Spatial Transcriptomics Cancer Research: 4 Core Strategies

Inquiry

For decades, our understanding of cancer has been shaped by a fundamental trade-off. We could either grind up a tumor to analyze its bulk genetic makeup or use single-cell RNA-sequencing (scRNA-seq) to create a detailed list of every cell type inside. This "cell soup" approach was revolutionary, giving us an unparalleled parts list for the tumor.

But it came at a high cost: we lost the "where."

We had a list of actors but no stage, no script, and no idea who was talking to whom. In the complex drama of cancer, context is everything. Why does a cancer cell at the edge of a tumor behave differently from one in the core? How are immune cells physically blocked from attacking? Why do therapies work in one region but fail in another?

This is the central challenge that spatial transcriptomics in cancer research solves. It moves us from a "cell soup" back to a high-resolution 3D map, overlaying the full transcriptome directly onto a tissue image.

Technologies like 10x Genomics Visium, now optimized for both fresh-frozen and archived FFPE samples, provide the "where" for every "what." This capability is game-changing. But having a map is not the same as having a plan.

The real question is: How do you use this map to ask the right questions?

Based on our experience helping hundreds of research teams, we've distilled the most successful approaches into four actionable, high-impact research strategies. This is your guide to planning your next breakthrough spatial omics project. These strategies represent a logical progression, moving from foundational mapping to dynamic tracking and, ultimately, to clinically relevant biomarker discovery.

Strategy 1: Map the Tumor's "Functional Neighborhoods" (e.g., Invasive Fronts, TLS)

The Concept: A tumor is not a uniform mass of cells. It is a rogue organ with a complex architecture. It has "functional neighborhoods"—like an industrial zone, a residential area, or a defensive border—each with a unique cellular composition and transcriptional signature. Your first goal is to create a map of these unknown territories.

The Strategy: Use unbiased spatial transcriptomics across an entire tissue section to identify spatially distinct clusters of gene expression. Then, ask why those regions are different. This approach reveals hidden structures that are critical to the tumor's function.

Case Study 1: The "Invasive Front" as a Strategic Hub

A tumor's edge is not just a simple boundary; it's a dynamic, functional interface. A 2023 study in Cell Research used high-resolution spatial data to precisely define a ~500µm-wide "invasive front" (IF) in liver cancer.

Spatially-resolved transcriptomic profiles in multiple regional sites in primary human liver cancer. (Wu, L., et al., Cell Res 2023)

• What they found: This IF was a "strategic hub" of intense metabolic and signaling activity. It was a hotbed of cancer-stromal-immune communication.
• Why it matters: The spatial map showed this neighborhood was uniquely enriched for specific macrophage populations and cancer-associated fibroblasts (CAFs) that were actively suppressing immune cells and remodeling the matrix to facilitate invasion. By defining this zone, researchers can now ask: "What specific pathways are active only at the invasive front, and can we target them to stop tumor progression?"

Case Study 2: Proving "Location Dictate Function" with TLS

Tertiary Lymphoid Structures (TLS) are ectopic lymph-node-like structures that can form within tumors, often associated with a positive prognosis. But are all TLS created equal?

• What they found: A 2024 Cancer Cell study on colorectal cancer liver metastases used spatial data to find the answer. They discovered that functional, antibody-producing TLS were not randomly located. They were spatially anchored by a specific niche of CCL19-producing fibroblasts.

Representative images and schematic classification of intratumoral TLS, highlighting spatial niches that support B-cell maturation and antibody production. (Xiong Y. et al. (2025) BMC Medicine).

• Why it matters: This proved that the location and cellular neighborhood of the TLS dictate its function. A TLS without this specific fibroblast support structure, even if it looked similar, was not producing anti-tumor antibodies. This spatial insight moves beyond just "counting TLS" to "functionally classifying TLS by their microenvironment."

Planning Your Project:

• (Callout Box) Planning a whole-slide FFPE project? The success of this strategy depends on getting the right coverage. Understanding read depth and spot density is critical for discovering these de novo structures.
• (Sidebar Note) Worried about your archived samples? Our validated protocols for FFPE sample quality ensure high data quality (high DV200) even from challenging blocks, allowing you to unlock your existing clinical cohorts. [See our FFPE Spatial Transcriptomics Service]

Ready to map your own tissue structures? Our Spatial Transcriptomics Services provide end-to-end support from slide prep to data visualization.

Strategy 2: Decode the TME "Social Network" with Ligand-Receptor Analysis

The Concept: Cells "talk" to each other through a physical language of ligands and receptors. For this "conversation" to happen, a cell expressing a ligand must be in close physical proximity to a cell expressing its cognate receptor. ScRNA-seq can tell you if these genes are expressed, but it can't tell you if the two cells are actually neighbors.

The Strategy: Use spatial transcriptomics to map the locations of different cell types (either by deconvolution from single-cell data or by spatial clustering). Then, use computational bioinformatics analysis to screen for all potential ligand-receptor (L-R) pairs between adjacent cells. This allows you to build a true-to-life "social network" of the tumor microenvironment (TME).

Workflow for detecting spatially co-expressed ligand–receptor pairs, clustering interaction patterns, and running pathway/differential analyzes on adjacent cell neighborhoods. (Liu Z. et al. (2023) Nature Communications).

Case Study: The "Cancer-Embryonic Neighborhood" Driving Resistance

Therapy resistance is often driven not by a single cell type, but by a supportive community.

• What they found: A 2024 Nature Cancer study in HCC identified a "multi-cell alliance" that drove recurrence and immunotherapy resistance. This wasn't a simple one-to-one interaction. It was a spatially co-located "neighborhood" composed of POSTN+ CAFs, FOLR2+ macrophages, and PLVAP+ endothelial cells.
• Why it matters: This "cancer-embryonic neighborhood" (resembling fetal development) creates a potent immunosuppressive and pro-angiogenic niche. A drug targeting only one member of this alliance might fail. Spatial analysis revealed that the entire ecosystem is the target. This moves the field from single-cell targets to "spatial neighborhood" targets, a much more robust strategy.

This type of cell-cell communication (ligand-receptor) spatial analysis is a core deliverable of our Intercellular Communication Analysis Services, which build and visualize these complex networks for you.

Strategy 3: Create a "Spatiotemporal Movie" of Therapy Response and Evolution

The Concept: Cancer is a dynamic process. The tumor you biopsy today is not the same as the tumor that exists after three rounds of chemotherapy, nor is it the same as the tumor that relapses six months later. To truly understand resistance and evolution, you must map the TME across time.

The Strategy: Design a longitudinal study with samples from different time points (e.g., pre-treatment vs. mid-treatment vs. post-treatment/relapse). By creating a series of "spatial snapshots," you can stitch them together to create a "spatiotemporal movie" of how the TME adapts, remodels, and evolves.

Spatial clustering and differential pathway activity distinguish responders from non-responders after neoadjuvant CABO/NIVO, illustrating immune-enriched regions versus proliferation/metabolism-dominated niches. (Zhang S. et al. (2023) Genome Medicine).

Case Study: Watching the TME Remodel Under Pressure

How does neoadjuvant chemoradiation (nCRT) change rectal cancer?

• What they found: A 2023 Cell Reports Medicine study did exactly this. They compared spatial data from pre-nCRT and post-nCRT patient samples. The results were striking. The therapy dramatically "remodeled" the entire TME, fundamentally altering the CAF populations.
• Why it matters: In patients with a good response, one set of CAF subtypes (e.g., CAF_PI16+) increased. In patients with a poor response, a different, pro-invasive set of CAFs (e.g., CAF_FAP+) took over and led to EMT and resistance. This "great replacement" of cell types was invisible to bulk analysis. This approach provides direct, visual evidence of how therapy works and how resistance emerges, identifying the specific cell players that determine patient outcomes.

This dynamic approach is a cornerstone of modern translational research. Our Spatial Genomics Services are designed to handle complex, multi-sample cohorts to help you track tumor evolution and therapy response over time.

Strategy 4: Discover Spatially-Aware Clinical Biomarkers (Beyond Gene Expression)

The Concept: This is the ultimate translational goal. For decades, our biomarkers have been spatially blind (e.g., "Is HER2 expression high?" or "What is the PD-L1 TPS score?"). But these metrics ignore a critical variable: location. A "hot" tumor (full of T-cells) where all T-cells are stuck in the stroma is functionally "cold."

The Strategy: Move beyond simple gene expression and create a new class of spatial biomarkers that integrate location as a variable. This means quantifying relationships:

• The distance from a cytotoxic T-cell to the nearest cancer cell.
• The density of mature TLS at the invasive front.
• The ratio of M2/M1 macrophages within a tumor nest vs. in the stroma.

Case Study: Cell State + Vessel Distance = A New Prognostic Indicator

In melanoma, a cell's potential to metastasize is a key prognostic question.

• What they found: A landmark 2022 Nature study performed a brilliant spatial quantification. They first identified a stem-like cell state (pre-EMT-NC) with high metastatic potential. But they didn't stop there. They asked, "Where are these cells?" The spatial data showed these cells were not randomly distributed. They were found "hugging" the blood vessels.
• Why it matters: They quantified this relationship and found a significant negative correlation: the more metastatic-potential a cell had, the closer it was to a blood vessel. The biomarker was not just "the presence of cell X." It was "the average distance of cell X to the nearest vessel." This is a quantifiable, powerful, and spatially-aware prognostic indicator that is impossible to discover without spatial context.

This type of analysis, which merges cell state with TME architecture (like vascular or neuronal niches), is the future of clinical biomarkers.

Your Spatial Transcriptomics Questions, Answered

You have the strategies, but you may still have practical questions. Here are the most common questions our scientists receive from researchers planning their first spatial transcriptomics cancer study.

Q1: What is the main difference between spatial transcriptomics and single-cell RNA-seq (scRNA-seq)?

The main difference is a trade-off between resolution and context. scRNA-seq provides a high-resolution list of every cell type but loses all location data. Spatial transcriptomics, like Visium, retains the location of gene expression, though typically at a "spot" resolution that captures 1-10 cells. The two methods are most powerful when used together, allowing you to use the scRNA-seq atlas to computationally map specific cell types back onto the spatial slide.

Q2: Can I use my archived FFPE samples for spatial transcriptomics?

Yes, absolutely. This is a major breakthrough, as platforms like 10x Visium FFPE are now optimized for formalin-fixed paraffin-embedded blocks. This allows you to unlock your invaluable existing archives of clinically-annotated patient samples for translational research. The key to success is RNA quality, and our team has optimized protocols to ensure high-quality data from your FFPE blocks.

Q3: What kind of bioinformatics analysis is required for a spatial project?

Spatial bioinformatics is a multi-layered process that goes far beyond standard RNA-seq analysis. After mapping the reads to the H&E image, the core analyzes involve identifying spatial domains through clustering, estimating the cell type composition in each spot via deconvolution, and then mapping the "social network" through neighborhood and ligand-receptor analysis. Our team provides this full, end-to-end bioinformatics support.

Q4: What is the typical resolution, and is it truly single-cell?

This is a critical distinction. Spatial capture technologies like 10x Visium offer whole-transcriptome data but at a "spot-level" resolution of 1-10 cells, making them exceptional for discovery. In contrast, in-situ technologies (like Xenium or MERSCOPE) provide true single-cell resolution but are targeted to a panel of genes, making them ideal for high-resolution cell mapping of known targets.

From Strategy to Execution: Start Your Spatial Omics Project

The evidence is clear: spatial transcriptomics in cancer research is no longer a niche technology. It is an essential tool for answering the next wave of critical questions in oncology.

These four strategies—mapping structures, decoding interactions, tracking dynamics, and discovering spatial biomarkers—provide a clear blueprint for asking deeper, more functional questions. The key is moving from this idea to a well-designed, statistically-powered, and expertly-executed project.

That's where we can help. Our team of Ph.D. scientists and bioinformaticians is here to help you design, run, and analyze your spatial data. We help you choose the right platform, optimize your sample prep, and deliver the actionable, publication-ready insights you need.

• Unsure which technology is right for your samples? [See our guide on How to Choose Spatial Transcriptomic Technologies,] or [contact our scientists directly] for a free consultation.
• Ready to get started? [Review our Sample Submission Guidelines.]

Let's build your molecular map, together.

Disclaimer: Our services are for Research Use Only (RUO) and are not intended for diagnostic, clinical, or personal use.

References

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2. Zhang, Yifan, et al. "CCL19-producing fibroblasts promote tertiary lymphoid structure formation enhancing anti-tumor IgG response in colorectal cancer liver metastasis." Cancer Cell 42.8 (2024): 1370-1385.
3. Li, Z., Pai, R., Gupta, S. et al. Presence of onco-fetal neighborhoods in hepatocellular carcinoma is associated with relapse and response to immunotherapy. Nat Cancer 5, 167–186 (2024).
4. Fan, J., Lu, F., Qin, T. et al. Multiomic analysis of cervical squamous cell carcinoma identifies cellular ecosystems with biological and clinical relevance. Nat Genet 55, 2175–2188 (2023).
5. Qin, Pengfei, et al. "Cancer-associated fibroblasts undergoing neoadjuvant chemotherapy suppress rectal cancer revealed by single-cell and spatial transcriptomics." Cell Reports Medicine 4.10 (2023).
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9. Li, Z., Wang, T., Liu, P. et al. SpatialDM for rapid identification of spatially co-expressed ligand–receptor and revealing cell–cell communication patterns. Nature Communications 14, 3995 (2023).
10. Zhang, S., Yuan, L., Danilova, L. et al. Spatial transcriptomics analysis of neoadjuvant cabozantinib and nivolumab in advanced hepatocellular carcinoma identifies independent mechanisms of resistance and recurrence. Genome Medicine 15, 72 (2023).
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