Spatial ATAC-seq Experimental Workflow and Principles

Inquiry

How chromatin is arranged within intact tissues remains one of the key questions in epigenomics. While conventional ATAC-seq has been widely used to identify open chromatin regions, it requires dissociating cells from their native context. In doing so, valuable information about tissue architecture and spatial relationships is lost. Spatial ATAC-seq overcomes this drawback by combining chromatin accessibility profiling with spatially encoded barcoding directly on tissue sections. With this approach, researchers can track not only which genome regions are accessible, but also where these regulatory events take place within the tissue landscape.

This article introduces the basic principles behind Spatial ATAC-seq, outlines its experimental workflow, and discusses practical considerations for generating reliable data. We also address common questions raised by researchers, such as the achievable resolution of the method and how it compares to single-cell ATAC-seq.

What is Spatial ATAC-seq

Spatial ATAC-seq is a recent adaptation of the ATAC-seq method that extends its power by preserving spatial information in tissue samples. In conventional ATAC-seq, cells must be dissociated before analysis, which means their original positions within a tissue are lost. Spatial ATAC-seq avoids this limitation by working directly on thin cryosections that are placed onto specially prepared slides containing spatially encoded DNA barcodes. Each barcode corresponds to a defined spot on the slide, effectively anchoring chromatin signals to precise tissue coordinates.

Once sequencing is complete, these spatial barcodes allow researchers to reconstruct a two-dimensional map of chromatin accessibility across the section. In practice, this means that open chromatin regions can be linked back to histological features such as cortical layers in the brain, tumor boundaries, or developmental gradients. This capability makes Spatial ATAC-seq particularly valuable for studies where the local context of gene regulation is critical, and where cell populations cannot be fully understood without considering their spatial environment.

ATAC-seq vs. Spatial ATAC-seq (Quick Comparison)

What are the Core Principles of Spatial ATAC-seq

The foundation of Spatial ATAC-seq lies in combining the chemistry of ATAC-seq with strategies that preserve spatial information. While the concept sounds complex, the method is built around three core ideas:

Schematic workflow of Spatial-ATAC-seq. (Deng, Yanxiang, et al., Nature 2022)

1. Tn5 Transposition

The process begins with the Tn5 transposase enzyme, which is known for its ability to cut open chromatin and insert sequencing adapters. This step, often called tagmentation, ensures that regions of accessible DNA are selectively tagged and ready for sequencing.

2. Spatial Barcoding

Unlike traditional ATAC-seq, where all fragments are pooled together, Spatial ATAC-seq uses glass slides or microfluidic devices that contain unique DNA barcodes arranged in a grid-like pattern. Each barcode corresponds to a defined spot on the tissue. When DNA fragments are generated, they pick up these barcodes, which act like coordinates on a map.

3. Image Registration

Before the DNA is processed further, the tissue section is imaged under a microscope. These images are later used to align the barcode coordinates with tissue morphology. This step ensures that accessibility data is not just abstract sequence information but is tied directly to histological features.

Together, these principles allow Spatial ATAC-seq to create high-resolution maps of chromatin accessibility that are anchored in real tissue architecture. Instead of losing context during sample preparation, researchers can now connect gene regulatory activity to the physical layout of a tissue.

What are the Experimental Steps of Spatial ATAC-seq

Although different laboratories may adapt the protocol to fit their needs, the general workflow of Spatial ATAC-seq follows a sequence of steps that move from intact tissue to a spatially resolved chromatin map:

Schematic workflow of spatial ATAC–RNA-seq and spatial CUT&Tag–RNA-seq. (Zhang, Di, et al., Nature 2023)

1. Tissue Preparation and Sectioning

Fresh-frozen tissue is the preferred starting material. Samples are sectioned into thin slices, typically in the 5–20 µm range, using a cryostat. Sections are carefully transferred onto slides that contain pre-printed DNA barcodes. Keeping the slices uniform in thickness is essential, as uneven sections can produce variable signals.

2. Chromatin Tagmentation with Tn5

The Tn5 transposase is applied to the tissue slices, where it selectively inserts sequencing adapters into regions of accessible chromatin. At the same time, DNA fragments become tagged with the spatial barcodes on the slide, recording their physical location. This step requires fine-tuning: too little activity may miss regulatory regions, while too much activity can create excess background.

3. Barcode Capture and Molecular Labeling

Each spot on the slide carries a unique DNA sequence that acts as a spatial "address." When chromatin fragments are released, they hybridize with these barcodes. The result is a coordinate system similar to pixels in an image, with each spot capturing the local accessibility profile.

4. Tissue Imaging

Before the DNA is further processed, the section is imaged with brightfield or fluorescence microscopy. These images serve as a reference, allowing molecular data to be overlaid with visible histological features. This imaging step is critical for ensuring that sequencing reads can be accurately matched back to tissue structure.

5. Crosslink Reversal and DNA Amplification

After fragments have been tagged and barcoded, crosslinks are reversed to release DNA. PCR amplification is then performed to generate enough material for sequencing. Care must be taken to avoid over-amplification, which can introduce bias and distort relative accessibility levels.

6. Library Construction and Sequencing

Amplified DNA fragments are prepared as sequencing libraries, typically for Illumina platforms. Each read now contains both genomic information (which region of chromatin was accessible) and spatial information (the barcode indicating tissue location). The depth of sequencing will determine how well rare or weak signals are detected.

7. Data Integration

Sequencing reads are aligned to the reference genome, and barcode information is used to reconstruct a spatial atlas of chromatin accessibility. When combined with histology images, this produces a detailed map showing how gene regulatory landscapes vary across different regions of the tissue.

Practical Considerations and Optimization Tips

Running a Spatial ATAC-seq experiment involves more than simply following a protocol. The quality of the data depends heavily on technical choices made during sample preparation and processing. Below are several points that researchers should pay close attention to, along with suggestions to improve experimental outcomes:

• Signal-to-Noise Ratio

Background signals can mask true chromatin accessibility patterns. A common cause is uneven contact between the tissue and the barcoded slide. Using uniform, thin cryosections and ensuring good slide adhesion can help reduce noise. Pre-testing section thickness for a given tissue type is often worthwhile.

• Barcode Design and Accuracy

Spatial barcodes must be carefully designed to avoid cross-contamination and misassignment. Even a small error in barcode handling can misplace signals on the spatial map. Many groups recommend validating barcode sets in pilot runs before scaling up.

• Tissue Compatibility

Different tissues behave differently. Brain tissue, for example, is soft and fragile, requiring extra stabilization during cryosectioning, while fibrous tissues such as muscle may need optimized pre-treatment for enzyme penetration. Adjusting sectioning speed and temperature can also reduce tissue tearing.

• Sample Handling

Repeated freeze–thaw cycles damage chromatin and compromise accessibility signals. Samples should be snap-frozen as soon as possible and stored at consistent low temperatures. If multiple sections are needed, prepare them in one session rather than repeatedly thawing the sample block.

• Imaging Quality

High-resolution imaging is not just for visualization—it is fundamental for accurate alignment between sequencing reads and histological features. Poorly focused or low-contrast images can complicate downstream integration. Using consistent imaging settings across experiments ensures reproducibility.

• PCR and Amplification Bias

Over-amplification during PCR can skew the relative abundance of accessible regions. Monitoring cycle numbers and including technical replicates can help detect and correct for amplification bias.

By anticipating these issues, researchers can greatly improve the robustness of their Spatial ATAC-seq experiments. Small adjustments in preparation—such as optimizing tissue thickness, validating barcodes, and ensuring consistent handling—often make the difference between noisy data and interpretable results.

What Resolution Can Spatial ATAC-seq Achieve

One of the first questions many researchers ask about Spatial ATAC-seq is: what level of resolution can it deliver? Unlike bulk ATAC-seq, which averages chromatin accessibility across thousands of cells, spatial approaches preserve positional information and can approach near single-cell resolution.

Most current implementations achieve a resolution on the scale of tens of micrometers—close to the size of individual cells. For example, pixels of ~20 µm have been reported, which is fine enough to distinguish accessibility patterns across neighboring cell groups within the same tissue. This enables researchers to track how open chromatin varies between cortical layers in the brain, between tumor cores and invasive edges, or across developmental zones in embryonic tissues.

However, the effective resolution is not determined by sequencing alone. Several technical factors influence how sharp the final map will be:

• Density of barcoded spots on the slide: higher density provides finer spatial grids.
• Tissue thickness: overly thick sections can cause signal mixing between adjacent cells.
• Imaging and registration quality: accurate overlay of histological images and molecular data is essential to maintain precision.

Spatial chromatin accessibility mapping of E13 mouse embryos. (Deng, Yanxiang, et al., Nature 2022)

While single-cell ATAC-seq can reach finer granularity, Spatial ATAC-seq offers the unique advantage of retaining positional context. This balance—slightly coarser resolution but preserved architecture—makes it particularly powerful for studying structured tissues such as brain regions, tumors, and developing organs.

How Does Spatial ATAC-seq Data Compare to scATAC-seq

Spatial ATAC-seq and single-cell ATAC-seq (scATAC-seq) share the same core goal—profiling open chromatin regions—but they take different approaches. scATAC-seq isolates individual nuclei or cells, producing high-resolution maps of accessibility at the single-cell level. Spatial ATAC-seq, in contrast, works on intact tissue slices, capturing chromatin accessibility in its native context.

From a data perspective, Spatial ATAC-seq can produce accessibility profiles that are close in quality to scATAC-seq when experiments are carefully optimized. The main trade-off is resolution: scATAC-seq offers single-cell granularity, while Spatial ATAC-seq typically provides near-cellular "pixel" resolution. What it loses in fine detail, however, it gains in context by preserving the physical arrangement of cells within tissues.

This distinction is crucial for certain biological questions. For instance, scATAC-seq excels at dissecting cellular heterogeneity, while Spatial ATAC-seq is better suited for understanding how regulatory states are distributed across tissue structures such as cortical layers, tumor margins, or developmental gradients. In many projects, the two approaches are complementary rather than competing.

Spatial ATAC-seq vs. scATAC-seq (Quick Comparison)

Conclusion

Spatial ATAC-seq has opened new possibilities for studying gene regulation in its true tissue context. By combining the enzymatic tagging of ATAC-seq with spatially encoded barcodes and high-resolution imaging, it overcomes a major limitation of conventional methods: the loss of spatial organization after cell dissociation. The result is a powerful tool that links chromatin accessibility to tissue architecture, revealing patterns that would otherwise remain hidden.

Compared with scATAC-seq, Spatial ATAC-seq provides slightly lower resolution but offers an invaluable advantage—preservation of spatial context. This makes it particularly suitable for research areas where structure matters, such as neuroscience (mapping chromatin states across cortical layers), cancer biology (profiling tumor microenvironments), and developmental biology (tracking regulatory gradients during organ formation).

For researchers planning to implement this method, careful attention to sample quality, sectioning precision, and imaging is critical to achieving reliable results. Many groups combine Spatial ATAC-seq with complementary approaches such as scATAC-seq or spatial transcriptomics to build a more comprehensive view of regulatory landscapes.

At CD Genomics, we support projects with professional sequencing and bioinformatics services, helping research teams move from raw samples to interpretable data. Our services are designed for research use only and aim to provide reliable, publication-ready results.

As technology advances, future directions will likely include integrating Spatial ATAC-seq with other omics layers, offering even deeper insight into how chromatin accessibility and gene expression interact within native tissue environments.

References

1. Deng, Yanxiang, et al. "Spatial profiling of chromatin accessibility in mouse and human tissues." Nature 609.7926 (2022): 375-383.
2. 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.
3. Noronha, Katelyn, et al. "1361 Revolutionizing epigenomic analysis in cancer: high-resolution spatial CUT&Tag and spatial ATAC-seq mapping at the single-nucleus level." (2024).
4. Zhang, Di, et al. "Spatial epigenome–transcriptome co-profiling of mammalian tissues." Nature 616.7955 (2023): 113-122.