In the intricate landscape of our tissues, a revolutionary new science is mapping the hidden conversations between cells.
Imagine understanding biological processes with a resolution that reveals not just which cells are involved, but exactly where they're located and how they interact with their neighbors. This is the promise of spatial omics, a family of technologies transforming biological research and clinical diagnostics.
While traditional methods required tissue dissociation—losing crucial spatial context—and could only examine a handful of markers at once, spatial omics preserves the intricate architecture of tissues while capturing thousands of molecular measurements simultaneously 3 6 . In this rapidly evolving field, a powerful new member has joined the spatial omics family, capable of capturing multiple layers of cellular information from the very same tissue sample.
The journey to spatial omics began with conventional tissue staining, which could only detect two to five markers in cells at a time, severely limiting our ability to distinguish between cell types in complex tissues 6 .
"Spatial omics stems from something very old, which is tissue staining. The -omics part comes from harnessing what genomics has done for a long time and putting that onto tissue."
The breakthrough came with the introduction of barcoding technology, which fundamentally changed the scale at which cellular analysis could be performed. Similar to how barcodes revolutionized commerce by accelerating checkout processes, biological barcodes allow researchers to rapidly analyze thousands of genes simultaneously 6 .
A significant breakthrough in spatial omics has emerged with the development of methods that can simultaneously profile multiple molecular layers from the same tissue sample. This new approach successfully combines spatial analysis of overall chromatin accessibility and gene expression on the same tissue sample (spatial ATAC–RNA-seq), as well as simultaneous co-analysis of histone modifications and gene expression (spatial CUT&Tag–RNA-seq) 1 .
The key innovation lies in a sophisticated two-dimensional barcoding system. Researchers introduced a grid of tissue pixels defined by spatial barcodes using two microfluidic chips placed sequentially in perpendicular directions on the tissue slice 1 . These introduce spatial barcodes Ai (i = 1–50 or 100) and Bj (j = 1–50 or 100) respectively to encode each pixel point.
This system covers an area of about 16 square millimeters with near single-cell resolution; each pixel point is 20 micrometers, and the total number of pixels encoded is up to 10,000 1 .
"Designing and optimizing the barcodes strategy is the most challenging part. We made a lot of efforts to ensure that the two omics assays can be compatible and addable, without interfering with each other."
| Technology Type | Molecular Layers Captured | Resolution | Key Advantage |
|---|---|---|---|
| Traditional Staining | Proteins (2-5 markers) | Cellular | Established, low cost |
| Spatial Transcriptomics | Gene expression (hundreds to thousands of genes) | Near single-cell | Comprehensive gene expression mapping |
| Spatial Proteomics | Protein abundance (dozens of proteins) | Single-cell | Direct protein measurement |
| New Multi-Omic Spatial Profiling | Chromatin accessibility + Gene expression OR Histone modifications + Gene expression | Near single-cell (20μm) | Multiple molecular layers from same sample |
Recent research demonstrates the power of performing spatial transcriptomics and spatial proteomics on the very same tissue section, providing unprecedented insights into molecular relationships within their native context 4 .
In a groundbreaking study on human lung cancer samples, researchers developed a wet-lab and computational framework to integrate multiple spatial omics modalities from the same tissue section 4 :
Consecutive lung carcinoma tissue sections were analyzed under different experimental conditions, with one set processed sequentially with Xenium (for transcriptomics), COMET (for proteomics), and H&E staining.
Spatial Transcriptomics: Using Xenium In Situ technology with a 289-gene human lung cancer panel to map gene expression.
Spatial Proteomics: Employing hyperplex immunohistochemistry with COMET technology using off-the-shelf primary antibodies for 40 protein markers.
Computational registration using Weave software allowed accurate alignment and annotation transfer across all modalities, creating a unified dataset that includes both transcript counts and protein marker intensities within the same cells 4 .
The integrated analysis revealed a crucial finding: systematically low correlations between transcript and protein levels within individual cells, consistent with prior findings but now resolved at cellular resolution 4 . This highlights the importance of multi-layer molecular analysis, as mRNA levels alone cannot reliably predict protein abundance.
The co-registered dataset enabled single-cell level comparisons of RNA and protein expression, revealing segmentation accuracy and facilitating detailed correlation analyses within individual tumor and non-tumor regions 4 .
| Analysis Type | Key Finding | Biological Significance |
|---|---|---|
| Transcript-Protein Correlation | Systematic low correlations between transcript and protein levels | mRNA levels alone poor predictors of protein abundance |
| Tumor Microenvironment | Distinct cellular organization in responsive vs. non-responsive patients | Spatial organization affects treatment outcomes |
| Methodological Insight | Feasibility of ST and SP on same section without compromising data quality | Enables more accurate multi-omic integration |
The spatial omics revolution relies on a sophisticated array of research reagents and technologies. Here are the key components driving this field forward:
| Reagent/Technology | Function | Application in Spatial Omics |
|---|---|---|
| Spatial Barcodes | Unique molecular identifiers that encode spatial location | Enables mapping of molecules to their tissue coordinates through perpendicular microfluidic chips 1 |
| Multiplexed Probes | Labeled DNA/RNA sequences or antibodies that bind specific targets | Allows detection of hundreds to thousands of genes or proteins through sequential imaging rounds 6 |
| Microfluidic Chips | Devices with microscopic channels for fluid manipulation | Create precise spatial arrays for barcode delivery and molecular capture 1 |
| Indexed Fluorescent Antibodies | Antibodies with oligonucleotide tags for cyclic detection | Enable highly multiplexed protein detection through sequential staining and imaging 3 |
| Orthologous Gene Panels | Cross-species conserved gene sets | Facilitates multi-species analysis and comparative biology in models like Nicheformer 2 |
| Computational Integration Tools | Software for data alignment and analysis (e.g., Weave) | Co-register multiple spatial modalities for unified analysis 4 |
The implications of these spatial omics advancements extend far beyond basic research. In cancer biology, spatial omics is revealing the complex architecture of the tumor microenvironment, with direct clinical implications .
Studies of breast cancer, for instance, have used spatial transcriptomics to identify potential prognostic biomarkers and reveal how immune cell interactions influence treatment response .
In developmental biology, researchers are applying spatial omics to understand processes like hematopoietic stem cell development.
"If you have a limited set of signaling pathways you're asking questions about, you can only find what you are looking for. With this spatial omics approach, we could ask how the pathways change."
The field is also advancing through computational innovations like Nicheformer, a transformer-based foundation model trained on both human and mouse dissociated single-cell and targeted spatial transcriptomics data 2 .
Pretrained on SpatialCorpus-110M, a collection of over 110 million cells, Nicheformer learns cell representations that capture spatial context and enables predictions of spatial context for dissociated cells 2 .
As spatial omics technologies continue to evolve, they're poised to transform both research and clinical practice. The integration with artificial intelligence and machine learning is particularly promising.
"Every spatial experiment we do is at least 100 thousand to 5 million cells on one slide. Over time, the technology will develop to learn what is or is not a cell, and what type of cell it is."
In clinical pathology, spatial omics offers the potential to revolutionize diagnostics.
"With spatial omics, we can expand this to the entire transcriptome with a spatial distribution. We can know exactly where genes are expressed in the tumor, and in what cell type."
While challenges remain—including costs, standardization, and data analysis complexity—the trajectory is clear. As these technologies become more accessible and comprehensive, they will undoubtedly uncover new biological insights and transform our understanding of health and disease in the context of tissue architecture.
The new member of the spatial omics family, capable of multi-layer molecular analysis from a single sample, represents more than just technical progression—it offers a more comprehensive, integrated view of biology in its native context, bringing us closer than ever to understanding the full complexity of living systems.