Spatial ATAC-seq & scRNA-seq Integration Strategy for Spatial Epigenomics

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Understanding how cells organize and regulate genes within tissues requires more than looking at one type of data. While spatial ATAC-seq provides a view of chromatin accessibility in place, scRNA-seq reveals the active transcriptional programs of individual cells. By bringing these two technologies together, researchers can not only identify cell types more accurately but also connect local chromatin states with gene expression patterns. This integration opens the door to a deeper understanding of spatial regulation, tissue development, and disease processes.

In this article, we outline practical strategies, available tools, and common challenges for integrating spatial ATAC-seq with scRNA-seq—helping research teams apply these methods to their own projects with confidence.

Why integrate spatial ATAC-seq with scRNA-seq

The main advantage of integration is that it connects "what is possible" with "what is happening." Spatial ATAC-seq identifies open chromatin regions across tissue sections, pointing to potential regulatory elements, but it does not always reveal which genes are being actively expressed. On the other hand, scRNA-seq captures gene expression at single-cell resolution but loses the spatial information when cells are dissociated. By integrating the two, researchers can place transcriptional programs back into their original tissue context and directly link active enhancers or promoters with downstream gene expression.

This combined view improves cell-type annotation and enables more precise discovery of rare or transitional cell populations that might be overlooked with either method alone. For example, developmental studies have shown that mapping spatial chromatin landscapes alongside transcriptional profiles helps distinguish closely related cell states and reveals how gene regulation unfolds across tissue gradients.

Another important benefit is in functional interpretation. With integration, researchers can test whether changes in chromatin accessibility align with differential gene expression, offering a mechanistic view of tissue remodeling in processes like embryogenesis, immune activation, or tumor growth.

Scheme to evaluate if multiome data help the integration of single-modality data. (Lee, et al., Genome Biol 2023)

Available tools and workflows for integration

There is no single "best" workflow for all projects. Instead, several community tools and frameworks are now available, each with strengths depending on dataset size, resolution, and research goals. Below is an overview of commonly used options:

• Seurat + Signac

A widely used R-based framework. Signac can calculate gene activity scores from ATAC peaks, while Seurat provides anchor-based integration to map ATAC-derived features onto RNA-defined clusters. This is a practical choice for many labs starting with integration tasks.

Distributions of ATACseq peaks-gene link statistics calculated using the Zscores method as implemented in Signac. (Leblanc, Francis JA, and Guillaume Lettre, Scientific Reports 2023)

• scvi-tools (PeakVI, scBasset)

A Python-based ecosystem for probabilistic modeling. It offers batch correction, denoising, and integration of sparse ATAC data with RNA datasets, making it suitable for large-scale projects where variability across samples is a concern.

• INSTINCT

A newer method designed for multi-sample spatial ATAC integration. It uses deep learning approaches (domain translation and graph attention) to align data across tissue sections or developmental stages while preserving biological signals. This is particularly valuable for comparative studies.

• Tangram and related spatial mapping tools

Tools like Tangram, SpaGE, and Cell2location focus on placing single-cell RNA profiles into spatial coordinates. While not designed solely for ATAC data, they can be combined with ATAC-derived gene activity matrices to enhance mapping accuracy.

Tangram learns spatial transcriptome-wide patterns at single-cell resolution from sc/snRNA-seq data and corresponding spatial data. (Biancalani, T., et al. Nat Methods 2021)

• Workflow managers like Snakemake pipelines

For groups handling multiple samples or needing reproducible automation, published Snakemake workflows provide a backbone for preprocessing, QC, and downstream integration in a standardized way.

Tip: Tool choice depends on your project's size, data sparsity, and spatial resolution. For smaller pilot datasets, Seurat + Signac may be enough. For cross-sample comparisons or high-dimensional datasets, INSTINCT or scvi-tools can provide more robust performance.

Practical workflow and tips

Even with powerful tools available, the quality of integration largely depends on how the data are prepared and processed. Below are some practical steps and recommendations drawn from published workflows and lab experiences:

1. Preprocessing and Quality Control

• Spatial ATAC-seq data: Begin with standard ATAC-seq QC — trim adapters, align reads, remove mitochondrial fragments, and check fragment size distribution. Consistent peak calling across slides or samples ensures comparability.
• scRNA-seq data: Normalize gene expression, identify variable features, and filter low-quality cells before integration.
• Balanced dataset design: Where possible, sample tissues in parallel for ATAC and RNA assays to reduce batch-related discrepancies.

2. Defining gene activity

Because ATAC-seq measures accessibility rather than expression, estimating gene activity is key for alignment. Tools like Signac's GeneActivity() function generate a gene activity matrix from peaks, which can then be mapped onto RNA-defined clusters. This step creates the "bridge" between modalities.

3. Barcode and metadata handling

For spatial assays, double-check that barcodes map consistently to the correct tissue coordinates. Misaligned barcodes can lead to inaccurate placement of cell types on tissue sections. Including metadata such as developmental stage or treatment condition helps during downstream batch correction.

4. Integration and co-embedding

• Use anchor-based integration (Seurat + Signac) or probabilistic models (scvi-tools) to align ATAC and RNA profiles.
• Co-embedding both modalities in the same UMAP or t-SNE visualization allows direct assessment of integration quality—clusters from ATAC should align closely with annotated RNA clusters.

5. Validation and interpretation

Always cross-check results with known marker genes or regulatory regions. If chromatin accessibility around a lineage-specific enhancer matches expression of its target gene in the same region, the integration is likely robust.

Insight: Many integration challenges come not from the tools themselves but from data preparation. Rigorous QC, careful metadata tracking, and validation against known biology are often the difference between a noisy co-embedding and a biologically meaningful map.

Case example: Mapping lung immune response via spatial ATAC and scRNA integration

In a compelling proof-of-concept study, researchers used a mouse model immunized with Klebsiella pneumoniae to explore local immune responses in the lung. By integrating spatial transcriptomics (Visium), scRNA-seq, and scATAC-seq, they created a multimodal map linking immune cell locations, gene expression, and chromatin accessibility within intact tissue sections (Xu, Zhongli, et al., Iscience 2022).

Key findings:

• The team developed a deconvolution pipeline to accurately infer which cell types were present at specific anatomical locations.
• Combining scATAC-seq with scRNA-seq improved the reliability of identifying cell types—especially lineage-specific T helper cells—compared to using transcript data alone.
• By overlaying all three data types spatially, the study revealed dynamic relocation patterns of T helper cells in response to immunization, matched with changes in their chemokine expressio.

Why this matters:

• High-resolution cellular context: This integration enables researchers to see not just which immune cells are present, but where they are and what regulatory programs they are activating in situ.
• Mechanistic insight: Linking chromatin accessibility to gene expression pinpoints regulatory regions driving immune responses—a critical step toward understanding immune cell behavior in tissue.
• Model for future studies: This approach provides a template for studying tissue dynamics in infection, inflammation, or tissue remodeling—not relying solely on dissociated single-cell data.

Benchmarking of scATAC-seq clustering and downstream analysis. (Li, Z., et al., Nat Commun 2021)

Challenges and integration considerations

While integration of spatial ATAC-seq with scRNA-seq is powerful, it is not without hurdles. Recognizing these challenges early can help researchers design more reliable analyses.

1. Batch effects

Differences in sample preparation, sequencing runs, or tissue processing can introduce technical variation. If uncorrected, this noise may obscure true biological signals. Tools such as scvi-tools or newer frameworks like INSTINCT provide batch-correction modules that help align datasets across experiments.

2. Data sparsity

ATAC-seq data are inherently sparse—most open chromatin sites are only detected in a small fraction of cells. This sparsity makes integration with scRNA-seq challenging, as the signal-to-noise ratio is lower. Generating a gene activity matrix and applying dimensionality reduction before integration can help stabilize results.

3. Spatial resolution versus expression coherence

Spatial assays offer different resolutions (e.g., 20–50 µm spots). At lower resolutions, each pixel may represent a mixture of cells, making it harder to match chromatin accessibility directly to single-cell RNA profiles. Deconvolution algorithms are often required to separate mixed signals and recover cell-type-specific information.

4. Computational demands

Integration workflows—especially when combining multiple large datasets—require substantial memory and processing power. Efficient tool selection matters: while Seurat + Signac is user-friendly, methods like Tangram or scvi-tools may scale better for larger projects.

5. Biological interpretation

Finally, even when integration runs smoothly, interpretation requires caution. Not every accessible region corresponds to active transcription, and not every expression change can be explained by local chromatin opening. Cross-validation with marker genes or external datasets is essential to avoid over-interpretation.

Takeaway: These challenges do not diminish the value of integration. Instead, they highlight the importance of careful experimental design, robust QC, and thoughtful tool choice. Addressing them directly ensures that spatial ATAC-seq and scRNA-seq integration delivers biologically meaningful insights rather than technical artifacts.

Conclusion: Recommended integration strategy

Bringing together spatial ATAC-seq and scRNA-seq is no longer just a technical experiment—it has become a practical way to uncover how gene regulation and cellular identity are organized in real tissue environments. By combining chromatin accessibility with transcriptional profiles, researchers can move from descriptive maps of where cells are, to mechanistic insights into why they behave that way.

A recommended workflow is straightforward:

1. Rigorous preprocessing of both ATAC and RNA data, with quality control at every step.
2. Gene activity estimation to bridge the two modalities.
3. Integration with accessible tools such as Seurat + Signac for smaller datasets, or scvi-tools and INSTINCT for larger, multi-sample studies.
4. Visualization and co-embedding to evaluate alignment and cluster consistency.
5. Biological validation using marker genes or known regulatory features to confirm results.

This strategy provides a reliable foundation for studies in developmental biology, neuroscience, immunology, and cancer research—anywhere spatial context and regulatory mechanisms matter.

Final note: Integration is not about chasing the most complex workflow but about aligning the right tools with your biological question. With careful design, spatial ATAC-seq and scRNA-seq integration can turn complex multi-omics data into clear, actionable insights for your research.

Partner with experts in spatial multi-omics integration

At CD Genomics, we specialize in providing high-quality spatial ATAC-seq sequencing and bioinformatics analysis to help researchers uncover chromatin accessibility in its true tissue context. When combined with customer-supplied scRNA-seq datasets, we can deliver powerful integration strategies that connect regulatory landscapes with transcriptional programs.

Our support includes:

• Spatial ATAC-seq sequencing and QC with strict data quality standards.
• Custom integration pipelines designed to combine spatial ATAC-seq with transcriptomic datasets provided by your lab.
• Research-focused services only — tailored for academic and industrial research, not for clinical or personal applications.
• Publication-ready deliverables, including processed data, integrative analyses, and visualization outputs.

With our expertise, your spatial ATAC-seq data can be transformed into actionable insights and integrated effectively with other omics layers to advance your research.

Contact us today to discuss how our sequencing and analysis services can support your spatial epigenomics projects.

References

1. Xu, Zhongli, et al. "Integrative analysis of spatial transcriptome with single-cell transcriptome and single-cell epigenome in mouse lungs after immunization." Iscience 25.9 (2022).
2. Berest, Ivan, and Andrea Tangherloni. "Integration of scATAC-Seq with scRNA-Seq data." Single cell transcriptomics: Methods and protocols. New York, NY: Springer US, 2022. 293-310.
3. Kumar Swain, Asish, Rajveer Singh Shekhawat, and Pankaj Yadav. "ScInfeR: an efficient method for annotating cell types and sub-types in single-cell RNA-seq, ATAC-seq, and spatial omics." Briefings in Bioinformatics 26.3 (2025): bbaf253.
4. Li, Haikuo, et al. "Spatially resolved genome-wide joint profiling of epigenome and transcriptome with spatial-ATAC-RNA-seq and spatial-CUT&Tag-RNA-seq. " Nature Protocols (2025): 1-35.
5. Lee, M.Y.Y., Kaestner, K.H. & Li, M. Benchmarking algorithms for joint integration of unpaired and paired single-cell RNA-seq and ATAC-seq data. Genome Biol 24, 244 (2023).
6. Leblanc, Francis JA, and Guillaume Lettre. "Major cell-types in multiomic single-nucleus datasets impact statistical modeling of links between regulatory sequences and target genes." Scientific Reports 13.1 (2023): 3924.
7. Biancalani, T., Scalia, G., Buffoni, L. et al. Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram. Nat Methods 18, 1352–1362 (2021).
8. Li, Z., Kuppe, C., Ziegler, S. et al. Chromatin-accessibility estimation from single-cell ATAC-seq data with scOpen. Nat Commun 12, 6386 (2021).