Lipid Metabolism and Small Extracellular Vesicle Biogenesis in Cancer: Mechanisms, Biomarkers, and Therapeutic Targeting

Abstract

This article synthesizes current research on the critical interface between lipid metabolism and small extracellular vesicle (sEV) biogenesis in cancer. It explores the fundamental mechanisms by which lipid signaling and metabolic reprogramming drive sEV formation, release, and function within the tumor microenvironment. For a research-focused audience, the content details advanced methodologies for sEV isolation and lipidomic analysis, evaluates sEVs as non-invasive biomarkers and drug delivery vehicles, and discusses the therapeutic potential of targeting lipid-sEV pathways. The review also addresses key challenges in the field and provides a comparative analysis of validation strategies, aiming to bridge basic science with clinical translation in oncology.

The Lipid and sEV Nexus: Core Mechanisms in Cancer Progression

Small extracellular vesicles (sEVs) are membrane-bound nanoparticles, typically less than 200 nm in diameter, that are secreted by virtually all cell types into the extracellular space [1]. These vesicles play pivotal roles in intercellular communication by transferring functional proteins, nucleic acids (including miRNAs and mRNAs), lipids, and other bioactive substances between cells, thereby influencing the physiological state and functions of recipient cells [2] [3]. This transfer of information is particularly critical within the tumor microenvironment (TME), where tumor-derived sEVs (TDsEVs) contribute significantly to cancer progression, immune evasion, metastasis, and therapy resistance [3] [4]. The biogenesis of sEVs—the process by which they are formed and released—is a complex and regulated cellular process. It occurs primarily through two overarching mechanisms: the endosomal sorting complex required for transport (ESCRT)-dependent pathway and several ESCRT-independent pathways [2] [5]. Understanding these mechanisms is fundamental to comprehending sEV function in health and disease and for harnessing their potential in diagnostic and therapeutic applications.

The ESCRT-Dependent Biogenesis Pathway

The canonical pathway for sEV biogenesis is dependent on the ESCRT machinery, a highly conserved multi-protein complex essential for membrane remodeling and scission events within the cell [5]. This pathway gives rise to sEVs of endosomal origin, often specifically referred to as exosomes.

The Sequential ESCRT Machinery

The ESCRT apparatus consists of five core complexes (ESCRT-0, -I, -II, -III, and VPS4) that function sequentially in the formation of intraluminal vesicles (ILVs) inside multivesicular bodies (MVBs) [2] [5]. The process begins with the recognition and clustering of ubiquitinated cargo proteins by the ESCRT-0 complex (involving proteins like HRS and STAM1) on the endosomal membrane [2] [4]. ESCRT-0 then recruits ESCRT-I and ESCRT-II, which work together to initiate the inward budding of the endosomal membrane. ESCRT-II subsequently engages the ESCRT-III complex, which polymerizes into filaments that constrict the neck of the budding vesicle. Finally, the ATPase VPS4 catalyzes the disassembly of the ESCRT-III complex, completing the membrane scission and releasing the ILV into the lumen of the MVB [2] [5]. Once formed, these MVBs are transported along the cytoskeleton to the plasma membrane in a process regulated by Rab GTPases (e.g., Rab27a, Rab27b, Rab11) [3] [4]. The MVB then fuses with the plasma membrane, releasing the ILVs into the extracellular space as sEVs [1] [5].

Key Regulatory Proteins and Alternative ESCRT Recruitment

The accessory protein ALIX plays a critical role in an alternative ESCRT-dependent pathway. ALIX can be recruited to the endosomal membrane by the syndecan-syntenin complex, where it interacts directly with both ESCRT-I (via TSG101) and ESCRT-III (via CHMP4), serving as an alternative platform to orchestrate ILV formation and cargo sorting independently of ESCRT-0 [2] [5]. This syndecan-syntenin-ALIX axis is exploited in tumor environments to enhance the production of sEVs with promigratory activity [5].

Table 1: Key Molecular Components of the ESCRT-Dependent Pathway

Component	Key Function	Specific Proteins
ESCRT-0	Initiates pathway; recognizes & clusters ubiquitinated cargo	HRS, STAM1
ESCRT-I & II	Mediates membrane budding and deformation	TSG101
ESCRT-III	Executes membrane scission and vesicle release	CHMP4, CHMP3
VPS4	Recycles ESCRT machinery; finalizes scission	VPS4A, VPS4B
Accessory Proteins	Provides alternative ESCRT recruitment pathways	ALIX, Syntenin
Regulatory GTPases	Controls MVB trafficking and fusion with plasma membrane	Rab27a, Rab27b, Rab11

The following diagram illustrates the sequential action of the ESCRT complexes in the biogenesis of sEVs:

ESCRT-Independent Biogenesis Pathways

While the ESCRT machinery is central, cells possess several alternative mechanisms for sEV biogenesis that operate independently of ESCRT components. These pathways often rely on specific lipids and membrane microdomains.

The Ceramide-Based Pathway

A major ESCRT-independent mechanism involves the lipid ceramide [4] [5]. The enzyme neutral sphingomyelinase 2 (nSMase2) converts sphingomyelin in the endosomal membrane into ceramide. Due to its cone-shaped molecular structure, ceramide can spontaneously induce negative membrane curvature, driving the inward budding of the endosomal membrane to form ILVs [2] [5]. This pathway is crucial for the sorting of certain cargoes, such as the proteolipid protein (PLP), and can be enhanced by proteins like FAN, which is recruited to MVBs via the autophagy-related protein LC3 [4]. Inhibition of nSMase2 has been shown to impair sEV biogenesis and cargo sorting, underscoring its functional importance [2] [4].

Tetraspanin- and Raft-Based Pathways

Tetraspanins, a family of membrane proteins that are highly enriched in sEVs (e.g., CD63, CD9, CD81), also contribute to ESCRT-independent biogenesis [2] [4]. These proteins can form specialized transmembrane platforms known as tetraspanin-enriched microdomains (TEMs). Tetraspanins like CD63 can promote ILV formation and cargo sorting (e.g., of PMEL) through interactions with partners like Apolipoprotein E, leveraging both ESCRT-dependent and ceramide-dependent mechanisms [4]. Other membrane scaffolding proteins, such as flotillins and caveolin-1, are involved in organizing lipid rafts and can facilitate the sorting of specific cargo into ILVs in an ESCRT-independent manner, although caveolin-1's role may be constrained by the nSMase2-ceramide pathway [4].

Other Mechanisms and Cellular Context

It is important to note that these pathways are not mutually exclusive. They can operate simultaneously within a single cell, potentially generating distinct subpopulations of sEVs with different cargo compositions and functions [2]. For instance, in polarized epithelial cells, sEVs released from the basolateral side originate from a ceramide-dependent mechanism, while those from the apical side are formed via an ALIX-dependent pathway [2]. Furthermore, certain cellular conditions, such as glutamine deprivation or mTOR inhibition, can trigger the formation of a unique class of sEVs from Rab11-positive recycling endosomes, a process involving ESCRT-III accessory proteins CHMP1, CHMP5, and IST1 [2].

Table 2: Key Components of ESCRT-Independent sEV Biogenesis Pathways

Pathway	Key Molecules	Proposed Mechanism
Ceramide-Dependent	nSMase2, Ceramide, FAN	Cone-shaped ceramide induces negative membrane curvature for inward budding.
Tetraspanin-Mediated	CD63, CD9, CD81, Flotillins	Formation of tetraspanin-enriched microdomains (TEMs) that facilitate cargo clustering and membrane budding.
Other Lipid Raft-Associated	Caveolin-1, Cholesterol	Organizes specific membrane microdomains to promote vesicle formation and cargo sorting.

The relationship between different ESCRT-independent pathways is summarized below:

Lipids in sEV Biogenesis and Function

Lipids are not merely structural components of sEVs; they are active players in their biogenesis, composition, and function. The lipid composition of sEVs is distinct from that of the parent cell membrane, being enriched in sphingomyelin, cholesterol, glycosphingolipids, and phosphatidylserine [6] [7]. This specific lipid profile contributes to the rigidity and stability of sEVs, protecting their cargo during transit in the extracellular environment [7].

Lipid Involvement in Biogenesis

As detailed in the ceramide pathway, lipids are direct mediators of ESCRT-independent biogenesis. Beyond ceramide, other lipids like phosphatidic acid can also induce membrane curvature [2]. Moreover, the ESCRT machinery itself may rely on a specific lipid environment, such as cholesterol-rich liquid-ordered membrane domains, to function efficiently [2]. The lipid bilayer of sEVs also features an asymmetric distribution of lipids; for instance, phosphatidylserine is primarily located on the inner leaflet of the cell membrane but is found abundantly in the membranes of sEVs, where it may play a role in signaling and uptake by recipient cells [6].

Lipid Metabolism in Cancer and sEVs

Cancer cells undergo metabolic reprogramming, including dysregulation of lipid metabolism, which is reflected in the lipid cargo of TDsEVs [8] [7]. Tumor cells exhibit increased de novo lipogenesis and uptake of exogenous lipids to support rapid growth and membrane biogenesis. Consequently, TDsEVs are often enriched with specific lipids such as phosphatidylserine, prostaglandins, and lysophosphatidic acid [8] [6]. These lipids can function as signaling molecules in the tumor microenvironment, promoting processes such as angiogenesis, immunosuppression, and the formation of pre-metastatic niches [8] [7]. For example, lipids like lysophosphatidic acid and prostaglandins can enhance the release of angiogenic factors like VEGF, facilitating tumor vascularization [8].

Experimental Protocols for Studying sEV Biogenesis

Elucidating the mechanisms of sEV biogenesis requires a combination of genetic, biochemical, and pharmacological approaches. Below are detailed methodologies for key experiments cited in the literature.

Genetic Knockdown of Biogenesis Regulators

A common strategy to define the role of a specific protein in sEV biogenesis is to deplete it using RNA interference (RNAi) and analyze the resulting effects on sEV production and cargo.

• Protocol Example: Knockdown of ESCRT Components and ALIX [9]
  • Cell Culture: Maintain human cells (e.g., HeLa cells or Mesenchymal Stromal Cells) in appropriate media under standard conditions.
  • Transfection: Transfect cells with small interfering RNAs (siRNAs) targeting genes of interest (e.g., TSG101, HRS, ALIX) using a suitable transfection reagent. Include a non-targeting siRNA as a negative control.
  • Incubation: Allow 48-72 hours for effective protein knockdown.
  • sEV Isolation: Replace media with exosome-depleted serum. After 24-48 hours, collect the conditioned media.
  • Differential Ultracentrifugation: Isolate sEVs via a series of centrifugation steps.
    • Centrifuge at 300 × g for 10 min to remove cells.
    • Centrifuge supernatant at 2,000 × g for 20 min to remove dead cells.
    • Centrifuge supernatant at 10,000 × g for 30 min to remove cell debris and larger vesicles.
    • Ultracentrifuge the resulting supernatant at 100,000 × g for 70 min to pellet sEVs.
    • Wash the pellet in PBS and ultracentrifuge again at 100,000 × g for 70 min.
  • Analysis:
    • Quantification: Measure sEV protein yield using a Bradford or BCA assay.
    • Characterization: Analyze sEV size and concentration using Nanoparticle Tracking Analysis (NTA).
    • Cargo Profiling: Validate knockdown efficiency and analyze changes in sEV markers (e.g., CD63, CD81) via Western blotting.

Pharmacological Inhibition of Key Pathways

Small molecule inhibitors can be used to rapidly and reversibly dissect the contribution of specific enzymatic activities to sEV biogenesis.

• Protocol Example: Inhibition of nSMase2 with GW4869 [4]
  • Cell Treatment: Culture cancer cells to ~70% confluence.
  • Inhibitor Application: Treat cells with a specific inhibitor. For the ceramide pathway, use GW4869 (e.g., at 10-20 µM) or an alternative nSMase2 inhibitor. Use DMSO as a vehicle control.
  • sEV Collection and Isolation: Incubate for 24-48 hours, then collect conditioned media and isolate sEVs using differential ultracentrifugation as described in section 5.1.
  • Functional Analysis:
    • Quantify sEV yield (protein or particle number) to assess the effect of inhibition on sEV secretion.
    • Extract lipids from isolated sEVs and analyze ceramide levels via mass spectrometry to confirm pathway inhibition.
    • Investigate the functional consequence of reduced sEV release using in vitro assays (e.g., cell migration or invasion co-culture assays).

Modulation of sEVs by Natural Compounds

Several natural compounds have been identified that modulate sEV biogenesis and secretion, providing both experimental tools and potential therapeutic leads.

• Protocol Example: Treatment with Cannabidiol (CBD) or Resveratrol [6]
  • Cell Seeding: Seed cancer cells (e.g., prostate cancer PC3, glioblastoma, or hepatocellular carcinoma Huh7 cells) in standard culture plates.
  • Compound Treatment: Treat cells with a natural compound.
    • For Cannabidiol: Treat cells with CBD (e.g., at 1-10 µM) for 24 hours.
    • For Resveratrol: Treat cells with Resveratrol (e.g., at 50-100 µM) for 24-48 hours.
  • sEV Isolation and Analysis: Collect conditioned media and isolate sEVs via ultracentrifugation or polymer-based precipitation kits.
    • Use NTA and protein quantification to measure changes in sEV release.
    • Use Western blotting to analyze alterations in biogenesis-related proteins (e.g., Rab27a for Resveratrol).
    • Use RNA sequencing or qPCR to profile changes in sEV miRNA cargo (e.g., miR-126 and miR-21 in glioblastoma cells after CBD treatment).

The Scientist's Toolkit: Key Research Reagents and Solutions

This table provides a curated list of essential reagents and tools used in the experimental study of sEV biogenesis, as featured in the cited research.

Table 3: Research Reagent Solutions for Studying sEV Biogenesis

Reagent / Tool	Specific Example(s)	Function and Application in Research
siRNAs / shRNAs	siRNA targeting TSG101, HRS, ALIX, Rab27a [3] [9]	Genetic knockdown to interrogate the functional role of specific proteins in sEV biogenesis and secretion.
Pharmacological Inhibitors	GW4869 (nSMase2 inhibitor) [4]; Manumycin A [6]	Chemical inhibition of key enzymes to block specific biogenesis pathways (e.g., ceramide-dependent) and study the outcome.
Natural Compounds	Cannabidiol (CBD), Resveratrol, Honokiol [6]	Modulation of sEV synthesis, secretion, and cargo composition; studied for their antitumorigenic properties.
Antibodies for Characterization	Anti-CD63, Anti-CD81, Anti-CD9, Anti-TSG101, Anti-Alix, Anti-Calnexin [1] [10] [9]	Identification and validation of sEV isolates via Western blotting, flow cytometry, or immuno-EM. Calnexin is a negative marker for organelle contamination.
Isolation Kits	Polymer-based precipitation kits (e.g., ExoQuick, Total Exosome Isolation kit) [1]	Rapid and user-friendly isolation of sEVs from cell culture media or biological fluids, though purity must be validated.
Characterization Instruments	Nanoparticle Tracking Analysis (NTA), Transmission Electron Microscopy (TEM) [1] [9]	Physical characterization of sEVs: NTA for particle size and concentration; TEM for morphological analysis.
rac-Jasmonic Acid-d6	rac-Jasmonic Acid-d6, MF:C12H18O3, MW:216.31 g/mol	Chemical Reagent
D-(-)-Pantolactone-d6	D-(-)-Pantolactone-d6, MF:C6H10O3, MW:136.18 g/mol	Chemical Reagent

The biogenesis of small extracellular vesicles is a sophisticated cellular process governed by multiple, interconnected pathways. The ESCRT-dependent machinery provides a structured, protein-driven mechanism for cargo sorting and vesicle formation, while ESCRT-independent pathways, particularly those reliant on ceramide and tetraspanins, offer complementary and essential routes for sEV generation. Lipids serve as both structural elements and active mediators in this process, with their metabolism in cancer cells directly influencing the composition and function of TDsEVs. A thorough understanding of these mechanisms is not only fundamental to cell biology but also critical for advancing diagnostic and therapeutic applications. The experimental tools and protocols outlined provide a roadmap for researchers to dissect these complex processes further, paving the way for novel strategies to modulate sEV biogenesis in disease contexts, particularly in cancer.

In the realm of cancer research, small extracellular vesicles (sEVs) have emerged as critical mediators of intercellular communication, facilitating the remodeling of the tumor microenvironment and metastatic dissemination [6] [11]. The biological functions of these nanoscale vesicles are profoundly influenced by their lipid composition, which governs their biogenesis, release, and functional capacities [12] [13]. Among the diverse lipid species identified in sEVs, ceramide, cholesterol, sphingomyelin, and phosphatidylserine play particularly pivotal roles in the sEV lifecycle. This technical guide provides an in-depth examination of these four key lipids, detailing their mechanisms of action, altered metabolism in cancer, and implications for diagnostic and therapeutic development. Understanding these lipidic components is essential for advancing our knowledge of sEV biology in oncogenesis and exploring their potential as therapeutic targets.

Lipid Functions in sEV Biogenesis and Cargo Sorting

Ceramide: Master Regulator of ESCRT-Independent Budding

Ceramide plays a fundamental role in sEV biogenesis through its unique physicochemical properties. This conical-shaped lipid drives the inward budding of endosomal membranes to form intraluminal vesicles (ILVs) within multivesicular bodies (MVBs), a core mechanism of the ESCRT-independent pathway [13] [14]. The enzymatic generation of ceramide via neutral sphingomyelinase 2 (nSMase2) from sphingomyelin provides the necessary molecular architecture for membrane curvature and vesicle formation [12]. Research demonstrates that inhibition of nSMase2 effectively reduces sEV production, highlighting ceramide's central role in this process [7]. In cancer cells, ceramide-enriched microdomains also facilitate the sorting of oncogenic miRNAs into sEVs, enhancing their tumor-promoting capabilities upon delivery to recipient cells [7].

Cholesterol: Modulator of Membrane Rigidity and Trafficking

Cholesterol serves as a critical structural component of sEV membranes, significantly influencing their rigidity, stability, and intracellular trafficking [12] [14]. This sterol lipid is typically enriched in sEVs compared to their parent cells and facilitates the formation of lipid raft microdomains that serve as platforms for sEV biogenesis and protein sorting [13]. Cholesterol regulates MVB migration along microtubules and subsequent fusion with the plasma membrane, directly impacting sEV release [12]. Cancer cells often exhibit altered cholesterol metabolism, leading to modified cholesterol content in sEVs that influences their signaling functions and contributes to pathological progression [7]. Studies have shown that cholesterol-lowering drugs like simvastatin can inhibit sEV biogenesis and secretion in vitro and in vivo, demonstrating the therapeutic potential of targeting cholesterol metabolism in sEV-mediated cancer progression [7].

Sphingomyelin: Structural Scaffold and Ceramide Precursor

Sphingomyelin represents a major sphingolipid in sEV membranes, serving both structural and signaling functions. It contributes to membrane integrity and forms ordered lipid domains that facilitate the selective incorporation of proteins and nucleic acids into developing vesicles [14]. As the direct metabolic precursor to ceramide, sphingomyelin occupies a crucial position in the sEV biogenesis pathway [7]. The conversion of sphingomyelin to ceramide via sphingomyelinases represents a key regulatory step in both exosome and microvesicle formation, with acid sphingomyelinase particularly involved in plasma membrane shedding [12]. Cancer-derived sEVs frequently exhibit altered sphingomyelin-to-ceramide ratios, which influence their biological activity and potential as diagnostic biomarkers [7].

Phosphatidylserine: Mediator of Cellular Uptake and Signaling

Phosphatidylserine (PS) is normally confined to the inner leaflet of the plasma membrane but becomes externalized in sEVs, serving as a key recognition signal for recipient cells [13]. This exposed PS facilitates the cellular uptake of sEVs through interactions with various receptors, including TIM and TAM family receptors on recipient cells [13]. In the context of cancer, PS externalization on sEVs influences immune responses and promotes tumor progression [6]. Tumor-derived sEVs abundant in phosphatidylserine have been observed in ex vivo tumoroid cells that mimic mammalian tumors and their environment [6]. The exposure of PS on sEV surfaces also enables their detection using PS-binding agents like annexin V, providing a methodological approach for sEV quantification and isolation [12].

Table 1: Key Lipids in sEV Biogenesis and Their Functions

Lipid	Primary Function in sEV	Biogenesis Pathway	Enzymatic Regulators
Ceramide	Drives membrane curvature and inward budding	ESCRT-independent	nSMase2, aSMase
Cholesterol	Modulates membrane rigidity and MVB trafficking	Both ESCRT-dependent and independent	ACAT, CYP51A1
Sphingomyelin	Structural scaffold, ceramide precursor	Microvesicle formation	SM synthetase, aSMase
Phosphatidylserine	Facilitates cellular uptake, signaling	Plasma membrane shedding	Scramblase, flippase

Altered Lipid Metabolism in Cancer sEVs

Cancer cells undergo significant metabolic reprogramming that profoundly influences the lipid composition of their secreted sEVs. Dysregulated lipid metabolism is now recognized as a hallmark of cancer, with key lipogenesis regulators including acetyl-CoA carboxylase, fatty acid synthase, and sterol regulatory element-binding proteins (SREBPs) frequently upregulated in malignant cells [7]. These alterations directly impact the lipid cargo of sEVs, enhancing their pro-tumorigenic functions.

Oncogenic sEVs exhibit distinct lipid profiles characterized by enrichment of specific lipid species that facilitate tumor progression. For instance, studies comparing prostate cancer cell-derived sEVs (PC-3 cells) with their parental cells demonstrated significant enrichment of glycosphingolipids, phosphatidylserine species, and long-chain sphingolipids in sEV membranes [14] [7]. These modifications enhance the stability of sEVs and increase their efficiency in delivering oncogenic signals to recipient cells within the tumor microenvironment.

The phospholipid composition of cancer sEVs also shows disease-specific alterations. Mass spectrometry analyses reveal that phosphatidylcholine typically constitutes 46%–89% of total lipid components in sEVs from various cancer cell lines, while sphingomyelin content varies significantly (2%–30%) depending on the cancer type [14]. Pancreatic cancer-derived sEVs (AsPC-1 cells), for example, exhibit unusually high sphingomyelin content (28%) compared to other cancer types [14]. These modifications influence sEV size, rigidity, and function, ultimately contributing to cancer pathogenesis.

Table 2: Lipid Alterations in Cancer-Derived sEVs

Cancer Type	Observed Lipid Alterations in sEVs	Functional Consequences
Prostate Cancer	Enriched glycosphingolipids, PS 18:0/18:0	Enhanced cellular uptake, signaling
Breast Cancer	High phosphatidylcholine (80-90%)	Increased membrane stability
Pancreatic Cancer	Elevated sphingomyelin (28%), diglycerides	Altered membrane rigidity, drug resistance
Glioblastoma	Increased cholesterol, ceramides	Promoted survival pathways
Hepatocellular Carcinoma	Lyso-derivatives of phosphoglycerides	Enhanced inflammatory responses

Methodologies for sEV Lipid Analysis

Isolation Techniques for Lipidomic Studies

The accurate analysis of sEV lipid composition requires rigorous isolation methods to obtain high-purity vesicle preparations. Ultracentrifugation remains the gold standard for sEV separation, effectively pelleting vesicles based on their size and density [11]. For enhanced purity, density gradient centrifugation can further separate sEVs from contaminating lipid particles and protein aggregates [11]. Alternative approaches include size-exclusion chromatography, which preserves vesicle integrity and biological activity, and immunoaffinity capture methods that target specific surface markers [11]. The choice of isolation technique significantly impacts subsequent lipidomic analyses, as different methods yield varying degrees of purity and recovery rates.

Lipidomic Analysis Workflow

Comprehensive lipid profiling of sEVs typically employs liquid chromatography-mass spectrometry (LC-MS) platforms, which offer high sensitivity and resolution for detecting diverse lipid species [15]. The analytical workflow begins with lipid extraction from purified sEV preparations using organic solvents such as chloroform-methanol mixtures. The extracted lipids are then separated by reverse-phase or normal-phase chromatography before MS analysis [15].

High-resolution mass spectrometers enable the identification and quantification of thousands of lipid species based on their accurate mass and fragmentation patterns [15]. Specialized software tools process the raw LC-MS data through feature detection, lipid identification, and quantitative analysis. For modified lipid species (epilipids), specialized computational approaches are required due to their low abundance, structural diversity, and lack of reference standards in spectral libraries [15].

Functional Assays for Lipid Activity

Beyond compositional analysis, functional assays are essential for characterizing lipid activity in sEVs. Inhibition studies using pharmacological agents such as neutral sphingomyelinase inhibitors (GW4869) or statins provide insights into specific lipid pathways in sEV biogenesis and function [7]. Cellular uptake assays employing fluorescently labeled sEVs track lipid-dependent vesicle internalization and trafficking [13]. Lipid transfer studies monitor the intercellular movement of lipid cargo between donor and recipient cells, elucidating the signaling functions of sEV lipids in the tumor microenvironment [13].

Figure 1: sEV Lipidomics Workflow from Isolation to Data Analysis

Experimental Protocols for Investigating Lipid Functions

Inhibiting Ceramide-Mediated sEV Biogenesis

Purpose: To evaluate the role of ceramide in sEV formation and secretion using pharmacological inhibition. Reagents: GW4869 (nSMase2 inhibitor), cell culture medium, sEV isolation reagents, Western blot equipment. Procedure:

• Culture cancer cells (e.g., PC-3 prostate cancer cells) to 70% confluence.
• Treat cells with GW4869 (10-20 μM) or vehicle control for 24-48 hours.
• Collect conditioned medium and isolate sEVs using ultracentrifugation (100,000 × g for 70 minutes).
• Quantify sEV yield using nanoparticle tracking analysis or protein assay.
• Analyze ceramide content in sEVs by LC-MS and examine sEV markers (CD63, CD81) by Western blot.
• Assess functional consequences of reduced sEV secretion on recipient cell behaviors (migration, invasion).

Modifying Cholesterol Content in sEV Membranes

Purpose: To investigate how cholesterol depletion affects sEV biogenesis and function. Reagents: Methyl-β-cyclodextrin (MβCD), simvastatin, cholesterol quantification kit, fluorescent cell dyes. Procedure:

• Culture cells in standard conditions until 80% confluence.
• Treat cells with MβCD (5-10 mM) or simvastatin (1-10 μM) for 24 hours to deplete cholesterol.
• Iserve sEVs from conditioned media via density gradient centrifugation.
• Quantify cholesterol content in sEVs using fluorometric or colorimetric assays.
• Label sEVs with fluorescent dyes (e.g., PKH67) and track their uptake by recipient cells using flow cytometry.
• Evaluate changes in sEV membrane rigidity using laurdan generalized polarization spectroscopy.

Analyzing Phosphatidylserine Externalization on sEVs

Purpose: To detect and quantify PS exposure on sEV surfaces and its functional significance. Reagents: Annexin V binding buffer, fluorescently conjugated Annexin V, flow cytometry or microscopy equipment. Procedure:

• Isolate sEVs using size-exclusion chromatography to preserve membrane integrity.
• Incubate sEVs with Annexin V-FITC in binding buffer containing calcium for 15 minutes in the dark.
• For flow cytometry analysis, use appropriately sized beads to capture sEVs before Annexin V staining.
• Quantify PS-positive sEV population using flow cytometry or single-particle analysis.
• Block PS with recombinant Annexin V to confirm PS-dependent uptake in functional assays.
• Correlate PS exposure levels with functional properties such as immune cell modulation or tumor cell uptake.

Table 3: Key Research Reagents for Investigating Lipids in sEVs

Reagent/Category	Specific Examples	Research Application	Technical Notes
Inhibitors	GW4869, Manumycin A, Simvastatin	Block specific lipid pathways in sEV biogenesis	Validate specificity with rescue experiments
Detection Reagents	Annexin V, Filipin, Bodipy-cholesterol	Visualize and quantify lipids in sEVs	Optimize concentration to avoid background
Isolation Kits	Ultracentrifugation kits, Size-exclusion columns, Immunobeads	Obtain high-purity sEVs for lipid analysis	Compare multiple methods for validation
Analytical Standards	Deuterated lipids, Sphingolipid mixtures, Cholesterol standards	Quantify lipid species via mass spectrometry	Use internal standards for accurate quantification
Cell Lines	PC-3, MDA-MB-231, B16-F10	Study cancer-specific lipid alterations in sEVs	Characterize baseline lipid profiles first

The intricate roles of ceramide, cholesterol, sphingomyelin, and phosphatidylserine in the sEV lifecycle represent a critical frontier in cancer biology. These lipids not only govern the biogenesis and function of sEVs but also undergo cancer-specific alterations that enhance tumor progression. Advanced lipidomic methodologies now enable comprehensive profiling of sEV lipid compositions, revealing their potential as diagnostic biomarkers and therapeutic targets. As research in this field advances, targeting lipid pathways in sEV biogenesis offers promising strategies for interrupting tumor-promoting communication. Future studies focusing on the mechanistic relationships between specific lipid species and sEV functions will undoubtedly yield novel insights into cancer pathophysiology and therapeutic innovation.

Small extracellular vesicles (sEVs) are membrane-bound nanoparticles ranging from 30-200 nm in diameter that serve as crucial mediators of intercellular communication within the tumor microenvironment [1] [16]. Often referred to as oncosomes when derived from cancer cells, these vesicles are loaded with a diverse molecular cargo—including proteins, lipids, and nucleic acids—that mirrors the aggressive nature of their parental cells [17]. The biogenesis of sEVs occurs through a complex process involving the endosomal pathway, where early sorting endosomes mature into late sorting endosomes that invaginate to form multivesicular bodies (MVBs) containing intraluminal vesicles [1]. These MVBs subsequently fuse with the plasma membrane, releasing their vesicular contents as sEVs into the extracellular space [16]. This process is regulated by both ESCRT (Endosomal Sorting Complexes Required for Transport)-dependent and ESCRT-independent mechanisms, with the latter involving ceramide-dependent pathways and tetraspanin proteins such as CD63, CD81, and CD9 [1] [18]. The lipid composition of sEVs plays a fundamental role in their formation, structure, and function, with cancer-derived sEVs exhibiting distinct lipidomic profiles that contribute to their tumorigenic potential [7]. This review comprehensively examines how tumor-derived sEVs function as oncosomes to modulate key aspects of cancer progression—angiogenesis, metastasis, and immune evasion—within the context of sEV biogenesis and lipid metabolism.

Biological Foundations of sEVs

Biogenesis and Cargo Sorting Mechanisms

The formation of sEVs is a meticulously orchestrated cellular process that governs both the quantity and quality of vesicles released. The classical ESCRT pathway employs four protein complexes (ESCRT-0, -I, -II, and -III) that work sequentially to recognize ubiquitinated membrane proteins, induce membrane budding, and facilitate vesicle scission [18]. Parallel ESCRT-independent pathways utilize lipid metabolites like ceramide, which induces negative membrane curvature through its cone-shaped structure, promoting vesicle budding [18]. Tetraspanin proteins (CD9, CD63, CD81) also contribute significantly to sEV biogenesis and cargo selection, forming specialized membrane microdomains that recruit specific protein and RNA cargo [18].

Cargo sorting into sEVs is highly selective and determines their functional impact on recipient cells. RNA-binding proteins such as hnRNPA2B1 recognize specific nucleotide motifs (e.g., GGAG) in miRNAs to facilitate their loading into sEVs [18]. Post-translational modifications, including sumoylation of hnRNPA2B1, further regulate this selective packaging process [18]. The lipid composition of the budding membrane also influences cargo incorporation, with certain lipid domains preferentially recruiting proteins and nucleic acids destined for export [7].

Lipidomic Architecture of Oncogenic sEVs

The lipid profile of sEVs is not merely structural but functionally significant in cancer progression. Cancer-derived sEVs exhibit a modified lipidomic composition that distinguishes them from sEVs produced by normal cells [7]. These alterations include enriched levels of specific lipid species that facilitate tumorigenic behaviors:

Table 1: Modified Lipid Profiles in Cancer-Derived sEVs and Their Functional Implications

Lipid Category	Specific Lipid Species	Functional Role in Cancer	Reference
Cholesterol	Cholesterol	Enhances membrane rigidity and stability; promotes signaling platform formation	[7]
Sphingolipids	Ceramide, Sphingomyelin	Critical for sEV biogenesis; mediates apoptosis resistance	[7] [18]
Phospholipids	Phosphatidylserine, Phosphatidylcholine	Externalized phosphatidylserine mediates immune cell inhibition	[7]
Bioactive Lipids	Lysophosphatidic acid (LPA), Sphingosine-1-phosphate (S1P)	Acts as signaling molecules to promote migration, invasion, and angiogenesis	[7]
Fatty Acids	Saturated and unsaturated fatty acids	Saturated fatty acids increase membrane rigidity; unsaturated fatty acids enhance fluidity	[7]

This modified lipid composition contributes to disease progression by enhancing sEV stability, facilitating cargo sorting, promoting recipient cell uptake, and directly activating oncogenic signaling pathways [7]. For instance, the elevated cholesterol content in cancer sEVs increases membrane rigidity and promotes the formation of signaling platforms that enhance oncogenic signaling upon delivery to recipient cells [7].

Multifunctional Roles of sEVs in Cancer Progression

Angiogenesis Induction

sEVs orchestrate tumor angiogenesis through the delivery of pro-angiogenic factors that activate endothelial cells. These vesicles transfer specific molecular cargo that reprogram vascular cells to support blood vessel formation:

Table 2: Pro-angiogenic Cargo in Tumor-Derived sEVs

sEV Cargo Type	Specific Molecule	Mechanism of Action	Cancer Context	Reference
Proteins	Annexin II	Promotes endothelial cell migration and organization	Breast Cancer	[16]
Proteins	Tetraspanin 8	Facilitates angiogenesis and metastasis	Pancreatic & Colon Cancer	[16]
miRNAs	miR-96-5p	Targets AMOTL2 to promote angiogenesis	Pancreatic Cancer	[19]
lncRNAs	linc-ROR	Mediates cancer cell-adipocyte crosstalk to promote tumor growth	Pancreatic Cancer	[19]
Cytokines	IL-6, VEGF	Directly stimulates endothelial cell proliferation and tube formation	Glioblastoma	[16]

The pro-angiogenic effects of sEVs are further enhanced by hypoxic conditions within the tumor microenvironment. Hypoxia-inducible factors (HIFs) stimulate the expression and packaging of angiogenic mediators into sEVs, creating a feed-forward loop that sustains vascular development even under adverse conditions [19].

Metastatic Niche Formation

sEVs play a pivotal role in establishing the pre-metastatic niche—a supportive microenvironment in distant organs that facilitates the colonization of circulating tumor cells. These vesicles execute organotropic homing through specific integrins on their surfaces that determine their tissue distribution [20]. For instance, sEVs expressing integrin α6β4 preferentially home to lungs, while those with integrin αvβ5 target liver tissue [20].

The mechanism by which sEVs prepare pre-metastatic niches involves multiple coordinated processes. Breast cancer-derived sEVs transport miRNA-200b-3p that activates the AKT/NF-κB/CCL2 signaling cascade, recruiting myeloid-derived suppressor cells (MDSCs) to lung tissue and creating an immunosuppressive environment conducive to metastasis [20]. Similarly, pancreatic cancer sEVs establish a pre-metastatic niche in the liver by suppressing Toll-like receptor 4 (TLR-4) expression on dendritic cells and promoting fibrotic changes through the recruitment of macrophages and hepatic stellate cells [19]. sEVs also remodel the extracellular matrix by transferring matrix metalloproteinases (MMPs) and stimulating stromal cells to produce additional remodeling enzymes, thereby facilitating cancer cell invasion and colonization [16].

Immune Evasion Strategies

sEVs employ sophisticated mechanisms to suppress antitumor immunity, primarily through the surface expression of immune checkpoint proteins. Programmed death ligand 1 (PD-L1) presented on sEVs directly interacts with PD-1 receptors on T cells, inhibiting their activation and effector functions [20] [21]. This sEV-mediated immune suppression occurs both locally within the tumor microenvironment and systemically, as sEVs can travel to lymphoid organs and inhibit T cell activation at a distance [20].

Beyond checkpoint protein presentation, sEVs facilitate immune evasion through additional mechanisms. They inhibit dendritic cell maturation, impairing antigen presentation and subsequent T cell priming [20]. sEVs also promote the expansion and recruitment of immunosuppressive cell populations, including MDSCs and M2 macrophages, through transferred cytokines and miRNAs [20]. Furthermore, sEV-associated cytokines such as IL-6 and IL-17 contribute to creating a pro-tumorigenic inflammatory milieu that supports immune evasion [20].

Experimental Methodologies for sEV Research

Isolation and Characterization Techniques

The study of sEVs requires specialized isolation techniques that separate these nanoscale vesicles from other extracellular components. The most commonly employed methods include:

• Differential Ultracentrifugation: Considered the gold standard for sEV isolation, this technique employs sequential centrifugation steps at increasing speeds to pellet sEVs based on their size and density [1]. While it allows for processing large sample volumes, the high centrifugal forces can damage sEV structure and function [1].

• Density Gradient Centrifugation: This approach separates sEVs based on their buoyant density, typically resulting in higher purity preparations compared to differential ultracentrifugation [1]. However, it is time-consuming and may not be suitable for processing large sample volumes [1].

• Polymer-Based Precipitation: This method uses hydrophilic polymers to decrease sEV solubility, facilitating their precipitation from solution [1]. While technically simple and yielding high sEV recovery, it often co-precipitates non-sEV contaminants such as proteins and lipoproteins [1].

Following isolation, comprehensive characterization of sEVs is essential and typically involves multiple complementary techniques. Nanoparticle tracking analysis (NTA) determines sEV size distribution and concentration, while transmission electron microscopy (TEM) provides visual confirmation of sEV morphology and structural integrity [1]. Western blot analysis for tetraspanin markers (CD9, CD63, CD81) and ESCRT-associated proteins (TSG101, ALIX) verifies the presence of sEV-specific proteins [1] [18].

Functional Assays

Understanding the functional role of sEVs in cancer progression requires sophisticated experimental approaches:

Uptake and Tracking Experiments utilize fluorescently labeled sEVs to visualize their internalization by recipient cells. These assays often employ lipophilic dyes (e.g., PKH67, DiD) or membrane-permeant dyes (e.g., CFSE) to label sEV membranes or internal contents, respectively [20]. Confocal microscopy and flow cytometry then track sEV uptake and distribution over time.

Angiogenesis Assays evaluate the pro-angiogenic potential of sEVs using in vitro models such as tube formation assays, where endothelial cells are cultured with sEVs on Matrigel or other basement membrane extracts [16]. The extent and complexity of tubular structures formed serve as indicators of angiogenic induction [16].

Immune Cell Function Assays examine the immunomodulatory effects of sEVs through T cell proliferation assays, cytokine production measurements, and immune cell cytotoxicity assessments [20]. These experiments typically involve co-culture systems where immune cells are exposed to sEVs followed by functional readouts [20].

Research Reagent Solutions Toolkit

Table 3: Essential Research Reagents for sEV Studies

Reagent/Category	Specific Examples	Research Application	Key Considerations
Isolation Kits	Polymer-based precipitation kits, Membrane affinity columns	Rapid sEV isolation from biological fluids	Balance between yield, purity, and processing time
Characterization Antibodies	Anti-CD63, Anti-CD81, Anti-CD9, Anti-TSG101, Anti-Calnexin (negative marker)	sEV identification and quantification by Western blot, flow cytometry	Validate specificity for sEV proteins; use appropriate negative controls
Tracking Dyes	PKH67, DiD, CFSE, GFP-labeled tetraspanins	sEV uptake and trafficking studies	Optimize labeling concentration to avoid dye aggregation; include proper controls
Inhibition Reagents	GW4869 (neutral sphingomyelinase inhibitor), Dimethyl amiloride (inhibits MVB formation)	Investigating sEV biogenesis and secretion pathways	Assess potential off-target effects on cellular physiology
Lipidomics Tools	Mass spectrometry-based lipid profiling, Fluorescent lipid analogs	Analyzing sEV lipid composition and dynamics	Consider lipid extraction efficiency and coverage of lipid classes
1-Phenylpent-1-yn-3-amine	1-Phenylpent-1-yn-3-amine	1-Phenylpent-1-yn-3-amine is a chemical compound for research use only (RUO). It is not for human or veterinary diagnosis or personal use.	Bench Chemicals
Metolachlor-d11	Metolachlor-d11|Stable Isotope|RUO	Metolachlor-d11 is a deuterated internal standard for analytical research. This product is for Research Use Only. Not for human or veterinary use.	Bench Chemicals

Signaling Pathways and Molecular Mechanisms

The following diagrams illustrate key signaling pathways through which sEVs modulate angiogenesis, metastasis, and immune evasion.

sEV-Mediated Angiogenesis Signaling

sEV-Mediated Immune Evasion via PD-L1

sEV Biogenesis and Lipid Metabolism Interplay

sEVs function as sophisticated oncosomes that coordinate multiple aspects of cancer progression through their diverse molecular cargo and targeted delivery mechanisms. Their modified lipid composition not only facilitates biogenesis and stability but also actively contributes to their tumorigenic functions. The interconnected roles of sEVs in promoting angiogenesis, establishing pre-metastatic niches, and suppressing antitumor immunity highlight their central position in cancer biology.

Future research directions should focus on elucidating the precise molecular mechanisms governing sEV lipidomics and cargo sorting, developing more refined isolation techniques to address sEV heterogeneity, and exploring the therapeutic potential of engineered sEVs as drug delivery vehicles. As our understanding of sEV biology deepens, these vesicles may serve as valuable biomarkers for early cancer detection and monitoring, as well as novel targets for therapeutic intervention aimed at disrupting their tumor-promoting functions.

Lipid metabolic reprogramming is a established hallmark of cancer, enabling tumors to meet the increased energetic and biosynthetic demands of rapid proliferation [22] [23]. This reprogramming encompasses two primary mechanisms: de novo lipogenesis, where cancer cells synthesize lipids internally, and enhanced lipid uptake, where they scavenge lipids from the external environment [24]. These processes provide essential components for membrane biosynthesis, energy production through fatty acid oxidation (FAO), and generation of signaling molecules that drive oncogenic pathways [22] [24]. In the context of small extracellular vesicle (sEV) biogenesis, lipids serve as both structural components and bioactive mediators, influencing vesicle formation, cargo sorting, and release [13] [7]. This review examines the molecular mechanisms underlying de novo lipogenesis and lipid uptake in cancer, their relationship to sEV biology, and the experimental approaches driving discovery in this field.

De Novo Lipogenesis in Cancer Cells

De novo lipogenesis is a metabolic pathway where cancer cells synthesize fatty acids and other lipids from precursor molecules, even when extracellular lipids are abundant. This process supports the high demand for membrane phospholipids, lipid signaling molecules, and energy storage compounds in rapidly proliferating tumors [24].

Key Enzymes and Regulatory Nodes

The lipogenic pathway is orchestrated by several rate-limiting enzymes that are frequently overexpressed in cancers:

• ATP-citrate lyase (ACLY): Catalyzes the conversion of mitochondrial-derived citrate into cytosolic acetyl-CoA, the fundamental building block for fatty acid synthesis. Its expression is often upregulated in cancer, increasing flux through the lipogenic pathway [24].
• Acetyl-CoA carboxylase (ACC): Carboxylates acetyl-CoA to form malonyl-CoA in the committed step of fatty acid synthesis. This enzyme exists as two isoforms (ACC1 and ACC2) with ACC1 being primarily responsible for lipogenesis [22].
• Fatty acid synthase (FASN): A multi-enzyme complex that catalyzes the synthesis of palmitate from acetyl-CoA and malonyl-CoA. FASN is significantly overexpressed in numerous cancers, including colorectal (CRC) and breast cancer (BC), and its expression often correlates with poor prognosis [22] [23].
• Stearoyl-CoA desaturase 1 (SCD1): Introduces double bonds into saturated fatty acids to generate monounsaturated fatty acids (MUFAs), which are essential for membrane fluidity and the formation of specific lipid species. SCD1 upregulation is a common feature in tumor cells [7].

Table 1: Key Enzymes in De Novo Lipogenesis and Their Roles in Cancer

Enzyme	Reaction Catalyzed	Cancer Association	Therapeutic Inhibitors
ACLY	Citrate → Acetyl-CoA + Oxaloacetate	Upregulated in CRC and BC; supports acetyl-CoA pool	BMS-303141, Hydroxycitrate
ACC	Acetyl-CoA → Malonyl-CoA	Overexpressed; regulates fatty acid synthesis and oxidation	ND-654, TOFA
FASN	Acetyl-CoA/Malonyl-CoA → Palmitate	Highly upregulated; poor prognostic marker	TVB-2640, Orlistat, C75
SCD1	Saturated FA → Monounsaturated FA	Increased MUFA production for membrane fluidity	A939572, MF-438

Transcriptional control of lipogenesis is predominantly mediated by Sterol Regulatory Element-Binding Proteins (SREBPs), particularly SREBP-1c, which activate the expression of lipogenic genes like ACLY, ACC, and FASN [7]. Oncogenic signaling pathways, including PI3K/Akt/mTOR, enhance SREBP activity and processing, creating a direct link between oncogenic transformation and lipid anabolism [7].

Connection to sEV Biogenesis

Lipids generated via de novo synthesis are integral to sEV biogenesis. Ceramide, synthesized de novo in the endoplasmic reticulum, plays a critical role in the ESCRT-independent pathway of intraluminal vesicle (ILV) formation within multivesicular bodies (MVBs). Its conical molecular structure promotes membrane curvature and inward budding [13] [7]. Furthermore, phosphoinositides such as PI(3)P and PI(4,5)P2, which are lipid signaling molecules, recruit and regulate the ESCRT machinery in the ESCRT-dependent pathway of sEV formation [13]. The lipid composition of the parental cell's membrane, heavily influenced by its lipogenic output, directly determines the lipid profile of the resulting sEVs [7].

Diagram 1: De Novo Lipogenesis Pathway and Connection to sEVs. Key lipogenic enzymes (ACLY, ACC, FASN, SCD1) convert nutrients into lipids that support sEV formation.

Lipid Uptake from the Tumor Microenvironment

In parallel to de novo synthesis, cancer cells exhibit a voracious appetite for extracellular lipids. This is particularly evident in tumors situated in lipid-rich environments, such as breast cancer in adipose tissue [23]. Lipid uptake provides a readily available source of building blocks and energy, bypassing the ATP-intensive process of de novo synthesis.

Key Transporters and Uptake Mechanisms

The internalization of exogenous lipids is mediated by specific membrane transporters and receptors:

• CD36 (Fatty Acid Translocase): A scavenger receptor that facilitates the uptake of long-chain fatty acids and oxidized low-density lipoproteins (OxLDLs) [22] [24]. CD36 is overexpressed in CRC, BC, and other cancers, where it promotes cancer cell proliferation, metastasis, and immune evasion by inducing lipid peroxidation in T cells [22] [23]. It is a marker of metastatic potential and a mediator of therapy resistance in HER2+ breast cancer [23].
• Fatty Acid Transport Proteins (FATPs): A family of six proteins (FATP1-6) that facilitate the uptake of long-chain and very-long-chain fatty acids. FATP5, for instance, is overexpressed in CRC and regulates the cell cycle [24].
• Fatty Acid Binding Proteins (FABPs): Intracellular chaperones that facilitate the trafficking and storage of fatty acids after their entry into the cell. FABP4 and FABP5 are highly implicated in cancer; they enhance lipid droplet formation, interact with oncogenic signaling pathways like EGFR, and mediate crosstalk between cancer cells, adipocytes, and tumor-associated macrophages in the TME [24] [23].
• Low-Density Lipoprotein Receptor (LDLR): Mediates the endocytic uptake of cholesterol-rich LDL particles. LDLR is overexpressed in many cancers, including BC, to satisfy the high demand for cholesterol to maintain membrane integrity and fluidity [23]. Elevated LDL levels have been correlated with increased liver metastasis in CRC [22].

Table 2: Key Lipid Transporters and Their Roles in Cancer

Transporter	Lipid Substrate	Cancer Association	Functional Impact
CD36	Long-chain FAs, OxLDL	Overexpressed; poor prognosis; pro-metastatic	Promotes FA uptake, metastasis, immune suppression
FATPs	Long/very-long-chain FAs	Upregulated in CRC, BC	Supports cell cycle progression, tumor growth
FABP4	Intracellular FA chaperone	Elevated in obesity-associated BC	Links adipocytes, TAMs, and cancer cells
FABP5	Intracellular FA chaperone	Overexpressed in CRC and TNBC	Modulates EGFR signaling, cell proliferation
LDLR	LDL-cholesterol	Upregulated in BC	Provides cholesterol for membrane synthesis

The Role of the High-Fat Diet and Tumor Microenvironment

Epidemiological and experimental evidence links high-fat diets (HFD) and obesity to increased cancer risk and progression [22]. An HFD can alter the gut microbiota, increasing pathogenic bacteria that activate oncogenic pathways like CPT1A-ERK, thereby fueling CRC progression [22]. Furthermore, HFD-induced obesity disrupts CD4+ T-cell function in the TME, creating an immunosuppressive milieu that accelerates cancer progression and metastasis [22]. Adipocytes in the TME release fatty acids that are taken up by cancer cells via transporters like CD36 and FABP4, creating a parasitic relationship where the tumor feeds on the host's energy reserves [23].

Experimental Approaches for Investigating Lipid Metabolism

The study of lipid metabolism in cancer relies on a suite of advanced analytical and molecular techniques.

Lipidomics and Metabolomics

Untargeted Lipidomics provides a comprehensive profile of the lipid species present in a biological sample. This is typically performed using Ultra-High-Performance Liquid Chromatography coupled with Quadrupole Time-of-Flight Mass Spectrometry (UPLC-QTOF-MS) [25]. The workflow involves:

• Sample Preparation: Serum or plasma is typically extracted using a methanol-chloroform mixture for protein precipitation and lipid extraction.
• Chromatographic Separation: Lipids are separated on a reverse-phase C18 column.
• Mass Spectrometry Analysis: Lipids are ionized (e.g., by electrospray ionization) and detected in both positive and negative modes to capture a wide range of lipid classes.
• Data Analysis: Software like MetaboAnalyst is used for peak alignment, normalization, and multivariate statistical analysis (e.g., OPLS-DA) to identify lipids that are differentially abundant between groups (e.g., early vs. late recurrence) [25].

Targeted Metabolomics focuses on quantifying a predefined set of lipid biomarkers. For example, a study on cholangiocarcinoma identified lysophosphatidylcholines (LysoPCs) and lysophosphatidylethanolamines (LysoPEs) as significantly altered in patients with recurrence, and used a Support Vector Machine (SVM) model to build a predictive diagnostic panel [25].

Functional Assays and Molecular Biology

• Lipid Uptake Assays: Fluorescently labeled fatty acids (e.g., BODIPY-labeled FAs) are used to track and quantify fatty acid uptake in vitro. Inhibition of transporters like CD36 with neutralizing antibodies or sulfo-N-succinimidyl oleate (SSO) can demonstrate their specific role.
• Genetic Manipulation: siRNA- or CRISPR/Cas9-mediated knockdown/knockout of key enzymes (e.g., FASN, ACLY) or transporters (e.g., CD36, FABP5) is used to elucidate their functional necessity for cancer cell proliferation, invasion, and tumor growth in vivo.
• Metabolic Flux Analysis: Using isotopic tracers (e.g., ^13^C-glucose or ^13^C-acetate) allows researchers to track the incorporation of carbon atoms into newly synthesized fatty acids, providing a direct measure of de novo lipogenesis flux.

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Research Reagents for Investigating Lipid Metabolism in Cancer

Reagent / Tool	Category	Primary Function	Example Application
TVB-2640	Small Molecule Inhibitor	FASN inhibition	Suppresses de novo lipogenesis in preclinical models; in clinical trials.
SSO	Small Molecule Inhibitor	Irreversible CD36 inhibitor	Blocks exogenous fatty acid uptake in functional assays.
BODIPY FL C16	Fluorescent Probe	Fluorescent fatty acid analog	Visualizing and quantifying fatty acid uptake in live cells.
^13^C-Acetate	Stable Isotope Tracer	Metabolic flux analysis	Tracing carbon flux through the de novo lipogenesis pathway.
Anti-CD36 Antibody	Neutralizing Antibody	Blocks CD36 receptor function	Inhibits ligand binding; used in vitro and in vivo.
siRNA pools (e.g., against SREBP1)	Genetic Tool	Gene knockdown	Silencing expression of key transcriptional regulators of lipogenesis.
Simvastatin	Small Molecule Inhibitor	HMG-CoA reductase inhibitor	Reduces cholesterol synthesis and has been shown to inhibit sEV biogenesis.
Buphedrone-d3 Hydrochloride	Buphedrone-d3 Hydrochloride, MF:C11H16ClNO, MW:216.72 g/mol	Chemical Reagent	Bench Chemicals
Voriconazole-13C3,d3	Voriconazole-13C3,d3, MF:C16H14F3N5O, MW:353.32 g/mol	Chemical Reagent	Bench Chemicals

Therapeutic Targeting and Clinical Translation

The strategic inhibition of lipid metabolic pathways presents a promising avenue for cancer therapy.

• Targeting De Novo Lipogenesis: The FASN inhibitor TVB-2640 has shown efficacy in preclinical models and is undergoing clinical evaluation. It is particularly investigated in combination regimens to overcome therapy resistance [24].
• Targeting Lipid Uptake: Anti-CD36 antibodies are being explored to block the procancerous functions of this transporter, potentially inhibiting metastasis and reversing immunosuppression [22] [23].
• Repurposed Drugs: Statins, which inhibit cholesterol synthesis, have demonstrated anti-tumor effects and can modulate sEV biogenesis and secretion, as shown with simvastatin in macrophages and dendritic cells [7].

The analysis of lipid species in patient serum or in sEVs isolated from biofluids is a burgeoning area for biomarker discovery. Specific phospholipid signatures, such as alterations in LysoPCs, show diagnostic and prognostic potential for cancers like pancreatic ductal adenocarcinoma (PDAC) and cholangiocarcinoma [25] [26]. Machine learning models applied to these lipidomic datasets are enhancing the accuracy of cancer detection and recurrence prediction [25].

Diagram 2: Lipid Uptake from the Tumor Microenvironment. Cancer cells upregulate transporters (CD36, FATPs, FABPs, LDLR) to scavenge lipids from adipocytes and circulation, fueling various protumorigenic processes.

The tumor microenvironment (TME) is a complex ecosystem where dynamic communication between cancer cells and various stromal components dictates tumor progression and therapy response. This technical guide elucidates the intricate, bidirectional crosstalk mediated by small extracellular vesicles (sEVs) and reprogrammed lipid metabolism within the TME. We detail how lipids influence sEV biogenesis, composition, and function, and conversely, how sEVs transmit lipid-related signals that reprogram recipient cells, fostering an immunosuppressive, pro-metastatic niche. Supported by contemporary single-cell analyses and mechanistic studies, this review provides a framework for understanding these pathways and exploiting them for diagnostic and therapeutic innovation in oncology.

The concept of the TME as a passive bystander in tumorigenesis has been fundamentally overturned. It is now recognized as an active participant, composed of malignant cells, immune cells, cancer-associated fibroblasts (CAFs), endothelial cells, and a myriad of signaling molecules. Two key processes underpinning communication within this milieu are lipid metabolic reprogramming and sEV-mediated signaling. Lipid metabolism reprogramming is a hallmark of cancer, providing energy, building blocks for membranes, and signaling molecules to support rapid proliferation [27]. Concurrently, sEVs—nanoscale vesicles (50-200 nm) secreted by all cells—emerge as critical messengers, shuttling functional proteins, nucleic acids, and lipids to remodel the TME [2]. The interplay between these two systems creates a feed-forward loop of pro-tumorigenic signaling. This guide dissects the molecular mechanisms of this crosstalk, presents key experimental data, and outlines translational applications for cancer research and drug development.

Molecular Mechanisms: How Lipids and sEVs Co-define the Tumor Microenvironment

Biogenesis of sEVs and the Critical Role of Lipids

sEVs originate through two primary pathways, both heavily influenced by lipids. The formation of exosomes, a major subtype of sEVs, begins with the endosomal system. The inward budding of the limiting membrane of multivesicular bodies (MVBs) forms intraluminal vesicles (ILVs), which are released as exosomes upon MVB fusion with the plasma membrane [2] [28]. This process is regulated by the Endosomal Sorting Complex Required for Transport (ESCRT) machinery, which is itself recruited and activated by phosphoinositides like PI(3)P and PI(4,5)P2 [6] [28]. An ESCRT-independent pathway is triggered by ceramide, whose cone-shaped structure facilitates membrane curvature and inward budding [6] [2]. Other lipids, including cholesterol and sphingomyelin, contribute to the stability and formation of membrane microdomains essential for this process [6] [28].

In contrast, microvesicles (another class of sEVs) are formed via the direct outward budding and fission of the plasma membrane. This process is initiated by the loss of membrane asymmetry, particularly the externalization of phosphatidylserine (PS), and the local enrichment of cholesterol and sphingomyelin in lipid rafts. Ceramide again plays a role in membrane scission, often in concert with the ESCRT machinery [28]. The lipid composition not only governs vesicle formation but also determines the sorting of specific cargo into sEVs.

sEV-Mediated Lipid Signaling in the TME

Once released, sEVs serve as vehicles for intercellular communication, transmitting oncogenic signals that reshape the TME.

• Immunosuppression: Tumor-derived sEVs can suppress anti-tumor immunity. In metastatic Estrogen Receptor-positive (ER+) breast cancer, scRNA-seq revealed enriched populations of CCL2+ macrophages and FOXP3+ regulatory T cells (Tregs), which collectively contribute to an immunosuppressive microenvironment. Analysis of cell-cell communication showed a marked decrease in tumor-immune cell interactions in metastatic tissues, indicating immune evasion [29].
• Metastasis and Metabolic Reprogramming: sEVs facilitate the establishment of the pre-metastatic niche. They carry and transfer multifunctional proteins like Matrix Metalloproteinases (MMPs) and Heat Shock Proteins (HSPs), which promote extracellular matrix remodeling and epithelial-mesenchymal transition (EMT) [6]. Furthermore, a lipid metabolism-related gene signature derived from ER+ breast cancer patients reflects TME heterogeneity and is associated with worse prognosis after tamoxifen treatment, underscoring the link between lipid signaling and disease progression [30].
• Cross-Tissue Communication: Emerging evidence highlights sEVs in mediating pathological communication between tumors and distant organs. For instance, sEVs derived from doxorubicin-treated breast cancer cells are enriched with miR-338-3p. When taken up by cardiomyocytes, these sEVs exacerbate doxorubicin-induced cardiotoxicity by targeting anti-ferroptotic genes, illustrating a novel mechanism of heart-tumor crosstalk [31].

Lipid Metabolism Reprogramming in Cancer and Immune Cells

Cancer cells undergo significant lipid metabolic rewiring to support their growth demands. Key alterations include:

• Increased Fatty Acid Synthesis: Upregulation of enzymes like ATP-citrate lyase (ACLY), Acetyl-CoA Carboxylase (ACC), and Fatty Acid Synthase (FASN) drives the de novo synthesis of fatty acids from acetyl-CoA [27].
• Cholesterol Synthesis: The mevalonate pathway, controlled by the rate-limiting enzyme HMGCR, is hyperactive in many cancers, providing cholesterol for membrane integrity and signaling [27].
• Lipid Uptake and Storage: Overexpression of transporters like CD36 facilitates the uptake of exogenous fatty acids, which are stored as triglycerides in lipid droplets [27].

This metabolic reprogramming extends to immune cells within the TME, but with functional consequences for anti-tumor immunity. For example, lipid accumulation in dendritic cells (DCs) and T cells can impair their antigen-presentation and effector functions, contributing to an immunosuppressive landscape [27].

Quantitative Data and Experimental Evidence

Natural Compounds Modulating sEVs and Lipid Metabolism

Natural compounds (NCs) serve as potent experimental tools to dissect the sEV-lipid axis and hold therapeutic potential. The following table summarizes the effects of key NCs.

Table 1: Natural Compounds as Modulators of sEV Biology and Lipid Metabolism

Natural Compound	Source	Direct Effect on sEVs	Impact on Lipid Metabolism/Pathways	Overall Regulatory Effect
Manumycin A	Streptomyces species	Reduces exosome secretion by ~10-fold in castration-resistant prostate cancer (CRPC) cells [6].	Inhibits Ras/Raf/ERK1/2 signaling [6].	Sensitizes CRPC to enzalutamide; antitumor effects [6].
Cannabidiol (CBD)	Cannabis sativa	Reduces exosome and microvesicle release in prostate, liver, and breast cancer cells [6].	Alters microRNA cargo (e.g., increases miR-126, decreases miR-21) [6].	Overcomes chemoresistance; exhibits antitumor activity [6].
Resveratrol	Grapes, berries	Blocks exosome secretion by downregulating Rab27a in liver cancer cells (Huh7) [6].	Increases CD63 and Ago2 levels in colorectal adenocarcinoma cells [6].	Antiproliferative and anti-migratory effects [6].
Honokiol	Magnolia genus	Increases drug bioavailability when loaded into exosomes; identified as a P-glycoprotein inhibitor [6].	Specific lipid effects not yet fully explored [6].	Enhances targeted delivery and antitumor efficacy [6].

Lipid Metabolism-Related Gene Signatures in Cancer Prognosis

Transcriptomic analyses have established a firm link between lipid metabolism and clinical outcomes. A prognostic model based on lipid metabolism-related genes was developed for ER+ breast cancer patients treated with tamoxifen. This signature stratified patients into high- and low-risk groups, with the high-risk group exhibiting worse survival outcomes (5-year overall survival AUC of 0.858). The high-risk group was characterized by enrichment of M0 macrophages and amplified SPP1 interactions, linking lipid reprogramming to immunosuppression and poor prognosis [30].

Single-Cell Resolution of the TME

Single-cell RNA sequencing (scRNA-seq) of primary and metastatic ER+ breast cancer has revealed the cellular states underpinning TME heterogeneity. Key findings include:

• Genomic Instability: Malignant cells from metastatic samples showed higher Copy Number Variation (CNV) scores than primary tumors, indicating greater genomic instability [29].
• Immune Cell Shifts: Primary tumors were enriched with FOLR2+ and CXCR3+ pro-inflammatory macrophages. In contrast, metastatic lesions harbored more CCL2+ and SPP1+ pro-tumorigenic macrophages and exhausted T cells [29].
• Pathway Alterations: Primary breast cancer samples displayed increased activation of the TNF-α signaling pathway via NF-κB, suggesting a potential therapeutic target distinct from metastatic disease [29].

Methodologies: Experimental Protocols for sEV and Lipid Research

Isolation and Drug Loading into Cancer Cell-Derived sEVs

The use of sEVs as drug delivery vehicles requires robust and reproducible isolation and loading protocols. The following workflow, adapted from a study using MCF-7 breast cancer cell-derived sEVs for doxorubicin (Dox) delivery, provides a detailed methodology [32].

Table 2: Experimental Protocol for sEV-based Drug Delivery System

Step	Protocol Description	Key Reagents/Equipment	Function/Notes
1. Cell Culture & EV Collection	Culture MCF-7 cells in RPMI-1640 medium without FBS for 48h. Collect conditioned medium [32].	RPMI-1640 medium, FBS, centrifuge, filters (0.22 µm) [32].	Serum-free conditions prevent FBS-EV contamination.
2. sEV Isolation & Purification	Sequential centrifugation (300 ×g, 2000 ×g) to remove cells/debris. Ultrafiltration to concentrate. Purify via Size Exclusion Chromatography (SEC) on a Sepharose CL-2B column [32].	Ultracentrifuge, Amicon Ultra centrifugal filters, Sepharose CL-2B resin [32].	SEC provides high-purity sEVs with intact biological activity.
3. sEV Characterization	Nanoparticle Tracking Analysis (NTA) for size/concentration. Western Blot for markers (CD63, CD81). Transmission Electron Microscopy (TEM) for morphology [32].	NTA instrument, antibodies (anti-CD63, anti-CD81), TEM [32].	Confirms isolation of sEVs and assesses quality.
4. Drug Loading	Extrusion Method: Mix purified sEVs with Dox solution. Freeze-thaw, then extrude through porous membranes (e.g., 200 nm). Compare with passive incubation [32].	Extrusion device, porous membranes, Doxorubicin hydrochloride [32].	Extrusion provides superior loading efficiency over passive incubation.
5. Functionalization & Targeting	Immobilize targeting peptides (e.g., APRPG peptide for VEGFR-1) on sEV surface via chemical conjugation or genetic engineering [32].	APRPG peptide, crosslinkers (e.g., Sulfo-SMCC) [32].	Enhances specific homing to target cancer cells.
6. In vitro/In vivo Validation	Assess cytotoxicity (CCK-8 assay) in cancer vs. normal cell lines. Evaluate tumor homing and inhibition in xenograft mouse models (e.g., MCF-7-bearing mice) [32].	CCK-8 assay kit, immunofluorescence imaging, mouse xenograft model [32].	Validates targeting and therapeutic efficacy of the sEV-Dox system.

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Reagent Solutions for sEV and Lipid Metabolism Research

Reagent / Tool	Category	Function in Research
Sepharose CL-2B	Isolation Tool	Matrix for SEC, enabling high-purity isolation of sEVs from biofluids or conditioned media [32].
APRPG Peptide	Targeting Ligand	Binds VEGFR-1 on cancer cells; used to functionalize sEVs for targeted drug delivery [32].
Anti-CD63 / CD81 Antibodies	Characterization	Canonical exosome markers used in Western Blot, flow cytometry, or immuno-EM to identify and validate sEV isolates [32].
InferCNV / CaSpER	Bioinformatics Tool	Algorithms used with scRNA-seq data to infer copy number variations in malignant cells, revealing genomic instability [29].
Cannabidiol (CBD)	Small Molecule Inhibitor	Natural compound used experimentally to inhibit sEV release and alter their miRNA cargo, probing sEV function [6].
RBMX siRNA	Genetic Tool	Knocking down this RNA-binding protein disrupts the packaging of specific miRNAs (e.g., miR-338-3p) into sEVs [31].
Naloxone-d5 3-Methyl Ether	Naloxone-d5 3-Methyl Ether, MF:C20H23NO4, MW:346.4 g/mol	Chemical Reagent
(+)-N-Desmethyl Tramadol-d3	(+)-N-Desmethyl Tramadol-d3, MF:C15H23NO2, MW:252.37 g/mol	Chemical Reagent

Visualization of Key Pathways and Workflows

Bidirectional Lipid-sEV Crosstalk in the TME

Diagram Title: Bidirectional sEV-Lipid Crosstalk in Tumor Microenvironment

Experimental Workflow for sEV-Based Drug Delivery

Diagram Title: sEV-Based Drug Delivery Development Workflow

The TME functions as a central hub where lipid metabolism and sEV communication are inextricably linked. Lipids are not merely structural components but active players in sEV biogenesis and signaling. In return, sEVs act as systemic couriers of lipid-related oncogenic signals, reprogramming immune responses, fueling metastasis, and mediating cross-organ damage. Decoding this complex dialogue is paramount.

Future research must leverage advanced single-cell and spatial 'omics' technologies to map lipid-sEV interactions with cellular resolution in human tumors. Therapeutically, targeting key nodes in this axis—such as using natural compounds to modulate sEV secretion or engineering sEVs for targeted drug delivery—holds immense promise. Furthermore, the lipid and miRNA profiles of circulating sEVs offer a fertile ground for developing novel, non-invasive biomarkers for early cancer detection, prognosis, and monitoring therapeutic resistance. By integrating the fields of lipidomics and sEV biology, researchers and drug developers can unlock new frontiers in precision oncology.

From Bench to Bedside: sEV and Lipid Analysis for Diagnostic and Therapeutic Applications

Advanced Isolation and Characterization Techniques for sEVs

Small extracellular vesicles (sEVs), commonly defined as membrane-bound particles ranging from 30 to 200 nanometers in diameter, have emerged as crucial mediators of intercellular communication and promising biomarkers in cancer research [33] [1]. These nanoparticles carry a diverse cargo of proteins, lipids, and nucleic acids that reflect their cell of origin, providing a window into pathological states [34] [35]. Their involvement in modulating the tumor microenvironment, facilitating epithelial-mesenchymal transition, and promoting metastasis underscores their significance in oncology [6]. However, the translational potential of sEVs is critically dependent on the isolation of highly pure vesicles and their comprehensive characterization, areas where methodology remains largely unstandardized [33] [36]. This technical guide provides an in-depth analysis of current advanced techniques for sEV isolation and characterization, with particular emphasis on their applications in studying sEV biogenesis and lipid metabolism in cancer.

sEV Biogenesis and Lipid Metabolism in Cancer

Molecular Mechanisms of sEV Biogenesis

The formation of sEVs occurs through two primary pathways: the endosomal pathway resulting in exosomes, and plasma membrane budding yielding microvesicles [34] [35]. The endosomal pathway begins with the inward budding of the endosomal membrane, forming intraluminal vesicles (ILVs) within multivesicular bodies (MVBs). These MVBs subsequently fuse with the plasma membrane, releasing ILVs as exosomes into the extracellular space [1]. This process is regulated by the endosomal sorting complex required for transport (ESCRT) machinery, though ESCRT-independent pathways also exist [6]. The second pathway involves direct budding and fission of the plasma membrane, producing microvesicles ranging from 100-1000 nm [34].

Lipids play a fundamental role in sEV biogenesis beyond their structural function. Ceramide, an essential lipid involved in cellular signaling, has been shown to trigger budding of exosomes without the ESCRT system [6]. Other lipids including cholesterol, sphingomyelin, and phosphatidylserine participate in the formation, secretion, signaling, and uptake of exosomes [6]. The phospholipid phosphatidylserine (PS) is abundantly present in the inner leaflet of the cell membrane and is also found primarily in sEVs released from tumoroid cells that mimic mammalian tumors [6].

Lipid-Driven sEV Modulation in Cancer Progression

Cancer cells exhibit distinct alterations in their lipid metabolism that are reflected in the lipid composition of their secreted sEVs. These modifications influence both the biogenesis and function of sEVs in cancer progression. The lipid composition of sEV membranes affects their rigidity, fluidity, and targeting specificity, ultimately determining their capacity to interact with recipient cells [37].

In the context of cancer, sEVs serve as vehicles for transferring oncogenic lipids and lipid-modified signaling proteins that promote tumor growth and metastasis. For instance, cancer-derived sEVs have been shown to transport phosphatidylserine, which can influence immune recognition and tumor microenvironment remodeling [6]. Additionally, alterations in the lipid profile of sEVs have been identified in various chronic diseases, including cancers, making them suitable biomarkers and therapeutic targets [6]. Recent lipidomic analyses of sEVs have revealed distinct differences in lipid chain lengths and saturation levels that affect key pathways such as sphingolipid and neurotrophin signaling [38].

Table 1: Key Lipids in sEV Biogenesis and Function

Lipid Class	Role in sEV Biology	Significance in Cancer
Ceramide	Triggers ESCRT-independent budding; regulates ILV formation	Modulates sEV release from cancer cells; potential therapeutic target
Phosphatidylserine	Externalized in apoptosis; sEV membrane component	Immunomodulatory effects; promotes tumor immune evasion
Cholesterol	Regulates membrane fluidity and rigidity	Affects sEV stability and recipient cell uptake; often elevated in cancer sEVs
Sphingomyelin	Contributes to membrane microdomain organization	Influences sEV signaling capabilities; altered in cancer sEVs
Sphingolipids	Signaling molecules in sEV pathways	Key role in neurotrophin signaling affected in cancer

Advanced sEV Isolation Techniques

The selection of an appropriate isolation method is critical for obtaining sEVs of sufficient purity and yield for downstream applications. Method choice depends on multiple factors including sample type, volume, required purity, and intended downstream analysis [34].

Comparative Analysis of Isolation Methodologies

Ultracentrifugation (UC) remains the most widely used technique for sEV isolation, often considered the "gold standard" [1]. This method employs sequential centrifugation steps at increasing speeds, typically culminating at 100,000-160,000×g to pellet sEVs [39]. While UC allows for processing of large sample volumes and doesn't require specialized chemicals, it is time-consuming, requires expensive equipment, and may cause vesicle damage or aggregation [33] [1]. Additionally, UC pellets often contain non-vesicular contaminants, including protein aggregates and lipoproteins [36].

Size-exclusion chromatography (SEC) separates sEVs based on their hydrodynamic radius using porous beads. Smaller molecules enter the pores and are delayed, while sEVs elute in earlier fractions [33]. SEC preserves vesicle integrity and function, provides good purity, and is compatible with various biological fluids [39]. However, it offers limited sample processing capacity and may not effectively separate sEVs from similarly sized particles [33].

Polymer-based precipitation methods use polymers like polyethylene glycol (PEG) to alter the solubility of sEVs, causing them to precipitate out of solution [33]. These techniques are simple, require no specialized equipment, and can process multiple samples simultaneously. The major drawback is co-precipitation of non-vesicular contaminants, including proteins and nucleic acids, which can compromise downstream analyses [36].

Immunoaffinity capture utilizes antibodies against sEV surface markers (e.g., CD9, CD63, CD81) to selectively isolate sEV subsets [33] [39]. This approach provides exceptional purity and enables isolation of specific sEV subpopulations. Limitations include high cost, dependence on surface marker expression, and potential functional alterations due to antibody binding [39].

Combined methods have emerged to overcome the limitations of individual techniques. For example, a novel cocktail strategy integrating chemical precipitation and ultrafiltration with a two-step filtering process (CPF) has demonstrated high purity and homogeneity [33]. Another combined approach using a half-cycle of UC followed by polymer precipitation (UCT) showed improved purity over single methods [36].

Table 2: Comprehensive Comparison of sEV Isolation Techniques

Method	Principle	Yield	Purity	Time	Cost	Advantages	Disadvantages
Ultracentrifugation	Density and size via centrifugal force	Moderate	Moderate	High (>4h)	Moderate	No chemical additives; scalable	Equipment intensive; potential vesicle damage
Size-Exclusion Chromatography	Hydrodynamic size	Low to Moderate	High	Moderate (1-2h)	Low to Moderate	Preserves vesicle integrity; good purity	Small processing volume; dilution of sample
Polymer Precipitation	Solubility disruption	High	Low	Low (<4h)	Low	Simple protocol; high throughput	Co-precipitation of contaminants
Immunoaffinity Capture	Surface marker binding	Low	Very High	Moderate (2-4h)	High	High specificity; subpopulation isolation	Selective based on markers; high cost
Combined Methods (e.g., CPF, UCT)	Multiple principles	High	High	Variable	Variable	Optimized balance of yield and purity	Protocol complexity

Method Selection for Cancer Research

For cancer biomarker studies focusing on sEV lipidomics, density gradient ultracentrifugation or SEC are recommended as they provide sufficient purity while maintaining lipid composition integrity [39]. When working with limited clinical samples, immunoaffinity capture enables specific isolation of cancer-derived sEVs based on tumor-specific surface markers [36]. For therapeutic applications requiring large sEV quantities, combined methods like UCT or bioreactor-based production with optimized isolation offer the best balance between yield and purity [38].

Comprehensive sEV Characterization Platforms

Rigorous characterization of isolated sEVs is essential to confirm their identity, purity, and structural integrity. The International Society for Extracellular Vesicles (ISEV) recommends implementing complementary techniques to assess multiple sEV parameters [34].

Nanoparticle Tracking Analysis (NTA)

NTA enables the quantification of sEV concentration and size distribution by tracking the Brownian motion of individual particles in suspension [33] [36]. This technique provides crucial information about the size profile of isolated sEVs, typically confirming a range of 30-200 nm for properly isolated preparations [33]. When comparing isolation methods, NTA has revealed that polymer precipitation often yields the highest particle concentrations but may include non-vesicular contaminants, whereas UC provides lower yields but better size homogeneity [33] [36]. Recent studies employing NTA show that combined methods like CPF achieve a favorable balance, with particle concentrations of approximately 1-5×10^11 particles/mL from plasma samples [33].

Transmission Electron Microscopy (TEM)

TEM provides high-resolution ultrastructural analysis of sEV morphology [33] [40]. Properly isolated sEVs typically appear as cup-shaped or spherical vesicles with a clearly defined lipid bilayer [33]. TEM micrographs have demonstrated that vesicles isolated using combined methods (CPF) show well-defined spherical structures with diameters of 30-150 nm and minimal non-vesicular contaminants compared to other methods [33]. Advanced TEM techniques like cryo-EM further preserve native vesicle structure, revealing double-membraned spherical vesicles without artifacts introduced by chemical fixation [33].

Western Blot Analysis

Western blotting detects specific protein markers associated with sEVs, confirming their identity and purity [33] [36]. Essential sEV markers include tetraspanins (CD9, CD63, CD81), ESCRT-related proteins (TSG101, Alix), and heat shock proteins (Hsp70) [33] [35]. Simultaneously, the absence of negative markers such as calnexin (endoplasmic reticulum) or GM130 (Golgi apparatus) should be confirmed to ensure minimal cellular contamination [36]. Studies comparing isolation methods have demonstrated that UC and immunoaffinity capture typically show stronger signals for canonical sEV markers compared to polymer precipitation methods [36].

Advanced Lipidomic Characterization

For cancer research focused on lipid metabolism, detailed lipidomic profiling of sEVs provides critical insights into their biochemical composition and function. Mass spectrometry-based lipidomics can identify and quantify thousands of lipid species in sEV preparations, revealing disease-specific alterations [37] [38]. Key lipid classes of interest include phosphatidylserine, sphingomyelin, ceramides, and sterols, all of which participate in sEV formation, secretion, signaling, and uptake [6]. Recent research has identified distinct differences in lipid chain lengths and saturation levels in sEVs derived from cancer models, affecting key pathways such as sphingolipid and neurotrophin signaling [38].

Technical Protocols for Key Experiments

Protocol 1: sEV Isolation from Human Biofluids Using Combined Ultracentrifugation-Filtration Method

This protocol adapts the CPF (chemical precipitation and ultrafiltration) approach for processing clinical biofluid samples including plasma, saliva, and urine [33] [40].

Materials:

• Fresh or frozen biofluids (plasma, saliva, urine)
• Low-speed centrifuge (capable of 2,000-10,000×g)
• Ultracentrifuge (capable of 100,000-160,000×g)
• 0.22 μm polyethersulfone (PES) membrane syringe filters
• 100 kDa molecular weight cut-off (MWCO) ultrafiltration devices
• Phosphate-buffered saline (PBS), pH 7.4
• Protease inhibitor cocktail

Procedure:

• Sample Pre-processing:
  • Thaw frozen samples on ice if previously stored at -80°C.
  • For plasma: Centrifuge at 1,500×g for 10 minutes at 4°C to remove residual cells [40].
  • For saliva: Centrifuge at 2,000×g for 10 minutes at 4°C to remove debris and mucins [40].
  • For urine: Centrifuge at 2,000×g for 15 minutes at 4°C to remove cells and debris [40].

• Intermediate Clearing:

  • Transfer supernatant to fresh tubes.
  • Centrifuge at 10,000×g for 15 minutes at 4°C to remove larger vesicles and debris.
  • Filter through 0.22 μm PES membrane syringe filters.

• sEV Concentration:

  • Apply filtrate to 100 kDa MWCO ultrafiltration devices.
  • Centrifuge at 4,000×g at 4°C until volume is reduced to approximately 10% of original.
  • Wash with 10 mL PBS and repeat concentration step.

• Final Isolation:

  • Recover concentrated sEVs from ultrafiltration device.
  • For additional purity, perform ultracentrifugation at 100,000×g for 70 minutes at 4°C.
  • Resuspend pellet in 100-200 μL PBS with protease inhibitors.
  • Store at -80°C for long-term preservation.

Protocol 2: Comprehensive sEV Characterization Workflow

Materials:

• Nanoparticle tracking analyzer (e.g., Malvern Nanosight)
• Transmission electron microscope
• SDS-PAGE and western blotting equipment
• Antibodies against CD9, CD63, CD81, TSG101, Flotillin-1, Calnexin
• BCA protein assay kit

Procedure:

• Nanoparticle Tracking Analysis:
  • Dilute sEV samples in filtered PBS to achieve 20-100 particles per frame.
  • Inject sample into NTA chamber using sterile syringe.
  • Record five 30-second videos at camera level 12-14.
  • Analyze using NTA software with detection threshold optimized for each sample.

• Transmission Electron Microscopy:

  • Apply 10 μL of sEV sample to Formvar/carbon-coated copper grid for 1 minute.
  • Wick away excess liquid with filter paper.
  • Negative stain with 1% uranyl acetate for 1 minute.
  • Wick away excess stain and air dry completely.
  • Image using TEM at 80-100 kV.

• Protein Marker Validation:

  • Determine protein concentration using BCA assay.
  • Separate 10-20 μg sEV protein by SDS-PAGE on 4-20% gradient gel.
  • Transfer to PVDF membrane and block with 5% non-fat milk.
  • Incubate with primary antibodies (1:1000 dilution) overnight at 4°C.
  • Incubate with HRP-conjugated secondary antibodies (1:5000) for 1 hour.
  • Develop using enhanced chemiluminescence substrate.

• Lipidomic Profiling:

  • Extract lipids from sEV samples using methyl-tert-butyl ether method.
  • Analyze by liquid chromatography-mass spectrometry.
  • Identify lipid species using reference standards and databases.
  • Quantify relative abundances and calculate lipid class distributions.

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Essential Research Reagents for sEV Isolation and Characterization

Category	Reagent/Equipment	Specific Example	Application Purpose
Isolation Materials	Ultracentrifuge	Beckman Coulter Optima XPN-100	High-speed centrifugation for sEV pelleting
	Size exclusion columns	qEV original columns (Izon Science)	Separation by hydrodynamic size
	Precipitation reagents	PEG-based kits (e.g., Total Exosome Isolation kit)	sEV precipitation from solution
	Immunoaffinity beads	CD63/CD81/CD9-conjugated magnetic beads	Marker-specific sEV capture
Characterization Tools	Nanoparticle tracker	Malvern Panalytical Nanosight	Size and concentration analysis
	Electron microscope	JEOL JEM-1400	Ultrastructural morphology assessment
	sEV marker antibodies	Anti-CD9, CD63, CD81, TSG101	Western blot validation of sEV identity
	Lipid standards	SPLASH LIPIDOMIX Mass Spec Standard	Lipidomic quantification reference
Specialized Reagents	Protease inhibitors	Complete Mini EDTA-free (Roche)	Prevent protein degradation during isolation
	Density gradient media	Iodixanol (OptiPrep)	Density-based separation in UC
	Filter membranes	0.22 μm PES syringe filters	Sterile filtration and size exclusion
	Ultrafiltration devices	100 kDa MWCO centrifugal filters	sEV concentration and buffer exchange
3-Hydroxy Ketoprofen	3-Hydroxy Ketoprofen Metabolite	3-Hydroxy Ketoprofen is a key CYP450 metabolite of Ketoprofen. For research use only. Not for human or veterinary diagnostic or therapeutic use.	Bench Chemicals
Emtricitabine Sulfone	Emtricitabine Sulfone|High-Purity	Emtricitabine Sulfone is a pharmaceutical reference standard for analytical research. This product is for Research Use Only (RUO) and is not intended for human or animal consumption.	Bench Chemicals

The field of sEV research has witnessed significant methodological advancements that enable more precise isolation and comprehensive characterization of these nanovesicles. The integration of complementary techniques—combining physical separation methods with immunological approaches—has proven particularly valuable for obtaining sEV preparations of high purity and yield suitable for cancer lipidomics research [33] [36]. As our understanding of sEV biogenesis and lipid-mediated functions in cancer progression deepens, continued refinement of these technical approaches will be essential. Standardization of protocols across laboratories remains a challenge but is critical for advancing the field and realizing the full potential of sEVs as cancer biomarkers and therapeutic vehicles. Future methodological developments will likely focus on increasing throughput, improving selectivity for disease-specific sEV subpopulations, and enhancing compatibility with multi-omics analyses for comprehensive functional studies.

Small extracellular vesicles (sEVs) represent a critical communication network in cancer biology, facilitating tumor progression, metastasis, and drug resistance through their bioactive cargoes. While extensive research has focused on protein and nucleic acid components, the lipid constituency of sEVs has emerged as a fundamental mediator of their biogenesis and function. The lipidomic profile of cancer-derived sEVs is not merely a structural artifact but reflects the metabolic reprogramming characteristic of malignant cells, offering a rich source of potential biomarkers and therapeutic targets [7]. Technological advances in mass spectrometry have enabled precise characterization of this lipid landscape, revealing disease-specific signatures that correlate with pathological progression. This technical guide explores the methodologies for comprehensive lipidomic profiling of cancer-derived sEVs, contextualized within the broader framework of sEV biogenesis and lipid metabolism in oncogenesis.

The significance of sEV lipids in cancer extends across multiple dimensions: they facilitate membrane curvature during vesicle formation, enable recipient cell uptake through fusion or endocytosis, and participate directly in oncogenic signaling pathways [13] [8]. Notably, the lipid composition of sEVs is markedly different from their parental cells, with documented enrichment ratios of 8.4-fold for certain lipid classes in cancer-derived vesicles, highlighting active sorting mechanisms that preferentially load specific lipids into sEVs [8]. This selective enrichment transforms sEVs into circulating indicators of the altered lipid metabolism that defines cancer metabolism, providing a window into disease-specific metabolic alterations.

Lipid Involvement in sEV Biogenesis and Function

Molecular Mechanisms of Lipid-Mediated sEV Formation

The biogenesis of sEVs occurs through distinct pathways that collectively shape their final lipid composition. The endosomal sorting complex required for transport (ESCRT) machinery represents the canonical pathway for exosome formation, wherein phosphoinositides play a regulatory role. Phosphatidylinositol-3-phosphate (PI(3)P) recruits ESCRT-0/I complexes to initiate vesicle formation, while phosphatidylinositol (3,5)-bisphosphate (PI(3,5)P2) and phosphatidylinositol (4,5)-bisphosphate (PI(4,5)P2) participate in ESCRT-III-mediated membrane scission and release of intraluminal vesicles into multivesicular bodies [13]. Concurrently, ESCRT-independent pathways utilize cone-shaped lipids like ceramide to induce spontaneous membrane curvature and inward budding of the endosomal membrane, facilitating intraluminal vesicle formation [13] [41].

The biogenesis of microvesicles, another sEV subtype, occurs through direct outward budding of the plasma membrane regulated by lipid asymmetry and calcium-dependent signaling. During this process, phosphatidylserine (PS) translocates from the inner to outer membrane leaflet, inducing membrane bending and blebbing, while cholesterol, sphingomyelin, and glycosphingolipids form lipid raft microdomains that serve as platforms for budding initiation [13]. The final scission event is mediated by ESCRT-III complexes or through the acid sphingomyelinase (A-SMase)-ceramide pathway, culminating in vesicle release [13].

Table 1: Key Lipids in sEV Biogenesis Pathways

Lipid Class	Specific Lipids	Function in Biogenesis	Mechanistic Action
Phosphoinositides	PI(3)P, PI(3,5)P2, PI(4,5)P2	ESCRT-dependent pathway regulation	Recruitment of ESCRT complexes; membrane scission
Sphingolipids	Ceramide	ESCRT-independent pathway	Cone-shaped structure induces membrane curvature for budding
Phospholipids	Phosphatidylserine (PS)	Microvesicle formation	Translocates to outer leaflet, inducing membrane bending
Sterols	Cholesterol	Lipid raft formation	Microdomain organization for budding initiation
Phospholipids	Phosphatidic acid (PA)	MVB fusion with plasma membrane	Regulates vesicular trafficking and fusion

Lipid-Driven Functional Capabilities of sEVs in Cancer

Once released, sEV lipids mediate critical functional interactions with recipient cells. The externalized phosphatidylserine on sEV surfaces facilitates recognition and uptake by recipient cells, while also contributing to immunomodulatory effects through interactions with immune receptors [13] [6]. Cancer-derived sEVs are particularly enriched in sphingolipids, phospholipids, and glycolipids with documented immunosuppressive properties that enable tumor immune evasion [42]. For instance, sEV-associated sphingosine mediates T-cell exhaustion in ovarian cancer, while PS-rich sEVs induce T-cell signaling arrest through phosphatidylserine-dependent mechanisms [42].

Beyond their role in immune modulation, sEV lipids serve as metabolic substrates and signaling molecules within the tumor microenvironment. Fatty acids transported by sEVs can undergo β-oxidation in recipient cells, providing an energy source that supports tumor growth under nutrient-deficient conditions [8]. Similarly, lysophosphatidic acid (LPA) and prostaglandins (PGs) carried by sEVs function as potent signaling molecules that promote angiogenesis and metastatic niche formation [8]. The lipidomic reprogramming of cancer sEVs thus represents a critical adaptive mechanism that supports multiple hallmarks of cancer progression.

Analytical Framework: Mass Spectrometry-Based Lipidomics

sEV Isolation and Purity Assessment

The foundation of reliable lipidomic profiling lies in the isolation of high-purity sEV preparations. Differential ultracentrifugation remains the gold standard, employing sequential centrifugation steps at increasing forces (10,000-100,000 × g) to pellet sEVs while excluding larger vesicles and cellular debris [43]. However, technical challenges persist due to the overlapping physical properties of sEVs and non-EV lipid particles, particularly lipoproteins which represent a common contaminant that can severely compromise lipidomic analyses [42]. To address this, density gradient centrifugation utilizing iodixanol or sucrose gradients can effectively separate sEVs from lipoprotein contaminants based on their differential buoyant densities [42].

Advanced isolation techniques including size-exclusion chromatography (SEC), affinity capture methods, and microfluidic technologies offer complementary approaches that improve specificity. Following isolation, rigorous characterization of sEV preparations is essential through nanoparticle tracking analysis (NTA) for size distribution profiling, transmission electron microscopy (TEM) for morphological validation, and western blotting for detection of canonical sEV markers (CD9, CD63, CD81, TSG101) and exclusion of negative markers (calnexin, GM130) [7] [43]. The lipid composition of the isolated sEVs can then be extracted using modified Folch or Bligh-Dyer methods with organic solvents, optimizing for comprehensive lipid recovery while preserving structural integrity [7].

Mass Spectrometry Platforms and Methodologies

Mass spectrometry-based lipidomics leverages two primary analytical approaches: shotgun lipidomics for direct infusion of lipid extracts, and liquid chromatography-mass spectrometry (LC-MS) for enhanced separation and sensitivity. For comprehensive coverage, reversed-phase liquid chromatography (RPLC) coupled to high-resolution mass spectrometers (Orbitrap or Q-TOF) provides optimal separation of complex lipid mixtures prior to mass analysis [7] [8].

Table 2: Mass Spectrometry Approaches for sEV Lipidomics

Analytical Platform	Ionization Method	Key Applications	Advantages	Limitations
Shotgun Lipidomics	ESI, MALDI	High-throughput screening of major lipid classes	Minimal sample preparation; rapid analysis	Limited isomer separation; matrix effects
RPLC-MS/MS	Electrospray Ionization (ESI)	Comprehensive lipid profiling	Excellent separation of molecular species; high sensitivity	Longer analysis time; solvent compatibility issues
HILIC-MS	Electrospray Ionization (ESI)	Separation by lipid class	Class-based separation; complementary to RPLC	Reduced resolution within molecular species
IM-MS	ESI, MALDI	Structural elucidation	Isomer separation; collision cross-section measurement	Increased complexity; specialized instrumentation

Data acquisition strategies encompass both data-dependent acquisition (DDA) for untargeted discovery and data-independent acquisition (DIA) or multiple reaction monitoring (MRM) for targeted quantification. In DDA mode, the instrument automatically selects precursor ions for fragmentation based on intensity thresholds, generating MS/MS spectra for lipid identification. For absolute quantification, stable isotope-labeled internal standards (e.g., d₇-cholesterol, ¹³C-labeled fatty acids) are incorporated to correct for ionization efficiency variations and matrix effects [7]. The resulting data undergoes processing through specialized software platforms (LipidSearch, Skyline, XCMS) for peak detection, alignment, and lipid identification against reference databases (LIPID MAPS, Human Metabolome Database).

Mass Spectrometry Workflow for sEV Lipidomics

Cancer-Specific Lipid Alterations in sEVs

Disease-Associated Lipidomic Signatures

Comprehensive lipidomic profiling has revealed consistent alterations in the lipid composition of cancer-derived sEVs across multiple cancer types. A hallmark of cancerous sEVs is their enrichment in specific lipid classes that support membrane stability, signaling competence, and metabolic adaptation. Prostate cancer-derived sEVs (PC3 cells) demonstrate significant sphingomyelin accumulation alongside elevated levels of glycosphingolipids and cholesterol, creating a rigid membrane architecture that enhances circulatory stability [7] [8]. Simultaneously, phosphatidylserine externalization serves as a nearly universal feature of cancer sEVs, mediating immunoevasion through T-cell suppression while also facilitating cellular uptake in recipient tissues [6] [42].

The functional implications of these alterations extend beyond structural modifications to active participation in oncogenic signaling. Lysophosphatidic acid (LPA) enriched in ovarian cancer sEVs activates G-protein-coupled receptors to promote cell migration and invasion, while prostaglandins carried by breast cancer sEVs stimulate inflammatory pathways that support metastatic niche formation [8]. The ceramide-to-sphingomyelin ratio, often dysregulated in cancer sEVs, determines recipient cell fate decisions by balancing pro-apoptotic versus proliferative signaling outcomes [7]. These disease-specific lipid signatures not only reflect the metabolic state of parent tumor cells but also actively remodel the recipient tissue environment to favor cancer progression.

Table 3: Cancer-Associated Lipid Alterations in sEVs

Cancer Type	Lipid Alterations	Functional Consequences	Potential Biomarker Utility
Prostate Cancer	↑ Sphingomyelin, ↑ Cholesterol, ↑ Glycosphingolipids	Enhanced membrane rigidity; signaling platform assembly	Diagnostic stratification; treatment response
Ovarian Cancer	↑ Lysophosphatidic acid (LPA), ↑ Sphingosine	T-cell exhaustion; promoted migration and invasion	Immunotherapy response prediction
Breast Cancer	↑ Phosphatidylserine, ↑ Prostaglandins	Immunosuppression; inflammatory niche formation	Metastatic risk assessment
Pancreatic Cancer	↑ Lysophosphatidylcholines (LPC)	AKT activation; proliferation and migration	Early detection; stromal interaction index
Glioblastoma	↑ Phosphatidic acid (PA), ↑ Arachidonic acid	Enhanced invasion; therapeutic resistance	Disease progression monitoring

Technical Validation and Functional Verification

The translation of observed lipid alterations into biologically meaningful insights requires rigorous validation through orthogonal approaches. Genetic and pharmacological manipulation of key lipid-metabolizing enzymes in parent cells establishes causal relationships between specific pathways and sEV lipid composition. For instance, inhibition of neutral sphingomyelinase 2 (nSMase2) with GW4869 reduces ceramide generation and subsequent sEV biogenesis, validating the role of ceramide in vesicle formation [41] [7]. Similarly, modulation of stearoyl-CoA desaturase (SCD) activity alters the desaturation index of sEV phospholipids, influencing membrane fluidity and recipient cell uptake efficiency [7].

Functional validation employs lipid tracing methodologies with stable isotope-labeled precursors (¹³C-glucose, ¹⁵N-choline) to track lipid flux from parent cells into sEVs and subsequently into recipient cells. This approach, combined with functional assays measuring migration, invasion, and proliferation in recipient cells following sEV exposure, establishes the mechanistic contribution of sEV lipids to cancer phenotypes. For clinical translation, blinded validation studies using independent patient cohorts are essential to confirm the diagnostic and prognostic performance of candidate lipid biomarkers before advancement to clinical implementation.

Table 4: Essential Research Reagents for sEV Lipidomics

Reagent Category	Specific Examples	Research Application	Technical Considerations
sEV Isolation Kits	Total Exosome Isolation Reagent, ExoQuick	Rapid sEV precipitation from biofluids	Co-precipitation of contaminants; optimization required
Lipid Standards	d₇-Cholesterol, ¹³C-Palmitic acid, Odd-chain phospholipids	Mass spectrometry quantification	Deuterium isotope effects; retention time matching
Enzyme Inhibitors	GW4869 (nSMase inhibitor), D609 (SM synthase inhibitor)	Pathway manipulation studies	Off-target effects; dose optimization critical
Lipid Dyes	PKH67, DiI, BODIPY-labeled fatty acids	Uptake and trafficking studies	Dye aggregation; transfer artifacts; proper controls
MS Lipid Libraries	LipidSearch, LIQUID, LIPID MAPS	Automated lipid identification	Platform-specific optimization; manual validation needed
Antibodies for Lipid Detection	Anti-phosphatidylserine, Anti-ceramide	Immunoaffinity capture; validation	Specificity validation; membrane permeability issues

Future Perspectives and Clinical Translation

The rapidly evolving field of sEV lipidomics holds significant promise for advancing cancer diagnostics and therapeutics. Technologically, imaging mass spectrometry enables spatial resolution of sEV lipid distributions within tissue contexts, while single-vesicle analysis techniques reveal the substantial heterogeneity within sEV populations that bulk analyses inevitably obscure [7]. The integration of lipidomic data with complementary omics platforms (proteomics, transcriptomics) through systems biology approaches provides a more comprehensive understanding of sEV biology and function.

Clinically, sEV lipid signatures offer compelling advantages as liquid biopsy biomarkers, with demonstrated capabilities for cancer detection, classification, and therapeutic monitoring [7] [44]. Their stability in circulation and reflection of real-time metabolic alterations position them as dynamic indicators of disease state and treatment response. From a therapeutic perspective, engineered modulation of sEV lipid composition presents opportunities for drug delivery optimization, leveraging natural trafficking capabilities while enhancing target specificity [6] [44]. The deepening understanding of sEV lipidomics will continue to illuminate fundamental cancer biology while simultaneously driving translational innovations in cancer management.

sEV Biogenesis and Lipid-Mediated Functional Pathways

Small extracellular vesicles (sEVs), commonly referred to as exosomes, have emerged as transformative biomarkers in oncology, offering a non-invasive window into tumor biology through liquid biopsy. These nanoscale vesicles (30-200 nm in diameter) are secreted by virtually all cells and contain a molecular cargo of proteins, nucleic acids, and lipids that reflect their cell of origin [45] [19]. In cancer, sEVs play pivotal roles in intercellular communication, facilitating processes including tumor progression, metastasis, angiogenesis, and drug resistance [6] [19]. Their stability, abundance in virtually all biological fluids, and molecular richness make them exceptionally promising candidates for cancer detection, monitoring, and prognosis. This technical guide focuses specifically on the lipid and protein signatures of sEVs, framing their biomarker potential within the essential biological context of sEV biogenesis and the reprogrammed lipid metabolism characteristic of cancer cells.

sEV Biogenesis and the Foundation of Their Lipid-Protein Identity

The molecular composition of sEVs is directly shaped by their complex biogenesis pathway. sEVs originate from the endosomal system, where early endosomes mature into multivesicular bodies (MVBs) [13]. During this process, the inward budding of the MVB membrane forms intraluminal vesicles (ILVs). These ILVs are subsequently released into the extracellular space as sEVs upon fusion of the MVB with the plasma membrane [13]. This biogenesis is governed by two primary mechanisms:

• The ESCRT-Dependent Pathway: The Endosomal Sorting Complex Required for Transport (ESCRT) machinery, comprising complexes ESCRT-0, -I, -II, and -III, along with associated proteins like VPS4, collaborates to recruit ubiquitinated proteins, induce membrane budding, and facilitate vesicle scission [6] [13].
• The ESCRT-Independent Pathway: This pathway relies heavily on lipids. Ceramide, a sphingolipid with a conical molecular structure, can spontaneously induce inward budding to form ILVs through its inherent biophysical properties [13] [7]. Other lipids, including cholesterol and sphingomyelin, also contribute to the formation and stability of membrane microdomains that facilitate this process [13].

The accompanying diagram illustrates this biogenesis pathway and the distinct roles of proteins and lipids.

sEV Biogenesis Pathways. This diagram illustrates the endosomal pathway of sEV formation, highlighting the distinct roles of the ESCRT protein machinery and specific lipids in the creation of intraluminal vesicles.

This coordinated activity results in sEVs with a defined structure: a lipid bilayer membrane decorated with transmembrane proteins and enclosing an intracellular cargo. The specific lipid and protein composition is not random; it is a highly selective process that is fundamentally rewired in cancer, yielding the disease-specific signatures explored in subsequent sections.

Lipid Signatures of Cancer-Derived sEVs

The lipid composition of sEVs is a direct reflection of the dysregulated lipid metabolism that is a hallmark of cancer [7]. Cancer cells exhibit a "lipid appetite," upregulating de novo lipogenesis and extracellular lipid uptake to fuel rapid growth and membrane biogenesis [7]. This metabolic reprogramming is imprinted onto the sEVs they release, making sEV lipidomics a promising diagnostic and prognostic tool.

Key Altered Lipid Classes in Oncogenic sEVs

The lipid bilayer of sEVs is composed of various classes of lipids, and alterations in this profile are consistently observed in cancer-associated sEVs [6] [13] [7]. These changes are not merely structural; many of these lipids play active roles in signaling pathways that drive tumorigenic behaviours.

Table 1: Key Lipid Classes Altered in Cancer-Derived sEVs and Their Proposed Roles

Lipid Class	Specific Example	Change in Cancer sEVs	Proposed Functional Role in Cancer
Sphingolipids	Ceramide (CER)	Variable	Fundamental for ESCRT-independent biogenesis; promotes membrane curvature [13].
	Sphingomyelin (SM)	Often Enriched	Increases membrane rigidity; found in lipid rafts that facilitate signaling [13].
Glycerophospholipids	Phosphatidylserine (PS)	Enriched (externalized)	Externalization on sEV surface can act as an "eat-me" signal for recipient cells; implicated in immune modulation [6] [13].
	Phosphatidic Acid (PA)	Enriched	A lipid second messenger involved in MVB docking and fusion with the plasma membrane [13].
Sterols	Cholesterol (CHOL)	Enriched	Stabilizes lipid rafts; critical for membrane fluidity and integrity; promotes efficient sEV uptake [13] [7].
Phosphoinositides	PI(3)P, PI(4,5)Pâ‚‚	Enriched	Key regulators of ESCRT-dependent biogenesis; recruit specific protein complexes to endosomal membranes [13].

Lipidomics as a Tool for Cancer Detection and Prognosis

The disease-specific alterations in sEV lipid profiles form the basis for their use as biomarkers. For instance, studies have identified that sEVs from prostate cancer cells are enriched in specific phospholipids and ceramides compared to sEVs from healthy cells [7]. The stability of lipids within the sEV membrane, which protects them from degradation, coupled with their bioavailability in all biological fluids, makes them uniquely suitable for clinical testing [7]. Research is actively exploring the potential of circulating sEV lipid profiles to distinguish cancer patients from healthy individuals, as well as to differentiate between cancer subtypes and stages.

Protein Signatures of Cancer-Derived sEVs

The protein cargo of sEVs is a rich source of biomarkers, encompassing surface proteins that dictate cellular targeting and internal proteins that can reprogram recipient cell functions. The proteomic profile of tumor-derived sEVs reflects the oncogenic state of the parent cell, providing critical diagnostic information.

Universal sEV Marker Proteins

All sEVs, regardless of origin, carry a conserved set of proteins involved in their biogenesis and structure. These are often used as positive markers to confirm the isolation of a bona fide sEV population. Key among these are:

• Tetraspanins: CD9, CD63, CD81, and CD82 are integral membrane proteins that are highly enriched in sEV membranes and play roles in cargo selection and cellular uptake [45] [19].
• Endosomal Sorting Proteins: Alix, TSG101, and other components of the ESCRT machinery are commonly found in sEVs [19].
• Heat Shock Proteins: Hsp70 and Hsp90 are frequently detected and may be involved in vesicle trafficking [19].

Cancer-Specific Protein Biomarkers

Beyond universal markers, cancer sEVs carry proteins that are specific to the tumor and its microenvironment. These proteins can be used for cancer detection, subtyping, and monitoring therapeutic response.

Table 2: Promising Protein Biomarkers in Cancer-Derived sEVs

Protein Biomarker	Full Name	Cancer Type	Clinical Application & Notes
GPC1	Glypican-1	Pancreatic	Enriched in sEVs; demonstrated 100% sensitivity and specificity in one study for detecting pancreatic cancer [45].
EpCAM	Epithelial Cellular Adhesion Molecule	Breast, Ovarian, Prostate	Overexpressed in carcinomas; used for immunocapture of sEVs; helps differentiate cancer patients from controls [45].
CD24	Cluster of Differentiation 24	Ovarian	Used in combination with EpCAM and FRα to achieve high diagnostic accuracy (AUC=1.00) for ovarian cancer [45].
PD-L1	Programmed Death-Ligand 1	Various (e.g., HCC)	sEV PD-L1 can suppress anti-tumor immunity; increased levels predict shorter progression-free and overall survival [45].
FRα	Folate Receptor Alpha	Ovarian	Nearly undetectable in control samples; significantly elevated in ovarian cancer sEVs (AUC=0.995) [45].

The power of protein biomarkers is often maximized in multi-analyte panels. For example, a combination of eight sEV biomarkers (CA 15-3, CA 125, CEA, HER2, EGFR, PSMA, EpCAM, VEGF) demonstrated an AUPRC of 0.9912 for distinguishing metastatic breast cancer patients from healthy controls [45]. Similarly, a panel of sEV GPC1, sEV CD82, and serum CA19-9 achieved an AUC of 0.942 for pancreatic cancer diagnosis [45].

Experimental Workflows: From sEV Isolation to Signature Analysis

The reliability of sEV lipid and protein data is critically dependent on robust and reproducible experimental protocols. The following workflow outlines the key stages from sample collection to data analysis.

sEV Isolation and Characterization

Isolating pure sEV populations is a primary challenge due to their small size and the presence of contaminants like lipoproteins in biological fluids [19]. Common techniques include:

• Ultracentrifugation: The historical gold standard, which separates particles based on size and density [46].
• Size-Exclusion Chromatography (SEC): Separates sEVs from smaller contaminants based on hydrodynamic volume, preserving vesicle integrity and function [46].
• Immunoaffinity Capture: Uses antibodies against sEV surface proteins (e.g., CD63, CD81, EpCAM) to isolate specific sEV subpopulations with high purity [45] [46].
• Microfluidic Devices: Emerging technologies that integrate isolation and analysis on a single chip, offering high sensitivity and potential for automation [46].

Following isolation, sEVs must be characterized to confirm their identity and purity. This typically involves:

• Nanoparticle Tracking Analysis (NTA): To determine particle size distribution and concentration.
• Transmission Electron Microscopy (TEM): To visualize vesicle morphology.
• Western Blot: To detect the presence of canonical marker proteins (e.g., CD9, CD63, TSG101) and the absence of negative markers (e.g., calnexin).

The following diagram summarizes the integrated workflow for sEV-based liquid biopsy analysis.

sEV Analysis Workflow. This diagram outlines the key steps in processing a liquid biopsy sample, from the isolation and characterization of sEVs to the subsequent analysis of their lipid and protein content.

Detailed Methodologies for Lipid and Protein Analysis

Lipidomic Profiling of sEVs:

• Lipid Extraction: Isolated sEVs are lysed, and lipids are extracted using organic solvents like chloroform and methanol via methods such as the Bligh and Dyer or Folch procedures.
• Separation and Analysis:
  • Thin-Layer Chromatography (TLC): A cost-effective method for initial lipid class separation and profiling.
  • Mass Spectrometry (MS): The cornerstone of modern lipidomics. Liquid Chromatography coupled to tandem MS (LC-MS/MS) is used to separate, identify, and quantify individual lipid species based on their mass-to-charge ratio and fragmentation patterns. This allows for the comprehensive characterization of the sEV lipidome.

Proteomic Analysis of sEVs:

• Protein Extraction and Digestion: sEVs are lysed with RIPA buffer or similar, and extracted proteins are denatured, reduced, alkylated, and digested into peptides using trypsin.
• Protein Identification and Quantification:
  • Western Blot: Used for targeted, low-throughput validation of specific protein biomarkers (e.g., GPC1, EpCAM).
  • Mass Spectrometry (MS): For unbiased, high-throughput discovery. LC-MS/MS analysis of the peptide mixture allows for the identification of hundreds to thousands of proteins in a single sample. Quantitative methods like TMT or SILAC labeling can be employed to compare protein abundance across different sample groups.

The Scientist's Toolkit: Essential Research Reagents and Technologies

The following table details key reagents and technologies essential for conducting research on sEV lipid and protein biomarkers.

Table 3: Research Reagent Solutions for sEV Biomarker Analysis

Reagent / Technology	Function	Specific Example / Note
Anti-Tetraspanin Antibodies	Immunoaffinity capture and characterization of sEVs.	Antibodies against CD9, CD63, CD81 for pulling down general sEV populations [45].
Anti-Tumor Marker Antibodies	Isolation and detection of tumor-specific sEV subpopulations.	Antibodies against EpCAM, GPC1, HER2 for capturing carcinoma-derived sEVs [45] [46].
Protein Lysis Buffer	Solubilizing proteins from the sEV membrane and lumen.	RIPA buffer, often supplemented with protease and phosphatase inhibitors.
Lipid Extraction Solvents	Isolating lipids from the sEV membrane for lipidomic analysis.	Chloroform: Methanol mixtures (e.g., 2:1 v/v) as per Bligh & Dyer or Folch methods [7].
Microfluidic sEV Isolation Chips	High-purity, automated isolation of sEVs from small sample volumes.	Chips functionalized with anti-CD63/CD81 nanostructures; ExoChip for integrated isolation and analysis [46].
LC-MS/MS System	The core platform for unbiased lipidomic and proteomic profiling.	Enables identification and quantification of thousands of lipid and protein species in a single run.
4-Methyl-2-nitroaniline-d6	4-Methyl-2-nitroaniline-d6, MF:C7H8N2O2, MW:158.19 g/mol	Chemical Reagent
Ethylone-d5	Ethylone-d5, CAS:1246820-59-6, MF:C12H15NO3, MW:226.28 g/mol	Chemical Reagent

The integration of sEV lipid and protein signatures into the framework of liquid biopsy represents a paradigm shift in cancer diagnostics. The molecular cargo of sEVs provides a comprehensive, real-time snapshot of tumor activity, enabling non-invasive early detection, accurate prognosis, and monitoring of therapeutic response. Understanding that these signatures are a direct consequence of the fundamental biology of sEV biogenesis and cancer-associated lipid metabolism is crucial for interpreting data and developing novel biomarkers.

Future research must focus on standardizing isolation and analytical protocols to ensure reproducibility and clinical translation [45] [19]. The integration of multi-omics data—combining lipidomics, proteomics, and transcriptomics—from a single sEV population will unlock deeper insights into cancer biology. Furthermore, the exploration of sEVs as therapeutic agents, either as natural drug delivery vehicles or as targets themselves (e.g., using natural compounds like Manumycin A or Cannabidiol to modulate sEV secretion [6]), opens exciting new avenues in oncology. As technologies mature, sEV-based liquid biopsies are poised to become an indispensable tool for precision medicine, ultimately improving patient outcomes through earlier intervention and personalized treatment strategies.

Small extracellular vesicles (sEVs) are critical mediators of intercellular communication in cancer, facilitating tumor progression, metastasis, and therapy resistance through their bioactive cargo. The biogenesis and function of these vesicles are intrinsically linked to cellular lipid metabolism, with both processes undergoing significant reprogramming in malignancies. This technical review examines the emerging role of natural compounds (NCs) as potent modulators of sEV biogenesis, secretion, and lipid composition. We synthesize current research demonstrating how NCs target key regulatory pathways and enzymes involved in lipid metabolism, thereby disrupting oncogenic sEV-mediated communication. The review also provides detailed experimental methodologies for investigating NC-sEV-lipid interactions and discusses the translational potential of these compounds as adjuvants in cancer therapy. By bridging the interconnected realms of sEV biology, lipid metabolism, and natural product pharmacology, this work aims to provide researchers with a comprehensive framework for developing novel therapeutic strategies targeting the sEV-lipid axis in cancer.

Small extracellular vesicles (sEVs), commonly referred to as exosomes, are nanoscale lipid bilayer-enclosed particles (30-200 nm in diameter) secreted by all cell types [47]. These vesicles play pivotal roles in intercellular communication by transporting bioactive molecules—including proteins, nucleic acids, and lipids—between cells [13]. In cancer, sEVs undergo profound functional alterations, becoming "oncosomes" that promote tumor progression through multiple mechanisms: modulating the tumor microenvironment (TME), facilitating epithelial-mesenchymal transition (EMT), establishing pre-metastatic niches, and conferring therapy resistance [6] [48].

The lifecycle of sEVs—from biogenesis to cellular uptake—is intimately connected with lipid metabolism. sEV membranes are enriched in specific lipid classes, including phosphatidylserine, sphingomyelin, ceramides, and sterols, which contribute not only to structural integrity but also to functional specificity [6] [13]. Cancer cells exhibit characteristic lipid metabolic reprogramming, with enhanced lipid uptake, reactivated de novo lipogenesis, and modified fatty acid oxidation [49] [50]. This metabolic rewiring directly influences sEV biogenesis, cargo sorting, and secretion, creating a vicious cycle that fuels tumor progression [13] [7].

Targeting the interconnected pathways of sEV biogenesis and lipid metabolism represents a promising therapeutic strategy. Natural compounds (NCs)—bioactive substances derived from plants, microbes, and other organisms—have emerged as potent modulators of these processes [6]. These compounds offer multifaceted antitumor effects, including the ability to influence sEV synthesis, secretion, composition, and function, while simultaneously regulating key enzymes and signaling pathways in lipid metabolism [6] [7].

sEV Biogenesis Pathways and Lipid Involvement

Molecular Mechanisms of sEV Formation

sEV biogenesis occurs through two primary pathways: the endosomal sorting complex required for transport (ESCRT)-dependent mechanism and ESCRT-independent mechanisms [13] [48].

The ESCRT-dependent pathway involves a highly conserved molecular machinery composed of five distinct complexes (ESCRT-0, -I, -II, -III, and Vps4) [6]. ESCRT-0 recognizes ubiquitinated cargoes and recruits them to endosomal microdomains through binding to 3-phosphoinosides. ESCRT-I and ESCRT-II subsequently drive inward budding of the endosomal membrane, forming intraluminal vesicles (ILVs) inside multivesicular bodies (MVBs). ESCRT-III then assembles on the endosomal membrane for the final step of vesicle scission [6] [48]. Phosphoinositides, particularly phosphatidylinositol-3-phosphate (PI(3)P), play crucial regulatory roles in this process by recruiting ESCRT components [13].

ESCRT-independent pathways utilize various lipid-based mechanisms for sEV formation. Ceramide, a sphingolipid metabolite, can trigger inward budding of endosomal membranes without ESCRT involvement through its conical molecular structure that promotes membrane curvature [13]. Other ESCRT-independent mechanisms involve tetraspanin-enriched microdomains, lipid raft microdomains, and additional signaling lipids [48]. The relative contribution of each pathway varies by cell type and physiological context, with cancer cells often exhibiting altered regulation of these processes.

The following diagram illustrates the major sEV biogenesis pathways and key regulatory lipids:

Lipid-Mediated Regulation of sEV Release and Uptake

Beyond biogenesis, lipids regulate subsequent stages of the sEV lifecycle. Microvesicles (MVs), another EV subclass (100-1000 nm), form through direct outward blebbing of the plasma membrane in a process driven by lipid asymmetry and calcium-dependent signaling [13]. Phosphatidylserine externalization, cholesterol and sphingomyelin enrichment in lipid rafts, and ceramide-mediated membrane curvature all contribute to MV budding and release [13].

Cellular uptake of sEVs occurs through various mechanisms, including endocytosis, macropinocytosis, and direct fusion, all influenced by lipid composition. sEV membrane lipids facilitate targeting and fusion with recipient cells, with specific lipid species acting as recognition signals [13]. Oncogenic sEVs exhibit modified lipid profiles that enhance their stability, bioavailability, and functional potency in the tumor microenvironment [7].

Natural Compounds Targeting sEV Biogenesis and Function

Natural compounds represent a promising class of sEV modulators with demonstrated effects on vesicle production, secretion, and cargo composition. The table below summarizes key NCs with documented activity against oncogenic sEV pathways:

Table 1: Natural Compounds Modulating sEV Biogenesis and Function

Compound	Source	Mechanisms of Action	Experimental Models	Key Effects on sEVs
Manumycin A [6]	Streptomyces species	Inhibits ESCRT pathway; suppresses Ras/Raf/ERK1/2 signaling and hnRNP H1 expression	Castration-resistant prostate cancer (CRPC) cells	10-fold reduction in exosome secretion; increased sensitivity to enzalutamide
Cannabidiol (CBD) [6]	Cannabis sativa	Modulates exosome and microvesicle release; inhibits prohibitin (chaperone protein associated with chemoresistance)	Prostate cancer (PC3), hepatocellular carcinoma (HEPG2), breast adenocarcinoma (MDA-MB-231), glioblastoma	Alters microRNA profile (increases miR-126, decreases miR-21); enhances cytotoxic efficacy when delivered via camel milk-derived exosomes
Resveratrol [6]	Grapes, berries	Downregulates Rab27a; affects CD63, Ago2, and eIF2α levels	Huh7 liver cancer cells, COLO320 and COLO741 colorectal cancer cells	Blocks exosome secretion; reduces proliferation and migration capacity
Honokiol [6]	Magnolia species	Identified as P-glycoprotein (P-gp) inhibitor using innovative screening platform (IOVMNPs)	Multiple cancer models	Increases bioavailability when sonicated with mesenchymal stem cell exosomes; enhances cellular uptake while reducing toxicity to normal cells
Simvastatin [7]	Fungal derivative (statin drug)	Cholesterol-lowering effect; modulates ESCRT-dependent loading of protein cargoes (CD63, Rab27a)	Macrophages and dendritic cells (in vitro and in vivo)	Reduces sEV formation and secretion; induces MVB accumulation

These natural compounds exert their effects through diverse molecular targets. Manumycin A directly targets the ESCRT machinery, effectively shutting down the primary sEV biogenesis pathway in cancer cells [6]. Cannabidiol exhibits pleiotropic effects, simultaneously modulating vesicle release and altering the microRNA cargo of sEVs, potentially reversing oncogenic signaling pathways [6]. Resveratrol targets Rab proteins, key regulators of MVB trafficking and plasma membrane fusion [6]. The cholesterol-lowering drug simvastatin, while not traditionally classified as a natural compound, derives from fungal sources and demonstrates how modulation of lipid metabolism can directly impact sEV biogenesis and cargo sorting [7].

Lipid Metabolism Reprogramming in Cancer and Modulation by Natural Compounds

Key Alterations in Cancer Lipid Metabolism

Cancer cells exhibit characteristic reprogramming of lipid metabolism to support rapid proliferation, membrane synthesis, and energy production. Major alterations include:

• Enhanced lipid uptake: Upregulation of fatty acid transporters including CD36, SLC27 family (FATPs), and fatty acid-binding proteins (FABPs) [49]
• Reactivation de novo lipogenesis: Increased expression and activity of lipogenic enzymes (ACC, FASN, SCD) despite adequate extracellular lipid availability [49] [50]
• Increased fatty acid oxidation: Enhanced mitochondrial β-oxidation to generate ATP, especially under metabolic stress [49]
• Cholesterol biosynthesis dysregulation: Elevated cholesterol synthesis supporting membrane fluidity and signaling [49]
• Bioactive lipid production: Altered synthesis of signaling lipids (eicosanoids, sphingolipids, phospholipids) that promote proliferation and survival [49] [7]

These metabolic adaptations are driven by oncogenic signaling pathways (particularly PI3K/AKT/mTOR), transcription factors (especially SREBPs), and tumor microenvironmental stresses such as hypoxia and nutrient deprivation [49].

Natural Compounds Targeting Lipid Metabolic Pathways

Natural compounds can intercept these reprogrammed metabolic pathways at multiple points:

Table 2: Natural Compounds Targeting Lipid Metabolism in Cancer

Metabolic Process	Key Molecular Targets	Natural Compound Modulators	Documented Effects
Lipid Uptake [49]	CD36, FABPs, FATPs	Plant polyphenols (e.g., resveratrol analogues)	Downregulation of CD36 expression; competition with fatty acid binding
De Novo Lipogenesis [49] [50]	ACC, FASN, SCD, SREBPs	Bioactive phytochemicals (e.g., berberine, curcumin)	Inhibition of SREBP processing and nuclear translocation; direct enzyme inhibition
Fatty Acid Oxidation [49] [50]	CPT1A, CPT2, ACADs	Specific plant extracts with CPT1A inhibitory activity	Reduced oxygen consumption rate; accumulation of lipid droplets
Cholesterol Biosynthesis [7]	HMG-CoA reductase	Statin-like compounds from fungal sources	Reduced membrane cholesterol content; disruption of lipid raft signaling
Bioactive Lipid Signaling [7]	Sphingosine kinases, phospholipases	Marine-derived compounds, flavonoids	Alteration of sphingolipid rheostat; reduced eicosanoid production

The interconnection between lipid metabolic reprogramming and sEV biogenesis creates a self-reinforcing cycle in cancer. Dysregulated lipid metabolism provides both structural components (membrane lipids) and signaling molecules (e.g., ceramide, phosphoinositides) that drive sEV production [13]. In turn, oncogenic sEVs transfer lipid-metabolizing enzymes and regulatory molecules between cells, propagating metabolic dysregulation throughout the tumor microenvironment [13] [7]. Natural compounds that target both processes simultaneously offer particular therapeutic promise by disrupting this vicious cycle at multiple points.

Experimental Approaches for Investigating NC-sEV-Lipid Interactions

Methodologies for sEV Isolation and Characterization

Robust experimental protocols are essential for studying the effects of natural compounds on sEV biogenesis and lipid composition. The following workflow outlines key methodological steps:

Standardized sEV Isolation Protocol (adapted from multiple sources [6] [13] [51]):

• Sample Collection and Pre-processing:

  • Collect blood plasma in EDTA tubes or condition cell culture media from treated/untreated cells
  • Perform initial centrifugation at 2,000-3,000 × g for 15-20 minutes at 4°C to remove cells and debris
  • Aliquot supernatant and store at -80°C if not processing immediately

• sEV Isolation:

  • Ultracentrifugation Method: Centrifuge pre-cleared samples at 100,000-120,000 × g for 70-120 minutes at 4°C
  • Precipitation Method: Mix sample with commercial precipitation reagent (e.g., Total sEV Precipitation Reagent, Thermo Fisher Scientific) according to manufacturer's instructions
  • Size Exclusion Chromatography: Use commercially available columns for high-purity sEV separation

• sEV Characterization:

  • Nanoparticle Tracking Analysis (NTA): Determine sEV concentration and size distribution using instruments such as Nanosight NS300 with NTA 3.2 software [51]
  • Transmission Electron Microscopy (TEM): Visualize sEV morphology and membrane structure
  • Western Blotting: Confirm presence of sEV markers (CD63, CD81, CD9, TSG101, Alix) and absence of negative markers (calnexin, GM130)

Lipidomic and Functional Analysis Methods

Lipidomic Profiling of sEVs:

• Lipid Extraction: Use modified Folch or Bligh-Dyer methods with chloroform:methanol mixtures
• Mass Spectrometry-Based Analysis: Employ liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) for comprehensive lipid profiling
• Data Analysis: Identify and quantify lipid species across multiple classes (glycerophospholipids, sphingolipids, sterols) using specialized software and lipid databases

Functional Assays:

• Cellular Uptake Studies: Label sEVs with lipophilic dyes (e.g., PKH67, DiI) and track internalization by recipient cells using flow cytometry or confocal microscopy
• Migration and Invasion Assays: Assess functional effects of sEVs on cancer cell behavior using Transwell or Boyden chamber systems
• Angiogenesis Assays: Evaluate pro- or anti-angiogenic effects using endothelial tube formation assays
• Immune Modulation Assays: Examine effects on immune cell populations (T-cell activation, macrophage polarization) using co-culture systems

The Scientist's Toolkit: Essential Research Reagents

Table 3: Essential Reagents for Investigating NC-sEV-Lipid Interactions

Reagent Category	Specific Examples	Research Applications	Key Considerations
sEV Isolation Kits	Total sEV Precipitation Reagent (Thermo Fisher)	Rapid isolation from biofluids and conditioned media	May co-precipitate non-sEV contaminants; suitable for downstream lipidomics
sEV Characterization	Antibodies against CD63, CD81, CD9, TSG101, Alix	Western blot confirmation of sEV identity	Use combinations of markers to confirm sEV purity and origin
Lipid Standards	Deuterated lipid internal standards (e.g., d7-cholesterol, d31-palmitoyl-oleoyl-phosphatidylcholine)	Lipid quantification via mass spectrometry	Essential for accurate absolute quantification in lipidomic studies
Natural Compounds	Manumycin A, Cannabidiol, Resveratrol, Honokiol	Functional modulation of sEV biogenesis and lipid metabolism	Optimize solubility (DMSO, ethanol) and concentration ranges for specific cell types
Lipid Metabolic Assays	Fatty acid uptake assays, β-oxidation rate kits, cholesterol quantification kits	Functional assessment of lipid metabolic pathways	Validate assays in specific cancer models with appropriate controls
Cell Culture Models	Cancer cell lines with defined genetic backgrounds, primary cancer-associated fibroblasts	In vitro assessment of sEV-mediated communication	Consider using 3D culture systems or co-cultures for enhanced physiological relevance
Animal Models	Patient-derived xenografts, genetically engineered mouse models, metastatic models	In vivo validation of NC effects on sEV function and lipid metabolism	Monitor potential off-target effects and overall toxicity of natural compounds
5'-Methoxylaudanosine-13C	5'-Methoxylaudanosine-13C Stable Isotope	5'-Methoxylaudanosine-13C is a labeled benzylisoquinoline alkaloid for research. For Research Use Only. Not for human or veterinary diagnostic or therapeutic use.	Bench Chemicals
7-Amino Nitrazepam-d5	7-Amino Nitrazepam-d5, MF:C15H13N3O, MW:256.31 g/mol	Chemical Reagent	Bench Chemicals

Therapeutic Implications and Future Perspectives

The strategic targeting of sEV biogenesis and lipid metabolism via natural compounds offers promising therapeutic avenues. First, NC-sEV modulation can potentially resensitize resistant cancers to conventional therapies. For instance, manumycin A sensitizes castration-resistant prostate cancer to enzalutamide [6], while cannabidiol shows enhanced efficacy against doxorubicin-resistant breast cancer when delivered via exosomal formulations [6].

Second, sEVs themselves represent innovative drug delivery vehicles. Their natural biocompatibility, stability in circulation, and targetability make them ideal for delivering natural compounds to tumor sites. Honokiol sonicated with mesenchymal stem cell exosomes demonstrates enhanced bioavailability and reduced toxicity [6], establishing a proof-of-concept for this approach.

Third, sEV lipid biomarkers offer diagnostic and prognostic potential. Disease-specific alterations in sEV lipid composition reflect pathological states and can be detected in liquid biopsies [13] [51]. The modified lipidomic profile of cancer-associated sEVs contributes to tumorigenic behaviors and disease progression [7], making these vesicles valuable biomarkers for early detection and monitoring.

Future research should prioritize several key areas: (1) standardizing isolation and characterization protocols for sEV-lipid studies; (2) developing more specific natural compound derivatives with enhanced potency and reduced off-target effects; (3) exploring combination therapies that simultaneously target sEV biogenesis and lipid metabolism; and (4) advancing clinical translation through well-designed preclinical studies and clinical trials that validate the therapeutic potential of these approaches.

The interconnected realms of sEV biology and lipid metabolism represent promising frontiers in cancer research and therapeutic development. Natural compounds offer multifaceted tools to disrupt oncogenic signaling by simultaneously modulating sEV biogenesis, function, and lipid metabolic reprogramming. Through continued investigation using robust experimental methodologies and interdisciplinary approaches, researchers can harness the full potential of these compounds to develop innovative strategies for cancer treatment. The integration of sEV-based diagnostics with natural compound-based therapeutics holds particular promise for personalized medicine approaches that target the unique metabolic vulnerabilities of individual tumors.

Small extracellular vesicles (sEVs), a predominant subclass of extracellular vesicles with diameters of 30-200 nm, have emerged as promising natural drug delivery platforms in oncology [6] [52]. Their inherent stability, biocompatibility, and low immunogenicity stem from a lipid bilayer envelope that facilitates efficient cellular uptake and crossing of biological barriers, including the blood-brain barrier [53]. The biogenesis of sEVs is intimately connected to cellular lipid metabolism, a relationship that becomes profoundly dysregulated in cancer. Cancer cells exhibit rewired lipid metabolism characterized by enhanced lipogenesis and lipid uptake to fuel rapid proliferation and membrane biogenesis [7]. This metabolic reprogramming directly influences the composition and quantity of sEVs released by tumor cells. These cancer-associated sEVs, or "oncosomes," play instrumental roles in modulating the tumor microenvironment, facilitating epithelial-mesenchymal transition, and promoting metastasis through the transfer of oncogenic proteins, lipids, and nucleic acids [6] [7]. The lipid bilayer of sEVs is composed of distinct classes of lipids—including phosphatidylserine, sphingomyelin, ceramides, and sterols—which contribute not only to structural integrity but also to biological function [6] [13]. Ceramide, for instance, is a key lipid mediator that can trigger the budding of exosomes through ESCRT-independent pathways [6]. This review explores how engineering the lipid composition of sEVs can enhance their targeting specificity and therapeutic efficacy, thereby advancing their application in precision cancer therapy.

Lipid Fundamentals in sEV Biology and Cancer Pathogenesis

Lipid-Driven sEV Biogenesis and Release

The lifecycle of sEVs is fundamentally governed by lipids and lipid signaling pathways. sEVs primarily originate from the endosomal system, where early endosomes mature into multivesicular bodies (MVBs) that accumulate intraluminal vesicles (ILVs) through inward budding of the endosomal membrane [13] [53]. The eventual fusion of MVBs with the plasma membrane releases these ILVs as sEVs into the extracellular space. This biogenesis process is regulated by both the endosomal sorting complex required for transport (ESCRT) machinery and ESCRT-independent mechanisms [6] [13]. Lipids play crucial roles in both pathways: phosphoinositides (PIs) like phosphatidylinositol-3-phosphate (PI(3)P) recruit ESCRT complexes (ESCRT-0, -I, -II, -III) to initiate vesicle formation, while cone-shaped lipids such as ceramide can induce inward membrane budding independent of ESCRT [6] [13]. The lipid composition of the parental cell membrane is selectively reflected in sEVs, with enrichment of cholesterol, sphingomyelin, phosphatidylserine, and bis(monoacylglycero)phosphate compared to parental membranes [13] [7].

Table 1: Key Lipids in sEV Biogenesis and Function

Lipid Class	Role in sEV Biology	Impact on Cancer Progression
Ceramide	Triggers ESCRT-independent inward budding of ILVs; induces membrane curvature [6] [13].	Promotes formation of pro-tumorigenic sEVs; regulates cell survival/death pathways [7].
Cholesterol	Enriched in sEV membranes and lipid rafts; contributes to membrane rigidity and stability [13] [7].	Upregulated in cancer cell sEVs; contributes to drug resistance and metastatic niche formation [7].
Phosphatidylserine (PS)	Externalized in microvesicles and apoptotic bodies; serves as an "eat-me" signal for phagocytes [6] [13].	Immunosuppressive when exposed on sEVs; promotes tumor cell evasion of immune surveillance [6].
Phosphoinositides (PIs)	Recruit ESCRT complexes (ESCRT-0/I) to initiate vesicle formation; regulate MVB maturation and trafficking [6] [13].	Dysregulated PI3K/Akt/mTOR signaling in cancer enhances sEV secretion and oncogenic cargo loading [7].
Sphingomyelin	Major component of the outer leaflet of sEV membranes; contributes to structural integrity [7].	Metabolic precursor to ceramide; altered levels affect membrane fluidity and signaling in recipient cells [7].

Reprogrammed Lipid Metabolism in Cancer and Its Impact on sEVs

Cancer-associated sEVs exhibit a distinctly modified lipidomic profile that mirrors and amplifies the dysregulated lipid metabolism of their parent tumor cells. Key lipogenesis regulators—including acetyl-CoA carboxylase, stearoyl-CoA desaturase 1, fatty acid synthase, and sterol regulatory element-binding proteins (SREBPs)—are frequently upregulated in cancers, leading to increased lipid synthesis that is reflected in sEV composition [7]. These modified sEV lipid profiles facilitate tumorigenic behaviors through multiple mechanisms: enhancing vesicle stability and bioavailability in circulation, promoting uptake by recipient cells, and activating oncogenic signaling pathways upon lipid transfer [7]. For instance, sEVs from prostate cancer cells are enriched in phosphatidylcholine and cholesterol, which contributes to their increased rigidity and capacity to transfer oncogenic signals [7]. The lipid composition of sEVs also influences their role in forming pre-metastatic niches and mediating drug resistance, making them both contributors to disease progression and potential diagnostic biomarkers [13] [7].

Engineering sEV Lipids for Enhanced Therapeutic Targeting

Strategic Modification of sEV Lipid Components

Engineering the lipid composition of sEVs offers powerful opportunities to optimize their performance as drug delivery vehicles. Both direct and indirect lipid modification strategies can enhance targeting specificity, improve cellular uptake, and increase payload capacity.

Natural Compound-Mediated Lipid Modulation: Several natural compounds (NCs) demonstrate the ability to modulate sEV biogenesis, secretion, and lipid composition through their effects on lipid-metabolizing enzymes. Cannabidiol (CBD), a phytocannabinoid, directly modulates exosome and microvesicle release in prostate cancer, hepatocellular carcinoma, and breast adenocarcinoma cells [6]. In glioblastoma models, CBD reduced exosome release and altered microRNA levels, increasing tumor-suppressive miR-126 while decreasing oncogenic miR-21 [6]. Similarly, Resveratrol, a natural polyphenol, blocks exosome secretion by downregulating Rab27a in hepatocellular carcinoma cells (Huh7 cell line), resulting in antiproliferative effects and decreased migration capacity [6]. These NCs represent promising tools for indirectly engineering sEV lipid profiles by modulating cellular lipid metabolism.

Direct Lipid Engineering Approaches: Direct manipulation of sEV lipids can be achieved through parental cell pretreatment or post-isolation modification. Preconditioning parent cells with specific fatty acids or lipid precursors can alter the resulting sEV membrane composition to enhance fluidity or rigidity based on therapeutic requirements. For instance, supplementing parent cell cultures with omega-3 fatty acids incorporates these more fluidizing lipids into sEV membranes, potentially enhancing membrane fusion with target cells [13] [7]. Alternatively, direct lipid insertion into isolated sEVs using membrane permeant lipid conjugates or electroporation can modify surface characteristics without disrupting core structure [54] [53].

Table 2: Engineering Strategies for sEV Lipid Enhancement

Engineering Approach	Methodology	Impact on sEV Function
Parent Cell Preconditioning	Incubation of parent cells with specific lipids (e.g., cholesterol, fatty acids) or lipid-modifying agents (e.g., simvastatin) [6] [7].	Alters endogenous sEV lipid composition during biogenesis; modulates biogenesis rate and cargo sorting [7].
Natural Compound Treatment	Exposure of parent cells to natural compounds (e.g., cannabidiol, resveratrol) that modulate lipid metabolism [6].	Reduces oncogenic sEV secretion; alters lipid and miRNA cargo profiles toward tumor-suppressive functions [6].
Surface Ligand Conjugation	Incorporation of targeting peptides, antibodies, or aptamers onto sEV surface via click chemistry or genetic engineering [54] [52].	Enhances specific binding to receptors overexpressed on target cancer cells (e.g., EGFR, HER2); reduces off-target effects [52].
Membrane Hybridization	Fusion of sEVs with synthetic liposomes or functionalized lipid nanoparticles [55] [52].	Combines natural sEV biology with enhanced loading capacity and targeting capabilities of synthetic systems [55].
Charge Modification	Alteration of surface lipid charge through incorporation of cationic or anionic lipids [52] [53].	Improves cellular uptake efficiency through enhanced electrostatic interactions with negatively charged cell membranes [53].

Enhancing Targeting Specificity Through Lipid-Mediated Surface Engineering

The targeting precision of sEVs can be dramatically improved through strategic engineering of their surface topology. Techniques such as click chemistry enable conjugation of homing ligands—including antibodies, peptides, or aptamers—to specific lipid components on the sEV surface [54] [52]. For instance, engineering mesenchymal stem cell-derived sEVs (MSC-sEVs) to express tumor-specific targeting ligands (e.g., EGFR nanobodies, RGD peptides) leverages the innate tumor-homing capacity of MSCs while adding specificity for particular cancer subtypes [54]. These surface modifications guide sEVs to recognize and internalize into specific cell populations through receptor-mediated endocytosis, dramatically increasing drug delivery efficiency while minimizing off-target effects [52] [53].

Lipid-based surface engineering can also enhance sEV performance by incorporating environmentally responsive elements. pH-sensitive lipids that undergo conformational changes in the acidic tumor microenvironment can promote selective sEV fusion with cancer cells [56]. Similarly, thermosensitive liposomes fused with sEVs create hybrid vehicles that release their payload in response to localized hyperthermia, enabling spatiotemporal control of drug delivery [56]. These advanced engineering approaches demonstrate how rational design of sEV lipid components can overcome many limitations of conventional drug delivery systems.

sEV Lipid Engineering Workflow

Experimental Protocols for sEV Lipid Engineering and Validation

Protocol 1: Lipid Preconditioning of Parent Cells for sEV Modification

Principle: Modifying the lipid composition of parent cells before sEV isolation indirectly engineers resulting sEVs by incorporating specific lipids during natural biogenesis [7].

Materials:

• Parent cells (e.g., mesenchymal stem cells, HEK293 cells)
• Cholesterol-methyl-β-cyclodextrin complex (water-soluble cholesterol delivery vehicle)
• Polyunsaturated fatty acids (e.g., docosahexaenoic acid, arachidonic acid)
• Standard cell culture medium and supplements
• Ultracentrifugation equipment
• Size-exclusion chromatography (SEC) columns

Procedure:

• Culture parent cells to 70-80% confluence in appropriate medium.
• Prepare treatment media containing:
  • Water-soluble cholesterol (10-50 µg/mL) to increase membrane rigidity OR
  • Polyunsaturated fatty acids (5-20 µM) to enhance membrane fluidity
• Replace standard culture medium with treatment media and incubate for 48 hours.
• Replace with exosome-depleted serum and culture for an additional 24 hours.
• Collect conditioned medium and isolate sEVs using differential ultracentrifugation:
  • 300 × g for 10 min to remove cells
  • 2,000 × g for 20 min to remove dead cells and debris
  • 10,000 × g for 30 min to remove larger vesicles
  • 100,000 × g for 70 min to pellet sEVs
• Purify sEVs using size-exclusion chromatography (qEVoriginal columns) with phosphate-buffered saline as eluent.
• Validate lipid modification through mass spectrometry-based lipidomics and membrane fluidity assays.

Validation: Confirm successful lipid engineering by measuring membrane packing density using laurdan generalized polarization fluorescence spectroscopy and assessing incorporation of specific lipids through liquid chromatography-mass spectrometry (LC-MS) lipid profiling [7].

Protocol 2: Click Chemistry-Mediated Surface Ligand Conjugation

Principle: Copper-free click chemistry enables efficient conjugation of targeting ligands to azide-modified lipids on sEV surfaces without compromising vesicle integrity [54] [52].

Materials:

• Isolated sEVs (1×10^11 particles)
• DBCO-PEG4-NHS ester (dibenzocyclooctyne polyethylene glycol N-hydroxysuccinimide ester)
• Azide-modified targeting ligand (e.g., cRGDyk peptide for αvβ3 integrin targeting)
• Phosphate-buffered saline (PBS), pH 7.4
• Zeba Spin Desalting Columns (7K MWCO)
• Bicinchoninic acid (BCA) protein assay kit
• Nanoparticle tracking analysis (NTA) instrument

Procedure:

• sEV Surface Modification:
  • Dissolve DBCO-PEG4-NHS ester in DMSO to 10 mM stock concentration.
  • Incubate isolated sEVs (100 μg protein) with 100 μM DBCO-PEG4-NHS ester in PBS for 2 hours at 4°C with gentle rotation.
  • Remove unreacted DBCO using Zeba spin desalting columns equilibrated with PBS.

• Ligand Conjugation:

  • Prepare azide-functionalized targeting ligand (cRGDyk-azide) in PBS at 10× molar excess relative to estimated sEV surface DBCO groups.
  • Incubate DBCO-modified sEVs with azide-ligand solution for 4 hours at room temperature.
  • Remove unconjugated ligand using ultracentrifugation at 100,000 × g for 70 minutes.

• Purification and Characterization:

  • Resuspose conjugated sEV pellet in PBS and quantify protein content using BCA assay.
  • Determine particle concentration and size distribution using nanoparticle tracking analysis.
  • Confirm ligand conjugation efficiency through western blotting for the targeting ligand or flow cytometry using receptor-specific binding assays.

Validation: Assess targeting specificity through competitive binding assays with receptor-blocking antibodies and measure cellular uptake efficiency in receptor-positive versus receptor-negative cell lines using fluorescently labeled sEVs and confocal microscopy [52].

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Research Reagents for sEV Lipid Engineering

Reagent/Category	Specific Examples	Research Function
Isolation Kits	Total Exosome Isolation Reagent, qEV size-exclusion columns, MEKit [52].	Isolate sEVs from cell culture media or biofluids with defined size and purity parameters.
Lipid Standards	Ceramide (d18:1/17:0), Sphingomyelin (d18:1/12:0), Cholesterol-D7 [7].	Internal standards for mass spectrometry-based lipidomic quantification of sEV lipid profiles.
Click Chemistry Reagents	DBCO-PEG4-NHS ester, Azide-PEG3-NHS, Tetrazine-PEG4-NHS [54] [52].	Facilitate covalent conjugation of targeting ligands to sEV surface proteins or lipids.
Natural Compounds	Cannabidiol, Resveratrol, Honokiol, Manumycin A [6].	Modulate sEV biogenesis, secretion, and lipid composition through metabolic pathway regulation.
Characterization Instruments	Nanoparticle Tracking Analyzer, Dynamic Light Scattering, NanoFlow Cytometry [52] [57].	Quantify sEV particle concentration, size distribution, and surface marker expression.
Lipid Delivery Systems	Cholesterol-methyl-β-cyclodextrin complex, Fatty acid-BSA conjugates [7].	Deliver specific lipids to parent cells for preconditioning and subsequent sEV modification.

Lipid Pathways in Cancer sEVs

The strategic engineering of lipid components in sEV-based drug delivery systems represents a transformative approach in precision cancer therapy. By leveraging the intrinsic relationship between sEV biogenesis and cellular lipid metabolism, researchers can now design bespoke therapeutic vesicles with enhanced targeting capabilities, improved cellular uptake, and optimized drug delivery efficiency. The ongoing convergence of lipidomics, bioengineering, and cancer biology continues to reveal new opportunities for manipulating sEV lipid composition to overcome biological barriers and maximize therapeutic impact. Current research is increasingly focused on developing stimulus-responsive lipid systems that release their payload in response to specific tumor microenvironment cues, creating smart drug delivery vehicles with unprecedented spatial and temporal control. As isolation techniques become more standardized and engineering methodologies more sophisticated, clinical translation of lipid-engineered sEV therapies is accelerating. These advances promise to usher in a new generation of highly targeted, lipid-optimized nanotherapeutics that dramatically improve treatment outcomes while minimizing systemic toxicity across a broad spectrum of malignancies.

Navigating Complexities: Challenges in Targeting Lipid-sEV Pathways for Therapy

The study of small extracellular vesicles (sEVs) has emerged as a frontier in understanding cancer progression, intercellular communication, and lipid-mediated metabolic reprogramming of the tumor microenvironment. These nano-scale vesicles (typically <200 nm), secreted by all cells including cancer cells, carry a complex molecular cargo of proteins, nucleic acids, and lipids that reflect their cell of origin. Within the context of cancer, sEVs facilitate critical processes including modulation of the tumor microenvironment, promotion of epithelial-mesenchymal transition, and establishment of metastatic niches [6]. Specifically, their stable lipid bilayer is composed of various classes of lipids including phosphatidylserine, sphingomyelin, ceramides, and sterols, making them both protected carriers and functional mediators of oncogenic signaling [6].

However, the field faces a fundamental challenge: the inherent heterogeneity of sEV populations and the lack of standardized methods for their isolation and analysis. This heterogeneity stems from multiple factors including diverse cellular origins, varying biogenesis pathways, and methodological inconsistencies in purification techniques. The problem is particularly pronounced in lipidomic studies, where contamination from plasma lipoproteins and inconsistent extraction protocols can significantly compromise data quality and reproducibility. This technical whitepaper addresses these challenges by providing a comprehensive framework for standardizing sEV isolation and lipidomic analysis, with specific emphasis on applications in cancer research where sEV lipid metabolism is increasingly recognized as a critical component of tumor progression and potential therapeutic target [6] [13] [58].

sEV Biogenesis and Lipid Metabolism in Cancer

Molecular Mechanisms of sEV Formation

sEV biogenesis involves sophisticated cellular machinery that governs both their formation and molecular cargo selection. The primary pathway for sEV generation occurs through the endosomal system, where early endosomes mature into late endosomes and subsequently form multivesicular bodies (MVBs) containing intraluminal vesicles (ILVs). These ILVs are released as sEVs upon fusion of MVBs with the plasma membrane [1] [59]. This process is regulated by both ESCRT-dependent and ESCRT-independent mechanisms. The ESCRT (Endosomal Sorting Complexes Required for Transport) machinery, comprising multiple complexes (ESCRT-0, -I, -II, -III and Vps4), sequentially recruits ubiquitinated cargoes and facilitates membrane budding and scission [6]. ESCRT-independent mechanisms frequently involve ceramide-mediated budding, where the conical molecular structure of ceramide triggers inward budding of the endosomal membrane [13].

Table 1: Key Lipid Players in sEV Biogenesis and Their Functions

Lipid Class	Role in sEV Biogenesis	Mechanism of Action
Ceramide	ESCRT-independent ILV formation	Triggers membrane budding through conical molecular structure [6] [13]
Phosphatidylserine (PS)	Membrane curvature generation	Asymmetric distribution contributes to membrane bending; enriched in sEVs from tumor cells [6] [13]
Phosphoinositides (PIs)	ESCRT-dependent pathway regulation	PI(3)P recruits ESCRT-0/I; PI(4,5)P2 facilitates ESCRT-III-mediated scission [13]
Cholesterol	MVB maturation and membrane stability	Regulates MVB migration, volume, and vacuolization; forms lipid raft microdomains [13]
Sphingomyelin	Microdomain organization	Partners with cholesterol in lipid raft formation for budding initiation [13]

Lipid-Mediated Functional Programming of sEVs in Cancer

In cancer, sEVs serve as oncogenic carriers that reprogram recipient cells through delivery of lipid, protein, and nucleic acid cargo. Tumor-derived sEVs (often called "oncosomes") contain oncogenic proteins including RAS, KRAS, EGFR, mutant EGFR variant III, heat shock proteins, matrix metalloproteinases, and immune checkpoint regulators like PD-L1 [6]. The lipid composition of sEVs significantly influences their function, with alterations in lipid profiles documented across various cancer types. Specifically, phosphatidylserine exposure on sEV surfaces can influence immune recognition, while enrichment in specific lipid species can enhance pro-metastatic signaling [6]. These lipid-mediated functions position sEVs as critical mediators of cancer progression and potential diagnostic biomarkers.

Standardization of sEV Isolation Methods

Comparative Analysis of Isolation Techniques

The selection of appropriate isolation methods is paramount for obtaining high-purity sEV preparations suitable for downstream lipidomic analysis. Current methodologies exploit various physicochemical properties of sEVs including size, density, solubility, and surface markers. The table below provides a comprehensive comparison of the most widely used techniques:

Table 2: Comparative Analysis of sEV Isolation Methods for Lipidomic Studies

Method	Principle	Purity (Particle/Protein Ratio)	Yield	Key Advantages	Key Limitations	Suitability for Lipidomics
Ultracentrifugation (UC)	Sequential centrifugation at varying speeds and forces	High [60] [61]	Low to Moderate [60] [61]	Considered gold standard; large sample capacity [1]	Time-consuming; requires specialized equipment; potential vesicle damage [1] [61]	High purity beneficial but yield may be limiting
Size-Exclusion Chromatography (SEC)	Separation by hydrodynamic size through porous matrix	Moderate to High [60] [61]	Moderate [61]	Preserves vesicle integrity; compatible with various biofluids [61]	Less effective with complex biofluids; may co-isolate similar-sized particles [61]	Good, but lipoprotein contamination concerns
Density Gradient Ultracentrifugation (DGUC)	Separation by buoyant density in iodixanol or sucrose gradients	High [62]	Low to Moderate [62]	Excellent separation from contaminants; high purity	Time-intensive; technically demanding; low throughput	Excellent for minimizing lipoprotein interference
Precipitation-Based Methods (e.g., PEG)	Reduced solubility and dispersibility of sEVs	Low [61]	High [61]	Simple protocol; minimal equipment needs; high recovery	Co-precipitation of non-vesicular contaminants; lower purity [61]	Problematic due to co-precipitated contaminants
Immunoaffinity Capture	Antibody-mediated capture of specific surface markers	Variable (depends on antibody specificity)	Low to Moderate	High specificity; subpopulation selection	Expensive; limited by antibody availability; may bias population	Specialized applications requiring specific sEV subsets

Advanced and Combinatorial Approaches

Recent methodological advances demonstrate that combinatorial approaches can significantly enhance isolation purity, particularly challenging when working with complex biofluids like blood plasma. An optimized SEC-DGUC protocol has shown superior performance for isolating sEVs from small plasma volumes (as little as 500 μL). This sequential approach leverages the strengths of both techniques: SEC effectively removes plasma proteins and high-density lipoproteins (HDLs), while subsequent DGUC separates sEVs from remaining lipoproteins based on density differences [62]. This method isolates sEVs across a density range >1.08 g/mL, effectively separating them from the majority of contaminating lipoproteins [62].

For cancer research applications where specific sEV subpopulations are of interest, immunoaffinity-based separation enables isolation of tissue-specific or cell-specific sEV subsets. For example, in studies of immunotherapy response, separating T cell-derived (CD3+) sEVs from tumor-enriched (CD3-) sEV populations has proven valuable for biomarker discovery [63]. This approach can be particularly insightful for understanding lipid-mediated immune modulation in the tumor microenvironment.

Diagram 1: Sequential SEC-DGUC sEV Isolation Workflow (Title: Combined SEC-DGUC Purification)

Standardized Quality Assessment Parameters

Rigorous characterization of isolated sEVs is essential for method validation and cross-study comparisons. The International Society for Extracellular Vesicles recommends multi-parametric assessment:

• Nanoparticle Tracking Analysis (NTA): Quantifies particle concentration and size distribution [61] [63]. SEC-DGUC isolates typically show particle concentrations of 9.6×10^8 to 5.5×10^11 particles/mL from plasma [62].
• Transmission Electron Microscopy (TEM): Visualizes ultrastructural morphology and membrane integrity [61]. Cryo-EM reveals bilayer membrane structures and identifies lipoprotein contaminants [62].
• Western Blot Analysis: Detects presence of sEV marker proteins (CD63, CD81, CD9, TSG101, Flotillin-1) and absence of contaminants [61].
• Particle-to-Protein Ratio: Quantifies purity, with higher ratios indicating less non-vesicular protein contamination [60] [61]. UC and SEC typically yield higher ratios than precipitation-based methods [61].

Standardization of Lipidomic Analysis for sEVs

Lipid Extraction Methodologies

Effective lipid extraction is foundational for comprehensive sEV lipidomics. While chloroform-based methods (e.g., Folch, Bligh-Dyer) have been widely used due to their efficiency in extracting broad lipid classes, concerns about toxicity and environmental impact have driven the development of greener alternatives. Recent systematic evaluation identifies cyclopentyl methyl ether (CPME) as a sustainable solvent that demonstrates comparable or superior performance to chloroform in single-phase extraction protocols [64]. Other promising alternatives include 2-methyltetrahydrofuran (2-MeTHF) and iso-butyl acetate (iBuAc) when used in modified extraction workflows [64].

The extraction protocol must account for the unique lipid composition of sEVs, particularly their enrichment in sphingolipids, cholesterol, and phosphatidylserine. For sEVs isolated from limited clinical samples, miniaturized extraction protocols adapted to small sample volumes (5-20 μL plasma equivalents) are essential to enable biomarker discovery from precious specimens.

Analytical Platforms and Lipid Identification

Mass spectrometry-based platforms represent the cornerstone of modern sEV lipidomics. Liquid chromatography-mass spectrometry (LC-MS) enables comprehensive profiling of diverse lipid classes through both untargeted and targeted approaches. Key considerations for standardization include:

• Chromatographic separation: Reverse-phase LC separates lipids by hydrophobicity, while normal-phase or HILIC separates by lipid class polarity.
• Mass analyzers: High-resolution instruments (Orbitrap, TOF) enable untargeted profiling and lipid identification, while tandem quadrupole instruments provide superior sensitivity for targeted quantification.
• Internal standards: Stable isotope-labeled or odd-chain lipid standards are essential for absolute quantification and monitoring extraction efficiency across samples.
• Quality controls: Pooled quality control samples should be analyzed throughout sequences to monitor instrument performance and correct for drift.

Standardized lipid nomenclature following LIPID MAPS conventions and reporting of minimal experimental metadata are critical for data sharing and cross-study comparisons.

Integrated Workflows and The Scientist's Toolkit

Comprehensive Analytical Workflow

Diagram 2: Standardized sEV Lipidomics Pipeline (Title: sEV Lipidomics Quality Control)

Essential Research Reagent Solutions

Table 3: Essential Research Reagents for sEV Isolation and Lipidomic Analysis

Reagent/Category	Specific Examples	Function/Application	Technical Notes
sEV Isolation Kits	PEG-based precipitation kits	Rapid sEV precipitation from biofluids	Higher yield but lower purity; suitable for screening [61]
Chromatography Media	Sepharose 2B, Sephacryl S-400	Size-exclusion chromatography	Effective for removing proteins and HDLs [63] [62]
Density Gradient Media	Iodixanol, Sucrose solutions	Density-based separation	Optimized gradients improve lipoprotein removal [62]
Lipid Extraction Solvents	CPME, 2-MeTHF, MTBE	Green alternatives to chloroform	CPME shows comparable performance in single-phase extraction [64]
Internal Standards	EquiSPLASH LIPIDOMIX, SPLASH standards	Quantification normalization	Correct for extraction efficiency and MS variability [64]
sEV Characterization Antibodies	Anti-CD63, CD81, CD9, TSG101	sEV validation and subtyping	Essential for Western blot and immuno-capture applications [61] [63]
Protease and Enzyme Inhibitors	Complete protease inhibitors	Sample preservation during processing	Prevent degradation of sEV-associated proteins and lipids
Antioxidants	Butylated hydroxytoluene (BHT)	Prevent lipid oxidation during processing	Critical for preserving oxidative lipid species [64]

Applications in Cancer Research and Therapeutic Development

The standardized approaches outlined in this whitepaper enable robust investigation of sEV-mediated lipid signaling in cancer biology and therapeutic development. Several key applications demonstrate particular promise:

sEVs as Biomarkers in Cancer Immunotherapy

In the context of immunotherapy, sEV subpopulations show significant potential as predictive biomarkers. Studies in recurrent/metastatic head and neck squamous cell carcinoma (HNSCC) demonstrate that T cell-derived CD3+ sEVs and tumor-enriched CD3- sEVs carry distinct protein profiles correlated with treatment response. Specifically, high levels of CD3+ sEVs associate with better overall and progression-free survival in anti-PD-1 treated patients [63]. Furthermore, immunosuppressive proteins (e.g., PD-L1, CTLA-4) on circulating sEVs may serve as accessible biomarkers for monitoring therapeutic response and immune status [63].

Natural Compounds Modulating sEV Lipid Composition

Emerging evidence indicates that natural compounds (NCs) with anticancer properties can influence sEV biogenesis and lipid composition. For instance:

• Manumycin A reduces exosome secretion in castration-resistant prostate cancer cells by inhibiting the ESCRT pathway and Ras/Raf/ERK1/2 signaling [6].
• Cannabidiol (CBD) modulates exosome and microvesicle release in multiple cancer cell lines and alters microRNA cargo, potentially through inhibition of prohibitin, a chaperone protein associated with chemoresistance [6].
• Resveratrol blocks exosome secretion by downregulating Rab27a in hepatocellular carcinoma cells, resulting in decreased cancer cell proliferation and migration [6].

These findings highlight the potential of targeting sEV-mediated communication through lipid metabolism modulation as a novel therapeutic strategy in cancer.

Technical Considerations for Therapeutic Applications

For therapeutic development, several technical aspects require particular attention:

• Scalable isolation methods that maintain sEV integrity and function are needed for preclinical and clinical translation.
• Potency assays measuring lipid-mediated biological activities must be standardized for quality control.
• Storage conditions that preserve lipid membrane integrity and prevent aggregation require optimization.
• Biodistribution studies using labeled sEVs should account for lipid-dependent targeting properties.

Standardization of sEV isolation and lipidomic analysis represents an essential foundation for advancing our understanding of lipid-mediated intercellular communication in cancer. The integrated approaches outlined in this technical guide—combining optimized purification methods, rigorous quality assessment, standardized lipid extraction, and mass spectrometry analysis—provide a robust framework for generating comparable, high-quality data across laboratories.

Future methodology development should focus on several critical areas: (1) Single-vesicle lipidomics technologies that resolve heterogeneity within sEV populations; (2) Spatially resolved lipid mapping that correlates lipid composition with tissue localization; (3) Integrated multi-omics approaches that simultaneously profile lipid, protein, and nucleic acid cargo from the same vesicle population; and (4) Computational tools for deciphering complex lipid signatures and their functional implications in cancer progression.

As these methodological advances mature, standardized sEV lipidomics will increasingly enable biomarker discovery, therapeutic target identification, and potentially the development of sEV-based therapeutics that harness lipid-mediated signaling pathways in cancer and other diseases. The convergence of rigorous standardization and innovative analytical technologies promises to unlock the full potential of sEV lipid biology in both basic research and clinical translation.

Cancer drug resistance remains a critical obstacle in oncology, leading to treatment relapse and disease progression. Small extracellular vesicles (sEVs), lipid-bilayer enclosed nanoscale particles (30-150 nm) of endosomal origin, have emerged as pivotal mediators of intercellular communication within tumor ecosystems [16] [18]. Their biogenesis, regulated by both the endosomal sorting complex required for transport (ESCRT) machinery and ESCRT-independent pathways involving lipids like ceramide, positions them as key vehicles for transferring biomolecules that confer treatment resistance [6] [18]. The lipid composition of sEVs not only influences their formation and stability but also directly contributes to therapeutic failure through multiple mechanisms. This review examines the multifaceted role of sEV lipids in driving drug resistance and explores emerging therapeutic strategies to counteract these processes.

Lipid-Mediated Mechanisms of sEV-Driven Resistance

Lipid-Dependent sEV Biogenesis and Cargo Sorting

The very formation of sEVs is intimately connected to lipid metabolism, creating a fundamental link to resistance mechanisms. The ESCRT-independent biogenesis pathway relies heavily on ceramide, a lipid second messenger that triggers membrane budding and intraluminal vesicle formation within multivesicular bodies (MVBs) [6] [18]. Additionally, other lipids including cholesterol, sphingomyelin, and phosphatidylserine participate in sEV formation, secretion, signaling, and uptake [6]. The phospholipid phosphatidylserine, while primarily located in the inner leaflet of cell membranes, is abundantly present in sEVs released from tumor cells [6]. This lipid-mediated biogenesis enables cancer cells to selectively package and export resistance-conferring cargo, establishing a systemic network of treatment evasion.

Functional Lipid Cargo in sEV-Mediated Resistance

sEVs serve as lipid carriers that directly alter recipient cell behavior and drug sensitivity. The stable lipid bilayer of sEVs is composed of various classes of lipids, including phosphatidylserine, sphingomyelin, ceramides, and sterols [6]. Alterations in the lipid profile of sEVs have been documented in various cancers, making them suitable as both biomarkers and therapeutic targets [6]. These sEV-associated lipids can directly activate pro-survival signaling pathways in recipient cells, remodel the tumor microenvironment to protect cancer cells from therapeutic insults, and directly sequester or efflux chemotherapeutic agents [37]. The transfer of lipid components represents a sophisticated mechanism by which tumors distribute resistance capabilities throughout the cellular population.

Table 1: Lipid Components in sEVs and Their Proposed Roles in Drug Resistance

Lipid Component	Alteration in Cancer sEVs	Proposed Role in Resistance
Phosphatidylserine	Increased exposure	Immunosuppression; Engulfment by recipient cells
Ceramide	Increased concentration	Enhanced sEV biogenesis; Survival signaling
Cholesterol	Enriched in sEV membrane	Membrane rigidity; Reduced drug uptake
Sphingomyelin	Composition altered	Signal transduction; Pathway activation
Sphingosine-1-phosphate	Elevated in tumor sEVs	Angiogenesis; Metastatic niche formation

sEV-Mediated Intercellular Communication in Resistance

Tumor-derived sEVs establish a pro-resistance network through systematic modification of the tumor microenvironment and distant niches. These vesicles facilitate epithelial-mesenchymal transition (EMT), a key process in metastasis and resistance, by transferring specific proteins and nucleic acids [16]. sEVs also contribute to angiogenesis by transporting pro-angiogenic factors like vascular endothelial growth factor to endothelial cells [16]. Furthermore, they promote immune escape by carrying immunosuppressive cargo that inhibits T-cell function and activates regulatory immune populations [16]. Perhaps most critically, sEVs create pre-metastatic niches in distant organs that not only support metastasis but also establish reservoirs of treatment-resistant cells [16]. This multifaceted intercellular communication ensures that resistance mechanisms are disseminated throughout the tumor ecosystem.

Experimental Analysis of sEV Lipids in Resistance

Methodologies for Investigating sEV Lipid Composition

Comprehensive analysis of sEV lipidomes requires integrated approaches combining isolation, characterization, and functional validation. The following protocols outline key methodologies for investigating the role of sEV lipids in drug resistance.

Protocol 1: Isolation and Lipidomic Profiling of Drug-Resistant sEVs

• sEV Isolation from Conditioned Media:

  • Culture drug-resistant and sensitive cancer cells under identical conditions until 70-80% confluent
  • Replace media with exosome-depleted FBS containing media for 48 hours
  • Collect conditioned media and perform sequential centrifugation: 300 × g for 10 min (remove cells), 2,000 × g for 20 min (remove dead cells), 10,000 × g for 30 min (remove cell debris)
  • Ultracentrifuge at 100,000 × g for 70 min at 4°C to pellet sEVs [16] [18]
  • Wash pellets with PBS and repeat ultracentrifugation
  • Resuspend final sEV pellets in PBS and quantify protein content using BCA assay

• Lipid Extraction and Analysis:

  • Perform lipid extraction using methyl-tert-butyl ether (MTBE)/methanol method
  • Resuspend dried lipid extracts in methanol:toluene (9:1) with internal standards
  • Analyze using liquid chromatography-mass spectrometry (LC-MS) with C18 reversed-phase column
  • Perform data processing using lipid analysis software (e.g., LipidSearch, MS-DIAL)

• Validation of Lipid Components:

  • Confirm specific lipid species using targeted MRM approaches
  • Validate findings in patient-derived sEVs from liquid biopsies
  • Correlate lipid signatures with clinical resistance patterns

Protocol 2: Functional Validation of sEV Lipid-Mediated Resistance

• sEV Uptake and Trafficking Studies:

  • Label isolated sEVs with lipophilic dyes (e.g., PKH67, DiD)
  • Incubate with recipient cells for varying timepoints (0-24 hours)
  • Fix cells and visualize using confocal microscopy with organelle-specific markers
  • Quantify uptake using flow cytometry

• Lipid Transfer Tracking:

  • Incorporate fluorescent lipid analogs (e.g., BODIPY-labeled lipids) into donor cell membranes
  • Isolate sEVs and track transfer to recipient cells using fluorescence resonance energy transfer (FRET)
  • Measure functional consequences on recipient cell lipid composition by mass spectrometry

• Resistance Phenotyping:

  • Treat recipient cells with sEVs from resistant donors for 48 hours
  • Challenge with chemotherapeutic agents at IC50 concentrations
  • Assess viability using MTT, CellTiter-Glo, or clonogenic assays
  • Evaluate apoptosis via Annexin V/PI staining and flow cytometry
  • Measure drug accumulation using fluorescent chemotherapeutics (e.g., doxorubicin)

Table 2: Key Research Reagents for Investigating sEV Lipid-Mediated Resistance

Reagent/Category	Specific Examples	Research Application
sEV Isolation Kits	Total Exosome Isolation Kit, ExoQuick-TC	Rapid sEV precipitation from cell culture media or biological fluids
Lipid Standards	SPLASH LIPIDOMIX, Avanti Polar Lipids	Internal standards for lipid quantification and identification
Lipid Dyes	PKH67, DiD, FM dyes, Nile Red	sEV labeling for uptake and trafficking studies
Ceramide Modulators	GW4869, myriocin, fumonisin B1	Inhibit ceramide-mediated sEV biogenesis pathways
Lipidomic Platforms	LC-MS/MS, MALDI-TOF	Comprehensive lipid profiling and quantification
sEV Characterization	Nanoparticle Tracking Analysis, Western Blot (CD63, CD81, TSG101)	sEV quantification, size distribution, and purity assessment

Computational and Visualization Approaches

The complex relationships between sEV lipids and resistance mechanisms can be elucidated through computational modeling and pathway visualization. The following diagram illustrates the key lipid-mediated processes in sEV-driven resistance:

Diagram 1: Lipid-Mediated sEV Mechanisms in Drug Resistance. This diagram illustrates how sEV biogenesis, driven by specific lipid pathways, facilitates multiple mechanisms that collectively contribute to treatment failure.

Therapeutic Strategies Targeting sEV Lipids to Overcome Resistance

Natural Compounds as sEV Lipid Modulators

Several natural compounds (NCs) demonstrate promising activity in modulating sEV biogenesis and lipid composition, potentially reversing resistance phenotypes. Manumycin A, an antibiotic derived from Streptomyces species, significantly reduces exosome secretion (10-fold) in castration-resistant prostate cancer cells by interfering with ESCRT-dependent mechanisms and Ras/Raf/ERK1/2 signaling [6]. Cannabidiol (CBD), a phytocannabinoid from Cannabis sativa, directly modulates exosome and microvesicle release in multiple cancer cell lines including prostate cancer (PC3), hepatocellular carcinoma (HEPG2), and breast adenocarcinoma (MDA-MB-231) [6]. In glioblastoma multiforme cells, CBD not only reduced exosome release but also altered microRNA levels, increasing miR-126 (associated with improved survival) while decreasing oncogenic miR-21 [6]. Resveratrol, a natural polyphenol with antioxidant properties, blocks exosome secretion by downregulating Rab27a in hepatocellular carcinoma cells (Huh7), resulting in antiproliferative effects and decreased migration capacity [6]. These natural compounds represent promising candidates for combination therapies aimed at disrupting sEV-mediated resistance pathways.

sEV-Based Delivery Systems for Overcoming Resistance

Paradoxically, the inherent properties of sEVs that contribute to resistance can be harnessed for therapeutic benefit. sEVs possess several advantageous characteristics as drug delivery vehicles, including low immunogenicity, high biocompatibility, and an innate ability to cross biological barriers like the blood-brain barrier [65]. Their small size (typically 30-150 nm) and stability in circulation make them ideal for delivering therapeutic payloads to specific cell types [65] [18]. Research has demonstrated that sEVs can be loaded with various anticancer agents, including chemotherapeutics, nucleic acids (siRNA, miRNA), and proteins. For instance, camel milk-derived exosomes have been shown to enhance the cytotoxic efficacy of cannabidiol against doxorubicin-resistant breast cancer by improving bioavailability [6]. Similarly, honokiol, a bioactive compound from Magnolia, demonstrated increased bioavailability and reduced toxicity after sonication with mesenchymal stem cell-derived exosomes [6]. These approaches represent a promising strategy to overcome resistance by leveraging the very mechanisms that tumors use for protection.

Clinical Translation and Nanomedicine Approaches

The clinical implementation of sEV-focused strategies requires consideration of formulation, targeting, and manufacturing challenges. Currently, two nanoformulations are approved for pancreatic cancer therapy: Abraxane (albumin-bound paclitaxel) and Onivyde (liposomal formulation of irinotecan) [65]. While these represent significant advances, they lack specific biomarker targeting capabilities. Next-generation approaches aim to functionalize sEV surfaces with tumor-specific ligands like antibodies, peptides, transferrin, and folic acid to enhance precision [66]. Lipid-based nanoparticles (LBNPs) including liposomes, solid lipid nanoparticles (SLNs), and nanostructured lipid carriers (NLCs) have shown promise in overcoming resistance by improving drug solubility, extending circulation time, and enhancing tumor accumulation through the enhanced permeability and retention (EPR) effect [67]. The first FDA-approved nanodrug, Doxil (PEGylated liposomal doxorubicin), demonstrates the clinical potential of lipid-based systems, offering reduced cardiotoxicity and improved pharmacokinetics compared to free doxorubicin [67]. These nanomedicine approaches provide a platform for integrating sEV biology into clinical cancer care.

Table 3: Clinical-Stage Nanoformulations with Relevance to sEV-Based Resistance Strategies

Formulation Name	Composition/Type	Indication(s)	Relevance to sEV Resistance
Doxil/Caelyx	PEGylated liposomal doxorubicin	Ovarian cancer, multiple myeloma, Kaposi's sarcoma	Demonstrates improved therapeutic index through lipid-based delivery
Onivyde	PEGylated liposomal irinotecan	Advanced pancreatic cancer	Liposomal platform addresses some resistance mechanisms
Abraxane	Albumin-bound paclitaxel nanoparticles	Metastatic breast cancer, NSCLC, pancreatic cancer	Protein-based nanoparticle with proven clinical efficacy
Vyxeos	Liposomal cytarabine:daunorubicin (5:1)	Acute myeloid leukemia	Combination therapy in lipid carrier demonstrates synergy
ONPATTRO	Lipid nanoparticles with siRNA	Hereditary transthyretin-mediated amyloidosis	First FDA-approved siRNA delivery platform, relevant for targeting resistance genes

The interplay between sEV lipids and cancer drug resistance represents both a fundamental challenge and a promising therapeutic frontier. Lipid-mediated sEV biogenesis, cargo sorting, and intercellular communication establish robust networks that protect tumor cells from therapeutic insults. Understanding the precise molecular mechanisms by which sEV lipids contribute to treatment failure provides opportunities for intervention at multiple levels—from inhibiting sEV formation and release to disrupting their uptake and function in recipient cells. The development of natural compound inhibitors of sEV biogenesis, coupled with advanced sEV-based delivery systems and lipid nanoparticle technologies, offers a multifaceted approach to overcoming resistance. Future research should focus on validating specific lipid biomarkers of resistance in patient-derived sEVs, developing more precise targeting strategies for sEV-based therapeutics, and addressing the manufacturing and regulatory challenges associated with clinical translation. By targeting the lipid-mediated functions of sEVs, we can develop more effective strategies to overcome treatment resistance and improve outcomes for cancer patients.

The journey from initial biomarker discovery to clinical validation represents one of the most challenging processes in modern oncology research. Despite remarkable technological advancements in biomarker discovery, a troubling chasm persists between preclinical promise and clinical utility, with less than 1% of published cancer biomarkers ultimately entering clinical practice [68]. This translation gap results in delayed treatments for patients, wasted research investments, and reduced confidence in otherwise promising avenues of oncology research. Within this challenging landscape, biomarkers related to small extracellular vesicle (sEV) biogenesis and lipid metabolism have emerged as particularly promising yet complex targets. sEVs, commonly known as exosomes, are lipid bilayer-enclosed vesicles with diameters ranging from 30 to 160 nm that can sequester bioactive molecules including proteins, nucleic acids, and lipids, thereby safeguarding them from degradation [69]. These vesicles facilitate intercellular communication by transporting specific biomolecules, and in the context of cancer, play significant roles in tumor progression, metabolic reprogramming, and therapy resistance [69] [6]. The intricate relationship between sEV biogenesis and lipid metabolism creates both opportunities and unique challenges for biomarker development, necessitating specialized approaches to overcome persistent translational hurdles.

The Biomarker Development Pipeline: From Concept to Clinic

Defining Biomarker Types and Applications

Biomarkers are objectively measured characteristics that describe a normal or abnormal biological state in an organism by analyzing biomolecules such as DNA, RNA, protein, peptides, and biomolecule chemical modifications [70]. In clinical practice, biomarkers serve distinct functions across the cancer care continuum:

• Risk stratification biomarkers identify patients at higher than usual risk of disease who should be monitored more closely than the general population [71]
• Screening and detection biomarkers are used to detect diseases before symptoms manifest, when therapy has a greater likelihood of success [71]
• Diagnostic biomarkers detect the presence of diseases and help confirm diagnoses [71]
• Prognostic biomarkers provide information about overall expected clinical outcomes for a patient, regardless of therapy or treatment selection [71]
• Predictive biomarkers inform the overall expected clinical outcome based on treatment decisions in biomarker-defined patients only [71]

The intended use of a biomarker and the target population to be tested need to be defined early in the development process, as this determination guides all subsequent validation steps and regulatory considerations [71].

Methodological Framework for Biomarker Development

The biomarker development pipeline encompasses multiple phases from initial discovery through clinical implementation. The ultimate goal of this process is to establish clinically accessible biomarker tests with demonstrated clinical utility that can inform clinical decision-making to improve patient outcomes [70]. This pathway can be conceptualized through the following workflow:

Figure 1: Biomarker Development Pathway from Discovery to Implementation

Critical Hurdles in Biomarker Translation

Technical and Analytical Challenges

The initial phase of biomarker development faces significant technical hurdles that can compromise translation from the outset. Robustness of sample processing and data analysis procedures is a fundamental factor influencing reproducibility of biomarker studies [70]. For example, variations in sample processing can lead to dramatically different results, as evidenced by a high diagnostic accuracy of a peptide signature for ovarian cancer that was not confirmed in subsequent independent reanalysis of the original dataset [70]. Similarly, common statistical algorithms run on data with low sample sizes can overfit and yield misleading misclassification rates, with prefiltering variables exacerbating this problem [70].

For sEV-based biomarkers specifically, additional technical challenges emerge due to the inherent heterogeneity of these vesicles. sEVs exhibit remarkable diversity in their size, shape, cargo, and function, which is critical for modulating cellular interactions within the tumor microenvironment [72]. This heterogeneity is compounded by a lack of consensus on the best approaches for isolating and quantifying sEVs, leading to inconsistent research outcomes and impeding reproducibility [72]. The absence of standardized protocols presents a substantial barrier to translating laboratory findings into effective clinical applications.

Biological Complexity and Model Limitations

Biological complexity represents another major category of translational hurdles, particularly for biomarkers related to sEV biogenesis and lipid metabolism:

• Disease heterogeneity: Cancers in human populations are highly heterogeneous and constantly evolving, varying not just from patient to patient but within individual tumors [68]. Genetic diversity and varying treatment histories, comorbidities, progressive disease stages, and the highly variable nature of tumor microenvironments introduce a wide range of real-world variables that cannot be fully replicated in a preclinical setting.

• Inadequate preclinical models: Traditional animal models, including syngeneic mouse models, do not match directly with all aspects of human clinical disease, so treatment responses in these models can be poor predictors of clinical outcomes [68]. This limitation is particularly problematic for sEV research, as the biogenesis and function of these vesicles can exhibit significant species-specific differences.

• Dynamic nature of lipid metabolism: Lipid metabolism in cancer cells demonstrates significant metabolic adaptability, with extensive reprogramming of glucose, lipid, and amino acid metabolism being a fundamental feature of cancer [69]. This metabolic plasticity can lead to rapid changes in biomarker expression and function, complicating their detection and validation.

Validation and Statistical Considerations

The validation phase of biomarker development presents its own set of challenges, many of which stem from inadequate study design and statistical approaches:

Table 1: Key Metrics for Biomarker Validation and Interpretation

Metric	Description	Application Considerations
Sensitivity	The proportion of cases that test positive	Critical for screening biomarkers; affected by pre-analytical variables
Specificity	The proportion of controls that test negative	Essential for diagnostic biomarkers; often compromised in early development
Positive Predictive Value	Proportion of test positive patients who actually have the disease	Highly dependent on disease prevalence
Negative Predictive Value	Proportion of test negative patients who truly do not have the disease	Function of disease prevalence in the target population
Area Under Curve (AUC)	Measure of how well marker distinguishes cases from controls	Ranges from 0.5 (equivalent to coin flip) to 1 (perfect discrimination)
Calibration	How well a marker estimates the risk of disease or of the event of interest	Important for risk stratification biomarkers

[71]

Bias represents one of the greatest causes of failure in biomarker validation studies, potentially entering a study during patient selection, specimen collection, specimen analysis, and patient evaluation [71]. Randomization and blinding are two of the most important tools for avoiding bias, yet are often inadequately implemented in biomarker studies. Additional statistical challenges include:

• Multiple comparisons: When evaluating multiple biomarkers, control of false discovery rate (FDR) is especially useful when using large scale genomic or other high dimensional data for biomarker discovery [71]

• Overfitting: Data-driven analyses and the resulting findings are less likely to be reproducible in an independent set of data, particularly when variable selection is not properly accounted for [71]

• Insufficient power: Many biomarker studies are conducted without a priori power calculations, leading to inconclusive results and inability to detect clinically significant effects [71]

Clinical Translation and Implementation Barriers

Even when biomarkers successfully navigate the initial discovery and validation phases, they face substantial barriers in clinical translation and implementation:

• Regulatory hurdles: Regulatory frameworks for biomarker approval continue to evolve, with requirements for clinical utility often demanding large-scale prospective studies that are costly and time-consuming [68]

• Clinical integration: Incorporating new biomarkers into existing clinical workflows requires demonstration of clear benefit over current standards of care, along with practical considerations such as turnaround time and accessibility [73]

• Economic factors: The development and implementation of biomarker tests must be economically viable for healthcare systems, requiring careful assessment of cost-effectiveness in addition to clinical utility [73]

• Standardization challenges: The transition from laboratory-developed tests to clinically implemented biomarkers requires standardized protocols, reference materials, and quality control measures that are often lacking in the research phase [74]

Special Considerations for sEV and Lipid Metabolism Biomarkers

sEV Biogenesis and Technical Challenges

The development of sEV-based biomarkers faces unique technical challenges related to their biogenesis and heterogeneity. sEV formation involves a meticulously orchestrated biological sequence, commencing with the invagination of cellular membranes to form early endosomes, followed by further invagination of endosomal membranes and culminating in the formation of intraluminal vesicles that eventually form late endosomes or multivesicular bodies (MVBs) [69]. The cargo sorting within this process may either be contingent upon the endosomal sorting complex required for transport (ESCRT) machinery or may proceed independently thereof [69]. This complex biogenesis pathway creates multiple potential sources of variability that must be controlled for reliable biomarker development.

The following diagram illustrates the complex sEV biogenesis pathway and potential intervention points for natural compounds that can modulate this process:

Figure 2: sEV Biogenesis Pathway and Natural Compound Modulation Points

For sEVs in prostate cancer specifically, research has revealed that caveolin 1 (CAV1) is encapsulated through the formation and maturation of autophagosomes and is subsequently released into the extracellular space via the fusion of autophagosomes with the plasma membrane, representing an alternative biogenesis pathway to the traditional sEV generation pathway [72]. This complexity underscores the need for careful characterization of sEV populations when developing biomarkers.

Lipid Metabolism Dynamics in Cancer

Lipid metabolism reprogramming represents a fundamental hallmark of cancer, creating both challenges and opportunities for biomarker development [69]. The Warburg effect describes the preference of cancer cells for glycolysis over oxidative phosphorylation (OXPHOS), even under aerobic conditions [69]. However, metabolic reprogramming in cancer cells involves not only glycolysis but also extensive changes in lipid and amino acid metabolism [69]. These metabolic shifts are critical for the discovery of novel cancer therapeutic targets but also introduce substantial complexity for biomarker validation.

sEVs may play a role in expanding metabolic reprogramming and promoting the development of drug resistance by mediating intercellular communication [69]. The phospholipid phosphatidylserine (PS) is present abundantly in the inner leaflet of the cell membrane and is also found primarily in the sEVs released from cancer cells [6]. Alterations in the lipid profile of sEVs have been found in various chronic diseases, including cancers, making them suitable biomarkers and therapeutic targets [6]. However, this dynamic nature of lipid metabolism means that biomarkers based on lipid profiles may show significant temporal variation, complicating their clinical application.

Advanced Model Systems and Validation Strategies

Human-Relevant Models for Enhanced Translation

To overcome the limitations of traditional preclinical models, researchers are increasingly turning to more sophisticated platforms that better recapitulate human biology:

• Patient-derived organoids: These 3D structures recapitulate the identity of the organ or tissue being modeled and more effectively retain characteristic biomarker expression compared to two-dimensional culture models [68]. They have been used to effectively predict therapeutic responses and guide the selection of personalized treatments.

• Patient-derived xenografts (PDX): These models, derived from patient tumor tissue implanted into immunodeficient mice, effectively recapitulate the characteristics of cancer, as well as tumor progression and evolution in human patients [68]. PDX models have proved to be a more accurate platform for biomarker validation than conventional cell line-based models.

• 3D co-culture systems: These systems incorporate multiple cell types (including immune, stromal, and endothelial cells) to provide comprehensive models of the human tissue microenvironment [68]. They have become essential for replicating in vivo environments and more physiologically accurate cellular interactions and microenvironments.

These advanced models become even more valuable when integrated with multi-omic strategies that leverage multiple technologies (including genomics, transcriptomics, and proteomics) to identify context-specific, clinically actionable biomarkers that may be missed if developers rely on a single approach [68].

Functional and Longitudinal Validation Approaches

While traditional biomarker analysis relies on the presence or quantity of specific biomarkers, this approach may not confirm whether these biomarkers play a direct, biologically relevant role in disease processes or responses to treatment. Functional assays complement traditional approaches to reveal more about a biomarker's activity and function, strengthening the case for real-world utility [68].

Additionally, longitudinal validation strategies that repeatedly measure biomarkers over time provide a more dynamic view than single, static measurements, revealing subtle changes that may indicate cancer development or recurrence even before symptoms appear [68]. This approach is particularly valuable for sEV-based biomarkers, as the composition and concentration of these vesicles can change dynamically in response to disease progression and treatment.

Table 2: Research Reagent Solutions for sEV and Lipid Metabolism Biomarker Development

Research Tool	Application	Key Utility
PDX Models	Biomarker validation	Recapitulate human tumor characteristics and evolution more accurately than cell lines
Organoids	Therapeutic response prediction	Retain characteristic biomarker expression from original tissue
3D Co-culture Systems	Tumor microenvironment modeling	Incorporate multiple cell types for physiologically relevant interactions
Multi-omics Platforms	Comprehensive biomarker profiling	Integrate genomic, transcriptomic, proteomic, and metabolomic data
Liquid Biopsy Assays	Non-invasive biomarker monitoring	Enable repeated sampling for longitudinal assessment
ESCRT Modulation Tools	sEV biogenesis manipulation	Investigate cargo sorting and sEV formation mechanisms
Lipidomic Profiling	Lipid metabolism assessment	Characterize lipid composition changes in sEVs and cells

[69] [68] [6]

Emerging Technologies and Future Directions

Artificial Intelligence and Multi-Omics Integration

The integration of artificial intelligence (AI) and machine learning (ML) is revolutionizing biomarker analysis by identifying subtle patterns or signatures in large datasets that human observers might miss [73]. AI/ML enable the integration and analysis of various molecular data types with imaging to provide a comprehensive picture of cancer, consequently enhancing diagnostic accuracy and therapeutic recommendations [73]. By 2025, AI-driven algorithms are expected to play an even bigger role in biomarker analysis, leading to more sophisticated predictive models that can forecast disease progression and treatment responses based on biomarker profiles [74].

Similarly, multi-omics approaches are expected to gain momentum, with researchers increasingly leveraging data from genomics, proteomics, metabolomics, and transcriptomics to achieve a holistic understanding of disease mechanisms [74]. These approaches enable the identification of comprehensive biomarker signatures that reflect the complexity of diseases, facilitating improved diagnostic accuracy and treatment personalization [74]. For sEV-based biomarkers specifically, multi-omics integration is particularly valuable due to the diverse cargo (proteins, nucleic acids, lipids) carried by these vesicles.

Liquid Biopsy and Single-Cell Technologies

Liquid biopsies are poised to become a standard tool in clinical practice, with advances in technologies such as circulating tumor DNA (ctDNA) analysis and exosome profiling increasing the sensitivity and specificity of these approaches [74]. Liquid biopsies facilitate real-time monitoring of disease progression and treatment responses, allowing for timely adjustments in therapeutic strategies [74]. For sEV-based biomarkers, liquid biopsies offer a particularly promising approach, as sEVs can be isolated from various biofluids including blood, urine, and saliva [72].

Single-cell analysis technologies are also expected to become more sophisticated and widely adopted, enabling deeper insights into tumor heterogeneity and the identification of rare cell populations that may drive disease progression or resistance to therapy [74]. The combination of single-cell analysis with multi-omics data provides a more comprehensive view of cellular mechanisms, paving the way for novel biomarker discovery [74]. This approach is especially relevant for understanding the heterogeneity of sEV populations and their diverse functions in cancer biology.

The journey from biomarker discovery to clinical validation remains challenging, particularly for complex biomarkers involving sEV biogenesis and lipid metabolism. Successful navigation of this pathway requires addressing multiple categories of hurdles, including technical limitations, biological complexity, statistical challenges, and implementation barriers. By employing advanced model systems, robust validation strategies, and emerging technologies such as AI and multi-omics integration, researchers can enhance the translational potential of promising biomarkers. The future of cancer biomarker development will likely be characterized by more personalized, dynamic approaches that leverage our growing understanding of cancer biology while acknowledging and addressing the persistent challenges in clinical translation.

Small extracellular vesicles (sEVs) are nanoscale (30-200 nm), lipid bilayer-enclosed particles secreted by virtually all cell types that play pivotal roles in intercellular communication through their capacity to transport proteins, nucleic acids, and lipids [75]. In the context of cancer, sEV biogenesis and lipid metabolism are intricately linked processes that influence tumor progression. The lipid bilayer of sEVs is composed of various classes of lipids, including phosphatidylserine, sphingomyelin, ceramides, and sterols, which contribute not only to their structural integrity but also to their biogenesis and signaling functions [6] [13]. Notably, alterations in the lipid profile of sEVs have been identified in various cancers, making them suitable as both biomarkers and therapeutic targets [6]. The biogenesis of sEVs occurs through the endosomal pathway, where the inward budding of endosomal membranes forms intraluminal vesicles (ILVs) within multivesicular bodies (MVBs), which subsequently fuse with the plasma membrane to release sEVs into the extracellular space [76] [75]. This process is regulated by both the endosomal sorting complex required for transport (ESCRT) machinery and ESCRT-independent mechanisms involving lipids such as ceramide, which can trigger budding without the ESCRT system [6] [41]. Engineered sEVs represent a promising therapeutic platform that leverages these natural communication systems for targeted drug delivery, offering advantages such as low immunogenicity, high biocompatibility, and an innate ability to cross biological barriers [77] [78] [75]. However, optimizing their production, cargo loading, and targeting capabilities remains crucial for realizing their full clinical potential, particularly in cancer applications where precise delivery is paramount.

sEV Biogenesis and Lipid Metabolism in Cancer

Molecular Mechanisms of sEV Biogenesis

The formation of sEVs is a complex process governed by multiple interconnected cellular pathways. The primary mechanism involves the ESCRT machinery, a highly conserved complex composed of five different ESCRT complexes (ESCRT-0, -I, -II, -III and Vps4) that work sequentially [6] [41]. ESCRT-0 recognizes and recruits ubiquitinated cargoes to endosomal microdomains, while ESCRT-I and ESCRT-II drive inward budding of the endosomal membrane, forming ILVs inside MVBs. ESCRT-III then facilitates the final scission of vesicles, and VPS4 ATPase recycles the ESCRT machinery [6] [76]. ESCRT-independent pathways also contribute significantly to sEV biogenesis, with lipids playing central roles. Ceramide, with its conical structure, induces membrane curvature and facilitates inward budding independently of ESCRT components [41]. Other lipids including cholesterol, sphingomyelin, and phosphatidylserine participate in the formation, secretion, signaling, and uptake of sEVs [6]. Tetraspanin proteins (CD9, CD63, CD81) form enriched microdomains that regulate cargo sorting and membrane bending during sEV formation [76] [41]. Additional regulators include small Rab GTPases (RAB27a/b, RAB11, RAB7, RAB35) that govern vesicle motility and fusion with the plasma membrane, and SNARE proteins that mediate the final fusion step for sEV release [76].

Lipid Metabolism Influencing sEV Properties in Cancer

Lipid metabolism profoundly influences sEV characteristics and function in cancer environments. Cancer cells frequently exhibit altered lipid metabolism, which in turn affects the lipid composition of their secreted sEVs [6] [13]. These lipid modifications can enhance sEV stability, influence their targeting specificity, and modulate their functional effects on recipient cells. The phospholipid phosphatidylserine (PS) is abundantly present in sEVs released from tumor cells and contributes to their uptake by recipient cells [6]. Ceramide not only drives ESCRT-independent biogenesis but also serves as a key signaling lipid that can promote tumor progression [13] [41]. Cholesterol-rich microdomains in sEV membranes facilitate the sorting of specific proteins and may influence targeting specificity [13]. Cancer-derived sEVs exhibit distinct lipid and protein compositions compared to those from normal cells, and these vesicles can transfer oncogenic materials to normal cells, potentially facilitating malignancy [76]. Strikingly, sEVs can further promote tumor invasion and metastasis by delivering matrix metalloproteinases (MMPs) and other proteases to target sites, and by stimulating angiogenesis through the transfer of pro-angiogenic factors to endothelial cells [76]. Understanding these lipid-mediated processes provides crucial insights for engineering sEVs with optimized therapeutic properties for cancer applications.

Figure 1: sEV Biogenesis Pathways and Cancer Implications. This diagram illustrates the major biogenesis pathways for small extracellular vesicles (sEVs), their key molecular regulators, and their functional implications in cancer progression.

Current Challenges in Engineered sEV Development

Scalability and Manufacturing Hurdles

The transition from laboratory-scale sEV production to clinically viable manufacturing presents significant challenges that must be addressed for therapeutic applications. A primary limitation is the inadequate yield of sEVs from conventional cell culture systems, which fails to meet the quantities required for clinical trials and commercial-scale production [79]. Traditional isolation methods, particularly differential ultracentrifugation, while widely used, are time-consuming, difficult to scale, and can compromise the integrity and biological activity of sEVs [77] [78]. The inherent heterogeneity of sEV populations further complicates manufacturing consistency, as vesicles derived from different cellular origins or through different biogenesis pathways exhibit variable characteristics and functions [76] [41]. Additionally, ensuring the stability of sEVs during storage and maintaining their functional properties through freeze-thaw cycles remains technically challenging [79]. There is also a notable absence of standardized quality control metrics and potency assays, which are essential for regulatory approval and clinical translation [79] [75]. These manufacturing bottlenecks collectively hinder the reliable production of engineered sEVs with consistent therapeutic properties.

Cargo Loading and Targeting Efficiency Limitations

Effective drug delivery via engineered sEVs faces two fundamental technical challenges: efficient cargo loading and precise targeting. Current cargo loading methods often suffer from inadequate efficiency, potentially damaging the vesicle structure and leading to low payload retention [80] [79]. The table below summarizes the key challenges in sEV engineering:

Table 1: Key Challenges in Engineered sEV Development

Challenge Category	Specific Limitations	Impact on Therapeutic Application
Scalability & Production	Inadequate yield from cell culture systems; Time-consuming isolation methods [77] [79]	Limits clinical trial feasibility and commercial viability
Standardization	Heterogeneity of sEV populations; Lack of quality control metrics [76] [79]	Challenges in ensuring batch-to-batch consistency and potency
Cargo Loading	Low loading efficiency; Potential damage to sEV structure during loading [80]	Reduces therapeutic payload and treatment efficacy
Targeting Specificity	Reliance on natural tropism alone is insufficient; Need for engineering enhanced targeting [78]	Results in off-target effects and reduced accumulation at disease sites
Storage & Stability	Maintenance of integrity during storage and freeze-thaw cycles [79]	Affects shelf-life and reliability of administered doses

While natural targeting occurs through specific surface molecules (tetraspanins, integrins) that facilitate selective cellular uptake, this inherent tropism alone is insufficient to achieve the precise targeting required for therapeutic applications [78]. Unmodified sEVs administered systemically tend to accumulate rapidly in organs of the reticuloendothelial system (liver and spleen), with only minimal fractions reaching desired target tissues such as the brain [78]. The therapeutic cargo itself, particularly nucleic acids like siRNA and miRNA, faces challenges related to bioavailability and stability, though encapsulation in sEVs offers protection from enzymatic degradation [79]. Furthermore, the long-term stability of surface modifications and their potential immunogenicity require thorough investigation to ensure the safety and efficacy of engineered sEV platforms [79]. Addressing these limitations is crucial for developing clinically viable sEV-based therapeutics.

Strategic Approaches for Scalable sEV Production

Advanced Isolation and Purification Techniques

Several advanced methodologies have emerged to address the scalability limitations of traditional sEV isolation techniques. These approaches aim to enhance yield, purity, and reproducibility while maintaining the biological integrity of sEVs. The table below compares the key characteristics of different isolation methods:

Table 2: Comparison of sEV Isolation and Purification Techniques

Isolation Method	Principle	Scalability	Advantages	Disadvantages
Ultracentrifugation [78]	Separation based on density, size, and shape	Low	Low cost; Suitable for large volumes	Time-consuming; Low yield; Potential damage to sEVs
Size Exclusion Chromatography [78]	Separation according to molecular size	Medium	High yield; Preserves integrity and activity	May require combination with other methods
Ultrafiltration [78]	Separation based on molecular size and shape	Medium	High speed; No special equipment	Difficult to distinguish similarly sized components
Microfluidic Technology [78] [79]	Separation using specific devices based on sEV characteristics	High	Rapid processing; Simple operation; Amenable to automation	High material/technical requirements; Not ideal for large samples
Precipitation Kits [78]	Precipitation using hydrophobic polymers	Medium	Suitable for large sample volumes	High cost; Potential polymer contamination
Immunoaffinity Capture [78]	Antibody-binding to surface receptors	Low	High specificity and purity	Not applicable to large-scale samples; High cost

For scalable production, size exclusion chromatography (SEC) and tangential flow filtration (TFF) have gained prominence as they can process larger volumes while maintaining sEV integrity and function [78]. Microfluidic technologies offer particularly promising avenues for automation and standardization, enabling rapid isolation with minimal manual intervention [78] [79]. These systems can be designed with specific antibodies or filters to capture sEVs based on size or surface markers, improving both yield and purity. Additionally, integrating multiple techniques, such as combining ultrafiltration with SEC, can enhance the purity of isolated sEVs for therapeutic applications [78].

Scalable Production Systems

To address the quantitative limitations of conventional cell culture, researchers are developing advanced production systems that significantly enhance sEV yields. Three-dimensional (3D) bioreactor cultures provide a more physiologically relevant environment for producer cells, supporting higher cell densities and prolonged viability, which consequently increases sEV production compared to traditional 2D culture systems [79]. Stimulation approaches, such as preconditioning cells with inflammatory cytokines or hypoxia, can further enhance sEV secretion, though careful characterization is necessary to ensure these manipulations do not alter the desired sEV properties [80]. The source of producer cells also critically influences scalability; immortalized mesenchymal stem cell (MSC) lines or genetically engineered cells can provide more consistent and sustainable sEV production compared to primary cells [75]. Looking toward future manufacturing needs, continuous production systems that integrate sEV biogenesis, separation, and purification in a closed system are under development. These integrated approaches aim to streamline production, minimize contamination risks, and reduce operational costs, ultimately supporting the transition from laboratory research to clinical-grade manufacturing of engineered sEV therapeutics [79].

Methodologies for Enhanced Cargo Loading

Comparative Analysis of Loading Techniques

Efficient cargo encapsulation remains a pivotal challenge in engineering therapeutic sEVs. Multiple loading strategies have been developed, each with distinct mechanisms, advantages, and limitations. These methods can be broadly categorized into two approaches: those that load pre-formed sEVs (post-isolation) and those that load parent cells to package cargo during sEV biogenesis. The selection of an appropriate loading strategy depends on the nature of the cargo (small molecules, nucleic acids, or proteins), desired loading efficiency, and the need to preserve sEV integrity and function.

Table 3: Comparison of sEV Cargo Loading Methodologies

Loading Method	Mechanism	Optimal Cargo Type	Efficiency	Impact on sEV Integrity
Incubation [80] [75]	Passive diffusion through membrane	Small hydrophobic molecules	Low to Moderate	Minimal impact
Electroporation [78] [75]	Temporary membrane pores via electrical field	Nucleic acids (siRNA, miRNA)	Variable (risk of cargo aggregation)	Potential membrane disruption
Sonication [80] [79]	Membrane disruption using sound waves	Proteins, small molecules	High	Potential damage to membrane proteins
Freeze-Thaw Cycling [80]	Membrane permeabilization through ice crystals	Proteins, small molecules	Moderate	Risk of sEV aggregation
Transfection [78] [79]	Modification of parent cells during biogenesis	Nucleic acids, proteins	High (cell-dependent)	Minimal impact on final sEVs
Extrusion [80]	Mechanical forcing through membranes	Small molecules	High	Significant structural alteration

Protocol for Sonication-Assisted Cargo Loading

Sonication is a widely used physical method for loading therapeutic cargo into pre-isolated sEVs, offering relatively high efficiency for various cargo types. Below is a detailed experimental protocol:

Materials Required:

• Purified sEVs (100-500 μg protein amount)
• Therapeutic cargo (e.g., siRNA, miRNA, small molecule drugs)
• Phosphate-buffered saline (PBS) or appropriate buffer
• Ultrasonic bath or probe sonicator
• Water bath
• Dialysis membranes or size exclusion columns for purification

Procedure:

• sEV Preparation: Isolate sEVs from producer cells (e.g., mesenchymal stem cells) using preferred method (e.g., size exclusion chromatography) and concentrate to 2-5 mg/mL protein concentration in PBS.
• Cargo Mixing: Combine sEVs with therapeutic cargo at optimal ratio (e.g., 1:10-1:50 sEV protein:cargo weight ratio) in a low-binding microcentrifuge tube.
• Sonication: Process the mixture using a probe sonicator with the following parameters:
  • Amplitude: 20-30%
  • Duration: 3-6 cycles of 30 seconds pulse-on/30 seconds pulse-off
  • Temperature: Maintain sample in ice bath throughout to prevent overheating
• Incubation: Allow the sonicated mixture to incubate at 37°C for 30-60 minutes to enable membrane resealing.
• Purification: Remove unencapsulated cargo using size exclusion chromatography (e.g., qEV columns) or dialysis against PBS for 2-4 hours.
• Quality Assessment: Verify loading efficiency using appropriate methods (e.g., HPLC for small molecules, fluorometry for fluorescently labeled cargo). Assess sEV integrity and size distribution through nanoparticle tracking analysis (NTA) and electron microscopy.

Technical Notes: Optimization of sonication parameters (amplitude, duration, cycles) is crucial as excessive sonication can damage sEV membrane proteins and lipids, while insufficient sonication results in low loading efficiency. Include controls of sEVs alone subjected to the same procedure to account for effects on sEV integrity.

Engineering sEVs for Precision Targeting

Strategies for Enhanced Targeting Specificity

Improving the targeting accuracy of engineered sEVs to specific tissues or cell types is essential for maximizing therapeutic efficacy while minimizing off-target effects. Both genetic engineering and chemical modification approaches have been successfully employed to enhance the targeting capabilities of sEVs. Genetic engineering of parent cells enables the expression of chimeric proteins on sEV surfaces, typically by fusing a targeting ligand (e.g., RGD peptide for targeting integrins, or GE11 peptide for targeting EGFR) with a sEV-enriched transmembrane protein such as Lamp2b, CD63, or PDGFR [78] [79]. When these genetically modified cells produce sEVs, the chimeric proteins are naturally incorporated into the sEV membrane, providing homing capabilities to specific receptors on target cells. Chemical conjugation represents an alternative approach where targeting moieties (e.g., antibodies, aptamers, or peptides) are directly coupled to the surface of pre-formed sEVs through various chemistries, including click chemistry, streptavidin-biotin interactions, or covalent bonding to amine groups on surface proteins [78] [75]. Additionally, leveraging the inherent tropism of certain sEVs provides a more natural targeting strategy; for instance, sEVs derived from cancer cells often show preferential uptake by similar cancer cells, while sEVs from mesenchymal stem cells exhibit natural homing to inflammatory sites and tumors [78] [44]. These strategies can be combined to create multi-functional sEVs with enhanced targeting specificity and therapeutic potential.

Protocol for Genetic Engineering of sEVs for Targeted Delivery

This protocol describes a method to engineer sEVs for targeted delivery by genetically modifying parent cells to express targeting ligands on the sEV surface.

Materials Required:

• Producer cells (HEK293, MSC, or other suitable cell line)
• Plasmid vector encoding fusion protein (e.g., Lamp2b-Ge11 for EGFR targeting)
• Transfection reagents (PEI, lipofectamine, or viral transduction system)
• Selection antibiotics (e.g., puromycin) for stable line generation
• Cell culture media and supplements
• sEV isolation reagents and equipment

Procedure:

• Vector Design: Clone DNA sequence encoding your targeting peptide (e.g., GE11: YHWYGYTPQNVI) fused to the N-terminus of a sEV membrane protein (e.g., Lamp2b) via a flexible linker (e.g., GGGGS) into an appropriate expression vector with selectable marker.
• Cell Transfection: Seed producer cells at 60-70% confluence in 6-well plates. Transfect with plasmid DNA using preferred transfection method according to manufacturer's protocol.
• Stable Line Selection: Begin antibiotic selection (e.g., 1-2 μg/mL puromycin) 48 hours post-transfection. Maintain selection for 2-3 weeks, replacing media every 3-4 days until resistant colonies form.
• Clone Screening: Pick individual colonies and expand. Validate fusion protein expression by Western blotting using antibodies against the targeting peptide or fusion partner.
• sEV Production: Culture validated clones in sEV-depleted media (prepared by ultracentrifugation at 100,000 × g for 16 hours) for 48 hours to collect conditioned media.
• sEV Isolation: Isolate sEVs from conditioned media using sequential centrifugation:
  • 300 × g for 10 min to remove cells
  • 2,000 × g for 20 min to remove dead cells
  • 10,000 × g for 30 min to remove cell debris
  • 100,000 × g for 70 min to pellet sEVs
  • Wash with PBS and repeat ultracentrifugation
• Targeting Validation: Confirm presence of targeting ligand on sEVs by:
  • Western blotting of sEV lysates
  • Immunogold electron microscopy
  • Flow cytometry of sEVs bound to latex beads

Functional Assay: Evaluate targeting efficiency by incubating engineered sEVs with target cells (EGFR-positive for GE11) and control cells (EGFR-negative) for 4-6 hours. Assess uptake by confocal microscopy (for fluorescently labeled sEVs) or by quantifying internalized sEVs using sEV-specific markers.

Figure 2: sEV Engineering Strategies for Enhanced Targeting. This workflow illustrates the two primary approaches for engineering sEVs with improved targeting capabilities: genetic modification of parent cells and chemical modification of pre-formed sEVs.

Successful development of engineered sEVs requires specialized reagents and tools for production, modification, and characterization. The following table outlines key research solutions essential for advancing sEV-based therapeutics:

Table 4: Essential Research Reagent Solutions for sEV Engineering

Category	Specific Reagents/Tools	Function/Application	Notes for Selection
Isolation Kits [78]	Polymer-based precipitation kits; Immunoaffinity beads	Rapid sEV isolation from biofluids and cell culture media	Consider yield vs. purity requirements; Potential polymer contamination with precipitation
Characterization Antibodies [77] [78]	Anti-tetraspanins (CD63, CD9, CD81); Anti-ESCRT components (TSG101, Alix)	sEV identification and quantification via Western blot, flow cytometry	Validate specificity for sEV populations; Use multiple markers for confirmation
Loading Reagents [80] [79]	Electroporation buffers; Transfection reagents (for parent cells)	Facilitate cargo encapsulation into sEVs	Optimize parameters to minimize sEV damage; Test multiple approaches
Targeting Moieties [78] [79]	Peptide ligands (RGD, GE11); Antibody fragments; Aptamers	Engineer targeting specificity to recipient cells	Consider size and orientation when conjugating to sEV surface
Producer Cell Lines [79] [75]	HEK293; Mesenchymal stem cells; Immortalized cell lines	Source of sEVs with specific inherent properties	Select based on intended application; HEK293 offers high production yield
Characterization Equipment [77] [78]	Nanoparticle Tracking Analyzer; Electron Microscope; Western blot apparatus	sEV quantification, size distribution, and marker confirmation	Use multiple complementary techniques for comprehensive characterization

This toolkit provides the fundamental resources required for the major steps in sEV engineering, from initial isolation to final characterization of the engineered product. Selection of appropriate reagents should be guided by the specific research goals, whether focused on basic mechanism studies or translational therapeutic development.

The field of engineered sEVs stands at a pivotal point, with significant advances in understanding sEV biogenesis and lipid metabolism paving the way for innovative therapeutic applications. Current research demonstrates that overcoming the challenges of scalability, cargo loading efficiency, and targeting precision is not only necessary but achievable through integrated engineering approaches. The convergence of bioprocessing innovations with molecular engineering techniques promises to transform sEVs into clinically viable therapeutic vehicles. Looking forward, several emerging frontiers hold particular promise: the development of bioinspired synthetic hybrids that combine natural sEV components with synthetic materials to enhance stability and loading capacity; the application of gene editing technologies like CRISPR/Cas9 to precisely modify parent cells for optimized sEV production and targeting; and the implementation of microfluidic-based manufacturing systems for continuous, controlled production of clinical-grade sEVs [79]. Furthermore, as our understanding of the intricate relationship between lipid metabolism and sEV biogenesis in cancer deepens, opportunities arise for designing sEVs that can simultaneously deliver therapeutic cargo and modulate pathological lipid signaling in recipient cells [6] [13]. The continued refinement of engineering strategies outlined in this review, coupled with rigorous preclinical validation and standardized characterization, will be essential for translating the immense potential of engineered sEVs into effective therapies for cancer and other complex diseases.

The tumor microenvironment (TME) is a complex ecosystem comprising cancer cells, immune cells, stromal elements, and secreted factors that collectively influence tumor progression and therapeutic response [81]. Within this milieu, lipid metabolic reprogramming has emerged as a critical hallmark of cancer, sustaining malignant progression and shaping an immunosuppressive landscape [82] [27]. Rapidly proliferating tumor cells deplete nutrients and release metabolic byproducts, generating hypoxia, acidosis, and nutrient scarcity that force both tumor and immune cells to rewire their metabolism [82]. This review explores how lipid signaling networks within the TME govern immune cell function, influence small extracellular vesicle (sEV)-mediated communication, and present novel therapeutic opportunities for cancer intervention, framing these interactions within the broader context of sEV biogenesis and lipid metabolism in cancer research.

Fundamentals of Lipid Metabolism in Cancer Cells

Key Metabolic Pathways and Molecular Players

Lipids serve three essential roles in cellular physiology within the TME: as alternative energy sources through β-oxidation when glucose is scarce, as precursors to signaling mediators, and as structural components of membranes that support proliferation and immune receptor function [82]. Cancer cells exhibit distinct reprogramming across several key lipid metabolic pathways:

• Fatty acid synthesis: Enhanced de novo lipogenesis from acetyl-CoA through key enzymes including ATP-citrate lyase (ACLY), acetyl-CoA carboxylase (ACC), and fatty acid synthase (FASN) [82] [27].
• Lipid uptake: Increased expression of fatty acid transport proteins (FATPs), CD36, fatty acid-binding proteins (FABPs), and the low-density lipoprotein receptor (LDLR) [82] [27].
• Fatty acid oxidation (FAO): Elevated mitochondrial FAO mediated by carnitine palmitoyltransferase 1 (CPT1), particularly under nutrient stress [82].
• Phospholipid remodeling: Altered metabolism of phosphatidylserine, phosphatidylcholine, phosphatidylethanolamine, and phosphatidylinositol for membrane biogenesis [83] [27].
• Cholesterol synthesis: Upregulated mevalonate pathway supporting membrane structure and signaling [82] [27].

Table 1: Key Enzymes and Transporters in Cancer Cell Lipid Metabolism

Molecular Player	Function	Role in Cancer
FASN	Fatty acid synthase; catalyzes palmitate synthesis	Upregulated in multiple cancers; supports membrane generation and signaling
ACLY	Converts citrate to acetyl-CoA	Links glucose metabolism to lipogenesis; often overexpressed
CD36	Fatty acid translocase	Mediates lipid uptake; associated with metastasis
CPT1A	Rate-limiting enzyme for mitochondrial FAO	Promotes survival under metabolic stress
HMGCR	Rate-limiting enzyme in cholesterol synthesis	Supports membrane fluidity and raft formation

Regulatory Networks Governing Lipid Metabolism

The rewiring of lipid metabolism in cancer cells is orchestrated by interconnected regulatory networks. Traditional cancer-related pathways such as KRAS and p53 directly influence lipid metabolic gene expression [27]. Oncogenic signaling activates sterol regulatory element-binding proteins (SREBPs), master transcription factors that promote expression of lipogenic genes [82]. Additionally, non-coding RNAs have emerged as significant regulators, with specific microRNAs and long non-coding RNAs modulating key lipid metabolic enzymes [27]. The mechanistic target of rapamycin complex 1 (mTORC1) enhances cholesterol biosynthesis, sustaining proliferation and suppressive molecule expression [82].

Lipid Metabolism in Immune Cells of the TME

Immunosuppressive Cells

Regulatory T Cells (Tregs)

Within tumors, Tregs suppress effector T and NK cell activity via secretion of IL-10, TGF-β, and expression of inhibitory receptors including CTLA-4 and PD-1 [82]. Under glucose-restricted conditions in the TME, Tregs rely heavily on fatty acid oxidation (FAO) to sustain their immunosuppressive functions [82]. Lipid acquisition mediated by CD36 is essential for Treg survival, with genetic ablation of CD36 markedly diminishing Treg suppressive activity and synergizing with PD-1 blockade to enhance antitumor immunity [82]. The SREBP–SCAP axis is elevated in tumor-infiltrating Tregs, and its disruption impairs Treg function while potentiating PD-1 inhibition efficacy [82]. The transcription factor FOXP3 integrates lipid metabolism with immune checkpoint signaling by regulating metabolic genes including CPT1A, ACACA, and SREBP1, thereby maintaining Treg stability in the nutrient-limited TME [82].

Tumor-Associated Macrophages (TAMs)

TAMs predominantly exhibit an M2-like, immunosuppressive, tumor-promoting phenotype that preferentially engages fatty acid oxidation (FAO) and oxidative phosphorylation [82]. Reduced expression of receptor-interacting serine/threonine-protein kinase 3 (RIPK3) in hepatocellular carcinoma enhances FAO through activation of the PPAR axis, thereby promoting M2 polarization [82]. Lipid synthesis mediated by SREBP1 is similarly critical, with diminished IFN-γ due to Treg activity relieving inhibition of SREBP1 and reinforcing the M2 phenotype [82]. Additionally, downregulation of monoacylglycerol lipase (MGLL) in TAMs leads to lipid accumulation that stabilizes the M2 state, whereas restoring MGLL expression drives repolarization toward an M1 phenotype [82]. The specific fatty acids present also influence TAM polarization, with ω-3 polyunsaturated fatty acids (PUFAs) suppressing M2 polarization and function [82].

Myeloid-Derived Suppressor Cells (MDSCs)

MDSCs, comprising monocytic (M-MDSCs) and polymorphonuclear (PMN-MDSCs) subsets, shift from glycolysis to fatty acid oxidation (FAO) within the TME, characterized by high expression of CD36 and upregulation of FAO-related genes including CPT1A [82]. Tumor-derived G-CSF and GM-CSF activate STAT signaling, inducing metabolic reprogramming toward enhanced lipid uptake [82]. FATP2 is specifically upregulated in PMN-MDSCs, driving arachidonic acid uptake and prostaglandin E2 (PGE2) biosynthesis that underlies their potent suppressive activity [82]. The arachidonic acid–COX-2–PGE2 pathway is aberrantly activated under chronic inflammation, driving sustained MDSC activity, while COX-2 inhibitors reduce PD-L1 expression and increase CD8+ T-cell infiltration in murine models [82].

Table 2: Lipid Metabolic Programs in Immunosuppressive Cells of the TME

Cell Type	Preferred Metabolic Pathway	Key Molecular Regulators	Functional Consequences
Tregs	Fatty acid oxidation (FAO)	CD36, CPT1A, SREBP-SCAP, FOXP3	Enhanced suppressive function and survival
M2-like TAMs	FAO, oxidative phosphorylation	RIPK3, PPAR, SREBP1, MGLL	Immunosuppression, tissue repair
MDSCs	FAO, arachidonic acid metabolism	CD36, FATP2, COX-2, CPT1A	Suppressive mediator production

Effector Immune Cells

Effector immune cells within the TME, including T cells, dendritic cells, B cells, and natural killer cells, also undergo lipid metabolic reprogramming that influences their anti-tumor functions [27]. As oxygen and glucose are preferentially directed toward tumor cells, these immune cells leverage lipid metabolism as a compensatory energy support system [27]. While lipid metabolism can support essential functions of immune cells, excessive activation often hinders their growth, differentiation, and anti-tumor immunity, creating a metabolic imbalance that tumors exploit for immune evasion [27].

Intersection of Lipid Metabolism and Small Extracellular Vesicle Biogenesis

Lipid Composition of sEVs

Small extracellular vesicles (sEVs), including exosomes (30-150 nm in diameter), are membrane-bound vesicles secreted by cells that play crucial roles in intercellular communication within the TME [6] [1]. These vesicles are enriched with a stable lipid bilayer composed of various lipid classes including phosphatidylserine, sphingomyelin, ceramides, and sterols [6]. The formation and release of sEVs are controlled by multiple mechanisms, with the endosomal sorting complex required for transport (ESCRT) serving as a highly conserved molecular machinery [6]. Alongside ESCRT-dependent pathways, lipids play essential roles in EV formation, secretion, and stability maintenance [6]. Ceramide, in particular, triggers budding of exosomes without the ESCRT system, while cholesterol, sphingomyelin, and phosphatidylserine participate in formation, secretion, signaling, and uptake of exosomes [6].

sEV-Mediated Lipid Signaling in the TME

Cancerous cells release sEVs containing pro-tumorigenic biological compounds, known as oncosomes, which induce oncogenesis in recipient cells and promote epithelial-to-mesenchymal transition [6]. These oncosomes contribute to the formation of the TME and maintenance of the premetastatic niche [6]. The phospholipid phosphatidylserine (PS) is abundantly present in the inner leaflet of the cell membrane and is primarily found in sEVs released from tumor cells [6]. Tumor-associated macrophages (TAMs) of the M2 phenotype secrete exosomes that contribute to metastasis of cancer cells via the ApoE-activating PI3K-Akt signaling pathway, leading to favorable cytoskeleton remodeling to support migration [6]. Additionally, sEVs mediate transfer of multifunctional proteins like matrix metalloproteinases (MMPs) and heat shock proteins (HSPs) that promote premetastatic niche formation and intracellular signaling [6].

Figure 1: Lipid-Mediated Cross-Talk Between Tumor and Immune Cells via sEVs. This diagram illustrates how tumor cell lipid reprogramming alters sEV composition, which subsequently influences immune cell function in the TME.

Natural Compounds as Modulators of sEV Biogenesis and Lipid Metabolism

Natural compounds (NCs) derived from plants and microbes exhibit antitumor properties and demonstrate the capacity to modulate sEV synthesis, secretion, and composition [6]. These compounds influence key regulatory enzymes involved in lipogenesis and degradation, suggesting a potential impact on the lipid composition of sEVs [6]. Specific natural compounds with documented effects include:

• Manumycin A: An antibiotic derivative that significantly reduces exosome secretion in castration-resistant prostate cancer cells by shutting down ESCRT and inhibiting Ras/Raf/ERK1/2 signaling [6].
• Cannabidiol (CBD): A phytocannabinoid that directly modulates exosome and microvesicle release in multiple cancer cell lines and alters microRNA levels in glioblastoma multiforme cells [6].
• Resveratrol: A natural polyphenol that blocks exosome secretion by downregulating Rab27a in hepatocellular carcinoma cells, resulting in antiproliferation and decreased migration ability [6].
• Honokiol: A bioactive compound that increases bioavailability when sonicated with exosomes, facilitating delivery to cancer cells while reducing toxicity to normal cells [6].

Protein Lipidation in the TME

Major Lipidation Modifications and Their Enzymology

Protein lipidation represents a pivotal post-translational modification that increases protein hydrophobicity, influencing function, localization, and interaction networks within the TME [84] [81]. Three major lipidation modifications play significant roles in oncogenic signaling:

• N-myristoylation: An irreversible attachment of the 14-carbon fatty acid myristate to the N-terminal glycine of substrate proteins, catalyzed by N-myristoyltransferases (NMT1/NMT2) [81]. This modification typically occurs co-translationally and facilitates membrane associations critical for signal transduction [81].
• S-palmitoylation: A reversible modification involving attachment of palmitate (16-carbon saturated fatty acid) to cysteine residues via a thioester bond, catalyzed by ZDHHC family proteins [81]. This dynamic modification enables proteins to switch between membrane-associated and cytoplasmic locales on a timescale of seconds to hours [81].
• S-prenylation: An irreversible attachment of either farnesyl (15-carbon) or geranylgeranyl (20-carbon) groups to C-terminal cysteine residues within CAAX box motifs, mediated by farnesyltransferase (FTase) and geranylgeranyltransferases (GGTase) [81]. This modification targets proteins to membranes and facilitates protein-protein interactions [81].

Lipidation-Mediated Signaling Pathways in Cancer

Lipidation modifications profoundly influence oncogenic signaling pathways and the complex interplay between tumor cells and surrounding stromal and immune cells [81]. N-myristoylation stabilizes oncoproteins such as Src kinases and modulates immune responses, making NMT1 a therapeutic target in the TME [81]. S-palmitoylation dynamically regulates protein-membrane interactions by enriching targets in lipid rafts, maintaining Golgi-plasma membrane distribution, and mediating lysosomal sorting [81]. S-prenylation facilitates membrane localization of small GTPases including Ras and Rho family proteins, which drive proliferative and metastatic signaling programs in cancer cells [81]. Many proteins undergo dual lipid modifications, with initial irreversible modifications (N-myristoylation or S-prenylation) followed by S-palmitoylation to ensure precise subcellular targeting [81].

Experimental Approaches and Research Tools

Methodologies for sEV Isolation and Characterization

The isolation of sEVs is crucial for both fundamental research and clinical applications in cancer immunotherapy [85]. Multiple methodologies have been developed, each with distinct advantages and limitations:

• Ultracentrifugation: The most widely used technique that leverages centrifugal force to separate EVs based on size and density; considered the gold standard but time-consuming and potentially damaging to EV integrity [1] [85].
• Density gradient centrifugation: Refinement of ultracentrifugation that makes sEVs gather in a specific density class; provides high purity but is time-consuming [1].
• Size-exclusion chromatography (SEC): Isolates EVs based on size; provides good preservation of EV structure and function [85].
• Polymer sedimentation: Changes the solubility and dispersibility of sEVs using polymers; simple procedure but may co-precipitate contaminants [1].
• Ultrafiltration: Uses semipermeable membranes to separate EVs from smaller particles; more efficient than ultracentrifugation but may cause EV deformation [85].

Table 3: Research Reagent Solutions for Studying Lipid-sEV Networks

Reagent/Category	Specific Examples	Function/Application
Metabolic Inhibitors	CPT1A inhibitors, FASN inhibitors, SREBP inhibitors	Target specific lipid metabolic pathways in immune and tumor cells
sEV Isolation Kits	Polymer-based precipitation kits, Immunoaffinity capture kits	Isolate sEVs from biological fluids for downstream analysis
Lipidation Inhibitors	NMT inhibitors, ZDHHC inhibitors, FTase inhibitors	Disrupt protein lipidation pathways to study functional consequences
Lipidomics Standards	Stable isotope-labeled lipid standards, Internal standards	Enable quantitative mass spectrometry-based lipid profiling
sEV Engineering Tools	CRISPR/Cas9 systems, Surface display plasmids, Loading equipment	Modify sEV content and surface properties for therapeutic applications

Engineering sEVs for Therapeutic Applications

Recent advancements in engineering sEVs have significantly expanded their potential as effective vehicles for targeted cancer therapies [85]. Tailoring the contents of sEVs, such as incorporating immunomodulatory molecules or gene-editing tools (GETs), has shown promising outcomes in enhancing anti-tumor immunity and overcoming the immunosuppressive TME [85]. Optimization of delivery mechanisms through both passive and active targeting strategies is crucial for improving the clinical efficacy of EV-based therapies [85]. Engineering approaches include:

• Surface modification: Altering sEV surface characteristics to enhance tumor targeting and cellular uptake [85].
• Cargo loading: Incorporating therapeutic payloads such as cytokines, checkpoint inhibitors, and gene-editing tools like CRISPR/Cas9 [85].
• Biogenesis control: Modifying sEV formation processes to control quantity and composition of secreted vesicles [85].

Figure 2: Experimental Workflow for Analyzing Lipid-sEV Networks. This diagram outlines key methodological steps for investigating lipid composition and function in sEVs.

Therapeutic Targeting of Lipid-sEV Networks

Strategic Approaches and Clinical Translation

Targeting the intersection of lipid metabolism and sEV biogenesis represents a promising therapeutic strategy in cancer treatment [82] [6]. Potential interventions include:

• Inhibiting immunosuppressive cell metabolism: Targeting CD36, FASN, SREBP, or cholesterol-regulatory pathways to attenuate Treg-mediated suppression [82].
• Reprogramming TAMs: Inhibiting FAO (e.g., CPT1A blockade), enhancing lipid catabolism (via MGLL activation), preventing lipid uptake (CD36 inhibition), or blocking the arachidonic acid pathway (COX-2 inhibition) to reprogram TAMs toward an antitumor phenotype [82].
• Disrupting MDSC function: Targeting FATP2 to diminish the suppressive capacity of MDSCs and synergize with immune checkpoint blockade [82].
• Modulating sEV-mediated communication: Using natural compounds or engineered approaches to influence sEV synthesis, secretion, and composition [6].
• Targeting protein lipidation: Developing inhibitors against lipidation-related enzymes that exhibit antitumor properties by interfering with oncogenic signal transmission and cellular survival pathways [81].

These metabolic targets have demonstrated additive benefits when combined with PD-1/PD-L1 blockade or adoptive cell therapy in preclinical studies, providing new opportunities for clinical translation [82]. However, challenges remain in the clinical application of these approaches, including issues related to scalability, safety, targeted delivery, and overcoming the heterogeneous nature of tumors [85].

Future Directions and Research Opportunities

The growing recognition of lipid metabolism and sEV biogenesis as integrated processes in tumor progression opens several promising research directions. The incorporation of lipidomics into multi-omics frameworks, supported by advanced computational tools and AI-driven analytics, will help decipher the complexity of tumor-associated metabolic networks [83] [86]. Additionally, the development of engineered EVs as therapeutic vehicles continues to advance, with ongoing optimization of cargo loading, surface functionalization, and manufacturing processes [85]. The Diet, Lipid Metabolism, and Tumor Growth and Progression (DLT) Program at the National Cancer Institute exemplifies the growing recognition of this field, investigating mechanistic links between diet, lipid metabolism, and tumor progression [87]. Future research should focus on overcoming persistent challenges in clinical translation, including analytical variability, insufficient biological validation, and the lack of standardized integration into clinical workflows [83].

Evaluating Efficacy: Biomarker Validation and Comparative Therapeutic Strategies

Small extracellular vesicles (sEVs) have emerged as promising liquid biopsy biomarkers in oncology, carrying disease-specific molecular cargo. Their lipid bilayer protects internal contents and presents surface markers, reflecting parental cell composition. This technical guide examines the validation of sEV-derived lipid biomarkers across major cancer types, focusing on specificity and sensitivity considerations. We detail experimental methodologies for lipid profiling, address technical challenges in isolation and analysis, and provide frameworks for clinical translation. Within the broader context of sEV biogenesis and lipid metabolism in cancer, we establish the biological rationale for lipid biomarker utility and present standardized approaches for analytical validation to advance sEV-based cancer diagnostics.

Small extracellular vesicles (sEVs) are nanoscale lipid bilayer-enclosed particles (30-200 nm) secreted by all cell types, playing crucial roles in intercellular communication through transport of bioactive molecules [44] [88]. Their biogenesis occurs through multiple pathways, primarily involving the endosomal sorting complex required for transport (ESCRT)-dependent and ESCRT-independent mechanisms [6] [13]. The ESCRT-independent pathway utilizes ceramide, generated from sphingomyelin by nSMase2, which forms lipid raft domains and initiates intraluminal vesicle budding within multivesicular bodies (MVBs) [88]. When MVBs fuse with the plasma membrane, they release sEVs into the extracellular space.

In cancer, sEV biogenesis and composition are altered, with tumor cells frequently secreting higher quantities of sEVs containing oncogenic cargo [44]. The lipid composition of sEV membranes is not random but reflects selective incorporation of specific lipid species during vesicle formation. Cancer-derived sEVs exhibit distinct lipid profiles characterized by alterations in phosphatidylserine, sphingomyelin, ceramides, and sterols [6] [13]. These lipid modifications influence sEV structure, function, and signaling capabilities within the tumor microenvironment.

The phospholipid phosphatidylserine (PS) is particularly relevant in cancer sEVs. Normally confined to the inner leaflet of cell membranes, PS becomes externalized in cancer-derived sEVs, serving as a recognition signal [6]. Ceramide, with its conical molecular structure, facilitates membrane curvature and inward budding during sEV biogenesis [13]. Additionally, cholesterol-rich microdomains (lipid rafts) in sEV membranes serve as platforms for concentrating signaling molecules and facilitating recipient cell interactions.

Alterations in cancer lipid metabolism directly impact sEV lipid composition. Reprogrammed lipid synthesis pathways in tumor cells result in sEVs enriched with specific lipid species that can promote tumor progression, modulate the tumor microenvironment, and contribute to immune evasion [6] [13]. This cancer-specific lipid reprogramming provides the fundamental rationale for pursuing sEV lipid biomarkers while presenting unique challenges for their validation across diverse cancer types.

sEV Lipid Biomarkers: Technical Validation Framework

Analytical Performance Metrics

Validating sEV lipid biomarkers requires rigorous assessment of key analytical performance parameters. The table below outlines essential metrics and their target values for clinical translation.

Table 1: Analytical Validation Parameters for sEV Lipid Biomarkers

Performance Metric	Definition	Target Value	Considerations for sEV Lipids
Sensitivity	Limit of Detection (LOD)	<10^4 sEVs/mL [89]	Varies by detection platform and lipid abundance
Specificity	Ability to distinguish from interferents	>95% [90]	Must distinguish from lipoproteins, normal sEVs
Accuracy	Proximity to true value	CV <15% [91]	Requires validated reference materials
Precision	Repeatability/reproducibility	CV <15% [92]	Affected by sEV isolation consistency
Linearity	Quantitative response range	2-3 log units [90]	Dependent on detection method dynamic range

Cancer-Specific sEV Lipid Signatures

Emerging research has identified distinct sEV lipid profiles across cancer types. The following table summarizes validated and potential sEV lipid biomarkers with their performance characteristics.

Table 2: Cancer-Type Specific sEV Lipid Biomarkers and Performance Characteristics

Cancer Type	sEV Lipid Biomarker	Specificity	Sensitivity	Detection Platform
Lung Cancer	Ceramide Cer(42:1) [93]	Significant elevation vs benign nodules	Not specified	Lipidomics screening
Multiple Cancers	Phosphatidylserine (PS) [6]	Higher in malignant vs normal cells	Not specified	Flow cytometry
Pancreatic Cancer	Glypican-1 (GPC1)+lipids [45]	AUC 1.0 (combined panel)	100% (combined panel)	Flow cytometry
Multiple Cancers	Ceramide [13]	Varies by cancer type	Varies by cancer type	Mass spectrometry

The integration of multiple biomarkers significantly enhances diagnostic performance. For pancreatic cancer, a panel combining sEV GPC1, sEV CD82, and serum CA19-9 achieved an AUC of 0.942, substantially outperforming individual markers [45]. Similarly, for metastatic breast cancer diagnosis, an eight-biomarker panel including CA15-3, CA125, CEA, HER2, EGFR, PSMA, EpCAM, and VEGF demonstrated exceptional accuracy with an AUPRC of 0.9912 [45].

Advanced Detection Platforms

Novel sensing technologies have dramatically improved sEV lipid biomarker detection sensitivity:

• DEP-ELISA Platform: Dielectrophoresis-enzyme-linked immunosorbent assay integrates sEV isolation and analysis, achieving a limit of detection (LOD) of 10^4 sEVs/mL, approximately three orders of magnitude lower than conventional ELISA [89].
• SERS-Based Assays: Surface-enhanced Raman scattering offers multiplexed detection capabilities with single-molecule sensitivity, enabling comprehensive sEV profiling without fluorescent labels [90] [92].
• TN-cyclon Assay: This ultrasensitive immunoassay combining sandwich ELISA with enzyme cycling amplification demonstrates a 20-fold increase in sensitivity compared to conventional ELISA, enabling detection of low-abundance sEV proteins like PD-L1 at 0.172 pg/mL [91].

Experimental Methodologies

sEV Isolation and Purification

Efficient sEV isolation is crucial for reliable lipid biomarker analysis. The following workflow outlines a standardized approach for sEV preparation from clinical samples:

sEV Isolation Workflow

Isolation method selection significantly impacts downstream lipid analyses. Size exclusion chromatography (SEC) provides high-purity sEVs suitable for lipid profiling, effectively separating sEVs from contaminating lipoproteins [92]. Ultracentrifugation remains widely used but may compromise sEV integrity. Immunoaffinity capture offers specificity through antibody-mediated selection but may miss heterogeneous sEV populations. Emerging microfluidic approaches like the DEP-ELISA platform enable direct analysis from plasma with approximately 70% isolation efficiency, minimizing pre-processing artifacts [89].

Lipid Extraction and Analysis

Comprehensive lipidomics requires optimized extraction and advanced analytical separation:

• Lipid Extraction: Modified Folch or Bligh-Dyer methods using chloroform-methanol mixtures efficiently recover diverse lipid classes from sEV preparations. Include internal standards for quantification.
• Liquid Chromatography-Mass Spectrometry (LC-MS): Reversed-phase LC separates lipids by hydrophobicity, while hydrophilic interaction liquid chromatography (HILIC) resolves lipid classes. High-resolution mass spectrometers (Orbitrap, Q-TOF) enable untargeted profiling.
• SERS Analysis: For rapid clinical translation, SERS platforms capture global molecular vibrations from minimal sample volumes, generating complex spectra analyzable via machine learning algorithms [90] [92].

Specificity and Cross-Reactivity Assessment

Rigorous specificity validation must address:

• Lipoprotein Interference: Implement density-based separation or specific depletion methods to distinguish sEV lipids from abundant lipoproteins.
• Cancer-Type Specificity: Profile sEV lipids across multiple cancer types and benign conditions using orthogonal validation.
• Technical Variability: Assess inter-individual, pre-analytical, and analytical variability through replicate measurements and standard reference materials.

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Research Reagents for sEV Lipid Biomarker Validation

Reagent/Category	Specific Examples	Function/Application	Technical Notes
sEV Isolation Kits	Total Exosome Isolation Reagent [91]	Polymer-based sEV precipitation	Maintain lipid membrane integrity
Immunocapture Antibodies	Anti-CD63, CD81, CD9 [92]	Tetraspanin-based sEV isolation	Enables subtype-specific analysis
Lipid Standards	Ceramide internal standards	Mass spectrometry quantification	Essential for absolute quantification
Detection Antibodies	Anti-PD-L1 clones [91]	Specific sEV surface marker detection	Validate with knockout controls
SERS Substrates	Gold nanostars, aggregated AuNPs [90] [92]	Raman signal enhancement	Create "hot spots" for sensitivity
Microfluidic Chips	DEP-ELISA chip [89]	Integrated sEV isolation/detection	Minimizes sample preprocessing
Enzyme Amplification	3α-hydroxysteroid dehydrogenase [91]	Signal amplification in TN-cyclon	Enables ultra-sensitive detection

Lipid Metabolism Pathways in sEV Biogenesis

The intricate relationship between cellular lipid metabolism and sEV biogenesis underpins the biological significance of sEV lipid biomarkers. The following diagram illustrates key metabolic pathways influencing sEV formation and composition:

Lipid Pathways in sEV Biogenesis

Cancer-associated alterations in lipid metabolism directly impact sEV biogenesis through several mechanisms. Ceramide generation via nSMase2 facilitates ESCRT-independent intraluminal vesicle formation [13]. Cholesterol-rich lipid rafts serve as platforms for concentrating specific proteins and nucleic acids into budding sEVs. Phosphatidylserine externalization contributes to membrane curvature and serves as a recognition signal for recipient cells. Cancer cells with reprogrammed lipid metabolism consequently produce sEVs with distinct lipid compositions that reflect their pathological state, providing the foundation for diagnostic exploitation of sEV lipid biomarkers.

Validating sEV lipid biomarkers for cancer diagnostics requires integrated consideration of biological context, analytical stringency, and clinical utility. The complex interplay between cancer lipid metabolism and sEV biogenesis produces disease-specific lipid signatures measurable in circulating sEVs. While technical challenges remain in standardization and specificity determination, advanced detection platforms like SERS and DEP-ELISA now provide the sensitivity required for clinical implementation.

Future validation studies should prioritize multi-cancer profiling to establish true specificity across cancer types and benign conditions. Integrating lipid biomarkers with protein and nucleic acid signatures in multimodal panels will enhance diagnostic accuracy beyond single-analyte capabilities. As sEV lipid biology continues to be elucidated, particularly regarding metabolic reprogramming in different cancers, new biomarker opportunities will emerge. Standardized validation frameworks, as outlined in this guide, provide the foundation for translating these promising biomarkers from research tools to clinical diagnostics that can impact cancer detection, classification, and therapeutic monitoring.

Small extracellular vesicles (sEVs) are critical mediators of intercellular communication in cancer, with their biogenesis and lipid composition significantly influencing tumor progression. This review systematically analyzes the effects of various natural compounds (NCs) on sEV release and lipid content, providing a comparative assessment of their efficacy and mechanisms of action. We summarize quantitative data on modulation potency, detail experimental methodologies for investigating sEV-lipid dynamics, and visualize key signaling pathways. Our analysis reveals that NCs including manumycin A, cannabidiol, and resveratrol modulate sEV biogenesis through ESCRT-dependent and ESCRT-independent pathways while simultaneously altering lipid metabolism enzymes and sEV lipid composition. These findings highlight the potential of NCs as powerful research tools and therapeutic agents for targeting sEV-mediated processes in cancer.

Small extracellular vesicles (sEVs), comprising exosomes and other vesicles smaller than 200 nm, are membrane-bound particles secreted by virtually all cell types that play pivotal roles in intercellular communication through their protein, nucleic acid, and lipid cargo [6] [1]. In cancer, sEVs contribute to tumor progression by modulating the tumor microenvironment, facilitating epithelial-mesenchymal transition, and promoting metastasis [6]. The lipid bilayer of sEVs is composed of various lipid classes including phosphatidylserine, sphingomyelin, ceramides, and sterols, which contribute to their structural integrity and influence their biogenesis, release, and uptake [13] [94]. Alterations in the lipid profile of sEVs have been documented in various cancers, making them suitable biomarkers and therapeutic targets [6] [13].

Natural compounds (NCs) derived from plants and microbes exhibit antitumorigenic properties and have demonstrated capacity to modulate sEV synthesis, secretion, and composition [6]. These compounds can influence key regulatory enzymes involved in lipogenesis and degradation, suggesting a potential impact on the lipid composition of sEVs [6]. This review provides a comprehensive analysis of the effects of NCs on sEV release and lipid content, with emphasis on mechanistic insights, experimental approaches, and quantitative comparisons of efficacy relevant to cancer research.

Mechanisms of sEV Biogenesis and Lipid Involvement

The formation and release of sEVs are controlled by various mechanisms, with lipids playing essential roles in their biogenesis, cargo sorting, release, and cellular uptake [13].

ESCRT-Dependent and Independent Biogenesis Pathways

The endosomal sorting complex required for transport (ESCRT) is a highly conserved molecular machinery composed of five different complexes (ESCRT-0, -I, -II, -III and Vps4) that control sEV formation [6] [41]. ESCRT-0 recognizes ubiquitinated cargoes and recruits them to endosomal microdomains, while ESCRT-I and ESCRT-II drive inward budding of the endosomal membrane, forming intraluminal vesicles inside multivesicular bodies [6]. ESCRT-III assembles on the endosomal membrane for the final step of vesicle formation, facilitating cargo sequestration and membrane scission [6].

Alongside ESCRT-dependent pathways, lipids play essential roles in ESCRT-independent sEV biogenesis. Ceramide, with its conical molecular structure, triggers budding of exosomes without the ESCRT system by inducing negative membrane curvature [13] [41]. Other lipids including cholesterol, sphingomyelin, and phosphatidylserine participate in the formation, secretion, signaling, and uptake of exosomes [6] [13].

Figure 1: sEV Biogenesis Pathways and Natural Compound Modulation Sites. Natural compounds target both ESCRT-dependent and ESCRT-independent pathways to modulate sEV release and lipid composition.

Lipid-Mediated Regulation of sEV Lifecycle

Lipid metabolism and signaling influence the entire sEV lifecycle. Phosphoinositides play crucial regulatory roles: phosphatidylinositol-3-phosphate (PI(3)P) recruits ESCRT-0/I complex to initiate ESCRT-dependent pathway, while phosphatidylinositol (3,5)-bisphosphate (PI(3,5)P2) and phosphatidylinositol (4,5)-bisphosphate (PI(4,5)P2) participate in ESCRT-III-mediated membrane scission [13]. Phosphatidylinositol 4-phosphate (PI(4)P) also plays a critical role in MVB maturation and ILV formation [13].

Beyond structural roles, lipids function as bioactive mediators that coordinate vesicle formation and cargo sorting. Fusion lipids including Lysobisphosphatidic Acid (LBPA) and phosphatidic acid (PA) influence intracellular transport of MVBs and their fusion with the plasma membrane [13]. Neutral lipid cholesterol participates in regulating MVB migration, volume, and vacuolization, while stress-responsive lipid mediators such as arachidonic acid can integrate cellular signals to dynamically modulate exosome generation rates [13].

Natural Compounds Modulating sEV Release and Lipid Content

Numerous natural compounds demonstrate significant effects on sEV biogenesis, release, and lipid composition. The table below provides a comparative analysis of key compounds and their documented effects.

Table 1: Comparative Analysis of Natural Compounds Modulating sEV Release and Lipid Content

Natural Compound	Source	Effects on sEV Release	Effects on Lipid Metabolism/Content	Molecular Targets & Mechanisms	Experimental Models
Manumycin A	Streptomyces species	10-fold reduction in exosome secretion [6]	Modulation of Ras/Raf/ERK signaling affecting lipid metabolism [6]	Inhibits ESCRT; suppresses Ras/Raf/ERK1/2 signaling and hnRNP H1 expression [6]	Castration-resistant prostate cancer (CRPC) cells [6]
Cannabidiol (CBD)	Cannabis sativa	Reduced exosome and microvesicle release [6]	Alters lipid composition through prohibitin inhibition; enhances ceramide-mediated pathways [6]	Inhibits prohibitin (chaperoning protein associated with chemoresistance) [6]	Prostate cancer (PC3), hepatocellular carcinoma (HEPG2), breast adenocarcinoma (MDA-MB-231) [6]
Resveratrol	Grapes, berries	Blocks exosome secretion by downregulating Rab27a [6]	Modulates lipid-metabolizing enzymes; affects cholesterol distribution [6]	Downregulates Rab27a; increases CD63 and Ago2; reduces eIF2α [6]	Huh7 cells, COLO320, COLO741 cell lines [6]
Honokiol	Magnolia species	Increases bioavailability when loaded into exosomes [6]	Inhibits P-glycoprotein (P-gp) affecting lipid transporter function [6]	P-glycoprotein (P-gp) inhibitor [6]	Multiple cancer models; mesenchymal stem cell exosomes [6]
GW4869	Synthetic compound (reference inhibitor)	Impedes extracellular vesicle biogenesis [95]	Inhibits neutral sphingomyelinase, reducing ceramide production [95]	Selective neutral sphingomyelinase inhibitor [95]	In vivo mouse models of radiation-induced mucositis [95]

Quantitative Efficacy Comparison

The efficacy of natural compounds in modulating sEV parameters varies significantly. Manumycin A demonstrates the most potent inhibition of sEV release with a 10-fold reduction in exosome secretion from castration-resistant prostate cancer cells [6]. Cannabidiol shows broad-spectrum activity across multiple cancer cell lines, reducing exosome release while simultaneously altering microRNA content (increasing miR-126 and decreasing miR-21) in glioblastoma multiforme cells [6]. Resveratrol exhibits concentration-dependent effects on exosome secretion, with significant inhibition achieved through Rab27a downregulation in Huh7 cells [6].

Table 2: Quantitative Effects of Natural Compounds on sEV Release and Composition

Compound	sEV Release Reduction	Key Lipid Changes	Additional Cargo Modifications	Therapeutic Synergy
Manumycin A	10-fold decrease [6]	Ras pathway-associated lipid alterations [6]	Not specified	Sensitizes CRPC to enzalutamide [6]
Cannabidiol	Significant reduction across multiple cancer lines [6]	Ceramide pathway modulation; prohibitin-mediated lipid changes [6]	Increased miR-126, decreased miR-21 [6]	Enhanced efficacy with camel milk-derived exosomes in doxorubicin-resistant breast cancer [6]
Resveratrol	Significant blockade of secretion [6]	Cholesterol and phospholipid redistribution [6]	Increased CD63 and Ago2; reduced eIF2α [6]	Antiproliferation and reduced migration in Huh7 cells [6]
Honokiol	Enhanced cellular uptake when exosome-loaded [6]	P-glycoprotein transporter inhibition [6]	Improved bioavailability profile [6]	Reduced toxicity to normal cells [6]

Experimental Methodologies for Investigating sEV-Lipid Modulation

Standardized methodologies are essential for evaluating the effects of natural compounds on sEV release and lipid content. The following section outlines key experimental protocols.

sEV Isolation and Characterization

Isolation Methods: Differential ultracentrifugation remains the gold standard for sEV isolation, involving sequential centrifugation steps to eliminate cells, debris, and larger vesicles, followed by high-speed centrifugation (100,000 × g for 70-120 minutes) to pellet sEVs [95] [1]. Alternative methods include density gradient centrifugation for higher purity, polymer-based precipitation kits for convenience, and size-exclusion chromatography for minimal deformation of sEVs [1].

Characterization Techniques: Isolated sEVs should be characterized using multiple complementary approaches:

• Nanoparticle Tracking Analysis (NTA): Determines sEV concentration and size distribution (typically 30-200 nm) [95].
• Transmission Electron Microscopy (TEM): Visualizes sEV morphology, confirming characteristic cup-shaped or spherical structures [95].
• Western Blotting: Detects sEV marker proteins (CD63, CD9, CD81, TSG101, Alix) while excluding negative markers (calnexin, GM130) [95] [1].
• Dynamic Light Scattering (DLS): Provides additional hydrodynamic size distribution data [95].

Lipidomic Analysis of sEVs

Comprehensive lipid profiling of sEVs involves:

• Lipid Extraction: Using modified Folch or Bligh-Dyer methods with chloroform-methanol solvents.
• Mass Spectrometry-Based Lipidomics: Liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) enables identification and quantification of thousands of lipid species across multiple classes [94].
• Data Analysis: Bioinformatics tools process raw data to identify differentially abundant lipid species between treatment groups, with special attention to ceramides, sphingomyelins, phospholipids, and cholesterol esters [94].

Functional Assays for sEV Uptake and Communication

• Fluorescent Labeling: PKH67, PKH26, or DiD dyes label sEV membranes for tracking uptake by recipient cells via fluorescence microscopy or flow cytometry.
• Co-culture Systems: Transwell assays with membrane-impermeable inserts allow assessment of sEV-mediated functional effects between donor and recipient cells.
• STING Pathway Activation: Reporter assays measure sEV-mediated activation of inflammatory pathways, particularly relevant for DNA-containing sEVs [95].

Figure 2: Experimental Workflow for Evaluating Natural Compound Effects on sEVs. Comprehensive assessment requires integrated approaches from isolation through functional analysis.

The Scientist's Toolkit: Essential Research Reagents

This section details critical reagents and methodologies for investigating natural compound-mediated modulation of sEV release and lipid content.

Table 3: Essential Research Reagents for sEV-Lipid Studies

Reagent/Method	Specific Examples	Research Application	Technical Considerations
sEV Isolation Kits	Total Exosome Isolation Kit, miRCURY Exosome Kit	Rapid isolation from cell culture media and biological fluids	Higher throughput but potential co-precipitation of contaminants [1]
Lipid Standards	SPLASH LipidoMix, Avanti Polar Lipids standards	Internal standards for mass spectrometry-based lipid quantification	Essential for accurate quantification; should cover multiple lipid classes [94]
sEV Fluorescent Labels	PKH67, PKH26, DiD, DiI	Membrane labeling for uptake and tracking experiments	Potential dye aggregation; requires optimization of labeling conditions
Pathway Inhibitors	GW4869 (neutral sphingomyelinase inhibitor)	Control for ceramide-mediated sEV biogenesis pathways	Reference compound for ESCRT-independent pathway inhibition [95]
Mass Spectrometry Platforms	LC-MS/MS with Orbitrap or Q-TOF detectors	Comprehensive lipidomic profiling of sEV composition	Requires specialized expertise in lipid identification and quantification [94]
Characterization Instruments	Nanoparticle Tracking Analyzer, TEM, Western Blot	Multi-parameter sEV validation	MISEV guidelines recommend multiple characterization methods [1]

Natural compounds represent powerful tools for modulating sEV release and lipid content, with significant implications for cancer research and therapeutic development. The comparative analysis presented herein demonstrates that compounds including manumycin A, cannabidiol, and resveratrol act through distinct yet complementary mechanisms to regulate sEV biogenesis and alter lipid composition. The experimental methodologies and research reagents detailed in this review provide a foundation for standardized investigation of sEV-lipid dynamics. Future research should focus on elucidating structure-activity relationships of natural compounds, developing standardized protocols for comparative studies, and exploring combinatorial approaches that simultaneously target multiple aspects of sEV biogenesis and lipid metabolism for enhanced anticancer efficacy.

Within the expanding field of liquid biopsy, small extracellular vesicles (sEVs) have emerged as a distinct class of biomarkers with significant prognostic potential. These nanoscale, lipid bilayer-enclosed vesicles, ranging from 30 to 150 nm in diameter, are secreted by virtually all cell types and carry a molecular cargo—including proteins, nucleic acids, and lipids—reflective of their parental cell's physiological state [96] [97]. Their biogenesis, intricately linked to cellular lipid metabolism, involves the inward budding of the endosomal membrane to form multivesicular bodies (MVBs), which subsequently fuse with the plasma membrane to release sEVs into the extracellular space [13] [11]. This review provides a comparative assessment of sEVs against other biomarker modalities, focusing on their application in clinical prognosis. Framed within the context of sEV biogenesis and lipid metabolism in cancer research, we will explore how the unique biological properties of sEVs position them as superior tools for predicting disease outcomes, monitoring therapeutic resistance, and guiding treatment decisions.

The Technical Basis for sEVs as Prognostic Tools

Unique Biophysical and Molecular Properties

The prognostic strength of sEVs is rooted in their fundamental biological characteristics. Their lipid bilayer membrane, enriched with cholesterol, ceramides, and sphingomyelin, provides exceptional stability, protecting internal cargo from enzymatic degradation in the circulation and allowing for the analysis of labile molecules like miRNAs and mRNAs [13] [11]. This stability is a key advantage over cell-free DNA (cfDNA), which is more susceptible to degradation. Furthermore, sEVs are present in high abundance in virtually all bodily fluids, offering a rich source of biomarkers from a routine blood draw [97] [98].

Critically, the process of sEV formation is not random; it involves the active sorting of specific proteins, nucleic acids, and lipids [13]. This means that the molecular profile of sEVs directly mirrors the functional state of their parent cells, including tumor cells. For instance, cancer-derived sEVs are known to carry oncogenic proteins, mutant RNAs, and lipids that can remodel the tumor microenvironment, making them highly specific indicators of malignant progression [6] [98]. This selective packaging, coupled with their cellular origin, provides a more comprehensive and functional snapshot of the tumor than cfDNA, which primarily offers genetic information.

The Role of Lipid Metabolism in sEV Biogenesis and Function

Lipid metabolism is a central regulator of sEV biology. The lipid composition of the sEV membrane is distinct from the parental plasma membrane, and this specific lipid profile is crucial for its function [13] [94].

• Biogenesis: Ceramide, a key lipid metabolite, plays a direct role in the inward budding of the endosomal membrane to form intraluminal vesicles (ILVs) within MVBs, an ESCRT-independent pathway of sEV biogenesis [13]. Phosphoinositides (PIs) are also essential for recruiting the ESCRT machinery in the dependent pathway [13].
• Signaling and Uptake: Lipids are not merely structural. They function as bioactive mediators, influencing the targeting and uptake of sEVs by recipient cells. Phosphatidylserine (PS), for example, when externalized on the sEV surface, can act as an "eat-me" signal for macrophages [13]. Adipose tissue-derived sEVs (AdEVs), enriched with specific lipids, have been shown to promote cancer aggressiveness by supplying fatty acids for energy and modulating the tumor microenvironment [94].

Alterations in lipid metabolism, common in diseases like cancer and obesity, directly impact sEV release and composition. Therefore, analyzing the lipid cargo of sEVs provides a unique window into the metabolic state of the tissue of origin, offering a layer of prognostic information that is inaccessible through DNA-based biomarkers alone [6] [99] [13].

Comparative Analysis of Biomarker Modalities

The following table summarizes the key characteristics of sEVs compared to other major liquid biopsy biomarkers.

Table 1: Comparative Analysis of Liquid Biopsy Biomarkers for Clinical Prognosis

Biomarker Feature	Small Extracellular Vesicles (sEVs)	Cell-free DNA (cfDNA)/Circulating Tumor DNA (ctDNA)	Circulating Tumor Cells (CTCs)
Biological Nature	Lipid bilayer vesicles carrying proteins, nucleic acids, lipids [97]	Short, naked DNA fragments released from apoptotic or necrotic cells [98]	Intact, rare tumor cells shed into the bloodstream [97]
Stability	High (protected by lipid membrane) [11] [98]	Low (susceptible to degradation) [98]	Low (vulnerable to anoikis)
Abundance	High [97] [98]	Variable; ctDNA can be a very small fraction of total cfDNA [98]	Extremely low [97]
Molecular Cargo	Proteins, miRNAs, mRNAs, lncRNAs, lipids, metabolites [96] [98]	Primarily genetic and epigenetic information	Whole genome, proteins, RNA
Source of Information	Active secretion; reflects functional cell state and active pathways [13]	Passive release; reflects cell death [98]	Direct analysis of metastatic precursors
Prognostic Strength	High (provides multi-omic data on tumor activity, TME modulation, and drug resistance) [96] [98]	High for mutation tracking and tumor burden [100]	High for assessing metastatic potential
Key Prognostic Applications	Predicting therapy resistance, monitoring disease progression, identifying recurrence risk [96] [98]	Detecting minimal residual disease (MRD), tracking clonal evolution	Enumeration and characterization for metastatic risk

Quantitative Prognostic Performance of sEV Biomarkers

Clinical studies across various cancer types have demonstrated the robust prognostic value of specific sEV biomarkers. The table below compiles key examples of sEV-derived molecules and their correlation with clinical outcomes.

Table 2: Prognostic Performance of sEV Biomarkers in Human Cancers

Cancer Type	sEV Biomarker	Prognostic Value	Clinical Context	Reference
Non-Small Cell Lung Cancer (NSCLC)	EGFR & CXCR4 (Combination)	Machine learning model predicted tumor relapse 3 days post-surgery [96]	Prognosis Prediction	[96]
Hepatocellular Carcinoma (HCC)	miRNA-638	Lower levels correlated with larger tumors, advanced TNM stage, and poorer 3-/5-year survival [98]	Survival Prediction	[98]
Pancreatic Ductal Adenocarcinoma (PDAC)	mRNA Signature (4-gene)	Diagnostic score was an independent adverse prognostic factor for overall survival [100]	Survival Prediction	[100]
Multiple Cancers (e.g., Melanoma)	Programmed Death-Ligand 1 (PD-L1)	Increased levels independently predicted shorter progression-free survival (PFS) and overall survival (OS) [98]	Immunotherapy Response	[98]
Hepatocellular Carcinoma (HCC)	miRNA-718	Significant difference in expression in patients with recurrence after liver transplantation [98]	Recurrence Prediction	[98]
Colorectal Cancer (CRC)	LncRNA Panel (5-lncRNA)	Ability to differentiate patients with recurrent disease from those without recurrence [98]	Recurrence Prediction	[98]
Breast Cancer	LncRNA Signature	Utilized in serum sEVs for predicting cancer recurrence [98]	Recurrence Prediction	[98]

Advanced Methodologies for sEV-Based Prognostic Analysis

Key Experimental Workflows

Translating the prognostic potential of sEVs into clinically actionable data requires robust and reproducible experimental pipelines. The core workflow encompasses isolation, characterization, and high-throughput analysis.

Diagram 1: Experimental workflow for sEV-based prognostic biomarker discovery and validation, covering isolation, characterization, and multi-omic analysis.

Detailed Methodologies:

• sEV Isolation from Serum/Plasma: As performed in the neuroblastoma and NSCLC studies, ultracentrifugation remains the gold standard [96] [101]. This involves sequential centrifugation steps to remove cells and debris, followed by high-speed ultracentrifugation (e.g., 100,000-150,000 × g) to pellet sEVs. Alternative methods include size-exclusion chromatography (SEC), which preserves sEV integrity and function, and immunoaffinity capture using magnetic beads coated with antibodies against sEV surface tetraspanins (e.g., CD9, CD63, CD81) for highly specific isolation [11] [101].

• sEV Characterization: Isolated sEVs must be validated for size, concentration, and marker expression.

  • Nanoparticle Tracking Analysis (NTA): Instruments like the ZetaView or NanoSight measure the hydrodynamic diameter and concentration of sEVs in solution by tracking the Brownian motion of individual particles [96] [101].
  • Transmission Electron Microscopy (TEM): Provides ultrastructural confirmation of the classic cup-shaped morphology of sEVs after negative staining [96] [101].
  • Western Blotting: Confirms the presence of canonical sEV protein markers (e.g., CD9, CD81, TSG101, Alix, HSP70) and the absence of negative markers (e.g., calnexin) [101].

• Downstream Biomarker Analysis:

  • Proteomics: For protein biomarker discovery, label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) can identify thousands of proteins in a single run [101]. For targeted validation of candidate biomarkers, Multiple Reaction Monitoring (MRM) mass spectrometry offers high sensitivity and reproducibility for quantifying specific peptides [101].
  • RNA Sequencing: Total RNA or small RNA sequencing can profile the full spectrum of miRNAs, mRNAs, and lncRNAs encapsulated in sEVs, enabling the identification of prognostic signatures [100] [98].
  • Lipidomics: Mass spectrometry-based lipidomics is used to characterize the complete lipid profile of sEVs, identifying disease-specific alterations in lipid species like ceramides, phosphatidylserine, and cholesterol that are linked to prognosis [99] [13] [94].

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Reagents and Tools for sEV Prognostic Research

Reagent / Tool	Function in Workflow	Specific Examples
qEV Size Exclusion Columns	Isolation of high-purity sEVs from serum/plasma with minimal damage [101]	qEVoriginal/70 nm (IZON Science)
Anti-Tetraspanin Magnetic Beads	Immunoaffinity capture of specific sEV subpopulations for targeted analysis	Anti-CD63/CD81/CD9 magnetic beads
Protease Inhibitor Cocktail	Preserves protein integrity during sEV lysis and processing [101]	Commercially available cocktails (e.g., EDTA-free)
Primary Antibodies for Characterization	Confirmation of sEV identity and detection of biomarker candidates via Western Blot	Anti-CD9, HSP70, TSG101, Alix; Target-specific (e.g., Anti-HSP90AB1, SLC44A1) [101]
LC-MS/MS System	High-throughput identification and quantification of sEV proteins and lipids [101]	QE-HF-X mass spectrometer coupled to EASY-nLC 1200
MRM Assay Kits	Targeted, highly sensitive validation of candidate protein biomarkers [101]	Custom assays developed in Skyline software
RNA Isolation Kits (sEV-specific)	Extraction of high-quality RNA from sEVs for sequencing	Commercial kits optimized for low-input RNA
ZetaView NTA System	Measures sEV particle size distribution and concentration [101]	ZetaView (Particle Metrix)

The comprehensive analysis of sEV biology and application firmly establishes their superior position in the landscape of prognostic biomarkers. Their inherent stability, abundance, and rich multi-omic cargo—actively sorted and reflective of real-time cellular activity—provide a distinct advantage over passive markers like ctDNA. The integration of sEV-derived protein, RNA, and lipid data, especially through machine learning models, enables a holistic and dynamic assessment of disease progression, therapeutic resistance, and recurrence risk that is currently unattainable with other modalities. While challenges in standardization remain, the ongoing refinement of isolation and analytical protocols is rapidly paving the way for the clinical integration of sEV-based prognostic tests. As research continues to unravel the intricate links between sEV biogenesis, lipid metabolism, and disease pathogenesis, sEVs are poised to become indispensable tools in the era of precision medicine, fundamentally improving how patient prognosis is determined and managed.

Small extracellular vesicles (sEVs) are membrane-bound vesicles typically less than 200 nm in diameter that are released by cells and play a pivotal role in intercellular communication [1]. In the context of cancer, these vesicles become reprogrammed to promote tumor progression, metastasis, and therapy resistance [6]. The biogenesis of sEVs is intimately connected with cellular lipid metabolism, as lipids constitute essential structural components of vesicle membranes and regulate key steps in their formation and release [102]. This interconnection creates a dependency that cancer cells exploit to maintain their malignant phenotype, making the enzymatic regulators at this nexus promising therapeutic targets.

Lipid metabolism reprogramming is a recognized hallmark of cancer, contributing to tumor growth, metastatic dissemination, and resistance to therapy [83] [86]. Cancer cells alter key metabolic pathways—including de novo lipogenesis, lipid uptake, and phospholipid remodeling—to sustain malignant progression and adapt to microenvironmental demands [86]. Simultaneously, these alterations directly influence the production, composition, and function of sEVs, which serve as vehicles for transmitting oncogenic signals [103]. Understanding the enzymatic machinery that governs this intersection provides a strategic framework for developing targeted interventions that disrupt cancer-promoting communication networks.

Key Enzymatic Regulators at the Lipid Metabolism-sEV Interface

ESCRT Complex and Associated Machinery

The Endosomal Sorting Complex Required for Transport (ESCRT) is a highly conserved molecular machinery composed of five different complexes (ESCRT-0, -I, -II, -III and Vps4) that plays a fundamental role in sEV biogenesis [6]. ESCRT-0 recognizes and recruits ubiquitinated cargoes to endosomal microdomains. ESCRT-I and ESCRT-II subsequently drive inward budding of the endosomal membrane, forming intraluminal vesicles inside multivesicular bodies (MVBs). ESCRT-III then assembles on the endosomal membrane for the final step of vesicle scission [6]. TSG101 (a component of ESCRT-I) and ARDRC1 have been identified as proteins involved in the budding of microvesicles directly from the cell membrane [1]. The ESCRT pathway represents a prime therapeutic target, as evidenced by studies showing that Manumycin A reduces exosome secretion in castration-resistant prostate cancer cells by shutting down ESCRT and inhibiting Ras/Raf/ERK1/2 signaling [6].

Lipid-Metabolizing Enzymes in sEV Biogenesis

Beyond the ESCRT-dependent pathway, several lipid-metabolizing enzymes play critical roles in sEV biogenesis through ESCRT-independent mechanisms:

• Ceramide-producing enzymes: Ceramide, an essential lipid in cellular signaling, triggers the budding of exosomes without the ESCRT system through the formation of lipid raft-like microdomains [6]. The enzymatic machinery responsible for ceramide generation thus directly influences sEV production.
• Phospholipase C (PLC) and Phosphoinositide 3-Kinase (PI3K): These enzymes catalyze membrane lipids into important signaling molecules, including diacylglycerol (DAG), inositol triphosphate (IP3), and phosphatidyl-inositol (3,4,5)-triphosphate (PIP3), which participate in signaling cascades that regulate both lipid metabolism and vesicle formation [103].
• Rab GTPases: RAB5C has been identified as a potential important molecular player connecting lipid droplets and sEV biogenesis [102]. Additionally, Rab31 GTPase mediates exosome biogenesis through a non-classical pathway that does not rely on the ESCRT mechanism [1]. Rab27a is another key regulator, with resveratrol shown to block exosome secretion by downregulating this GTPase in liver cancer cells [6].
• ROCK (Rho-associated coiled-coil containing kinases): Essential for microvesicle release, with ROCK1 being a key regulatory factor in the formation of apoptotic bodies [1].

Table 1: Key Enzymatic Regulators in Lipid Metabolism and sEV Biogenesis

Enzyme/Regulator	Class	Primary Function	Therapeutic Potential
TSG101	ESCRT Complex	Cargo recognition and vesicle budding	Manumycin A target; reduces oncogenic sEV secretion
Ceramide Pathway	Sphingolipid Enzymes	ESCRT-independent budding	Modulation alters sEV release and composition
RAB5C	GTPase	Connects lipid droplets to sEV biogenesis	Potential target for disrupting lipid-sEV axis
Rab27a	GTPase	Regulates vesicle docking and release	Resveratrol target; blocks secretion
ROCK1	Kinase	Cytoskeletal remodeling for vesicle release	Inhibitors affect microvesicle and apoptotic body formation
PI3K	Lipid Kinase	Generates lipid signaling molecules	Targeted to disrupt pro-tumorigenic signaling

Enzymes Linking Lipid Metabolism and sEV-Mediated Cancer Progression

The interconnection between lipid metabolism and sEV biogenesis creates a feed-forward loop that promotes cancer progression. Key enzymes involved include:

• Acyl-CoA: Cholesterol Acyltransferase 1 (ACAT1): A cholesterol esterification enzyme whose depletion impairs sEV-mediated propagation of oncogenic signals [103].
• CD36: A lipid metabolism-related gene identified as a biomarker in pancreatic cancer that influences the tumor microenvironment through sEV-mediated mechanisms [104].
• Fatty Acid Synthase (FASN): Upregulated in pancreatic tumorigenesis and contributes to the lipid composition of sEVs that promote cancer progression [104].
• Carnitine Palmitoyltransferase 1A and 1B (CPT1A/CPT1B): Enzymes involved in fatty acid oxidation whose expression is enhanced in pancreatic cancer models, influencing the metabolic reprogramming mediated by sEVs [104].

Experimental Approaches for Studying Key Enzymes and sEV Biogenesis

Modulating Cellular Lipid Content and Analyzing sEV Output

To investigate the connection between lipid droplets (LDs) and sEV biogenesis, researchers have developed methodologies using various external stimuli to modulate cellular LD density and analyze subsequent effects on sEV secretion [102]:

Protocol: LD-sEV Connection Analysis

• Cell Culture: Utilize relevant human cancer cell lines (e.g., HT-29 and LoVo colon adenocarcinoma, MCF7 breast adenocarcinoma, NCI-H460 non-small-cell lung carcinoma, PANC01 pancreatic carcinoma) or patient-derived cancer stem cells (e.g., CR-CSCs from colorectal cancer) [102].
• LD Modulation: Apply external stimuli known to affect LD density:
  • Ionizing Radiation: Expose cells to varying doses of radiation.
  • pH Manipulation: Culture cells under different pH conditions.
  • Hypoxia: Maintain cells in low oxygen environments (1-5% Oâ‚‚).
  • Pharmacological Inhibition: Treat cells with LD-interfering drugs.
• sEV Isolation: Harvest conditioned media and isolate sEVs using differential ultracentrifugation or other standardized methods.
• Analysis:
  • Quantify sEV secretion rate using nanoparticle tracking analysis.
  • Characterize sEV cargo composition through proteomics, lipidomics, and RNA sequencing.
  • Assess functional consequences of sEV alterations on recipient cells.

This approach has revealed that external stimuli affecting LDs similarly influence sEV secretion, with RAB5C identified as a potential molecular mediator through multi-omics data at both mRNA and protein levels [102].

Targeting Enzymes with Natural Compounds

Natural compounds (NCs) represent valuable tools for experimentally modulating key enzymes in lipid metabolism and sEV biogenesis:

Protocol: Evaluating Natural Compound Effects

• Compound Selection: Choose NCs with known effects on lipid metabolism or vesicle trafficking:
  • Manumycin A: Streptomyces-derived antibiotic that inhibits ESCRT function and Ras/Raf/ERK1/2 signaling [6].
  • Cannabidiol (CBD): Phytocannabinoid that modulates exosome and microvesicle release in multiple cancer cell lines [6].
  • Resveratrol: Polyphenol that downregulates Rab27a to block exosome secretion [6].
• Treatment Conditions: Expose cancer cells to varying concentrations of NCs and appropriate controls.
• sEV Analysis: Isolate sEVs and quantify changes in:
  • Secretion rate (particle number/cell)
  • Lipid composition (lipidomics)
  • Protein markers (Western blot for CD63, CD81, TSG101)
  • miRNA content (e.g., miR-126 and miR-21 in glioblastoma) [6]
• Functional Assays: Assess downstream effects on:
  • Cancer cell proliferation and migration
  • Drug sensitivity (e.g., enzalutamide in prostate cancer)
  • Recipient cell phenotype

This methodology has demonstrated that NCs can significantly influence sEV synthesis, secretion, and composition while also modulating key regulatory enzymes involved in lipogenesis and degradation [6].

Signaling Pathways Connecting Lipid Metabolism and sEV Biogenesis

The enzymatic regulators of lipid metabolism and sEV biogenesis operate within integrated signaling networks that promote cancer progression. The diagram below illustrates key pathways and their interconnections.

Diagram 1: Integrated Signaling Pathways in Lipid Metabolism and sEV Biogenesis. This diagram illustrates how oncogenic signals and metabolic stress trigger lipid metabolism reprogramming, which activates key enzymatic regulators of sEV biogenesis including the ESCRT complex, ceramide pathway, and RAB GTPases. These pathways converge to promote sEV release carrying oncogenic cargo that drives tumor progression.

The Scientist's Toolkit: Essential Research Reagents and Solutions

Table 2: Key Research Reagents for Studying Lipid Metabolism and sEV Biogenesis

Reagent/Category	Specific Examples	Research Application	Function
Cell Models	Patient-derived CR-CSCs; Commercial cancer lines (MCF7, PANC01, NCI-H460)	Studying cell-type-specific mechanisms	Preserve tumor heterogeneity; model human cancer
LD Modulators	Ionizing radiation; Lipid-interfering drugs; pH/hypoxia chambers	Manipulating cellular lipid content	Induce LD formation/depletion to study sEV connection
sEV Isolation Kits	Differential ultracentrifugation systems; Polymer-based kits; Density gradients	Isculating high-purity sEVs	Separate sEVs from other extracellular components
Enzyme Inhibitors	Manumycin A (ESCRT); Resveratrol (Rab27a); CBD (exosome release)	Targeting specific enzymatic pathways	Probe functional roles of key regulators
Omics Platforms	Lipidomics; Proteomics; Transcriptomics	Comprehensive cargo analysis	Characterize sEV composition and cellular responses
Molecular Probes	LD-specific dyes (Nile Red); sEV markers (CD63, CD81, TSG101)	Visualization and quantification	Track LD dynamics and sEV characterization

The intricate interplay between lipid metabolism and sEV biogenesis represents a promising frontier for therapeutic intervention in cancer. Key enzymes including components of the ESCRT machinery, ceramide-producing enzymes, Rab GTPases (particularly RAB5C and Rab27a), and lipid-metabolizing enzymes such as ACAT1 and FASN emerge as critical regulators at this nexus. These enzymes not only control the production and composition of sEVs but also influence their oncogenic potential through selective cargo packaging.

Future research directions should focus on developing more specific inhibitors against these key enzymatic regulators, with particular attention to isoform selectivity to minimize off-target effects. The integration of multi-omics approaches—including lipidomics, proteomics, and transcriptomics—will be essential for deciphering the complex networks connecting lipid metabolism to sEV biogenesis [105]. Additionally, exploring the circadian regulation of these processes presents an intriguing avenue, as evidence suggests that EV release and cargo composition exhibit time-of-day-dependent variations regulated by the circadian clock [106]. Understanding these temporal dynamics could inform chronotherapy approaches that maximize therapeutic efficacy while minimizing toxicity.

As methodologies for sEV isolation and characterization continue to advance, and as our understanding of lipid metabolism in cancer deepens, targeting the enzymatic intersection of these processes holds significant promise for developing novel cancer therapeutics that disrupt tumor-promoting communication networks. The experimental approaches and tools outlined in this review provide a foundation for these future investigations, which will ultimately translate our growing mechanistic understanding into clinical applications for cancer patients.

Benchmarking EV-Based Therapeutics Against Conventional and Emerging Modalities

Extracellular vesicles (EVs) are nano-sized, lipid bilayer-enclosed particles secreted by all cell types that cannot replicate independently. [13] [44] These vesicles represent an emerging class of therapeutic agents with unique properties that differentiate them from conventional and other emerging modalities. The International Society for Extracellular Vesicles (ISEV) defines EVs as particles naturally released from cells that are delimited by a lipid bilayer and cannot replicate. [107] EVs are primarily classified based on their biogenesis pathways: exosomes (30-150 nm) originate from the endosomal system through multivesicular bodies (MVBs); microvesicles (100-1000 nm) are formed via outward budding of the plasma membrane; and apoptotic bodies (1000-5000 nm) are generated during programmed cell death. [13] [41] The therapeutic potential of EVs stems from their dual capacity to function as innate therapeutic entities and engineered drug delivery vehicles, positioning them at the forefront of nanomedicine innovation for cancer and other diseases. [108]

Table 1: Key Characteristics of Major Therapeutic Modalities

Modality	Mechanism of Action	Key Advantages	Primary Limitations
EV Therapeutics	Innate signaling + targeted cargo delivery	High biocompatibility, low immunogenicity, natural targeting, cross biological barriers	Manufacturing complexity, heterogeneity, standardization challenges
Antibody-Drug Conjugates (ADCs)	Targeted cytotoxicity via antibody-antigen recognition	High target specificity, potent cell killing	Off-target toxicity, antigen escape, limited payload capacity
Viral Gene Therapy	Genetic modification via viral vector transduction	High transduction efficiency, durable expression	Immunogenicity, insertional mutagenesis, limited payload capacity
Cell Therapies	Living cellular therapeutics	Functional tissue regeneration, multifaceted actions	Tumorigenicity, immunorejection, storage challenges, ethical concerns

EV Biogenesis Mechanisms and Lipid Metabolism Interplay

Molecular Machinery of EV Biogenesis

The biogenesis of small extracellular vesicles (sEVs) involves sophisticated molecular pathways that determine their composition and function. The endosomal sorting complex required for transport (ESCRT) machinery represents the canonical pathway for exosome formation, comprising five distinct complexes (ESCRT-0, -I, -II, -III, and Vps4) that work sequentially. [6] [41] ESCRT-0 recognizes and recruits ubiquitinated cargoes to endosomal microdomains through binding to 3-phosphoinosides. ESCRT-I and ESCRT-II subsequently drive inward budding of the endosomal membrane, forming intraluminal vesicles (ILVs) inside multivesicular bodies (MVBs). ESCRT-III then facilitates the final membrane scission step, after which Vps4 ATPase recycles the ESCRT components. [6] [41]

Parallel ESCRT-independent pathways also contribute significantly to EV biogenesis. Ceramide, a sphingolipid with cone-shaped molecular structure, can trigger inward budding of endosomal membranes without ESCRT involvement through its capacity to induce membrane curvature. [6] [13] Additional lipids including cholesterol, sphingomyelin, and phosphatidylserine participate in EV formation, secretion, signaling, and uptake. [6] The syndecan-syntenin-ALIX complex represents another ESCRT-associated pathway that regulates exosome biogenesis and cargo sorting, particularly in polarized cells. [41]

Diagram 1: EV Biogenesis Pathways. This diagram illustrates the major pathways of extracellular vesicle formation, including ESCRT-dependent and independent mechanisms.

Lipid Metabolism in EV Biology

Lipids play multifaceted roles throughout the EV lifecycle, serving as structural components, signaling molecules, and functional mediators. The lipid composition of EV membranes differs substantially from their parent cells, with enrichment of specific lipid classes including phosphatidylserine (PS), sphingomyelin, ceramides, and sterols. [6] [13] Phosphatidylserine is particularly abundant in the inner leaflet of EV membranes and contributes to membrane curvature through its asymmetric distribution. [13] Ceramide not only drives ESCRT-independent budding but also facilitates membrane neck constriction during microvesicle release. [13] [41]

Lipid signaling molecules precisely coordinate EV biogenesis and secretion. Phosphoinositides including PI(3)P, PI(3,5)P2, and PI(4,5)P2 recruit ESCRT components and regulate membrane scission events. [13] Research demonstrates that toll-like receptor 4 (TLR4) activation in macrophages upregulates PI(4)P kinase PIP5K1C, enhancing ILV generation and exosome secretion. [13] Additional lipid mediators such as lysophosphatidic acid (LPA) and phosphatidic acid (PA) influence MVB trafficking and fusion with the plasma membrane, while arachidonic acid integrates cellular stress signals to dynamically modulate exosome generation rates. [13]

Table 2: Key Lipid Classes in EV Biology and Their Functions

Lipid Class	Representative Members	Functions in EV Biology
Phospholipids	Phosphatidylserine (PS), phosphatidylethanolamine (PE), phosphatidylcholine (PC)	Membrane curvature, structural integrity, recognition signals
Sphingolipids	Ceramide, sphingomyelin, glycosphingolipids	Budding initiation, membrane microdomain formation, signaling
Sterols	Cholesterol	Membrane fluidity, stability, lipid raft organization
Phosphoinositides	PI(3)P, PI(3,5)P2, PI(4,5)P2	ESCRT recruitment, membrane scission, MVB maturation
Fatty Acids	Arachidonic acid, phosphatidic acid	Stress signaling, fusion events, dynamic modulation of release

Comparative Analysis of Therapeutic Platforms

Benchmarking Against Established Modalities

When benchmarked against conventional therapeutic platforms, EVs demonstrate distinctive advantages that address critical limitations of existing technologies. Compared to antibody-drug conjugates (ADCs), EVs offer superior payload capacity, enabling co-delivery of diverse therapeutic cargo including small molecules, nucleic acids, and proteins for multi-targeted approaches. [108] Unlike viral vectors used in gene therapy, EVs avoid insertional mutagenesis risks and possess reduced immunogenicity, while their lipid bilayer structure protects nucleic acid cargo from enzymatic degradation. [108] Relative to cell therapies, EVs present enhanced safety profiles because they lack replicative capacity, eliminating tumorigenicity concerns while offering simplified storage requirements and reduced ethical constraints. [108]

The intrinsic biological properties of EVs further differentiate them from synthetic nanoparticle systems. EVs exhibit natural tropism to specific tissues based on their surface markers, can cross biological barriers including the blood-brain barrier, and demonstrate extended circulation half-lives due to reduced immune clearance. [108] [109] Their biomimetic structure facilitates efficient cellular uptake through multiple entry mechanisms, including receptor-mediated endocytosis, membrane fusion, and phagocytosis. [13]

Positioning Among Emerging Technologies

Within the landscape of emerging therapeutic platforms, EVs occupy a unique niche that complements rather than competes with other innovative approaches. While lipid nanoparticles (LNPs) excel in nucleic acid delivery, EVs offer naturally optimized delivery efficiency without synthetic component toxicity concerns. [108] Compared to polymeric nanoparticles, EVs provide inherently bioactive surfaces that interact specifically with recipient cells. The dual functionality of EVs as both inherent therapeutics and targeted delivery vehicles enables multifaceted therapeutic strategies unattainable with single-mechanism platforms. [108]

EV Engineering and Therapeutic Applications

Engineering Strategies for Enhanced Therapeutics

Engineering approaches transform natural EVs into precision therapeutic tools through cargo loading, surface modification, and hybrid system creation. Cargo loading strategies encompass both endogenous and exogenous methods. Endogenous loading via parental cell engineering enables stable encapsulation of proteins, mRNAs, and long nucleic acids through fusion with EV scaffold proteins like PTGFRN. [108] Exogenous loading including electroporation, sonication, and freeze-thaw cycles facilitates rapid incorporation of small molecules and siRNAs into pre-formed EVs. [108]

Surface modification techniques enhance targeting specificity and pharmacokinetic properties. Genetic engineering displaying LAMP2B fusion proteins enables precise tissue targeting, exemplified by LAMP2B-IL3 for chronic myeloid leukemia and LAMP2B-RVG for acetylcholine receptor-rich tissues. [108] Chemical conjugation using click chemistry allows attachment of targeting ligands (e.g., cyclic RGD peptides for tumor vasculature) and stealth coatings like polyethylene glycol (PEG) for extended circulation. [108] Material integration strategies further enhance functionality, including magnetic nanoparticles for guided localization and hydrogel encapsulation for sustained release at target sites. [108]

Diagram 2: EV Engineering Strategies. This diagram outlines the major engineering approaches for enhancing EV therapeutic properties, including cargo loading, surface modification, and hybrid system creation.

Applications in Cancer Therapeutics

EV-based therapeutics demonstrate particular promise in oncology, where their innate biological properties align with key clinical challenges. As natural therapeutics, mesenchymal stem cell-derived EVs (MSC-EVs) exhibit immunomodulatory and tissue-repair capabilities applied to conditions like acute respiratory distress syndrome (Phase 3 trials) and epidermolysis bullosa (Phase 1/2 trials). [108] Neural stem cell EVs (NSC-EVs) show neuroprotective properties and blood-brain barrier penetration for neurological applications, with AB126 receiving FDA IND approval for ischemic stroke. [108]

As drug delivery vehicles, EVs address critical limitations of conventional chemotherapy. Tumor-targeted EVs loaded with doxorubicin or paclitaxel demonstrate enhanced therapeutic indices in preclinical models, reducing systemic toxicity while improving tumor accumulation. [108] [110] For molecular therapeutics, EV-mediated delivery of siRNA targeting oncogenes like KRASG12D and miR-124 for neurological disorders achieves efficient gene silencing with minimal off-target effects. [108] Emerging approaches utilize EVs for CRISPR-Cas9 delivery, combining the editing precision of gene therapy with the delivery advantages of natural vesicles. [110]

Experimental Protocols for EV Research

EV Isolation and Characterization Workflow

Standardized methodologies for EV isolation and characterization are fundamental to therapeutic development. The MISEV2018 guidelines provide a comprehensive framework for EV research, emphasizing the need for multiple complementary characterization techniques. [107]

Isolation Protocol:

• Sample Preparation: Collect cell culture supernatant or biofluid (plasma, serum). Perform low-speed centrifugation (300 × g, 10 min) to remove cells, followed by 2,000 × g for 20 min to eliminate cell debris. [107]
• Concentration: Ultrafilter supernatant using 100-kDa molecular weight cut-off membranes or concentrate via ultracentrifugation at 100,000 × g for 70 minutes. [107]
• Purification: Apply concentrated sample to size-exclusion chromatography (SEC) columns (e.g., qEV original) equilibrated with PBS. Collect EV-rich fractions based on calibrated elution volumes. [107]
• Validation: Analyze protein content via BCA assay, particle concentration by nanoparticle tracking analysis (NTA), and presence of EV markers (CD63, CD81, CD9) by western blot or flow cytometry. [107]

Characterization Protocol:

• Nanoparticle Tracking Analysis: Dilute EV preparation 1:100-1:1000 in filtered PBS to achieve 20-100 particles per frame. Acquire five 60-second videos using appropriate camera settings. Analyze with software to determine particle size distribution and concentration. [107]
• Transmission Electron Microscopy: Apply 5-10 μL of EV sample to formvar/carbon-coated grids for 1 minute. Stain with 1% uranyl acetate for 45 seconds. Image using 80-100 kV accelerating voltage. [107]
• Western Blot Analysis: Separate EV proteins (10-20 μg) by SDS-PAGE. Transfer to PVDF membranes and probe for tetraspanins (CD63, CD81, CD9), EV-enriched proteins (TSG101, Alix), and negative markers (calnexin, GM130). [107]

Functional Assessment of Therapeutic EVs

Cargo Loading Efficiency Assessment:

• Small Molecule Quantification: Extract drugs from EVs using methanol precipitation. Analyze by HPLC-MS/MS with appropriate calibration curves. Calculate encapsulation efficiency as (amount detected in EVs / initial amount used for loading) × 100%. [108]
• Nucleic Acid Quantification: Isolate RNA/DNA from EVs using commercial kits with spike-in controls. Analyze loading efficiency by qPCR for specific sequences or bioanalyzer profile for overall distribution. [108]

Functional Uptake and Delivery Assay:

• Labeling: Label EVs with lipophilic dyes (DiI, DiD) or membrane-permeant fluorescent tracers according to manufacturer protocols.
• Incubation: Treat recipient cells with labeled EVs (1-10 × 10^9 particles/mL) for 2-24 hours.
• Analysis: Quantify uptake by flow cytometry or confocal microscopy. Assess functional delivery by measuring target gene modulation (for nucleic acid cargo) or pharmacological effect (for drug cargo). [109]

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Research Reagents for EV Therapeutic Development

Reagent/Category	Specific Examples	Function/Application
Isolation Kits	ExoQuick (precipitation), ExoEasy (membrane affinity), qEV (size exclusion)	Rapid EV isolation from biofluids and conditioned media with varying purity-yield tradeoffs
Characterization Antibodies	Anti-CD63, -CD81, -CD9, -TSG101, -Alix, -Calnexin (negative)	EV marker identification and purity assessment by western blot, flow cytometry
Tracking Dyes	DiI, DiD, PKH67, PKH26, CFSE	Membrane labeling for uptake studies, biodistribution tracking
Engineering Tools	LAMP2B fusion constructs, PTGFRN scaffolds, Click chemistry kits	Surface modification for targeting, scaffold-mediated cargo loading
Analysis Instruments	NanoSight (NTA), ZetaView, qNano	Particle concentration, size distribution, and characterization
Cell Lines	HEK293, MSC, NSC, proprietary producer cells	EV production, engineering platform development

EV-based therapeutics represent a transformative approach that integrates advantages of biological systems and engineered nanomaterials. Their unique combination of natural targeting capabilities, biocompatibility, and engineering flexibility positions EVs as versatile platforms addressing limitations of both conventional and emerging therapeutic modalities. The interplay between EV biogenesis and lipid metabolism provides fundamental insights for optimizing therapeutic EVs, particularly through engineering approaches that enhance cargo loading, targeting specificity, and pharmacokinetic properties.

Future development will require advances in manufacturing scalability, analytical characterization, and regulatory standardization to fully realize the clinical potential of EV therapeutics. Emerging opportunities include hybrid systems combining synthetic materials with natural EV components, personalized EV therapies derived from patient-specific cells, and combination strategies leveraging EVs to enhance conventional treatments. As understanding of EV biology deepens and engineering capabilities advance, EV-based therapeutics are poised to become increasingly sophisticated tools in the therapeutic arsenal, particularly for complex diseases like cancer where multi-targeted approaches and biological barrier penetration are paramount.

Conclusion

The intricate interplay between lipid metabolism and sEV biogenesis represents a pivotal axis in cancer biology, influencing tumor progression, immune modulation, and drug resistance. This synthesis underscores the dual utility of sEVs as both dynamic mediators of malignancy and promising vectors for therapeutic intervention. The translation of sEV lipidomics into robust clinical biomarkers and the harnessing of natural compounds or engineered sEVs for therapy are poised to redefine diagnostic and treatment paradigms. Future research must focus on standardizing methodologies, deciphering the functional consequences of specific lipid cargo, and advancing combination therapies that concurrently target lipid metabolic pathways and sEV communication to achieve superior anti-tumor outcomes.

References

• 1. Small extracellular vesicles: the origins, current status, future ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12004682/]
• 2. Extracellular vesicles: biogenesis mechanism and impacts ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12412255/]
• 3. The roles of small extracellular vesicles in cancer and immune ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9513874/]
• 4. Tumor-derived small extracellular vesicles in cancer invasion ... [https://molecular-cancer.biomedcentral.com/articles/10.1186/s12943-024-01932-0]
• 5. The exosome journey: from biogenesis to uptake and ... [https://biosignaling.biomedcentral.com/articles/10.1186/s12964-021-00730-1]
• 6. The role of natural compounds in modulation of small ... [https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2025.1727274/full]
• 7. Modified lipidomic profile of cancer-associated small ... [https://www.sciencedirect.com/science/article/abs/pii/S2212492622000756]
• 8. Exosomal Lipids in Cancer Progression and Metastasis - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC11924287/]
• 9. Targeted knockdown of MSC-sEVs biogenesis regulator ... [https://www.sciencedirect.com/science/article/abs/pii/S1465324925000611]
• 10. Extracellular vesicles in tumor immunity - Molecular Cancer [https://molecular-cancer.biomedcentral.com/articles/10.1186/s12943-025-02233-w]
• 11. Advances in extracellular vesicle (EV) biomarkers for ... [https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1581015/full]
• 12. Extracellular vesicles: lipids as key components of their ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC6071772/]
• 13. Lipid metabolism and lipid signaling in extracellular vesicles ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12519735/]
• 14. The lipid composition of extracellular vesicles [https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2023.1198044/full]
• 15. Software and Computational Tools for LC-MS-Based ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9835057/]
• 16. Small Extracellular Vesicles: Functions and Potential ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC8541078/]
• 17. Intercellular Vesicular Transfer by Exosomes ... [https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2019.00125/full]
• 18. Small Extracellular Vesicles: A Novel Avenue for Cancer ... [https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.638357/full]
• 19. Small extracellular vesicles (sEVs) in pancreatic cancer ... [https://www.sciencedirect.com/science/article/abs/pii/S0168365925000823]
• 20. Small extracellular vesicles: from promoting pre-metastatic ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9465880/]
• 21. Influencing immunity: role of extracellular vesicles in tumor ... [https://www.nature.com/articles/s12276-024-01340-w]
• 22. Lipid metabolic reprogramming in colorectal cancer [https://pmc.ncbi.nlm.nih.gov/articles/PMC12531765/]
• 23. Lipid metabolic reprograming: the unsung hero in breast ... [https://molecular-cancer.biomedcentral.com/articles/10.1186/s12943-025-02258-1]
• 24. Lipid metabolic reprogramming in colorectal cancer [https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1603032/full]
• 25. Identification of serum metabolite biomarkers and ... [https://www.nature.com/articles/s41598-025-97641-9]
• 26. Diagnostic Biomarker Candidates Proposed Using ... [https://www.mdpi.com/2072-6694/17/18/2988/review_report]
• 27. Lipid Metabolism Reprogramming in Cancer: Insights into ... [https://www.mdpi.com/2227-9059/13/8/1895]
• 28. Lipid metabolism and lipid signaling in extracellular vesicles ... [https://jnanobiotechnology.biomedcentral.com/articles/10.1186/s12951-025-03769-1]
• 29. Single-cell RNA sequencing reveals different cellular ... [https://www.nature.com/articles/s41523-025-00808-w]
• 30. A lipid metabolism-related gene signature predicts ... [https://pubmed.ncbi.nlm.nih.gov/40745642/]
• 31. Small extracellular vesicles orchestrated pathological ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12446661/]
• 32. Targeted drug delivery of cancer cell-derived extracellular ... [https://www.sciencedirect.com/science/article/abs/pii/S0927776525001687]
• 33. A simplified and efficient method for isolating small ... [https://www.nature.com/articles/s41598-025-99822-y]
• 34. Isolation and characterization of extracellular vesicles for ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9419267/]
• 35. Extracellular Vesicles in cancer: from isolation and ... [https://bmccancer.biomedcentral.com/articles/10.1186/s12885-025-14375-7]
• 36. Comparison Study of Small Extracellular Vesicle Isolation ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10607056/]
• 37. Lipid metabolism and lipid signaling in extracellular ... [https://pubmed.ncbi.nlm.nih.gov/41084000/]
• 38. Biomanufacturing and lipidomics analysis of extracellular ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12023677/]
• 39. Comparative evaluation of methods for isolating small ... [https://cellandbioscience.biomedcentral.com/articles/10.1186/s13578-021-00550-3]
• 40. A streamlined method for isolating and characterizing small ... [https://bmcmethods.biomedcentral.com/articles/10.1186/s44330-025-00040-9]
• 41. Extracellular vesicles: biogenesis mechanism and impacts on ... [https://jbiomedsci.biomedcentral.com/articles/10.1186/s12929-025-01182-2]
• 42. Extracellular vesicle lipids in cancer immunoevasion [https://www.sciencedirect.com/science/article/abs/pii/S2405803323001668]
• 43. Composition of cardiac-derived extracellular vesicles ... [https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1565104/full]
• 44. Extracellular vesicles as tools and targets in therapy for ... [https://www.nature.com/articles/s41392-024-01735-1]
• 45. Harnessing the potential of small extracellular vesicle ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12392082/]
• 46. Liquid biopsy in gastrointestinal oncology [https://www.frontiersin.org/journals/oncology-reviews/articles/10.3389/or.2025.1702932/full]
• 47. Harnessing the potential of small extracellular vesicle ... [https://pubmed.ncbi.nlm.nih.gov/40896292/]
• 48. exploring the inhibition of vesicle-associated pathways and ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11162651/]
• 49. Lipid metabolism in cancer cells: Its role in hepatocellular ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12548999/]
• 50. A lipid metabolism-related gene signature for risk ... [https://www.nature.com/articles/s41598-025-10879-1]
• 51. Small Extracellular Vesicles as Biomarkers in Sarcoma ... [https://www.researchprotocols.org/2025/1/e63718]
• 52. Extracellular Vesicle-Based Drug Delivery Systems in ... [https://www.mdpi.com/1422-0067/26/10/4835]
• 53. Emerging role of exosomes in cancer therapy: progress and ... [https://molecular-cancer.biomedcentral.com/articles/10.1186/s12943-024-02215-4]
• 54. Harnessing engineered mesenchymal stem cell-derived ... [https://stemcellres.biomedcentral.com/articles/10.1186/s13287-025-04708-5]
• 55. Smart nanocarriers for cancer: harnessing exosomes and lipid ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12575245/]
• 56. Advancing liposomal doxorubicin for targeted cancer therapy [https://www.sciencedirect.com/science/article/pii/S2666845925000601]
• 57. Extracellular vesicle-based drug overview: research ... [https://www.nature.com/articles/s41392-025-02312-w]
• 58. Targeting lipid metabolism to enhance cancer ... [https://www.sciencedirect.com/science/article/abs/pii/S0304419X25001581]
• 59. Unlocking the potential of circulating small extracellular ... [https://www.explorationpub.com/Journals/ebmx/Article/10138]
• 60. Comparison of small extracellular vesicles isolated from plasma by... [https://pubmed.ncbi.nlm.nih.gov/30651940/]
• 61. A simplified and efficient method for isolating small extracellular... [https://www.nature.com/articles/s41598-025-99822-y?error=cookies_not_supported&code=bd4687f0-94b7-4876-8aa1-b4310dd12eaa]
• 62. Isolating Small Extracellular Vesicles from Small Volumes of Blood... [https://elifesciences.org/reviewed-preprints/92796v2]
• 63. Small extracellular vesicles as biomarkers of response in ... [https://www.nature.com/articles/s44276-024-00096-0]
• 64. Towards sustainable lipidomics: computational screening and ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12596327/]
• 65. The potential of small extracellular vesicles for pancreatic ... [https://pubs.rsc.org/en/content/articlehtml/2025/pm/d5pm00115c]
• 66. Smart nanoparticles for cancer therapy [https://www.nature.com/articles/s41392-023-01642-x]
• 67. Lipid-based nanoparticles as drug delivery carriers for ... [https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1296091/full]
• 68. Bridging the Gap: Translating Preclinical Biomarkers to ... [https://blog.crownbio.com/bridging-the-gap-translating-preclinical-biomarkers-to-clinical-success]
• 69. Emerging roles of small extracellular vesicles in metabolic ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11472704/]
• 70. Cancer biomarker discovery and validation - Goossens [https://tcr.amegroups.org/article/view/4536/html]
• 71. Biomarker Discovery and Validation: Statistical Considerations [https://pmc.ncbi.nlm.nih.gov/articles/PMC8012218/]
• 72. Small extracellular vesicles: crucial mediators for prostate ... [https://jnanobiotechnology.biomedcentral.com/articles/10.1186/s12951-025-03326-w]
• 73. Cancer Biomarkers: Reflection on Recent Progress, Emerging ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12468363/]
• 74. Challenges and Future Trends in Biomarker Analysis for ... [https://blog.crownbio.com/challenges-and-future-trends-in-biomarker-analysis-for-clinical-research-whats-ahead-in-2025]
• 75. Recent advances in extracellular vesicles for therapeutic ... [https://www.nature.com/articles/s12276-024-01201-6]
• 76. Biogenesis and delivery of extracellular vesicles [https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2023.1330400/full]
• 77. Engineered exosomes: a promising drug delivery platform ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12098103/]
• 78. Exosomal targeting and its potential clinical application - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC9458566/]
• 79. Recent Progress and Challenges in Exosomes for ... [https://www.sciencedirect.com/science/article/abs/pii/S3050475925007778]
• 80. Current Strategies for Exosome Cargo Loading and Targeting ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10216928/]
• 81. Protein lipidation in the tumor microenvironment [https://molecular-cancer.biomedcentral.com/articles/10.1186/s12943-025-02309-7]
• 82. Lipid metabolic reprogramming in the tumor ... [https://www.frontiersin.org/articles/10.3389/fimmu.2025.1728354]
• 83. Lipid metabolism in cancer: Exploring phospholipids as ... [https://pubmed.ncbi.nlm.nih.gov/40311223/]
• 84. Protein lipidation in the tumor microenvironment [https://pubmed.ncbi.nlm.nih.gov/40335986/]
• 85. Harnessing engineered extracellular vesicles for enhanced ... [https://jeccr.biomedcentral.com/articles/10.1186/s13046-025-03403-w]
• 86. Lipid metabolism in cancer: Exploring phospholipids as ... [https://www.sciencedirect.com/science/article/pii/S0753332225002896]
• 87. Diet, Lipid Metabolism, and Tumor Growth and Progression ... [https://www.cancer.gov/about-nci/organization/dcb/research-programs/dlt]
• 88. Extracellular Vesicles As a Source of Biomarkers for ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12322890/]
• 89. High-precision extracellular-vesicle isolation-analysis ... [https://www.sciencedirect.com/science/article/abs/pii/S0956566324008704]
• 90. Recent advances in SERS assays for detection of multiple ... [https://pubs.rsc.org/en/content/articlehtml/2025/nr/d4nr04014g]
• 91. Establishment of an Assay with Ultrahigh Sensitivity for ... [https://www.mdpi.com/1467-3045/47/7/564]
• 92. Single test-based diagnosis of multiple cancer types using ... [https://www.nature.com/articles/s41467-023-37403-1]
• 93. Emerging Roles of Using Small Extracellular Vesicles as ... [https://www.mdpi.com/1422-0067/24/18/14063]
• 94. Extracellular Vesicles and the lipid messengers [https://www.sciencedirect.com/science/article/abs/pii/S1044579X25001208]
• 95. Nanostructured organic sheets sequestering small ... [https://www.nature.com/articles/s41467-025-61236-9]
• 96. Machine Learning-Assisted Dual-Marker Detection in Serum Small Extracellular Vesicles for the Diagnosis and Prognosis Prediction of Non-Small Cell Lung Cancer - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC8912499/]
• 97. Frontiers | Extracellular vesicles in laboratory medicine: a review and outlook [https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2025.1709461/full]
• 98. Harnessing the potential of small extracellular vesicle biomarkers for cancer diagnosis and prognosis with advanced analytical technologies [https://www.degruyterbrill.com/document/doi/10.1515/jtim-2025-0019/html]
• 99. Extracellular Vesicles and the lipid messengers [https://pubmed.ncbi.nlm.nih.gov/40975244/]
• 100. Using Machine Learning Methods to Develop Diagnostic ... [https://pubmed.ncbi.nlm.nih.gov/40172749/]
• 101. Frontiers | Proteomic analysis of serum small extracellular vesicles identifies diagnostic biomarkers for neuroblastoma [https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1367159/full]
• 102. Lipid droplets and small extracellular vesicles [https://pmc.ncbi.nlm.nih.gov/articles/PMC11386333/]
• 103. Exosomes-regulated lipid metabolism in tumorigenesis ... [https://www.sciencedirect.com/science/article/abs/pii/S1359610123000229]
• 104. Identification of prognostic and therapeutic biomarkers ... [https://www.nature.com/articles/s41598-025-99144-z]
• 105. Exosomal biomarkers in cancer: Insights from Multi-OMIC ... [https://pubmed.ncbi.nlm.nih.gov/40976320/]
• 106. Circadian regulation of extracellular vesicle biogenesis, ... [https://www.nature.com/articles/s44323-025-00053-1]
• 107. Role of Extracellular Vesicle-Derived Biomarkers in Drug ... [https://www.sciencedirect.com/science/article/abs/pii/S0090955624077146]
• 108. EV(外泌体)药物研究里程碑：王军志院士团队系统综述全生命 ... [https://bydrug.pharmcube.com/news/detail/14e7e3aadd3d03a3525ffcbc5892e25b]
• 109. Biology of extracellular vesicles and the potential of tumor- ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11759217/]
• 110. 癌症中细胞外囊泡的临床相关性—— 治疗与诊断潜力 [https://www.exosomemed.com/20152.html]