10x Single-Cell ATAC-Seq Service
The regulatory genome — promoters, enhancers, silencers, and insulators — encodes the instructions that govern when and where genes are expressed. CD Genomics delivers an end-to-end 10x single-cell ATAC-seq (scATAC-seq) service — spanning nuclei isolation, library construction on the 10x Chromium platform, high-depth sequencing, and comprehensive bioinformatics analysis — for academic and biopharma teams investigating tumor heterogeneity, immune cell epigenetics, developmental biology, and drug target discovery.
What our 10x scATAC-seq service delivers:
- Single-nucleus chromatin accessibility profiling at ≥25,000 read pairs per nucleus
- Eight-module bioinformatics pipeline: peak calling, clustering, TF motif enrichment, footprinting, differential accessibility, and gene regulatory network reconstruction
- Multi-omics integration with scRNA-seq via label transfer or co-embedding; paired single-cell Multiome (ATAC + RNA) also available
- Publication-ready deliverables: UMAP embeddings, TF motif heatmaps, genome browser tracks, and differential accessibility reports
Technology Overview: How Single-Cell ATAC-seq Works
The Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) leverages a hyperactive Tn5 transposase loaded with sequencing adapters. When incubated with isolated nuclei, Tn5 preferentially inserts adapters into nucleosome-free regions of open chromatin, simultaneously fragmenting DNA and tagging accessible loci — a process termed tagmentation. In the 10x Chromium scATAC-seq workflow, individual nuclei are partitioned into nanoliter-scale Gel Bead-in-Emulsion (GEM) droplets, each containing a unique barcode. Within each droplet, tagmentation occurs on the chromatin of a single nucleus, and all resulting fragments are labeled with the same cell-specific barcode. Following droplet breakup, pooled fragments undergo library amplification and paired-end sequencing.
The primary benefit of single-cell resolution is the ability to distinguish cell-type-specific chromatin accessibility programs that are averaged out in bulk ATAC-seq. This enables the identification of regulatory elements active in rare or transient cell populations, as well as the reconstruction of gene regulatory networks at cell-type resolution.
| Dimension | scATAC-seq | Bulk ATAC-seq | scRNA-seq |
|---|---|---|---|
| Resolution | Single nucleus | Population average | Single cell |
| Analyte | Chromatin accessibility | Chromatin accessibility | Gene expression (mRNA) |
| Cell heterogeneity | Distinguishable | Masked | Distinguishable |
| Cell-type-specific regulatory elements | Directly identified | Inferred indirectly | Not directly measured |
| TF footprinting | Supported at cell-type resolution | Supported at population level | Not supported |
| Data sparsity | High (binary per locus) | Low | Moderate |
Service Workflow: From Sample to Chromatin Accessibility Map
The 10x scATAC-seq workflow proceeds through six integrated stages with quality control at each checkpoint.
- Sample preparation and QC
Fresh or cryopreserved cell suspensions and tissue specimens are evaluated for viability (>85% recommended) and cell count. Samples that do not meet QC thresholds are flagged for consultation before proceeding.
- Nuclei isolation
Tissues are dissociated and cells are lysed under optimized detergent conditions to release intact nuclei while preserving chromatin architecture. Critical: DNA-binding dyes are strictly avoided during isolation, as these can alter DNA conformation and compromise ATAC-seq fidelity.
- Tn5 tagmentation
Isolated nuclei are incubated with hyperactive Tn5 transposase, which simultaneously fragments and tags accessible chromatin regions. QC checkpoint: Tagmentation efficiency is monitored via fragment size QC.
- 10x Chromium single-cell partitioning
Tagmented nuclei are loaded onto the 10x Chromium Controller, where individual nuclei are encapsulated with barcoded Gel Beads in GEMs. Each droplet receives 500–10,000 nuclei per channel, depending on experimental design.
- Library preparation and sequencing
Barcoded fragments are amplified via PCR to generate indexed sequencing libraries. Libraries are sequenced on Illumina platforms at a recommended depth of ≥25,000 read pairs per nucleus (paired-end 50 bp). QC checkpoint: Raw sequencing QC metrics including Q30 scores and read quality assessment.
- Bioinformatics analysis
Raw sequencing data are processed through a dedicated analysis pipeline, yielding peak matrices, cell clusters, TF motif enrichment profiles, and publication-ready visualizations. QC checkpoint: Comprehensive QC report including TSS enrichment score, fraction of reads in peaks (FRiP), and per-nucleus unique fragments.
Our 10x scATAC-seq Advantages
Industry-standard platform
Powered by the 10x Genomics Chromium platform, delivering reproducible single-nucleus partitioning and high-quality chromatin accessibility data. Validated across multiple cell types and tissue sources.
Deep bioinformatics expertise
Eight-module analytical pipeline spanning QC, peak calling, cell clustering, TF motif enrichment, footprinting, differential accessibility, and gene regulatory network reconstruction. Every module delivers publication-grade outputs with comprehensive methods documentation.
Multi-omics integration
Seamless computational integration of scATAC-seq and scRNA-seq data via label transfer or co-embedding. Paired single-cell Multiome (ATAC + RNA, same-cell) and Spatial ATAC-seq services also available for multi-modal studies.
Proven track record
Client studies supported by our scATAC-seq service published in leading journals including Genome Biology. Dedicated team of bioinformaticians with domain expertise in epigenomics and chromatin biology.
Sample flexibility
Compatible with fresh cell suspensions, cryopreserved cells, tissue specimens, and blood samples. Expert consultation available for challenging sample types, including low-input and archival specimens.
Bioinformatics Analysis Pipeline
Our standardized scATAC-seq bioinformatics pipeline encompasses eight core analytical modules, each delivering publication-grade outputs. These modules are executed sequentially, with quality metrics reported at every step.
Core chromatin accessibility analysis (included)
- Raw data preprocessing and QC: Read alignment, barcode processing, fragment filtering. TSS enrichment score, FRiP, and per-nucleus unique fragments compiled into a comprehensive QC report.
- Peak calling and count matrix: Accessible chromatin peaks called on aggregated or per-cluster data; binary or TF-IDF-normalized peak-by-cell count matrix for downstream analysis.
- Dimensionality reduction and clustering: Latent semantic indexing (LSI) with singular value decomposition (SVD); graph-based clustering via Louvain or Leiden algorithm.
- Cell type annotation: Label transfer from matched scRNA-seq reference using gene activity scores; cross-referencing of cluster-specific marker peaks against cis-regulatory element databases.
Advanced regulatory analysis
- TF motif enrichment: JASPAR position weight matrix scanning against cluster-specific peaks; per-cell TF motif deviation scores via chromVAR for cell-type-specific regulatory program identification.
- TF footprinting: Tn5 insertion bias analysis at active motifs to infer TF occupancy, distinguishing actively bound TFs from those with merely accessible motifs.
- Differential accessibility: Pairwise comparisons identifying differentially accessible regions (DARs) annotated with genomic context (promoter, enhancer, intron, intergenic) and linked to nearby genes.
- Gene regulatory network reconstruction: Integration of DARs with TF motif enrichment results to construct cell-type-specific regulatory networks linking TFs to target genes.
Advanced custom analysis options are available for projects requiring deeper investigation: integration with Hi-C or promoter-capture data for enhancer–promoter looping validation, trajectory analysis for developmental or differentiation time courses, and cross-species conservation analysis of regulatory elements. For project-specific analysis design, contact our team.
Demo Results
The following figures represent the types of analysis outputs delivered with each 10x scATAC-seq project. These results are generated from the analysis of ~5,000 PBMC nuclei profiled using the 10x Chromium scATAC-seq workflow. Actual deliverables are tailored to your experimental design and biological questions.
Panel A — Quality control and fragment size distribution: The fragment size distribution exhibits the characteristic nucleosomal periodicity of successful ATAC-seq libraries, with peaks at sub-nucleosomal (<147 bp), mono-nucleosomal (~147 bp), and di-nucleosomal (~294 bp) fragment lengths. Key QC metrics: >50,000 unique fragments per nucleus, TSS enrichment score >8, and FRiP >0.3 — all exceeding community benchmarks for high-quality scATAC-seq data.
Panel B — UMAP clustering and cell type resolution: Unsupervised clustering resolves eight major PBMC populations: CD4+ T cells, CD8+ T cells, B cells, NK cells, CD14+ monocytes, CD16+ monocytes, dendritic cells, and hematopoietic progenitors. Cell type annotations are confirmed via label transfer from a matched scRNA-seq PBMC reference dataset.
Panel C — TF motif enrichment heatmap: Cell-type-specific TF motif activity reveals known lineage-defining regulators: CEBPB and CEBPD motifs are enriched in monocyte clusters, TCF7 and LEF1 motifs dominate T cell populations, PAX5 and EBF1 motifs characterize B cells, and IRF8 and TCF4 motifs mark dendritic cells. This pattern recapitulates established hematopoietic TF regulatory networks.
Panel D — Differential accessibility and genome browser track: Genome browser tracks at the CD14 locus illustrate differential chromatin accessibility between CD14+ monocytes (open) and T cells (closed), with accessible peaks concentrated at the CD14 promoter and intronic enhancer regions, confirming cell-type-specific regulatory architecture.
scATAC-seq Sample Requirements
| Sample Type | Recommended Input | Viability Threshold | Notes |
|---|---|---|---|
| Fresh cell suspension | ≥1 × 105 cells | >85% | Wash 2× with PBS; resuspend in recommended buffer |
| Cryopreserved cells | ≥5 × 105 cells | >80% post-thaw | Use DMSO-free cryopreservation medium when possible |
| Fresh tissue | ≥50 mg | N/A | Flash-freeze immediately; avoid RNAlater for ATAC applications |
| Blood (PBMCs) | ≥2 mL whole blood | N/A | Process within 4 hours of collection; use EDTA tubes |
Critical QC considerations:
- DNA-binding dyes are strictly prohibited during nuclei isolation or staining — these intercalate into DNA and alter chromatin conformation, compromising ATAC-seq accuracy.
- Nuclei should be counted on a hemocytometer or automated counter before loading onto the 10x Chromium Controller. The optimal loading concentration is 500–10,000 nuclei per channel.
- For multi-omics studies combining scATAC-seq and scRNA-seq, we recommend splitting a single cell suspension into two aliquots (one for each modality), ensuring the same biological starting material.
Multi-Omics Integration: scATAC-seq + scRNA-seq
Combining scATAC-seq with scRNA-seq data from the same biological system provides a multi-layered view of gene regulation — linking chromatin state to transcriptional output. CD Genomics supports multiple integration strategies tailored to your experimental design and biological question.
| Strategy | Approach | Best For |
|---|---|---|
| Label transfer | Cell-type annotations from scRNA-seq clusters are projected onto scATAC-seq data using shared correlation structures between gene activity scores and gene expression profiles. No matched cells required. | Independent scATAC and scRNA datasets from the same biological system; most widely adopted integration approach |
| Co-embedding | Both modalities projected into a shared low-dimensional latent space using canonical correlation analysis (CCA), enabling joint visualization of cells from both data types. | Exploratory analysis requiring integrated visualization across modalities |
| Peak–gene association | Differentially accessible peaks linked to nearby or distal genes via correlation of peak accessibility with gene expression across cell populations. | Enhancer–promoter interaction mapping; candidate regulatory element prioritization |
For projects requiring simultaneous capture of chromatin accessibility and gene expression from the same single cell, we also offer the 10x Genomics Single-Cell Multiome ATAC + Gene Expression service, which directly profiles both modalities in a single workflow. Additional spatial context can be obtained through our Spatial ATAC-seq service. For single-cell transcriptome-only profiling, see our 10x scRNA-seq service.
scATAC-seq Applications
Tumor heterogeneity and clonal evolution
Reveal epigenomic heterogeneity within tumors, identifying regulatory subprograms that distinguish drug-tolerant persister cells, metastatic subclones, and therapy-resistant populations. Combined with mtDNA mutation tracing, scATAC-seq enables clonal lineage reconstruction across disease progression.
Immune microenvironment profiling
Map chromatin accessibility landscapes of tumor-infiltrating lymphocytes (TILs) to uncover TF programs associated with T cell exhaustion, macrophage polarization, and NK cell dysfunction. These insights guide immunotherapy target discovery and biomarker development.
Developmental biology
Track epigenomic trajectories accompanying cell fate decisions during embryogenesis, organogenesis, and stem cell differentiation. Chromatin priming — the opening of lineage-specific regulatory elements prior to gene activation — can be detected and quantified at single-cell resolution.
Drug target and biomarker discovery
Identify condition-specific regulatory elements and their associated TFs, pinpointing candidate drug targets. Chromatin-based biomarkers derived from scATAC-seq provide novel tools for patient stratification and treatment response monitoring.
Neuroscience
Build cell-type-specific chromatin accessibility atlases of the brain, revealing regulatory programs underlying neuronal diversity, synaptic plasticity, and neurodegenerative disease risk loci mapped to non-coding regulatory regions.
Case Study: Single-Cell Multi-Omics Traces the CLL Disease Continuum
This independently published study demonstrates the power of single-cell ATAC-seq in resolving disease-relevant regulatory programs. It is presented as a representative example of the analytical depth achievable with this technology — it is not a CD Genomics client project.
Source: Rathgeber AC, Fernandes SM, Nagler A, et al. Single-cell epigenetic and transcriptomic states across the continuum of monoclonal B cell lymphocytosis to chronic lymphocytic leukemia. Genome Biology 27, 175 (2026).
Background: Chronic lymphocytic leukemia (CLL) is preceded by monoclonal B cell lymphocytosis (MBL), yet the molecular events driving the transition from premalignant MBL to clinically overt CLL remain poorly understood. Identifying epigenomic alterations at the earliest disease stages may reveal therapeutic opportunities for disease interception.
Methods: The study applied an integrated multi-modal single-cell strategy — combining scATAC-seq, scRNA-seq, surface protein profiling (CITE-seq), and mitochondrial DNA (mtDNA) mutation tracing — to profile 168,533 single-cell chromatin accessibility (mtscATAC-seq) profiles and matched transcriptomic data from peripheral blood mononuclear cells of 22 patients and 2 healthy controls, using the 10x Chromium Single Cell ATAC Kit v2. mtDNA mutations served as endogenous lineage barcodes, enabling simultaneous readout of chromatin state and clonal origin from the same single cells.
Results: UMAP visualization of scATAC-seq profiles revealed that low-count MBL (LC-MBL) cells already exhibit CLL-like chromatin accessibility landscapes, including open chromatin at B cell regulatory elements and enrichment of CLL-associated TF motifs such as TCF4 and NFAT. High-count MBL (HC-MBL) and clinical CLL samples showed remarkably stable epigenomic and transcriptomic profiles, suggesting the critical molecular transition occurs early — at the emergence of monoclonal B cell populations — rather than at the HC-MBL–CLL clinical boundary. TF motif enrichment analysis identified EBF1, BACH1/2, and POU2F2 as factors with reduced activity in MBL/CLL B cells relative to physiologic B cells, while TCF4 and NFAT motifs gained accessibility.
Conclusion: This study demonstrates that scATAC-seq can identify early epigenomic alterations that precede clinical disease. The identified TFs and regulatory elements represent potential targets for intercepting CLL at the premalignant stage. For researchers applying scATAC-seq to oncology, this work illustrates how single-cell chromatin accessibility data — combined with multi-omics integration — can resolve biologically and clinically meaningful regulatory programs across the full spectrum of disease progression.
Frequently Asked Questions
References
- Buenrostro JD, Giresi PG, Zaba LC, Chang HY, Greenleaf WJ. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat Methods. 2013;10(12):1213-1218. DOI: 10.1038/nmeth.2688.
- De Rop FV, Hulselmans G, Flerin C, et al. Systematic benchmarking of single-cell ATAC-sequencing protocols. Nat Biotechnol. 2024;42(6):916-926. DOI: 10.1038/s41587-023-01881-x.
- Satpathy AT, Granja JM, Yost KE, et al. Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion. Nat Biotechnol. 2019;37(8):925-936. DOI: 10.1038/s41587-019-0206-z.
- Stuart T, Srivastava A, Madad S, Lareau CA, Satija R. Single-cell chromatin state analysis with Signac. Nat Methods. 2021;18(11):1333-1341. DOI: 10.1038/s41592-021-01282-5.
- Hao Y, Hao S, Andersen-Nissen E, et al. Integrated analysis of multimodal single-cell data. Cell. 2021;184(13):3573-3587.e29. DOI: 10.1016/j.cell.2021.04.048.
