TL;DR

Spatial epigenomics brings chromatin accessibility and histone mark profiling into intact tissue sections using spatial ATAC-seq and spatial CUT&Tag. This guide explains core principles, study design questions, and practical use cases in development, oncology, neuroscience, and immunology, along with common pitfalls and analysis tips for spatial epigenomic sequencing and bioinformatics.

Figure 1. Conceptual overview of spatial epigenomics combining tissue architecture with chromatin accessibility and histone mark profiles.

Why Spatial Epigenomics Is Reshaping Biology and Medicine

Spatial epigenomics is attracting growing interest because many research programs have reached the limits of bulk and dissociated-cell assays. When chromatin data lose tissue context, the most informative biology is often blurred or completely hidden.

From bulk averages to spatially resolved regulation

Bulk ATAC-seq or ChIP-seq measures average signal across all cells in a sample. Even ATAC-seq performed on dissociated cells provides per-cell resolution but discards spatial information once nuclei are isolate.

In structured tissues, this can create several problems:

• Cortical layers, germinal centers, or tumor niches are mixed into a single averaged profile.
• Opposing regulatory programs in neighboring regions can cancel each other out.
• Rare but important microenvironments, such as invasive fronts or tertiary lymphoid structures, are diluted below detection.

Spatial ATAC-seq and spatial CUT&Tag directly address these issues. They preserve tissue architecture and overlay chromatin accessibility or histone mark information on top of morphology, imaging, or spatial transcriptomics, turning "where is this cell?" into "what is this cell doing here?"

Figure 2. Bulk epigenomic assays average signals across cells, whereas spatial epigenomics preserves tissue structure and region-specific regulation.

Key use cases across development, cancer, brain, and immunity

Spatial epigenomic profiling is particularly useful when questions are explicitly spatial, for example:

• Developmental biology
  • How regulatory landscapes change along anatomical axes in embryos, organoids, or regenerating tissues.
• Oncology and tumor microenvironment
  • How chromatin states differ between tumor core, invasive margin, stromal regions, and adjacent normal tissue.
• Neuroscience
  • How epigenetic programs define cortical layers, hippocampal subfields, or specific neuronal and glial populations.
• Immunology
  • How regulatory states differ between germinal centers, T-cell zones, border regions, and ectopic lymphoid structures.

These scenarios go beyond "which peaks are present" and ask "where in the tissue do these regulatory programs actually operate?" Spatial epigenomics gives a way to answer both at once.

How funders and journals drive spatial epigenetics adoption

Major journals increasingly publish work that combines spatial ATAC-seq or spatial CUT&Tag with transcriptomics, imaging, and genetic data. Spatial epigenomic maps are becoming part of reference atlases for organs and disease models.

At the same time, funders and reviewers are starting to expect spatial context for complex questions such as:

• How non-coding variants act in specific microenvironments.
• How cell states are organized along developmental or pathological trajectories in real tissues.
• How regulatory programs align with histology and functional readouts.

For many research teams, the key challenge is no longer whether to use spatial epigenomics, but how to choose between different spatial epigenomics methods and design a robust, interpretable study.

Spatial Epigenomics 101 – Core Concepts and Study Design Questions

Definition. Spatial epigenomics is the mapping of chromatin states and protein–DNA interactions in intact tissue sections while preserving spatial coordinates.

Instead of extracting nuclei into a tube, spatial epigenomic workflows operate directly on thin sections mounted on specially prepared slides or microfluidic devices.

What does spatial epigenomic profiling actually measure?

In practice, spatial epigenomic profiling uses two complementary measurement types:

• Spatial chromatin accessibility
  • Measured by spatial ATAC-seq.
  • Uses Tn5 transposase to tag open chromatin regions and assign them spatial barcodes.
• Spatial protein–DNA interactions
  • Measured by spatial CUT&Tag .
  • Uses antibodies and a pA-Tn5 fusion protein to profile specific histone modifications or transcription factor binding sites.

Both approaches generate sequencing reads that can be mapped back to "pixels" or "spots" with known positions in the tissue. These pixels can then be aligned with H&E images, immunofluorescence, or spatial transcriptomics.

Key design questions before you choose a platform

Before launching a spatial epigenomic sequencing project, it is helpful to clarify:

• Biological goal
  • Are you exploring the regulatory landscape broadly, or testing a specific histone mark or transcription factor?
• Sample type and preservation
  • Fresh-frozen brain or tumor? Archived FFPE block? Small biopsy or large resection?
• Desired spatial resolution
  • Do you need cell-level resolution, or are larger regional domains sufficient?
• Integration needs
  • Will you combine spatial epigenomic data with spatial transcriptomics, bulk RNA-seq, proteomics, or imaging?
• Available sections and replicates
  • How many tissue sections and biological replicates per condition can be allocated?

Our article Spatial ATAC-seq Experimental Workflow and Principles in the Resource section describes how these choices influence platform selection, resolution, and experimental layout.

What outputs will you interpret?

Spatial epigenomic datasets contain more than just peak lists. Typical outputs include:

• Spatial maps of accessible chromatin or histone marks.
• Candidate enhancers, promoters, and other regulatory elements with coordinates.
• Clusters or domains of pixels with similar epigenetic signatures.
• Links between regulatory elements, gene expression, and GWAS or eQTL variants.

Thinking about interpretation in advance helps shape your spatial epigenomics service request and the bioinformatics analysis plan.

Inside Spatial ATAC-seq – Mapping Chromatin Accessibility in Situ

Spatial ATAC-seq is a spatial epigenomics method that profiles open chromatin in tissue sections and tags each fragment with a spatial barcode.

It extends conventional ATAC-seq by adding positional information, converting "anonymous" peaks into features anchored to real tissue coordinates.

How does spatial ATAC-seq work step by step?

Figure 3. Typical spatial ATAC-seq workflow from tissue sectioning and in situ tagmentation to spatially resolved chromatin accessibility maps.

Protocols differ slightly among platforms, but a typical microfluidic-style spatial ATAC-seq workflow includes:

1. Tissue sectioning and placement

   Thin cryosections, often around 10 µm, are cut and mounted onto a slide compatible with spatial barcoding. Uniform section thickness improves reproducibility.

2. Fixation and permeabilization

   Mild fixation stabilizes morphology while preserving chromatin accessibility. Over-fixation is a frequent cause of low signal, so pilot tests across a small fixation gradient are very helpful.

3. In situ transposition

   Tn5 transposase, loaded with sequencing adapters, is applied directly onto the tissue. It inserts adapters into accessible regions and fragments DNA in a single step.

4. Spatial barcoding

   Microfluidic channels or pre-printed barcode grids deliver coordinate-encoded oligos. Each channel intersection or spot corresponds to a unique pixel that tags local chromatin fragments.

5. Library preparation and sequencing

   Tagged fragments are collected, amplified, and sequenced on a standard NGS platform. Reads are then demultiplexed by barcode to reconstruct a spatial chromatin accessibility map.

Microfluidic vs solid-phase spatial ATAC-seq

Two common strategies are used to deliver spatial barcodes:

• Microfluidic spatial ATAC-seq
  • Barcodes are introduced through orthogonal microfluidic channels that flow across the section.
  • Offers flexible grid designs but requires precise control of flow and alignment.
• Solid-phase spatial ATAC-seq
  • Barcodes are pre-printed or immobilized directly on the slide surface.
  • Simplifies handling but uses fixed spot layouts and resolutions.

In our spatial epigenomics projects, microfluidic layouts are often recommended for exploratory developmental studies, while solid-phase layouts work well for standardized screens across many samples.

Where spatial ATAC-seq excels

Spatial ATAC-seq is particularly well suited for discovery and global mapping, such as:

• Building chromatin accessibility atlases across embryonic organs or axes.
• Delineating cortical layers, hippocampal subfields, or other brain regions based on regulatory profiles.
• Characterizing tumor microenvironment heterogeneity by mapping core, edge, immune-rich zones, and stromal regions.

Because it captures all accessible regions, spatial ATAC-seq is ideal for finding new enhancers or regulatory modules before moving to targeted methods.

Practical tips from project experience

Based on common patterns across projects, a few practical suggestions:

• Prioritize section quality

  Folds, tears, and ice crystal damage explain many failed libraries. Good cryosectioning and handling pay off more than any downstream rescue.

• Pilot fixation and permeabilization

  Test at least two or three conditions on adjacent sections before processing a full cohort. Small adjustments often make a noticeable difference in library quality.

• Monitor simple QC metrics

  Look at fragment size distribution, library complexity, and fraction of reads in peaks. Comparing your metrics to public datasets or examples from Spatial ATAC-seq Tools and Datasets is a good way to calibrate expectations.

CD Genomics offers a spatial ATAC-seq service that combines optimized wet-lab workflows with spatial epigenomic bioinformatics analysis, delivering interpretable chromatin accessibility maps for research use.

Inside Spatial CUT&Tag – Targeted Histone Marks and Transcription Factors

Spatial CUT&Tag is a targeted spatial epigenomics method that uses antibodies and a pA-Tn5 fusion protein to map specific histone modifications or transcription factor binding sites in tissue sections.

Rather than surveying all open chromatin, spatial CUT&Tag focuses on predefined regulatory features such as H3K27ac, H3K4me3, H3K27me3, or a particular transcription factor.

How does spatial CUT&Tag work from tissue to sequencing?

Figure 4. Spatial CUT&Tag uses antibodies and pA-Tn5 to map specific histone marks or transcription factor binding sites in their native tissue context.

A typical spatial CUT&Tag workflow involves these key steps:

1. Tissue preparation

   Cryosections are fixed and permeabilized to preserve morphology while allowing antibody penetration. Blocking steps reduce nonspecific binding.

2. Primary antibody incubation

   A validated primary antibody recognizing the chosen histone mark or protein is incubated with the tissue. Incubation time and buffer conditions are optimized to maximize specific signal.

3. pA-Tn5 targeting

   A protein A–Tn5 fusion carrying sequencing adapters is added. Protein A binds the Fc region of the primary antibody, positioning Tn5 near the target loci.

4. Activation and cutting

   Divalent cations activate Tn5, which cuts and tags DNA near the antibody-bound regions, integrating adapters into nearby fragments.

5. Spatial barcoding and library preparation

   Microfluidic channels or barcoded slides then introduce spatial coordinates. Tagged DNA fragments are collected, amplified, and sequenced.

The result is a spatial map of enrichment for the selected histone mark or transcription factor across the tissue.

When to choose spatial CUT&Tag over spatial ATAC-seq

Spatial CUT&Tag is often the better choice when:

• You have a specific regulatory hypothesis.
  • For example, testing whether H3K27me3 is enriched in a particular layer or niche.
• You want to distinguish active vs repressed regions using marks such as H3K27ac or H3K27me3.
• A particular transcription factor is central to your project, and you want to see its binding pattern in situ.

Spatial ATAC-seq can point you to regions of interest, but spatial CUT&Tag provides a sharper lens on a small set of critical regulators.

Case-style examples across development, brain, and disease

Typical examples of spatial CUT&Tag applications include:

• Embryonic organ specification
  • Distinct histone mark signatures for heart, liver, and brain primordia profiled in a single embryo section.
• Cortical layer development
  • Layer-specific patterns of activating and repressive marks that track neuron subtype identities and transcription factor activity.
• Disease models
  • Redistribution of repressive marks in demyelination or neuroinflammation models, highlighting affected tracts and glial populations.
  • Changes in active promoter marks near genes implicated in cancer progression or neurodegeneration.

These use cases show how spatial CUT&Tag connects known molecular regulators to precise anatomical regions.

Antibody selection, controls, and quality checks

In spatial CUT&Tag, antibody performance is critical. Practical experience suggests:

• Prefer antibodies with existing CUT&Tag or ChIP-seq validation when available.
• Include no-primary controls to estimate background and positive controls using marks with well-known genomic patterns.
• Evaluate replicate concordance and enrichment over expected genomic features instead of relying only on peak counts or read depth.

CD Genomics' spatial CUT&Tag service includes antibody consultation, assay optimization, and spatial epigenomic data analysis, designed for research teams that want targeted, mechanism-focused maps.

Spatial ATAC-seq vs Spatial CUT&Tag – Choosing the Right Tool for Your Project

This section helps you choose the right spatial epigenomics service by comparing spatial ATAC-seq and spatial CUT&Tag side by side. The goal is to match each method to your biological question, sample type, and desired level of detail.

Figure 5. Spatial ATAC-seq offers broad, discovery-oriented maps of chromatin accessibility, whereas spatial CUT&Tag provides targeted, mechanism-focused profiles for selected marks or factors.

The table below summarizes the main differences so you can quickly align a spatial epigenomic sequencing project with your needs:

Many research teams use this comparison as a starting point when designing a spatial epigenomic sequencing and bioinformatics analysis workflow.

Detection targets and information depth

• Spatial ATAC-seq
  • Captures all accessible chromatin regions within each pixel.
  • Provides a broad, hypothesis-generating view of regulatory potential.
• Spatial CUT&Tag
  • Profiles chosen histone marks or transcription factors with high specificity.
  • Supports detailed analysis of particular regulatory states or pathways.

If you are exploring an unknown regulatory landscape, spatial ATAC-seq is usually the first step. If you are focused on a few defined regulators, spatial CUT&Tag gives sharper answers.

Sample requirements and compatibility

Several practical factors also matter:

• Tissue preservation
  • Spatial ATAC-seq was initially optimized for fresh-frozen sections, but FFPE spatial ATAC-seq protocols now expand the method to archived blocks. Our FFPE Spatial ATAC-seq Sample Preparation & QC Guide explains how pre-analytical steps influence data quality.
  • Spatial CUT&Tag can be more sensitive to fixation intensity, especially for certain epitopes.
• Input area and section count
  • Discovery projects often scan larger areas using spatial ATAC-seq.
  • Targeted spatial CUT&Tag may focus on specific regions or structures of interest.
• Signal-to-noise expectations
  • ATAC-seq distributes reads across all open regions, which can dilute weaker elements.
  • CUT&Tag concentrates reads near targeted marks but is strongly dependent on antibody quality.

Aligning method choice with your biological question

A simple decision logic we often use in project discussions:

• Global mapping of regulatory potential → Start with spatial ATAC-seq.
• Testing specific histone marks or transcription factors → Use spatial CUT&Tag.
• Integrating chromatin, marks, and RNA → Consider a combined strategy.

For example, you might run spatial ATAC-seq to identify key enhancer regions and then design spatial CUT&Tag assays for H3K27ac or a candidate transcription factor. Our Resource article Spatial ATAC-seq & scRNA-seq Integration Strategy for Spatial Epigenomics discusses how to further connect these maps with high-resolution transcriptome data or spatial transcriptomics.

From Bench to Insight – Data Analysis, Integration and Next Steps

Generating spatial epigenomic libraries is only the first half of the journey. Turning raw reads into biological insight requires a structured spatial epigenomic bioinformatics analysis workflow.

Core bioinformatics pipeline for spatial epigenomic data

A typical analysis pipeline for spatial ATAC-seq or spatial CUT&Tag includes:

1. Pre-processing

• Demultiplex barcodes, filter low-quality reads, and align to the reference genome.

2. Peak calling and quantification

• Identify accessible or enriched regions and quantify signal per peak per pixel.

3. Spatial mapping and image registration

• Assign each pixel to a coordinate and align the data with histology or fluorescence images.

4. Clustering and domain detection

• Group pixels based on epigenetic similarity to reveal layers, compartments, or microenvironments.

5. Motif and regulatory analysis

• Enrich for transcription factor motifs, annotate peaks to nearby genes, and explore affected pathways.

6. Multi-omics integration

• Integrate spatial epigenomic maps with spatial transcriptomics or bulk RNA-seq to connect regulatory potential with gene expression.

CD Genomics provides spatial epigenomic bioinformatics analysis as part of its spatial epigenomics services, delivering annotated peak sets, spatial domain maps, and integrative reports tailored to your study type.

Interpreting results without over-stating conclusions

Spatial epigenomic datasets are rich, but interpretation must stay grounded:

• Treat chromatin accessibility or mark enrichment as regulatory potential, not proof of gene expression changes.
• Combine multiple lines of evidence—motifs, gene expression, pathways, and known biology—before proposing new regulatory models.
• Use reference datasets when possible, especially for canonical structures such as cortical layers or lymphoid zones.

In practice, spatial epigenomics is most powerful when used to narrow down candidate regions, regulators, or pathways for subsequent functional experiments.

Examples of project outcomes and measurable impact

Realistic and useful outcomes from spatial epigenomic sequencing projects include:

• Identification of region-specific enhancers in embryonic or diseased tissue.
• Clearer separation of tumor core versus invasive edge regulatory programs, improving target prioritization.
• Discovery of epigenetically distinct niches in lymphoid organs or brain regions that were not obvious from transcriptomics alone.

These outputs translate into shorter candidate lists, more focused validation work, and stronger mechanistic stories in publications and grant proposals.

How to start a spatial epigenomics project with expert support

Starting a spatial epigenomics project does not require designing every detail alone. CD Genomics offers:

• Spatial epigenomics services covering spatial ATAC-seq and spatial CUT&Tag assay setup, sequencing, and bioinformatics analysis.
• Study design support to match platform, resolution, and sequencing depth with your tissue type and research question.
• Integration options with spatial transcriptomics and other high-resolution datasets to build a comprehensive view of gene regulation in space.

All services are provided for research use only and are not intended for clinical or personal applications.

To move from concept to data:

• Explore the Resource articles mentioned here for deeper technical background and case ideas.
• Share a brief description of your tissue types, available material, and key hypotheses with our team.
• Request a consultation to decide whether spatial ATAC-seq, spatial CUT&Tag, or a combined spatial epigenomic sequencing project best fits your next study.

FAQs: Spatial Epigenomics, Spatial ATAC-seq, and Spatial CUT&Tag

1. How is spatial epigenomics different from spatial transcriptomics?

Spatial transcriptomics measures RNA abundance and tells you where genes are expressed in a tissue section. Spatial epigenomics measures chromatin accessibility and histone marks, which indicate regulatory potential and help explain why certain genes are turned on or off. Combining both layers often provides the clearest picture of cell identity and state.

2. How much tissue do I need for a spatial ATAC-seq or spatial CUT&Tag project?

Most platforms work with standard cryosections in the 5–10 µm range and tissue areas comparable to a histology slide. For discovery-style maps, several adjacent sections per condition and at least two biological replicates are recommended. During project planning, CD Genomics will help estimate tissue requirements based on your chosen spatial epigenomics workflow and resolution.

3. Can I use FFPE samples for spatial epigenomic sequencing?

Yes, in many cases. FFPE spatial ATAC-seq protocols have been developed to mitigate cross-linking and can be applied to selected archived tissues. However, pre-analytical steps such as deparaffinization, retrieval, and QC are critical. Our FFPE Spatial ATAC-seq Sample Preparation & QC Guide outlines recommended practices. For spatial CUT&Tag, FFPE compatibility depends strongly on the target and antibody.

4. How do I decide between spatial ATAC-seq and spatial CUT&Tag?

If your main goal is to discover active regulatory regions and build a broad map of chromatin accessibility, spatial ATAC-seq is typically the first choice. If you already have a focused hypothesis around specific histone modifications or transcription factors, spatial CUT&Tag provides more targeted, mechanism-oriented insight. Many projects use both methods in sequence for complementary perspectives.

5. What sequencing depth is typically required for spatial epigenomic experiments?

There is no single depth that fits all projects. Many studies start with tens of thousands of reads per pixel and adjust up or down based on tissue complexity, desired resolution, and budget. A small pilot run on a subset of sections is often the best way to calibrate depth and refine your spatial epigenomic sequencing and bioinformatics analysis plan. CD Genomics will suggest depth ranges tailored to your project rather than a one-size-fits-all number.

References

1. Buenrostro, J.D., Giresi, P.G., Zaba, L.C., Chang, H.Y., & Greenleaf, W.J. (2013). Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nature Methods, 10, 1213–1218.
2. Kaya-Okur, H.S., Wu, S.J., Codomo, C.A., et al. (2019). CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nature Communications, 10, 1930.
3. Deng, Y., Bartosovic, M., Ma, S., et al. (2022). Spatial profiling of chromatin accessibility in mouse and human tissues. Nature, 609, 375–383.
4. Deng, Y., Su, G., Qin, X., et al. (2022). Spatial-CUT&Tag: spatially resolved chromatin modification profiling at the cellular level. Science, 375, 681–686.
5. Guo, P., Chen, Y., Mao, L., et al. (2025). Spatial profiling of chromatin accessibility in formalin-fixed paraffin-embedded tissues. Nature Communications, doi:10.1038/s41467-025-60882-3.
6. Li, H., Bao, S., Farzad, N., et al. (2025). Spatially resolved genome-wide joint profiling of epigenome and transcriptome with spatial-ATAC-RNA-seq and spatial-CUT&Tag-RNA-seq. Nature Protocols, 20, 2383–2417.
7. Zhang, D., Deng, Y., Ma, S., et al. (2023). Spatial epigenome–transcriptome co-profiling of mammalian tissues. Nature, 616, 113–122.
8. Farzad, N., Enninful, A., Fan, R., et al. (2024). Spatially resolved epigenome sequencing via Tn5 transposition and deterministic DNA barcoding in tissue. Nature Protocols, 19, 3389–3425.
9. Llorens-Bobadilla, E., Chellappa, K., Roth, G., et al. (2023). Solid-phase capture and profiling of open chromatin by spatial ATAC. Nature Biotechnology, 41, 1085–1088.
10. Lammi, M.J., & Qu, C. (2024). Spatial transcriptomics, proteomics, and epigenomics as tools in tissue engineering and regenerative medicine. Bioengineering, 11(12), 1235.
11. Systematic analysis identifies a connection between spatial and genomic epigenetic variation. (2024). Cell Systems, S2405-4712(24)00303-X.