Spatial ATAC-Seq Service
Chromatin accessibility governs which genomic regions are available for transcription factor binding and gene activation — yet traditional ATAC-seq methods lose the tissue context in which these regulatory decisions occur. CD Genomics delivers an end-to-end spatial ATAC-seq service that maps open chromatin across intact tissue sections, preserving the spatial coordinates of every accessible region. From in situ Tn5 tagmentation and spatial barcoding through sequencing and comprehensive bioinformatics, our service enables researchers to identify spatially restricted regulatory elements, map enhancer landscapes across tissue domains, and reveal epigenomic heterogeneity that bulk and single-cell approaches miss.
- Genome-wide chromatin accessibility profiling with spatial coordinates preserved — map open chromatin, enhancers, and promoters across intact tissue sections
- In situ Tn5 tagmentation + microfluidic spatial barcoding workflow — no tissue dissociation, no loss of spatial context
- Comprehensive bioinformatics: peak calling, spatial clustering, TF motif enrichment, differential accessibility, and genome browser tracks — all delivered as publication-ready figures
- Multi-omics integration with spatial transcriptomics and spatial CUT&Tag for multi-layered tissue epigenomic atlases
Spatial ATAC-Seq — Technology Overview
How Spatial ATAC-Seq Works: In Situ Tagmentation Meets Spatial Barcoding
Spatial ATAC-seq performs Tn5 tagmentation directly on intact tissue sections. After fixation to preserve chromatin architecture, hyperactive Tn5 transposase loaded with sequencing adapters is applied to permeabilized tissue, where it preferentially inserts adapters into nucleosome-free open chromatin regions. The tagged DNA fragments are then captured onto a spatially barcoded surface — typically a microfluidic chip or glass slide with oligonucleotide barcodes arranged in a grid pattern — that records the two-dimensional coordinates of each accessibility event. After tissue digestion, the barcoded fragments are released, amplified, and sequenced. The resulting data links each chromatin accessibility read to its precise location within the tissue section, generating a spatially resolved map of the open chromatin landscape.
This approach solves two fundamental limitations of traditional ATAC-seq: (1) bulk ATAC-seq averages chromatin accessibility across all input cells, masking tissue-region-specific regulatory programs, and (2) single-cell ATAC-seq (scATAC-seq) requires tissue dissociation, destroying spatial information about where accessible regulatory elements operate within the tissue architecture. Spatial ATAC-seq preserves both chromatin state and spatial coordinates in a single workflow.
What Spatial ATAC-Seq Reveals
| Information Layer | What It Tells You |
|---|---|
| Spatial chromatin accessibility | Which genomic regions are open and accessible in each tissue domain |
| Spatial enhancer/promoter maps | Where regulatory elements are active across tissue architecture |
| TF binding site accessibility | Which transcription factor motifs are accessible in specific tissue regions |
| Cell-type-specific epigenomic programs | Chromatin state differences between adjacent cell populations in situ |
| Disease-associated regulatory alterations | How chromatin accessibility changes in pathological tissue regions |
Technical foundation: The spatial ATAC-seq method was first described by Deng et al. (Nature, 2022), who combined in situ Tn5 transposition chemistry with microfluidic deterministic barcoding to achieve spatially resolved chromatin accessibility profiling at genome scale. A complementary solid-phase capture approach was independently developed by Llorens-Bobadilla et al. (Nature Biotechnology, 2023). The technology has since been extended to multi-omics workflows (spatial ATAC-RNA-seq, Nature Protocols, 2025) and applied to diverse tissue types.
Technology Value & Our Advantages
Spatial chromatin mapping at genome scale
Profile open chromatin across entire tissue sections with spatial coordinates preserved. No tissue dissociation — every regulatory element is mapped to its native tissue location.
Multi-tissue compatibility
Validated on brain, spleen, melanoma, PDX, kidney, and embryonic tissues. Protocol optimization available for challenging and novel tissue types.
In situ tagmentation preserves chromatin state
Fixation prior to Tn5 treatment locks chromatin architecture in place, minimizing artifacts from tissue handling and permeabilization.
Comprehensive bioinformatics
Peak calling, spatial clustering, TF motif enrichment (JASPAR/HOMER), differential accessibility, genome browser tracks, and pathway enrichment — all delivered as publication-ready figures with detailed methods documentation.
Multi-omics integration capability
Pair spatial ATAC-seq with spatial transcriptomics or spatial CUT&Tag for multi-layered epigenomic atlases. Compatible with label transfer and co-embedding integration approaches.
Publication-ready deliverables
All outputs formatted for direct use in manuscripts: high-resolution figures (PDF/PNG/SVG), processed data matrices, interactive HTML reports, and complete analysis parameter logs.
Spatial ATAC-Seq Workflow
Our spatial ATAC-seq workflow proceeds through five integrated stages with quality control at each checkpoint.
- Tissue Preparation & Fixation — Fresh frozen tissue sections (10–20 µm) are placed onto the spatially barcoded capture surface and cross-linked to preserve chromatin architecture. QC: Morphology assessed via brightfield imaging.
- Permeabilization & In Situ Tn5 Tagmentation — Tissue is permeabilized and hyperactive Tn5 transposase inserts sequencing adapters into accessible open chromatin regions. QC: Tagmentation efficiency monitored per tissue type.
- Spatial Barcoding & Fragment Capture — Tagged DNA fragments hybridize to spatially barcoded oligonucleotides on the capture surface, encoding tissue coordinates.
- Library Preparation & Sequencing — Barcoded fragments are amplified into indexed libraries and sequenced on Illumina platforms at 50,000–100,000 read pairs per spatial unit. QC: Library size distribution and Q30 scores validated.
- Data Processing & Analysis — Reads are demultiplexed by spatial barcode, aligned to the reference genome, and processed to generate a spatially indexed chromatin accessibility matrix for downstream bioinformatics.
Bioinformatics Analysis Pipeline
Our standardized spatial ATAC-seq bioinformatics pipeline encompasses six core analytical modules, each delivering publication-grade outputs with QC metrics reported at every step.
Core Analysis Pipeline (Included)
| Module | Description | Key Outputs |
|---|---|---|
| 1. Raw Data Preprocessing & QC | Read quality filtering (Cutadapt), alignment to reference genome (BWA/Bowtie2), spatial barcode demultiplexing, duplicate removal | QC report: reads per spatial unit, alignment rate, FRiP, TSS enrichment score |
| 2. Peak Calling | Identification of accessible chromatin regions using MACS2 or spatial-aware peak callers. Peaks called per spatial domain and aggregated across tissue | BED/bigBed peak files, peak-by-spatial-unit count matrix |
| 3. Dimensionality Reduction & Spatial Clustering | Latent semantic indexing (LSI) followed by UMAP/t-SNE projection. Spatial-aware clustering to identify chromatin-state-defined tissue domains | UMAP/t-SNE plots, spatial cluster map overlaid on tissue image |
| 4. TF Motif Enrichment Analysis | JASPAR/HOMER position weight matrix scanning against domain-specific peaks. chromVAR for per-spatial-unit TF motif deviation scores | TF motif enrichment heatmaps, per-domain motif activity rankings |
| 5. Differential Accessibility Analysis | Pairwise comparison of chromatin accessibility between spatial domains. Annotation of differentially accessible regions (DARs) with genomic context | Volcano plots, annotated DAR tables, genomic context pie charts |
| 6. Genome Browser Visualization | Generation of bigWig coverage tracks for IGV/UCSC genome browser. Spatial-domain-specific tracks for comparative visualization | bigWig files, IGV session files |
Advanced Analysis (Optional Add-ons)
| Analysis | Description |
|---|---|
| Spatial trajectory analysis | Inference of epigenomic state transitions along spatial gradients (e.g., tumor invasion front, developmental axes) |
| Multi-omics integration (ATAC + RNA) | Joint analysis of spatial ATAC-seq and spatial transcriptomics from adjacent sections; peak-gene linkage, enhancer-promoter interaction mapping |
| Cross-species regulatory conservation | Comparison of chromatin accessibility patterns between mouse and human for translational relevance |
| Custom publication figure generation | Tailored visualization for manuscript submission |
Demo Results
The following figure illustrates the types of analysis outputs delivered with each spatial ATAC-seq project. All visualizations are representative examples generated from mouse brain tissue sections profiled using the spatial ATAC-seq workflow.
Representative spatial ATAC-seq outputs. Top left: Spatial chromatin accessibility clusters mapped onto tissue. Top right: TF motif enrichment heatmap across spatial domains. Bottom: Analysis composite with genome browser tracks per domain (left), differential accessibility volcano plot (center), and pathway enrichment dot plot (right).
Note: All figures shown are representative results. Actual outputs vary by tissue type, sample quality, and project parameters.
Data Deliverables
Primary Data Outputs
| Deliverable | Description |
|---|---|
| Raw sequencing data | Demultiplexed paired-end reads with spatial barcode information |
| Spatially indexed accessibility matrix | Chromatin accessibility counts per genomic region per spatial unit |
| Aligned reads | Coordinate-sorted alignment files with spatial barcode tags |
| Peak files | Called accessible chromatin regions; genome browser-compatible coverage tracks |
| Spatial metadata | Spatial barcode-to-coordinate mapping, tissue image alignment parameters |
Analysis Reports
| Deliverable | Description |
|---|---|
| Comprehensive QC report | All quality metrics: reads per spatial unit, alignment rate, FRiP, TSS enrichment, spatial coverage |
| Spatial clustering report | UMAP/t-SNE plots, spatial cluster maps, cluster-specific peak annotations |
| TF motif enrichment report | Per-domain motif enrichment tables and heatmaps, chromVAR deviation scores |
| Differential accessibility report | Differentially accessible region tables, volcano plots, genomic context annotations |
| Genome browser visualization files | Per-domain coverage tracks for interactive visualization |
| Interactive analysis report | Complete analysis report with embedded interactive figures |
| Publication-ready figures | High-resolution figures formatted for manuscript submission |
| Analysis parameter log | Full record of software versions and parameter settings for reproducibility |
Raw data is delivered via secure file transfer. Data retention follows project agreement terms, typically 6–12 months post-delivery, with extended storage options available upon request.
Spatial ATAC-Seq Sample Requirements
| Sample Type | Recommended Input | Fixation/Preservation | Shipping Conditions | Notes |
|---|---|---|---|---|
| Fresh frozen tissue (OCT-embedded block) | Tissue section ≤ capture area; block submitted as-is | OCT-embedded cryomold | Dry ice (−80°C) | Avoid repeated freeze-thaw cycles |
| Pre-cut tissue sections on slides | 2–3 slides per sample; 10–20 µm thickness | Slide mailer, sealed | Dry ice (−80°C) | Sections must remain frozen during transport |
| FFPE tissue block | Standard FFPE block | Formalin-fixed, paraffin-embedded | Room temperature | Modified protocol; consult for feasibility |
| Brain tissue | Fit within capture area | OCT block preferred | Dry ice (−80°C) | Anatomical orientation documentation recommended |
| Tumor tissue | Multiple sections recommended | OCT block preferred | Dry ice (−80°C) | Necrotic regions may reduce data quality |
Tissue Quality Recommendations
- RIN ≥ 7 recommended for fresh frozen tissue
- DV200 ≥ 50% as complementary metric for moderately degraded samples
- Avoid DNA-binding dyes during tissue processing — these intercalate into DNA and alter chromatin conformation, compromising ATAC-seq fidelity
- Provide backup samples when possible (recommended: 2–3 additional sections)
- Morphological integrity — well-preserved tissue architecture without visible ice crystal damage, necrosis, or folding
Samples with borderline quality metrics (RIN 5–7, DV200 30–50%) are evaluated on a case-by-case basis. For FFPE specimens, a modified spatial epigenomics protocol may be applicable — consult our team for a feasibility assessment.
Spatial ATAC-Seq Applications
Tumor Microenvironment & Cancer Epigenomics
Map chromatin accessibility across tumor regions — including the tumor core, invasive margin, and adjacent stroma. Identify spatially restricted enhancer activation, regulatory programs driving tumor heterogeneity, and epigenomic alterations associated with immune evasion.
Neuroscience & Brain Architecture
Profile chromatin accessibility across anatomically defined brain regions in tissue sections. Map enhancer landscapes underlying cortical layer identity, track chromatin priming during myelination, and identify region-specific regulatory elements associated with neurological disease.
Developmental Biology
Track spatiotemporal chromatin accessibility dynamics during embryogenesis and organogenesis. Identify chromatin priming events — the opening of lineage-specific regulatory elements prior to gene activation — at single-tissue-domain resolution.
Immunology & Tissue Organization
Characterize chromatin state differences between immune cell populations in their native tissue context. Map regulatory element activation in lymphoid follicles, tertiary lymphoid structures, and inflammatory infiltrates without tissue dissociation artifacts.
Drug Target & Biomarker Discovery
Identify condition-specific regulatory elements and their associated target genes for therapeutic targeting. Chromatin-based biomarkers derived from spatial ATAC-seq provide spatially resolved readouts of drug target engagement and treatment response in preclinical models.
Spatial ATAC-Seq vs scATAC-Seq vs Spatial CUT&Tag
Choosing the right epigenomic profiling approach depends on your biological question, sample type, and the molecular layer you need to interrogate. The comparison below highlights key differences to guide method selection.
| Dimension | Spatial ATAC-Seq | scATAC-Seq | Spatial CUT&Tag |
|---|---|---|---|
| Molecular target | Open chromatin (genome-wide) | Open chromatin (genome-wide) | Specific histone modification or protein (targeted) |
| Spatial context | Preserved — tissue coordinates retained | Lost — requires tissue dissociation | Preserved — tissue coordinates retained |
| Cell-type resolution | Inferred from spatial domains | Direct — per-nucleus barcoding | Inferred from spatial domains |
| Genome-wide coverage | Whole-genome chromatin accessibility | Whole-genome chromatin accessibility | Targeted to antibody-defined loci |
| Sensitivity per locus | Moderate | Moderate | High (antibody-directed enrichment) |
| Multi-omics compatible | Compatible with spatial RNA-seq co-profiling | Compatible with scRNA-seq integration | Compatible with spatial RNA-seq co-profiling |
| Sample types | Fresh frozen (primary); FFPE (modified) | Fresh or cryopreserved nuclei | Fresh frozen (primary); FFPE (modified) |
| Data sparsity | High (binary per locus) | High (binary per locus) | Low-moderate (targeted enrichment) |
| Best for | Unbiased discovery of spatially regulated chromatin states | Cell-type-specific regulatory program identification | High-sensitivity profiling of specific histone marks in tissue context |
Method Selection Guide
- Choose Spatial ATAC-seq when: You need genome-wide, unbiased chromatin accessibility maps with tissue spatial context preserved. Ideal for exploratory studies mapping enhancer landscapes and identifying spatial epigenomic domains.
- Choose scATAC-seq when: Cell-type resolution is the primary goal and tissue spatial context is not required. Ideal for building cell-type-specific regulatory atlases.
- Choose Spatial CUT&Tag when: You are targeting a specific histone modification (e.g., H3K27ac for active enhancers, H3K4me3 for promoters) with high sensitivity in tissue context.
Related Services: Learn about our scATAC-seq service, explore our Spatial CUT&Tag service, or see our 10x Multiome ATAC + RNA service.
Case Study: Spatial Chromatin Accessibility Mapping During Mouse Organogenesis and Human Breast Cancer
This independently published study demonstrates the power of spatial ATAC-seq in resolving tissue-region-specific chromatin accessibility programs across development and disease. It is presented as a representative example of the biological insights achievable with this technology — it is not a CD Genomics client project.
Background
Understanding chromatin accessibility in a spatially resolved manner is critical for identifying regulatory programs that define cell identity, guide differentiation, and go awry in disease. Spatial ATAC-seq addresses this by profiling open chromatin directly on intact tissue sections.
Methods
Llorens-Bobadilla et al. (2023) performed Tn5 tagmentation on mouse embryo tissue sections (E12.5, E13.5, E15.5) and human breast cancer specimens mounted onto barcoded capture slides (55 µm spot resolution). Data were validated with snATAC-seq, Visium spatial transcriptomics, and pseudotime analysis.
Results
Unsupervised clustering identified 11 major clusters precisely aligned with anatomical landmarks. Approximately 18,000 differentially accessible peaks showed tissue-specific patterns. In human breast cancer, spatial ATAC identified tumor-specific accessibility at the HER2 locus and myeloid cell infiltration in the tumor microenvironment.
Conclusion
This study demonstrated spatial ATAC-seq's capacity to resolve tissue anatomy from epigenomic data, track developmental regulatory dynamics, and identify disease-associated chromatin alterations — insights inaccessible to bulk and single-cell approaches.
Source: Llorens-Bobadilla E, Zamboni M, Marklund M, et al. Solid-phase capture and profiling of open chromatin by spatial ATAC. Nat Biotechnol 41, 1805–1816 (2023). DOI: 10.1038/s41587-022-01603-9. CC BY 4.0.
Frequently Asked Questions (FAQ)
References
- Deng Y, Bartosovic M, Kukanja P, Zhang D, Liu Y, Su G, Enninful A, Bai Z, Castelo-Branco G, Fan R. "Spatial profiling of chromatin accessibility in mouse and human tissues." Nature, vol. 609, 2022, pp. 375–383.
- Llorens-Bobadilla E, Zamboni M, Marklund M, Bhalla N, Chen X, Hartman J, Frisén J, Ståhl PL. "Solid-phase capture and profiling of open chromatin by spatial ATAC." Nature Biotechnology, vol. 41, 2023, pp. 1805–1816.
- Li H, Bao S, Farzad N, Qin X, Fung AA, Zhang D, Bai Z, Tao B, Fan R. "Spatially resolved genome-wide joint profiling of epigenome and transcriptome in single cells." Nature Protocols, vol. 20, 2025, pp. 1419–1454.
- Kartiganer Z, Rojas G, Riccio M, Tyree A, Noronha K, Wetzel M, Barnett J, McGann J, Garbarino J, Massucci D, Chafi NS, Decker S, McDaniels A, Sabina J, Levchenko D, Perez J, Ng C, Wang K. "Improved cell-type identification with spatial epigenomics 96-channel microfluidic platform (spatial ATAC-seq)." GEN Biotechnology, vol. 2, no. 6, 2023, pp. 503–514.
- Chen X, Li K, Wu X, Li Z, Jiang Q, Cui X, Gao Z, Wu Y, Jiang R. "Descart: a method for detecting spatial chromatin accessibility patterns with inter-cellular correlations." Genome Biology, vol. 25, 2024, 323.
