BD Rhapsody™ Single-Cell ATAC-seq Services
Single-cell chromatin accessibility profiling on the BD Rhapsody microwell platform — resolve regulatory heterogeneity, identify cell-type-specific enhancers and transcription factor footprints, and eliminate batch effects through sample multiplexing of up to six samples per cartridge run.
Why CD Genomics for BD Rhapsody scATAC-seq:
- Sample multiplexing — up to 6 samples pooled in a single cartridge lane using antibody-oligo nuclear tagging, eliminating inter-batch technical variation
- Gentle microwell capture — gravity-based cell settling into microwells preserves nuclear integrity for fragile and primary cell types
- Broad cell throughput — validated for 500 to 50,000 nuclei per lane, compatible with fresh and cryopreserved specimens
- Multi-omics ready — standalone scATAC-seq or paired ATAC + whole transcriptome (WTA) from the same single nuclei for integrated gene regulatory network analysis
How BD Rhapsody scATAC-seq Works
BD Rhapsody single-cell ATAC-seq (Assay for Transposase-Accessible Chromatin with sequencing) profiles open chromatin regions at single-cell resolution using a microwell-based capture system. Unlike droplet-based platforms, BD Rhapsody uses gravity-driven cell settling to load single nuclei into microwells — a gentler approach that reduces shear stress on nuclei and preserves chromatin integrity.
The ATAC-seq chemistry uses Tn5 transposase to simultaneously fragment open chromatin and insert sequencing adapters ("tagmentation"). In closed chromatin regions, Tn5 cannot access the DNA, so sequencing reads concentrate in nucleosome-depleted regulatory regions — promoters, enhancers, and other cis-regulatory elements.
Sample multiplexing through nuclear antibody-oligo tagging is the platform's defining advantage. Each sample is labeled with a unique oligonucleotide-conjugated antibody that binds nuclear surface proteins. Tagged nuclei from up to six samples are pooled and processed in a single cartridge lane, then computationally demultiplexed after sequencing. This eliminates the batch effects that confound cross-sample comparisons in sequentially processed scATAC-seq experiments.
| Key Platform Specifications | |
|---|---|
| Capture mechanism | Gravity-based microwell settling |
| Cell throughput range | 500–50,000 nuclei per cartridge lane |
| Sample multiplexing | Up to 6 samples per lane via nuclear antibody-oligo tags |
| Sample types | Fresh or cryopreserved single-cell suspensions, primary tissues, cultured cells |
| Multi-omics capability | Standalone ATAC or paired ATAC + mRNA (WTA) from same nuclei |
| Sequencing | Illumina paired-end 50×50 bp, 50,000 read pairs per cell recommended |
| Pipeline | BD Single-Cell Multiomics Pipeline v3.0 (bwa-mem2 → sinto → MACS2 → Signac → chromVAR) |
Service Workflow
- Study Design and Sample Preparation
Nuclei are isolated from fresh or cryopreserved samples using optimized lysis protocols, then stained with BD Nuclear Sample Tag antibodies for multiplexing experiments. QC Checkpoint: Nuclei yield, concentration, and morphology assessment by microscopy. Aggregates and debris removed by filtration.
- Tagmentation and Single-Cell Capture
Tn5 transposase simultaneously fragments open chromatin and inserts sequencing adapters. Tagmented nuclei are loaded onto the BD Rhapsody microwell cartridge where gravity settling places single nuclei into individual microwells containing barcoded capture beads. QC Checkpoint: Nuclei loading efficiency and microwell occupancy rate.
- Library Preparation
Cell-barcoded ATAC fragments are amplified in-well, and separate sample tag libraries are generated for multiplexed runs. Libraries are purified, size-selected, and quantified. QC Checkpoint: Library fragment size distribution — characteristic nucleosome-free peak at ~260 bp and mononucleosome peak at ~420 bp on Bioanalyzer, concentration, and adapter-dimer levels.
- Sequencing
Libraries are sequenced on Illumina platforms (NovaSeq or NextSeq series) at 50,000 read pairs per cell, paired-end 50×50 bp. QC Checkpoint: Q30 scores ≥85%, sequencing saturation assessment, and per-cell read distribution analysis.
- Primary Data Processing
Raw FASTQ files are processed through the BD Single-Cell Multiomics Pipeline: read quality filtering (minimum read length 30 bp, mean base quality ≥20), bwa-mem2 alignment (MAPQ ≥30), sinto fragment generation, MACS2 peak calling, and Signac cell-by-peak matrix construction. QC Checkpoint: TSS enrichment score (>15 expected; ≥23 typical), fraction of reads in peaks (>20%), nucleosome-free fragment percentage (>65%), and valid cell barcode count.
- Bioinformatics Analysis and Data Delivery
Secondary analysis includes dimensionality reduction (TF-IDF + LSI), UMAP/t-SNE clustering, cell-type annotation, differential accessibility analysis, transcription factor motif enrichment (chromVAR), and publication-ready visualization. QC Checkpoint: Final data review against project specifications before delivery.
Sample Requirements
| Requirement | Specification |
|---|---|
| Sample type | Fresh or cryopreserved single-cell suspensions, primary tissues, cultured cells |
| Species | Human, mouse, and additional species (case-by-case evaluation) |
| Cell viability | >80% viable cells recommended; nuclei isolation protocols are optimized per sample type |
| Cell input range | 500–50,000 nuclei per sample (platform validated range) |
| Sample multiplexing | Up to 6 samples per cartridge lane via nuclear antibody-oligo tagging (same species) |
| Cell size range | Standard nuclear size; oversized nuclei (>40 µm) may require protocol adjustment |
| Buffer | BD OMICS-One Nuclei Buffer or equivalent optimized nuclei suspension buffer |
| Shipping | Fresh tissue in transport medium on wet ice; cryopreserved cells on dry ice; frozen tissue on dry ice |
Tissue types successfully processed include brain, spleen, lymph node, lung, liver, kidney, tumor biopsies, PBMCs, and sorted cell populations. For fibrous or fatty tissues, optimized nuclei isolation protocols are applied. Contact our team during project planning for tissue-specific guidance.
Bioinformatics Analysis
All BD Rhapsody scATAC-seq projects include a standard bioinformatics pipeline. Analysis scope is matched to your experimental design and biological question.
Standard Analysis (Included)
- Raw data QC and comprehensive ATAC-seq signal quality assessment (TSS enrichment, FRiP, nucleosome periodicity, fragment size distribution)
- Read alignment, cell barcode demultiplexing, and sample demultiplexing for multiplexed runs
- Peak calling (MACS2) and peak annotation — genomic distribution: promoter, exon, intron, intergenic
- Cell-by-peak matrix construction and low-quality cell filtering
- Dimensionality reduction (TF-IDF normalization → LSI)
- Unsupervised clustering with UMAP/t-SNE visualization
- Cell-type annotation using chromatin accessibility signatures and marker gene activity scores
- Differential accessibility analysis between cell types or experimental conditions
- QC report with sequencing metrics, cell statistics, and sample-level summaries
Advanced Analysis (Optional)
- Transcription factor motif enrichment and TF activity deviation analysis (chromVAR)
- Differential peak-associated gene GO/KEGG pathway enrichment
- Pseudotime trajectory inference and cell-state transition analysis (Cicero, Monocle)
- Peak-gene association and co-accessibility network construction
- Integration with scRNA-seq data for joint gene regulatory network inference
- Multi-omics integration: paired ATAC + RNA from the same single nuclei (multiomic assay)
- Custom visualization — interactive HTML reports, publication-quality figures
Analysis deliverables are compatible with R (Signac, Seurat, ArchR, chromVAR), Python (Scanpy, epiScanpy, scvi-tools), and Loupe Browser. All analysis parameters, software versions, and intermediate files are documented for reproducibility.
Deliverables
| Deliverable | Description |
|---|---|
| Raw sequencing data | Demultiplexed FASTQ files for ATAC and sample tag libraries |
| Processed peak matrix | Cell-by-peak count matrix in standard formats (h5ad, mtx, h5) |
| Peak annotation file | Genomic annotation of called peaks — promoter, exon, intron, intergenic, TSS-proximal |
| Cell metadata | Cell-level QC metrics, cluster assignments, cell-type annotations, and sample-of-origin labels |
| Clustering report | UMAP/t-SNE visualizations, cluster-specific peak heatmaps, annotation summaries |
| Differential accessibility tables | Differentially accessible peaks between cell types or conditions, with fold changes, adjusted p-values, and associated gene annotations |
| TF motif enrichment report (if ordered) | chromVAR TF motif deviation scores, ranked motif tables, per-cell motif activity matrices |
| Trajectory analysis (if ordered) | Pseudotime ordering, Cicero co-accessibility networks, and branch point analysis |
| Bioinformatics report | Methods documentation, full QC metrics, analysis parameter logs, and publication-ready figures |
| Data archive | All intermediate analysis files, scripts, and processing logs for reproducibility |
Applications
Tumor heterogeneity and epigenetic subclone detection
Resolve intratumoral chromatin accessibility heterogeneity — identify rare epigenetic subclones with distinct regulatory landscapes that drive drug resistance or metastasis. Paired ATAC + RNA from the same single nuclei links chromatin state to transcriptomic output for gene regulatory network inference in cancer.
Immunology and immune cell epigenomics
Map chromatin accessibility landscapes across immune cell subsets — naive, effector, memory, and exhausted states — at single-cell resolution. Identify cell-type-specific enhancers and transcription factor binding dynamics during immune activation, differentiation, and exhaustion. Sample multiplexing enables batch-free comparison of multiple time points, treatment conditions, or donor samples.
Developmental biology and cell fate specification
Trace chromatin accessibility dynamics along developmental trajectories. Identify pioneer transcription factors and cis-regulatory elements that prime gene expression programs before transcriptional activation. Pseudotime analysis of scATAC-seq data reveals the epigenetic sequence of cell fate commitment.
Neuroscience and brain cell atlas
Profile chromatin regulatory landscapes in neuronal and glial subtypes from complex brain regions. scATAC-seq resolves cell-type-specific enhancers linked to neurological disease risk variants identified in GWAS. Nuclei-based protocols are compatible with frozen brain tissue banks — see snRNA Sequencing Services for matched transcriptomic profiling.
Drug development and target discovery
Identify cell-type-specific regulatory elements as therapeutic targets. Evaluate drug-induced chromatin remodeling at single-cell resolution — determine whether a compound affects all cells uniformly or targets specific subpopulations. TF motif analysis reveals which upstream regulators drive drug response or resistance.
Spatial epigenomics integration
Single-cell ATAC-seq provides the cellular-resolution chromatin accessibility reference for spatial epigenomic data integration. Paired scATAC-seq and spatial ATAC-seq from matched specimens enable spatially resolved mapping of cell-type-specific regulatory elements. See our Spatial Epigenomics services for matched spatial epigenomic profiling.
Case Study: Disease-Critical Brain Cell Types Identified by Single-Cell Chromatin Accessibility and Transcriptome Profiling
Background
Genome-wide association studies (GWAS) have identified thousands of noncoding genetic variants associated with neurological and psychiatric diseases. Because these variants often reside in cell-type-specific regulatory elements, linking them to the causal cell types and biological mechanisms requires profiling chromatin accessibility at single-cell resolution across brain development.
Methods
Researchers profiled chromatin accessibility and gene expression from human fetal and adult brain specimens using single-cell ATAC-seq and single-cell RNA-seq. Multi-omics integration linked open chromatin regions to their target genes and to GWAS-identified disease variants. TF motif enrichment analysis identified transcription factors enriched at disease-associated regulatory elements.
Results
The integrated analysis revealed that disease-associated genetic variants were highly enriched in cell-type-specific open chromatin regions — not in broadly accessible promoters, but in distal enhancers active in specific neuronal and glial populations. Fetal brain cell types showed distinct disease enrichments from adult cell types: neurodevelopmental disorder variants were enriched in fetal neuronal progenitors, while neurodegenerative disease variants mapped preferentially to adult microglia and astrocyte regulatory elements.
Conclusion
Single-cell ATAC-seq, integrated with single-cell RNA-seq, resolves cell-type-specific regulatory programs that bulk approaches cannot detect. By mapping disease-associated noncoding variants to the specific cell types and regulatory elements in which they function, scATAC-seq provides a mechanistic bridge from GWAS to disease biology — informing target discovery and cell-type-directed therapeutic strategies.
Reference: Kim S, Truong B, Jagadeesh K, et al. Leveraging single-cell ATAC-seq and RNA-seq to identify disease-critical fetal and adult brain cell types. Nature Communications. 2024;15:563.
Adapted from Kim et al. (2024) Nature Communications.
BD Rhapsody scATAC-seq vs. 10x Genomics scATAC-seq — Platform Comparison
Both BD Rhapsody and 10x Genomics offer single-cell ATAC-seq with paired multi-omics capability. The choice between platforms depends primarily on your study design structure.
| Feature | BD Rhapsody scATAC-seq | 10x Genomics scATAC-seq |
|---|---|---|
| Capture mechanism | Gravity-based microwell settling | Pressure-based droplet encapsulation |
| Sample multiplexing | Up to 6 samples per lane via nuclear antibody-oligo tags | Not available (one sample per lane) |
| Batch effect control | Inherent — all samples processed in a single reaction | Requires computational batch correction (Harmony, scVI) |
| Nuclei gentleness | Higher — no shear stress from microfluidics | Lower — nuclei pass through microfluidic channels under pressure |
| Cell throughput range | 500–50,000 nuclei per lane | 500–10,000 nuclei per sample (targeted) |
| Multi-omics | ATAC + mRNA (WTA) from same nuclei | ATAC + mRNA (Single Cell Multiome ATAC + Gene Expression) |
| Cost per sample | Lower (shared cartridge runs via multiplexing) | Higher (dedicated lane per sample) |
| Bioinformatics pipeline | BD Single-Cell Multiomics Pipeline v3.0 | Cell Ranger ARC |
| Best for | Multi-condition comparisons, time series, dose-response studies, fragile cell types | Standard single-sample scATAC-seq, deep characterization of one condition |
When to choose BD Rhapsody: Your study design involves multiple samples, conditions, or time points where batch effects would confound biological interpretation — sample multiplexing eliminates this problem at the experimental level rather than relying on computational correction.
When to choose 10x Genomics: Your study requires deep characterization of a small number of samples, or your experimental design does not require cross-sample comparison, and the broader 10x software ecosystem (Cell Ranger, Loupe Browser) is preferred.
Both platforms are available at CD Genomics. For 10x-based scATAC-seq, see Single-Cell ATAC Sequencing Services. For paired ATAC + RNA multi-omics, see Single-Cell ATAC + RNA-seq.
Frequently Asked Questions (FAQ)
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. Nature 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. Nature Biotechnology. 2019;37(8):925–936. DOI: 10.1038/s41587-019-0206-z.
- Granja JM, Corces MR, Pierce SE, et al. ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nature Genetics. 2021;53(3):403–411. DOI: 10.1038/s41588-021-00790-6.
- Schep AN, Wu B, Buenrostro JD, Greenleaf WJ. chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data. Nature Methods. 2017;14(10):975–978. DOI: 10.1038/nmeth.4401.
- Kim S, Truong B, Jagadeesh K, et al. Leveraging single-cell ATAC-seq and RNA-seq to identify disease-critical fetal and adult brain cell types. Nature Communications. 2024;15:563. DOI: 10.1038/s41467-024-44742-0.
