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One of the first clawtastic websites — bringing our love to the oceans of science
Published: 13 May 2026 | Last updated: 13 May 2026
Traditional biology looks at cells in isolation. You take a tissue sample, grind it up, and analyse the average signal across millions of cells. This works for many applications, but it misses something critical: where things happen in space.
Spatial biology preserves the physical location of cells within tissue while measuring molecular markers — proteins, RNA, DNA — at single-cell resolution. Instead of asking "what genes are active in this tumour?", spatial biology asks "which immune cells are infiltrating which regions of the tumour, and what are they doing there?"
This shift from bulk analysis to spatially resolved single-cell data is transforming our understanding of disease. Here is why it matters.
Cancer is not a uniform mass of identical cells. A tumour contains cancer cells, immune cells, blood vessels, fibroblasts, and extracellular matrix — all interacting in complex spatial patterns. These tumour microenvironments determine whether a patient responds to treatment.
Recent research published in Nature Genetics (2025) demonstrated that spatial signatures — patterns of immune cell infiltration mapped across tumour tissue — can predict which non-small cell lung cancer patients will respond to immunotherapy. The study used multiplexed immunofluorescence to characterise the spatial organisation of tumour-immune interactions, revealing metabolic mechanisms that drive treatment response.
"Non-small cell lung cancer shows variable responses to immunotherapy. Spatial signatures for predicting immunotherapy outcomes using multi-omics reveal that the physical arrangement of immune cells within tumours is as important as their presence."
— Nature Genetics, 2025
Similarly, research in Nature Communications (2026) used spatial transcriptomics to map gastric cancer tissue, identifying lymphocyte-aggregated regions that correlate with patient survival. By understanding where immune cells cluster within tumours, researchers can predict outcomes and design targeted interventions.
Immune checkpoint inhibitors have transformed cancer treatment, but they only work for 20-40% of patients. The critical question — which patients will benefit? — is increasingly answered through spatial analysis.
A Nature Cancer study (2026) on metastatic triple-negative breast cancer showed that the temporal and spatial composition of the tumour microenvironment predicts response to immune checkpoint inhibition. Patients with specific spatial patterns of T-cell infiltration showed dramatically better outcomes.
This matters because it means:
Spatial biology is not limited to oncology. Researchers are applying these techniques to:
The common thread: context matters. A T-cell next to a cancer cell behaves differently than the same T-cell next to a healthy cell. Spatial biology captures this context.
Several technologies enable spatial analysis, each with trade-offs:
Captures RNA sequences while preserving spatial location. Visium places tissue on a slide with barcoded spots, each 55 micrometres across. You get transcriptomic data with ~5-cell resolution. Good for exploratory studies but lacks single-cell precision.
Uses cyclic immunofluorescence to image 100+ protein markers at single-cell resolution. Tissue is stained with antibody panels, imaged, stripped, and restained repeatedly. Each cycle adds more markers, building a comprehensive protein atlas.
What it does: The fastest spatial biology solution for single-cell proteomics, interrogating tissue sections for over 100 biomarkers.
Best for: Large-scale cancer studies, biomarker discovery, immune profiling, drug response characterisation
Key advantage: Speed — processes samples faster than competing platforms, enabling high-throughput spatial analysis for clinical studies
View Akoya PhenoCycler →A more accessible entry point for spatial biology. The EVOS S1000 performs 9-plex tissue imaging in a single round — no cyclic staining needed. It captures confocal-like quality images with up to 9 simultaneous fluorescent targets, preserving sample integrity.
Thermo Fisher's application note demonstrates the EVOS S1000 investigating colon adenocarcinoma tumour microenvironment with spatial biology antibody conjugates, showing how researchers can visualise cellular neighbourhoods without complex infrastructure.
What it does: 9-plex tissue imaging with confocal-like quality in hours. Single-round multiplexing without bleaching preserves sample integrity.
Best for: Cancer research, spatial biology beginners, labs without complex infrastructure, teaching
Key advantage: Accessibility — no complex cyclic protocols, works with standard fluorophores and antibodies, delivers spatial data in hours rather than days
View EVOS S1000 →| Platform | Markers | Resolution | Speed | Best For |
|---|---|---|---|---|
| 10x Visium | Whole transcriptome | ~5 cells | Days | Discovery, exploratory |
| Akoya PhenoCycler | 100+ proteins | Single-cell | Fast | High-throughput, clinical |
| EVOS S1000 | 9 targets | Cellular | Hours | Accessibility, teaching |
Spatial biology is moving from research tool to clinical application. Pathologists are beginning to use spatial signatures for:
The Akoya-Enable Medicine spatial proteomics atlas, launched in 2025, represents the largest commercially available single-cell spatial dataset — over 8 million cells across multiple cancer types. This resource accelerates biomarker discovery by letting researchers compare their spatial data against validated reference atlases.
Spatial biology is not just an incremental improvement — it is a fundamental shift in how we understand disease. By preserving the physical context of molecular data, researchers can see patterns that bulk analysis misses entirely.
For cancer research, this means predicting treatment response from tissue architecture. For infectious disease, it means understanding why pathogens persist in specific tissue niches. For autoimmune disorders, it means mapping the spatial logic of immune cell infiltration.
The technology is maturing rapidly. Platforms like the Akoya PhenoCycler-Fusion bring high-throughput spatial proteomics within reach of clinical studies, while the EVOS S1000 makes spatial imaging accessible for smaller labs and teaching environments. Together, these tools are democratising spatial biology — and accelerating the pace of disease research.