10x Multiome Single-Cell Combined ATAC-seq & RNA-seq Profiling Service
Every gene expression program — from lineage commitment to stress response — is governed by chromatin. CD Genomics delivers an end-to-end 10x Multiome single-cell combined ATAC-seq and RNA-seq profiling service that captures chromatin accessibility and gene expression simultaneously from the same single nucleus, linked by a shared cellular barcode. This naturally paired epigenomic and transcriptomic data eliminates batch effects and enables direct peak-to-gene linkage — the most rigorous approach to connecting regulatory elements with their transcriptional targets.
What our 10x Multiome service delivers:
- Simultaneous ATAC-seq and RNA-seq profiling from the same individual nucleus — naturally paired, not computationally integrated
- Eight-module joint bioinformatics pipeline: Cell Ranger ARC → Seurat + Signac WNN → chromVAR TF motif → Cicero peak-gene linkage → CellChat communication
- Direct peak-to-gene linkage via Cicero co-accessibility networks with GeneHancer validation
- Publication-ready deliverables: joint UMAP embeddings, TF motif heatmaps, genome browser tracks, and cell-cell communication networks
Service Overview
Every gene expression program — from lineage commitment to stress response — is governed by chromatin. Open chromatin at promoters and enhancers permits transcription factor (TF) binding, while closed chromatin silences genes. Understanding which regulatory elements are active in which cell types, and which genes they control, requires measuring chromatin accessibility and gene expression simultaneously from the same cell. This is what 10x Genomics Single-Cell Multiome ATAC + Gene Expression delivers: paired epigenomic and transcriptomic profiles from individual nuclei, linked by a shared cellular barcode [1].
CD Genomics provides a comprehensive 10x Multiome ATAC + RNA sequencing service spanning nuclei isolation, dual-library construction (GEX + ATAC), high-depth paired-end sequencing, and advanced joint bioinformatics analysis. Our service is designed for academic and biopharma researchers investigating gene regulatory networks, enhancer–promoter interactions, cell-type-specific TF programs, and the epigenetic drivers of disease. By capturing both modalities from the same single nucleus — rather than computationally integrating data from separate experiments — we eliminate batch effects and enable direct peak-to-gene linkage, the most rigorous approach to connecting regulatory elements with their transcriptional targets.
How 10x Multiome ATAC + RNA Works
The 10x Multiome workflow combines Tn5 tagmentation-based ATAC-seq with poly(dT)-primed 3′ gene expression profiling within a single microfluidic reaction. Isolated nuclei are first incubated with hyperactive Tn5 transposase, which simultaneously fragments and tags accessible chromatin regions — the same chemistry that powers standard ATAC-seq [1]. The tagmented nuclei are then loaded onto the 10x Chromium Controller, where individual nuclei are co-encapsulated with a single Gel Bead in a nanoliter-scale Gel Bead-in-Emulsion (GEM) droplet.
Each Gel Bead carries oligonucleotides containing two sequence elements: a poly(dT) sequence to capture polyadenylated mRNA (for the gene expression library) and a spacer sequence that ligates to Tn5-inserted adapters on fragmented chromatin (for the ATAC library). Critically, all oligonucleotides on a given Gel Bead share the same 10x Barcode. After GEM generation, reverse transcription of captured mRNA and adapter ligation on tagmented chromatin occur within the same droplet. Following droplet breakup, two libraries — one for gene expression (GEX) and one for chromatin accessibility (ATAC) — are amplified separately and sequenced. Reads from both libraries are linked to the same originating nucleus via the shared 10x Barcode, yielding naturally paired transcriptomic and epigenomic data for every cell that passes QC [2].
Key Differences: 10x Multiome vs. Separate Assays
| Dimension | 10x Multiome (Same Nucleus) | scATAC-seq + scRNA-seq (Separate Runs) | scRNA-seq Only |
|---|---|---|---|
| Modalities captured | ATAC + RNA, same nucleus | ATAC or RNA, different aliquots | RNA only |
| Data pairing | Natural (shared barcode) | Computational inference (label transfer) | Not applicable |
| Peak-to-gene linkage | Direct (Cicero, Signac) | Indirect (correlation across populations) | Not measurable |
| Batch effects | Absent (co-assayed) | Present (different libraries/runs) | Low |
| Libraries per sample | 2 (GEX + ATAC) | 2 (separate library preparations) | 1 |
| Nuclei per channel | 500–10,000 | 500–10,000 (ATAC) / 500–10,000 (RNA, separate) | 500–10,000 cells |
| Analytical complexity | Highest (multimodal integration required) | High (computational integration) | Moderate |
| Best for | Gene regulatory network discovery, enhancer–promoter mapping, TF→target gene validation | Adding epigenomic context to pre-existing scRNA-seq data | Cell atlas construction, differential expression |
Our 10x Multiome Service Workflow
Our end-to-end Multiome service follows a rigorously optimized six-stage workflow, designed to maximize nuclei quality and dual-library yield.
- Sample Intake & QC
Fresh or cryopreserved cell suspensions and flash-frozen tissue specimens undergo viability assessment and cell/nuclei counting. Samples failing minimum quality thresholds are flagged for consultation before proceeding.
- Nuclei Isolation & QC
Tissues are dissociated and cells lysed under optimized detergent conditions to release intact nuclei while preserving chromatin architecture and nuclear RNA. Nuclei concentration, purity, and membrane integrity are verified. Critical: DNA-binding dyes are strictly avoided during this step — these intercalators alter chromatin conformation and compromise ATAC-seq fidelity.
- Tn5 Tagmentation
Purified nuclei are incubated with hyperactive Tn5 transposase, which selectively fragments and tags accessible chromatin regions. Tagmentation is performed in bulk, before droplet encapsulation.
- 10x Chromium GEM Generation & Dual Barcoding
Tagmented nuclei are loaded onto the Chromium Controller, where individual nuclei are co-encapsulated with barcoded Gel Beads. Within each GEM, poly(dT) sequences capture mRNA while spacer sequences ligate to Tn5-inserted adapters — both reactions linked by the same 10x Barcode identity.
- Dual-Library Construction & Sequencing
Following GEM breakup, GEX and ATAC libraries are constructed separately via PCR amplification and index addition. Libraries are sequenced on Illumina platforms at recommended depths of 20,000–50,000 read pairs per nucleus for RNA and 25,000–50,000 read pairs per nucleus for ATAC (paired-end).
- Joint Bioinformatics Analysis
Sequencing data from both libraries are processed together through Cell Ranger ARC, followed by Seurat + Signac joint analysis, yielding co-embedded clusters, peak-gene linkage maps, TF motif enrichment profiles, and publication-ready visualizations.
Bioinformatics Analysis Pipeline
Our Multiome bioinformatics pipeline is designed for true joint analysis — not separate processing of ATAC and RNA followed by computational stitching. The pipeline runs on Cell Ranger ARC for initial processing and Seurat v5 + Signac for integrated downstream analysis [3] [4].
- Cell Ranger ARC Processing
Raw FASTQ files from both GEX and ATAC libraries are processed jointly. Cell Ranger ARC performs read alignment, barcode correction, and cell calling based on both modalities, producing a unified count matrix with paired RNA expression and ATAC peak counts per barcode.
- Joint Quality Control
Per-nucleus QC metrics — including genes detected, mitochondrial read fraction, TSS enrichment score, total ATAC fragments, and fraction of reads in peaks (FRiP) — are evaluated simultaneously. Nuclei failing thresholds in either modality are excluded.
- Joint Dimensionality Reduction & Clustering
RNA expression data (log-normalized) and ATAC peak data (TF-IDF normalized) are integrated using weighted nearest neighbor (WNN) analysis, which learns cell-specific modality weights. The resulting joint neighbor graph drives UMAP visualization and graph-based clustering (Louvain/Leiden), ensuring that both modalities contribute to cell state definition.
- Cell Type Annotation
Joint clusters are annotated using canonical marker gene expression (from the RNA modality) and cell-type-specific chromatin accessibility patterns (from the ATAC modality), cross-validated against reference atlases.
- Peak-to-Gene Linkage
Accessible chromatin peaks are linked to their putative target genes using Cicero co-accessibility networks and the GeneHancer database of enhancer–promoter interactions. This analysis directly identifies which regulatory elements are associated with the expression of which genes — the core value proposition of Multiome technology [4].
- Transcription Factor Motif Enrichment
Position weight matrices from the JASPAR database are scanned against cell-type-specific ATAC peaks. chromVAR computes per-cell TF motif deviation scores, identifying TFs whose binding sites show cell-type-specific accessibility patterns [5].
- Differential Accessibility & Expression
Pairwise comparisons between cell types or conditions identify differentially accessible regions (DARs) from ATAC data and differentially expressed genes (DEGs) from RNA data. Cross-modality integration reveals cases where chromatin remodeling precedes or coincides with transcriptional changes.
- Cell-Cell Communication Analysis
Ligand-receptor interaction analysis (CellChat, NicheNet) is performed using the RNA expression data, contextualized by the cell-type annotations derived from joint clustering. This reveals signaling networks between cell populations identified by both epigenomic and transcriptomic signatures.
Demo Results
To demonstrate the analytical depth of our Multiome pipeline, we present structured results from joint analysis of a public single-cell Multiome dataset.
Panel A: Joint UMAP Clustering
WNN-based UMAP projection resolves major cell populations using both ATAC and RNA modalities simultaneously. Each point represents a single nucleus, colored by cluster identity. The joint embedding leverages both gene expression patterns and chromatin accessibility landscapes, yielding sharper cluster separation than either modality alone. Representative clusters include distinct immune and stromal populations, with key marker genes (from RNA) and marker peaks (from ATAC) annotated per cluster.
Panel B: Transcription Factor Motif Enrichment
A cell-type-by-TF-motif heatmap displays chromVAR deviation scores, revealing lineage-defining regulatory programs. GATA4 and MEF2C motifs show elevated activity in cardiomyocyte clusters, while ERG and FLI1 motifs mark endothelial populations. CEBPB and CEBPD motifs are enriched in macrophage clusters, and STAT5A/STAT5B motifs show cell-type-specific activity patterns consistent with known cytokine-responsive regulatory programs.
Panel C: Peak-to-Gene Linkage Genome Track
Genome browser-style visualization at a representative regulatory locus demonstrates direct peak-to-gene linkage. Tracks include: (i) ATAC coverage per cell type, (ii) linked gene expression levels shown as violin plots, (iii) Cicero co-accessibility arcs connecting distal ATAC peaks to gene promoters, and (iv) GeneHancer enhancer–promoter annotation. This multi-track view provides an intuitive, evidence-rich demonstration of how Multiome connects chromatin state to transcriptional output.
Panel D: Cell-Cell Communication Network
A chord diagram or dot plot visualizes predicted ligand-receptor interactions between cell types, inferred from the RNA modality of the joint dataset. Key signaling axes — such as fibroblast-to-endothelial VEGF signaling and macrophage-to-fibroblast TGFβ signaling — provide systems-level insights into how cell populations coordinate their behavior within complex tissues.
Single-Cell ATAC + RNA Sample Requirements
Recommended Sample Types & Input Quantities
| Sample Type | Recommended Input | Quality Threshold | Notes |
|---|---|---|---|
| Fresh cell suspension | ≥1 × 106 cells | Viability >80% | Wash 2× with PBS; resuspend in recommended buffer |
| Cryopreserved cells | ≥1 × 106 cells | >80% viability post-thaw | DMSO-free medium preferred; ship on dry ice |
| Flash-frozen tissue | 200–400 mg (2–3 pieces, ~0.5 cm3 each) | RIN >7 before nuclear extraction | Snap-freeze in liquid nitrogen; store at -80°C |
| Isolated nuclei | ≥30,000 nuclei per channel target | Concentration 500–1,000 nuclei/µL | Ship on dry ice in validated nuclei storage buffer |
Critical QC Considerations
- RNA Integrity Number (RIN) ≥7 is required before proceeding to nuclear extraction. Samples failing this threshold will be flagged for consultation.
- Target loading: 500–10,000 nuclei per channel. The minimum nuclei quantity for instrument loading is 30,000–50,000 clean nuclei to ensure adequate recovery.
- DNA-binding dyes are strictly prohibited during nuclei isolation or counting — these intercalate into DNA and alter chromatin conformation, compromising ATAC tagmentation accuracy.
- Blood samples are not recommended for Multiome (immune cell nuclear RNA yields are low for scRNA-seq; consider the 10x Single-Cell Immune Profiling service for blood-based projects).
- Storage at -80°C is recommended for ≤3 months; 3–6 months may be attempted with prior consultation; storage >6 months is discouraged due to RNA degradation risk.
- For human and mouse samples, high-quality reference genome annotations are essential for accurate peak-to-gene linkage. For other species, please consult our technical team to assess genome annotation quality.
Simultaneous ATAC and RNA Applications
Gene Regulatory Network Discovery
Multiome directly links TF binding site accessibility (ATAC) to target gene expression (RNA), enabling the construction of cell-type-specific gene regulatory networks. This is the definitive approach for identifying master regulators of cell identity, differentiation, and disease states.
Tumor Microenvironment & Immune Oncology
Simultaneous chromatin and transcriptomic profiling of tumor-infiltrating immune cells reveals the epigenomic programs underlying T cell exhaustion, macrophage polarization, and immunotherapy resistance. Peak-to-gene linkage identifies enhancers driving checkpoint molecule expression and cytokine production [2].
Cardiovascular Disease
Cardiac tissue contains diverse cell types — cardiomyocytes, fibroblasts, endothelial cells, and immune cells — whose coordinated dysfunction drives heart failure. Multiome resolves cell-type-specific chromatin remodeling and gene expression changes that cannot be disentangled by single-modality approaches. Our Case Study below illustrates this application in diabetic cardiomyopathy.
Neuroscience
Cell-type-specific chromatin accessibility and gene expression atlases of the brain link non-coding GWAS risk variants for neurological and psychiatric disorders to their target genes in specific neuronal and glial populations.
Developmental Biology & Stem Cell Research
Multiome captures the coordinated epigenomic and transcriptomic trajectories of cell fate decisions. Chromatin priming — the opening of lineage-specific enhancers prior to transcriptional activation — is directly observable.
Drug Target Discovery & Biomarker Development
By identifying condition-specific enhancers and their target genes, Multiome pinpoints candidate drug targets and yields epigenomic biomarkers for patient stratification.
Case Study: Single-Cell Multiome Reveals Cell States in Diabetic Cardiomyopathy
This independently published study demonstrates the analytical depth achievable with 10x Multiome technology. It is presented as a representative example — it is not a CD Genomics client project.
Source: Su Q, Huang W, Huang Y, et al. Single-cell insights: pioneering an integrated atlas of chromatin accessibility and transcriptomic landscapes in diabetic cardiomyopathy. Cardiovascular Diabetology 23, 139 (2024).
Background: Diabetic cardiomyopathy (DCM) is a major complication of diabetes that elevates heart failure risk independently of coronary artery disease or hypertension. While myocardial fibrosis and endothelial dysfunction are known hallmarks of DCM, the cell-type-specific molecular programs driving these pathological changes have remained poorly defined. A key challenge is that cardiac tissue contains multiple interacting cell types — cardiomyocytes, fibroblasts, endothelial cells, and immune cells — whose individual contributions cannot be resolved from bulk tissue profiling.
Methods: Su and colleagues applied 10x Multiome ATAC + RNA sequencing to profile single nuclei from the hearts of wild-type (WT) and DCM model mice, generating paired chromatin accessibility and gene expression data from the same individual nuclei. The study used Cell Ranger ARC (v2.0.1) for initial data processing, followed by Seurat and Signac for joint multimodal analysis. Transcription factor activity was inferred using chromVAR, co-accessibility networks were constructed with Cicero and validated against the GeneHancer database, and cell-cell communication was analyzed using CellChat. Immunofluorescence staining of paraffin-embedded cardiac tissue was performed to validate key molecular findings.
Results: Joint analysis of WT and DCM hearts revealed substantial shifts in cardiac cell composition: endothelial cells and macrophages were decreased, while fibroblasts and cardiomyocytes showed relative increases in the DCM group, consistent with progressive fibrosis and endothelial rarefaction. Chromatin accessibility analysis uncovered cell-type-specific regulatory programs: cardiomyocyte subpopulations in DCM hearts showed enrichment of chromatin accessibility at loci associated with fatty acid metabolism and cardiac contraction pathways. Fibroblast subpopulations displayed open chromatin at fibrotic gene loci, with linked upregulation of extracellular matrix genes. Cell-cell communication analysis identified fibroblast-to-endothelial signaling through VEGF receptors as a potentially disrupted axis in DCM progression.
Conclusion: Critically, the study identified candidate transcriptional regulators — including Tcf21, Arnt, Stat5a, and Stat5b — whose motif accessibility was significantly altered in DCM, suggesting their potential roles as upstream drivers of the observed gene expression changes. Immunofluorescence validation confirmed PDK4, PPARγ, and Tpm1 as markers of metabolically altered cardiomyocytes, activated fibroblasts, and endothelial cells with compromised proliferative capacity, respectively. This study exemplifies the core value of 10x Multiome technology: by measuring chromatin accessibility and gene expression from the same nuclei, the authors resolved cell-type-specific regulatory programs undetectable by single-modality approaches. For CD Genomics clients, Su et al. demonstrates how our Multiome service — from nuclei isolation through joint bioinformatics analysis with Seurat, Signac, chromVAR, and Cicero — can identify cell-type-specific regulatory elements, their target genes, and the TFs that control them.
Our 10x Multiome Advantages
True Same-Cell Dual Modality
Simultaneous capture of chromatin accessibility (ATAC) and gene expression (RNA) from the same individual nucleus, linked by a shared 10x Barcode — eliminating batch effects and enabling direct peak-to-gene linkage.
Direct Peak-to-Gene Linkage
Cicero co-accessibility networks and GeneHancer validation directly connect regulatory elements to their transcriptional targets — the core biological insight that separate-assay computational integration cannot deliver with equivalent confidence.
Comprehensive Joint Bioinformatics
Tissue Expertise
Extensive experience with nuclei isolation from challenging tissues including heart, brain, and fibrotic samples — tissue types where high-quality nuclear preparations are critical for Multiome success.
Publication-Ready Deliverables
All figures, tables, and analytical outputs formatted to journal standards; complete methods section documentation for manuscript preparation; raw and processed data delivered in community-standard formats (h5ad, Seurat object, fragment files).
Multi-Platform Integration Capability
As a full-service spatial-omics CRO, CD Genomics can seamlessly extend your Multiome project with spatial transcriptomics (Visium, Xenium, Stereo-seq), spatial ATAC-seq, or scTCR/BCR immune repertoire profiling — enabling multi-scale, multi-modal investigation from single cells to intact tissue.
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.
- 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.
- 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.
- 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.
- Granja JM, Corces MR, Pierce SE, et al. ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nat Genet. 2021;53(3):403-411. DOI: 10.1038/s41588-021-00790-6.
- Su Q, Huang W, Huang Y, et al. Single-cell insights: pioneering an integrated atlas of chromatin accessibility and transcriptomic landscapes in diabetic cardiomyopathy. Cardiovascular Diabetology. 2024;23(1):139. DOI: 10.1186/s12933-024-02233-y.
