Spatial ATAC-seq Tools and Datasets

Inquiry

Spatial ATAC-seq is an emerging approach that allows researchers to explore chromatin accessibility within the spatial context of tissues. Instead of studying bulk samples alone, this method connects regulatory DNA landscapes to their precise location in a section of tissue. For researchers interested in development, disease models, or tissue heterogeneity, it provides a powerful way to link open chromatin with cellular organization.

In this article, we bring together a set of practical resources to help you get started:

• Public datasets that you can access for benchmarking or exploratory analysis.
• Published protocols and open-source pipelines to guide experimental design and data processing.
• Technical support from our platform, offering sequencing and bioinformatics solutions tailored for research projects.

By combining these resources, researchers can save time, build reliable workflows, and focus on the biological questions that matter most.

Available Public Spatial ATAC-seq Datasets

When you're planning experiments or benchmarking analysis pipelines, it's invaluable to start with real-world spatial ATAC-seq datasets. Here are key resources you can explore:

• GEO dataset: [GSE278007] – A high-profile dataset profiling chromatin accessibility in mouse and human FFPE tissues using spatial ATAC-seq. It's freely available via GEO and summarized on OmicsDI, offering benchmark-quality spatial epigenomic data.
• OmicsDI search – A handy metadata aggregator that helps you discover spatial ATAC-related datasets across multiple repositories, saving you time.

Visualization panels from the OmicsDI homepage.

• GeneBook-like aggregators – Although less specialized, platforms in the GeneBook family collect published spatial genomics datasets inclusive of spatial ATAC-seq entries.

At-a-Glance Summary

** Why this matters:**

• Downloading GSE278007 allows you to test your analysis workflows against high-quality, spatially resolved chromatin data.
• Aggregators like OmicsDI and GeneBook help uncover additional datasets you might otherwise miss. This aids both experimental design and validation across samples, tissues, and conditions.

Experimental Protocols & Open-Source Analysis Pipelines

To make spatial ATAC-seq practical in your lab, you need both well-documented protocols and robust analysis pipelines. A growing number of resources are already available to support experimental design and data interpretation.

Key Protocol Resources

• AtlasXomics Protocols – AtlasXomics provides detailed workflow descriptions for their spatial ATAC-seq assays, covering tissue preparation, transposition, and library generation. This is one of the most widely cited practical starting points.
• Yale University protocols on protocols.io – The Yale team who first introduced spatial ATAC-seq has shared experimental procedures via protocols.io. This includes information on cryosectioning, Tn5-based transposition, and spatial barcoding.

Open-Source Pipelines & Analysis Tools

• GitHub Snakemake workflow – A community-maintained Snakemake pipeline is available on GitHub for spatial ATAC-seq. It provides automated steps for quality control, read alignment, peak calling, and spatial integration.
• nf-core/atacseq – While originally designed for conventional ATAC-seq, this standardized pipeline (built on Nextflow) is often adapted by researchers for spatial data, especially for peak calling and QC.

A pipeline summary of nf-core/atacseq (Image source: nf-core/atacseq)

• MACS3 peak caller – A widely used tool for ATAC-seq analysis, suitable for detecting accessible chromatin regions from spatial ATAC-seq data as well.

Practical Notes

• Always confirm spatial barcodes or coordinate formats before running pipelines—mismatches can cause errors in downstream mapping.
• For reproducibility, use workflow managers like Snakemake or Nextflow so your pipeline can be easily shared or scaled.
• Combine peak calling with spatial visualization tools (e.g., overlay peaks on tissue images) to make results interpretable in a biological context.

Our Platform's Support for Spatial ATAC-seq

While open datasets and community pipelines are a great starting point, many research groups also benefit from having a technical partner to handle the complexity of spatial ATAC-seq experiments and data analysis. Our platform provides end-to-end support designed specifically for academic and research projects.

What We Offer

• Experimental support – Assistance with tissue preparation, library construction, and sequencing optimized for chromatin accessibility in spatial contexts.

Schematic workflow of spatial ATAC. (Llorens-Bobadilla, E., et al., Nat Biotechnol 2023)

• Bioinformatics expertise – From raw read processing to peak calling, annotation, and spatial integration, our team delivers analysis pipelines aligned with current best practices.
• Customized workflows – We adapt protocols or pipelines to fit specific sample types (e.g., mammalian tissues, plant slices, or other specialized materials).
• Publication-ready outputs – Deliverables include figures, processed data tables, and annotation files that can be directly incorporated into manuscripts or downstream studies.

Why Work With a Dedicated Platform?

• Save time – Outsourcing technical steps allows researchers to focus on biological interpretation.
• Increase reproducibility – Standardized lab and analysis workflows reduce variability between experiments.
• Gain flexibility – Support for both small pilot studies and larger comparative projects.

Recommendations & Practical Tips

Choosing the right combination of datasets, protocols, and analysis tools depends heavily on your sample type and research goals. Below are some practical guidelines to help you navigate spatial ATAC-seq projects more effectively:

Spatial-ATAC-seq design, workflow and data quality. (Deng, Y., et al., Nature 2022)

Selecting Resources by Research Focus

• Tissue-specific studies: If your goal is to study brain, tumor, or other structured tissues, begin with reference datasets like GSE278007 to set expectations for coverage and resolution.
• Cross-species or non-standard samples: For less common models (plants, non-mammalian tissues), start by adapting open protocols (e.g., AtlasXomics or protocols.io workflows) and test compatibility on pilot samples.
• Benchmarking pipelines: Use open-source workflows such as GitHub spatial_atac or nf-core/atacseq to validate your pipeline before scaling up.

Practical Workflow Advice

• Confirm spatial barcode format early – Mismatches between tissue imaging coordinates and sequencing barcodes are a common source of analysis errors.
• Prioritize quality control – Assess fragment size distribution, duplication rates, and alignment statistics before moving into spatial mapping.
• Integrate visualization tools – Overlay ATAC-seq peaks on histological images to interpret accessible regions in a biological context.

Strategic Use of External Support

• Use public datasets to refine your questions and test pipelines.
• Apply open-source workflows for reproducibility and cost control.
• Rely on platform support when you need scalability, advanced bioinformatics, or publication-ready outputs.

By combining open resources with platform-based support, researchers can achieve both flexibility and reliability—minimizing trial-and-error while maximizing biological insight.

Case Study: Solid-Phase Spatial ATAC

Objective

Demonstrate how solid-phase spatial ATAC profiles chromatin accessibility on tissue sections and how the resulting maps align with anatomy and expression programs in development and disease. (Llorens-Bobadilla, E., et al., 2023)

Method snapshot (what the team did)

• Fresh-frozen sections were placed on barcoded slides, immunostained and imaged to register tissue coordinates. Tn5 transposition was performed directly in permeabilized sections. Tagmented DNA was captured on the slide via a splint oligo, ligated, extended, and amplified to build spatially barcoded libraries.
• The study profiled mouse embryos across developmental stages and included a human breast tumor example to show applicability in pathology.

Spatial ATAC uncovers spatiotemporal patterns of regulatory element accessibility underlying gene expression. (Llorens-Bobadilla, E., et al., Nat Biotechnol 2023)

Data & code (reproducible entry points)

• Mouse data: GEO GSE214991 (raw and processed matrices).
• Human sequencing data: SciLife Data Repository (DOI in article).
• Analysis code: GitHub marzamKI/spatial_atac (the authors' pipeline).

Key results you can emulate

• Quality hallmarks: strong TSS enrichment and nucleosome periodicity in spatially barcoded fragments.
• Anatomy-aware clustering: unsupervised analysis recovered ~dozen major clusters whose spatial projections matched embryonic anatomical landmarks across sections and stages.
• Regulatory element discovery: thousands of differentially accessible peaks and distal elements correlated with local gene expression patterns after co-accessibility analysis; motif enrichment pointed to expected regulators in CNS, mesenchyme, and liver regions.
• Multimodal concordance: integration with snATAC-seq atlases and spatial transcriptomics showed high agreement; accessibility at loci (for example, cortical progenitor vs. neuronal regions) tracked corresponding expression modules.
• Tumor section example: spatial maps highlighted accessibility at ERBB2 and myeloid-associated loci in defined tumor–immune interfaces on adjacent sections.

How to reproduce a minimal analysis in your lab

1. Download GSE214991 (mouse) and the authors' GitHub pipeline; keep raw/, processed/, images/, metadata/ in separate folders; record tool versions.
2. QC first: verify TSS enrichment and fragment-length structure before spatial mapping.
3. Peak calling & mapping: call peaks (e.g., MACS3) and generate a peak × spot count matrix; project clusters back to tissue coordinates to check anatomical coherence.
4. Interpretation: run differential accessibility, motif enrichment, and co-accessibility to nominate candidate regulators and distal elements that explain spatial expression patterns; optionally co-analyze with a matched spatial transcript dataset.

What to look for (sanity checks)

• Spatial clusters that align with known tissue regions.
• Accessible distal elements linked to nearby genes whose expression is regionally elevated.
• Concordant trends when integrating with external nuclei-resolved ATAC references or spatial expression maps.

Takeaway

Solid-phase capture on barcoded slides enables robust, anatomically coherent spatial accessibility maps across development and in tumor tissue. The public GSE214991 dataset plus the authors' GitHub workflow provide a ready-made starting point to validate your own pipeline and reporting templates.

Conclusion & Future Outlook

Spatial ATAC-seq is opening a new chapter in epigenomics by linking chromatin accessibility with spatial organization inside tissues. With public datasets, open protocols, and established pipelines now available, researchers can begin exploring this technology without starting from scratch.

To recap, this blog highlighted:

• Where to find datasets such as GEO's GSE278007 for benchmarking.
• Protocols and open-source pipelines including AtlasXomics, Yale workflows, and GitHub repositories for reproducible analysis.
• Our platform's research support, offering customized workflows and bioinformatics expertise.
• Practical recommendations on choosing the right combination of resources for different project types.

Looking ahead, spatial ATAC-seq will likely become a cornerstone technique for studying developmental processes, tumor microenvironments, and tissue heterogeneity. As more datasets and tools are released, the research community will benefit from improved reproducibility, scalability, and biological interpretation.

If you are considering spatial ATAC-seq for your next project, we encourage you to explore the resources shared here and reach out to our team for research-focused technical support. By combining open data with expert guidance, you can accelerate your path from experiment to discovery.

References

1. Carraro, Caterina, et al. "Chromatin accessibility profiling of targeted cell populations with laser capture microdissection coupled to ATAC-seq." Cell reports methods 3.10 (2023).
2. Llorens-Bobadilla, E., Zamboni, M., Marklund, M. et al. Solid-phase capture and profiling of open chromatin by spatial ATAC. Nat Biotechnol 41, 1085–1088 (2023).
3. Deng, Y., Bartosovic, M., Ma, S. et al. Spatial profiling of chromatin accessibility in mouse and human tissues. Nature 609, 375–383 (2022).