Spatial genomics has emerged as a transformative approach for understanding the complex architecture of biological tissues. By preserving spatial context while profiling gene expression, this technology offers unique insights into how cells interact within their native microenvironments. In oncology, spatial genomics enables researchers to map tumor heterogeneity, identify functional niches, and uncover molecular signatures linked to therapeutic response. In developmental biology, it reveals the spatial and temporal orchestration of gene activity that shapes organ formation and tissue differentiation.

This article explores real-world applications of spatial genomics in oncology and spatial genomics in developmental biology, drawing on practical research experiences, reproducibility considerations, and strategies for integrating spatial data into high-resolution tissue maps.

Spatial Genomics in Cancer Research: Real-World Use Cases

The spatial organization of cancer cells and their surrounding microenvironment plays a critical role in tumor progression, therapeutic response, and resistance mechanisms. Spatial genomics enables researchers to capture these relationships at high resolution—offering insights that bulk and conventional cellresolved sequencing alone cannot provide.

Overview of spatial transcriptomic insights into solid tumors. (Cilento et al., J Cancer Res Clin Oncol, 2024)

Key Research Highlights and Applications

• Breast Cancer – Mapping Immune Cell Niches

A widely cited study in Nature Cancer showed that spatial transcriptomics defined distinct cellular neighborhoods within breast tumors. Immune infiltration was found to be spatially clustered rather than uniform—patterns that correlated with tumor aggressiveness and patient prognosis. (Ali et al., Nature Cancer, 2020)

→ Research value: Explains differential responses to immunotherapy.

• Colorectal Cancer – Tumor–Stroma Interface Profiling

In Cell, researchers integrated spatial transcriptomics with immunohistochemistry to characterize tumor–stroma boundaries. They discovered that signaling pathways such as TGF-β and WNT were selectively activated in specific microenvironment regions. (Pelka et al., Cell, 2021)

→ Research value: Identifies microenvironment-specific biomarkers often missed in bulk profiling.

• Melanoma – Spatial Multi-Omics for Therapy Resistance

A spatial multi-omics study combining transcriptomic and proteomic data in situ uncovered immune-suppressive niches enriched with regulatory T cells within melanoma tumors. (Ji et al., Cell, 2020)

→ Research value: Reveals localized immune suppression as a potential target for combination therapies.

Takeaway for Research Teams

• Spatial genomics serves not merely as a diagnostic tool, but as a comprehensive framework for mapping tumor heterogeneity and decoding cancer–immune interactions in their native spatial context.
• By incorporating spatial data into oncology research, teams can refine hypothesis generation and experimental design—leading to results that are more biologically grounded and translationally relevant.

Spatial Genomics in Developmental Biology: Examples & Innovative Techniques

Understanding how tissues and organs emerge during development requires capturing both spatial and temporal patterns of gene regulation. Spatial genomics in developmental biology examples shine in this realm, revealing cell‐level organization within complex, developing structures.

Graphical summary depicts the sequencing of normal and ARM rat embryos on GDs 14–16 using spatial transcriptomics. (Wang et al., Cell Biol Toxicol,2024).

Representative Research Examples

• Mouse Organogenesis – Stereo-seq High-Resolution Atlas

Researchers employed Stereo-seq—a DNA nanoball-based spatial transcriptomics technology—to map gene expression across whole mouse embryos at subcellular resolution, creating a 3D spatial transcriptional atlas of organ formation (Chen et al., Cell).

→ Research value: Enables fine-scale mapping of gene expression dynamics during embryonic development.

Stereo-seq enables high-definition spatially resolved transcriptomics with large field of view. (Chen et al., Cell)

• Human Brain Development – Regional Spatiotemporal Transcription Mapping

In a large-scale study spanning gestational weeks GW6–GW23, scientists combined high-resolution cellresolved RNA-seq with spatial profiling to construct a multi‐region human brain atlas. This work revealed spatially organized radial glial cell subtypes and their contribution to neuronal regionalization in cortex and ventral forebrain (Li et al., Cell).

→ Research value: Offers key insights into the spatial logic of human neurodevelopment across gestational time.

• Drosophila Embryo & Adult Brain – Fine-Scale Spatial Transcriptomics

Using spatial transcriptomics, researchers mapped hundreds of mRNA species in adult fruit fly brains and bodies, identifying previously unknown cell types and spatial mRNA localization patterns (Janssens et al., eLife).

→ Research value: Validates spatial annotation of cell clusters from Fly Cell Atlas, demonstrating cellular organization across tissues.

Emerging Techniques Enhancing Developmental Biology Research

• Stereo-seq & DBiT-seq Platforms provide ultra-high spatial resolution (nanometer-level) with large field of view—ideal for developmental atlases in model organisms and embryo.
• Multimodal Spatial Omics integrates transcriptomics with other omics layers (e.g. proteomics, epigenomics), offering a comprehensive view of developmental regulation.
• Computational Integration Tools and AI Models align spatial datasets from different developmental stages, enabling dynamic reconstruction of tissue lineage maps.
• In Situ Perturbation Studies, such as CRISPR perturbations combined with spatial profiling, offer precise functional validation within native spatial contexts.

Experimental Experience and Recommendations for Data Reproducibility

Reproducibility is a cornerstone of spatial genomics research. Achieving reliable and interpretable results depends not only on the quality of the experimental data but also on the consistency of the workflow and transparency in analysis. The following points summarise practical recommendations based on published studies and handson experience from research laboratories.

1. Standardised Workflows and Data Sharing

Establishing a standard operating procedure (SOP) for tissue handling, sectioning, fixation, and library preparation is essential. Variations in these steps can introduce technical biases that obscure biological signals. Where possible, adopt established data-sharing formats that include raw data, metadata, and analysis scripts. Frameworks such as MOSAIK have been designed to unify outputs from multiple spatial platforms, streamline quality control, and ensure that datasets can be meaningfully compared across projects.

2. Platform Selection and Experimental Planning

Different spatial technologies offer varying strengths. For example, Visium and Xenium platforms provide broad gene coverage with moderate resolution, whereas Stereo-seq or DBiT-seq deliver subcellular resolution at the cost of smaller gene panels. The choice should be guided by the biological question: mapping large tissue regions may require high coverage, while identifying fine cellular interactions may demand higher resolution. Consistency in sample preparation across batches is critical to avoid confounding technical variation.

3. Quality Control and Normalisation

RNA integrity metrics (e.g., RIN, DV200) remain useful indicators of sample quality, but researchers should note that informative data can still be recovered from challenging samples such as FFPE tissue if processed carefully. Downstream, robust normalisation is essential. Methods like SCTransform or model-based spatial variance analysis can help minimise batch effects and improve comparability across datasets.

4. Assessing Clustering Robustness and Spatial Patterns

When identifying cell clusters or spatially variable genes, validating the stability of results is as important as the initial analysis. Tools such as STEAM can evaluate clustering robustness, while spatial gene benchmarks can help select the most reliable algorithms for detecting locationspecific expression patterns.

5. Multi-Section Alignment and 3D Reconstruction

Many biological questions require integrating information from multiple tissue sections or samples. This can be challenging due to differences in section orientation, distortion, and expression variability. Advanced alignment methods—ranging from image registration to graph-based spatial mapping—can support the reconstruction of threedimensional tissue atlases, allowing a more complete view of tissue organisation.

Research Team Checklist

Technical Insights: Reconstructing Tissue Maps with Spatial Genomics

Reconstructing an accurate tissue map requires more than capturing raw spatial expression data—it demands the integration of molecular profiles with histological and structural information. By combining sequencing-based spatial technologies with imaging and computational modelling, researchers can create high-resolution, multi-layered maps that reveal both cell identity and spatial relationships.

1. Sequencing-Based Spatial Profiling

Technologies such as Stereo-seq, Visium, and Slide-seqV2 generate spatially resolved gene expression data directly from tissue sections. While each platform differs in resolution, coverage, and throughput, they all provide the foundation for mapping cellular organisation.

• Strength: Captures whole-transcriptome patterns across large areas.
• Consideration: Resolution limits may require integration with complementary imaging methods.

2. Imaging-Based Spatial Mapping

Approaches like seqFISH+, MERFISH, and RNAscope enable direct visualisation of hundreds to thousands of transcripts within intact tissues.

• Strength: High spatial precision, often at subcellular resolution.
• Consideration: Typically limited to predefined gene panels.

3. Integrating Spatial Data with Histological Context

Overlaying molecular data onto H&E or immunofluorescence images aligns gene expression with tissue morphology. This step improves interpretation by linking molecular signatures to anatomical structures. Image registration tools ensure accurate pixel-level alignment between datasets.

4. Computational Reconstruction and Multi-Section Alignment

When reconstructing 3D tissue maps, data from multiple consecutive sections or related samples must be spatially aligned.

• Graph-based models and image registration algorithms can connect cellular coordinates across sections.
• Integrative pipelines such as spCLUE unify expression domains across datasets, enabling consistent annotation.

5. Multi-Modal Integration for Functional Mapping

Combining transcriptomics with proteomics, chromatin accessibility (ATAC-seq), or spatial metabolomics provides a richer view of tissue function. For example, integrating protein expression data can validate transcriptional findings and identify posttranscriptional regulation within specific tissue domains.

Key Insight for Research Teams:

A wellconstructed tissue map is more than a visual reference—it is an analytical framework that can be interrogated to explore cell–cell interactions, spatial signalling gradients, and microenvironmental niches. The combination of sequencing, imaging, and advanced computational analysis allows researchers to move from static snapshots to dynamic, functionally annotated tissue atlases.

Researcher Q&A: Applying Spatial Genomics in Cancer and Developmental Biology

Q1: What are the advantages of using spatial genomics in cancer research?

A1: Spatial genomics retains the physical context of gene expression, enabling researchers to map tumour heterogeneity, characterise microenvironmental niches, and identify cell–cell interactions that drive disease progression. For example, spatial transcriptomics can reveal immune cell clusters that influence immunotherapy outcomes, a detail often missed by bulk RNA-seq.

Q2: How does spatial genomics contribute to developmental biology?

A2: In developmental biology, spatial genomics allows scientists to track how gene activity changes across time and space during organ formation. This makes it possible to reconstruct developmental trajectories, identify transient cell types, and understand how spatially restricted signalling pathways (e.g., Notch, BMP) shape tissue morphogenesis (Asp et al., Science, 2019).

Q3: How can researchers improve reproducibility in spatial transcriptomics studies?

A3: Reproducibility can be improved by standardising sample preparation, implementing consistent quality control metrics, and sharing both raw and processed data alongside analysis scripts. Using unified analysis frameworks such as MOSAIK or adopting benchmarking tools like STEAM for clustering stability can further ensure results are comparable across studie.

Future Outlook: Technological Improvements and Research Directions

Spatial genomics is rapidly evolving from a niche technology into an essential tool for understanding tissue biology. As both the experimental platforms and computational methods advance, several key trends are expected to shape its future applications in oncology and developmental biology.

1. Higher Resolution and Expanded Coverage

Next-generation spatial transcriptomics platforms are moving toward subcellular resolution without compromising wholetranscriptome coverage. Techniques such as Stereo-seq are already enabling nanoscale mapping of embryonic tissues. This progress will allow researchers to detect subtle cellular processes such as localised mRNA transport or synaptic gene expression changes in neural tissues.

2. 3D and Time-Resolved Spatial Atlases

Reconstructing tissues in three dimensions and tracking them over developmental time will become more routine. Combining multi-section spatial alignment with light-sheet microscopy and computational modelling can produce dynamic, spatiotemporal atlases of organogenesis or tumour evolution.

3. Multi-Omics Integration for Functional Insights

Integrating transcriptomic data with proteomics, chromatin accessibility (ATAC-seq), and spatial metabolomics will provide a more complete picture of tissue function. In cancer research, this approach could reveal how transcriptional programs align—or misalign—with protein activity in specific microenvironments.

4. AI-Driven Data Interpretation

Machine learning models are becoming crucial for analysing complex spatial datasets. Graph neural networks and spatially aware embedding models can identify hidden cell–cell interaction patterns, predict tissue architecture, and even infer gene expression from histological images.

5. Standardisation and Collaborative Databases

Efforts to create community-adopted standards for spatial genomics data will improve reproducibility and data sharing. Initiatives like the Spatial Omics Data Coordination Center aim to centralise datasets, analysis pipelines, and reference atlases, allowing researchers worldwide to build on each other’s work without repeating foundational experiments.

Key Message for Research Teams:

The future of spatial genomics lies in greater resolution, richer multi-omics integration, and improved data interoperability. For oncology, this means mapping tumours with unprecedented detail; for developmental biology, it means creating comprehensive, dynamic atlases of how organisms build themselves. Adopting these emerging tools early can position research groups at the forefront of spatial biology discovery.

Next Steps: Bringing Spatial Genomics into Your Research

Spatial genomics is no longer just a proof-of-concept technology—it has become a critical tool for dissecting complex biological systems in their native context. Whether you are investigating tumour–immune interactions in oncology or mapping cellular trajectories in developmental biology, a welldesigned spatial genomics experiment can provide insights that traditional methods simply cannot capture.

At CD Genomics, we support research teams with:

• End-to-end spatial genomics workflows – from sample preparation advice to data integration strategies.
• Platform selection guidance – ensuring your chosen technology matches your resolution, coverage, and throughput needs.
• Advanced bioinformatics – including spatial domain detection, cell–cell communication analysis, and 3D tissue reconstruction.
• Data reproducibility assurance – using standardised pipelines and quality control checkpoints to ensure results can be validated and compared across studies.

Our focus is exclusively on research applications. We work closely with academic groups, research institutes, and industry R&D teams to deliver reliable, reproducible, and publicationready data.

Interested in exploring spatial genomics for your project?

Contact our scientific team to discuss your research objectives and receive a customized workflow proposal.

References

1. Cilento, M. A., Sweeney, C. J., & Butler, L. M. (2024). Spatial transcriptomics in cancer research and potential clinical impact: a narrative review. Journal of cancer research and clinical oncology, 150(6), 296.
2. Chen, A., Liao, S., Cheng, M., Ma, K., Wu, L., Lai, Y., ... & Wang, J. (2022). Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell, 185(10), 1777-1792.
3. Li, Y., Li, Z., Wang, C., Yang, M., He, Z., Wang, F., ... & Jiao, J. (2023). Spatiotemporal transcriptome atlas reveals the regional specification of the developing human brain. Cell, 186(26), 5892-5909.
4. Jasper Janssens, Pierre Mangeol, Nikolai Hecker, Gabriele Partel, Katina I Spanier, Joy N Ismail, Gert J Hulselmans, Stein Aerts, Frank Schnorrer (2025) Spatial transcriptomics in the adult Drosophila brain and body eLife 13:RP92618
5. Wang, C. Y., Li, M. Y., Li, S. Y., Wei, X. G., Yuan, Z. W., Tang, X. B., & Bai, Y. Z. (2024). Spatial transcriptomics reveals gene interactions and signaling pathway dynamics in rat embryos with anorectal malformation. Cell Biology and Toxicology, 40(1), 34.
6. Jung, N., & Kim, T. K. (2023). Spatial transcriptomics in neuroscience. Experimental & molecular medicine, 55(10), 2105-2115.