BD Rhapsody™ Single-Cell CUT&Tag Services
Single-cell chromatin profiling of histone modifications and transcription factor binding on the BD Rhapsody microwell platform — use antibody-guided Tn5 tagmentation to map cell-type-specific regulatory landscapes, identify rare epigenetic subpopulations, and eliminate batch effects through sample multiplexing of up to six samples per cartridge run.
Why CD Genomics for BD Rhapsody scCUT&Tag:
- Antibody-guided chromatin targeting — protein A-Tn5 fusion enzyme directs tagmentation to histone marks or transcription factors of interest, yielding high signal-to-noise profiles at single-cell resolution
- Gentle microwell capture — gravity-based cell settling into microwells preserves nuclear integrity for fragile and primary samples that may not tolerate microfluidic shear stress
- Sample multiplexing — up to 6 samples pooled in a single cartridge lane using antibody-oligo nuclear tagging, eliminating inter-batch technical variation across conditions, time points, or donors
- Multi-omics ready — pair scCUT&Tag with scWTA from the same single nuclei for concurrent chromatin state and transcriptome profiling
How BD Rhapsody scCUT&Tag Works
BD Rhapsody single-cell CUT&Tag (Cleavage Under Targets and Tagmentation) profiles histone modifications and transcription factor binding at single-cell resolution using antibody-guided Tn5 tagmentation. Unlike scATAC-seq, which measures general chromatin accessibility, scCUT&Tag uses a target-specific antibody to direct the Tn5 transposase to precise chromatin features — active promoters (H3K4me3), active enhancers (H3K27ac), Polycomb-repressed regions (H3K27me3), gene bodies (H3K36me3), or specific transcription factor binding sites.
The CUT&Tag chemistry uses a protein A-Tn5 (pA-Tn5) fusion protein: protein A binds the Fc region of the target antibody, positioning the Tn5 transposase at antibody-bound chromatin sites. Upon magnesium activation, Tn5 simultaneously fragments the DNA and inserts sequencing adapters ("tagmentation") at these targeted loci. Because tagmentation occurs only where the antibody-pA-Tn5 complex is bound, scCUT&Tag achieves substantially higher signal-to-noise ratios than ChIP-seq while requiring 1,000–100,000-fold fewer cells.
On the BD Rhapsody platform, antibody-stained nuclei are loaded onto the microwell cartridge where gravity-driven settling places single nuclei into individual microwells containing barcoded capture beads. The gentle gravity-based loading avoids the shear stress of pressure-based droplet microfluidics, preserving chromatin integrity in fragile cell types. For multi-sample studies, sample multiplexing via nuclear antibody-oligo tags enables up to six samples to be processed in a single cartridge lane, eliminating batch effects at the experimental level.
| 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 |
| Target types | Histone modifications (H3K27ac, H3K4me3, H3K27me3, H3K4me1, H3K36me3, H3K9me3), transcription factors, chromatin-associated proteins |
| Sample types | Fresh or cryopreserved single-cell suspensions, primary tissues, cultured cells; nuclei-based protocol compatible with frozen tissue |
| Multi-omics capability | Standalone CUT&Tag or paired CUT&Tag + mRNA (WTA) from same nuclei |
| Sequencing | Illumina paired-end 50×50 bp, 25,000–50,000 read pairs per cell recommended |
| Pipeline | Data processing pipeline (bwa-mem2 → sinto → MACS2/SEACR → Signac → chromVAR) |
Service Workflow
- Study Design and Antibody Validation
Target antibodies are qualified by in-solution binding and tagmentation tests to confirm signal specificity before committing full sample cohorts. Nuclei isolation protocols are optimized per sample type and target. Sample multiplexing is configured using BD Nuclear Sample Tag antibodies for multi-sample studies. QC Checkpoint: Antibody specificity and tagmentation efficiency in test nuclei.
- Antibody Staining and pA-Tn5 Binding
Nuclei are permeabilized and incubated with target-specific primary antibody, followed by pA-Tn5 fusion protein binding to the antibody Fc region. For multiplexed runs, nuclei are also labeled with sample tag antibodies. QC Checkpoint: Antibody labeling efficiency and nuclei morphology assessment.
- Tagmentation and Single-Cell Capture
Magnesium-activated tagmentation simultaneously fragments antibody-bound chromatin and inserts sequencing adapters. Tagmented nuclei are loaded onto the BD Rhapsody microwell cartridge, where gravity settling distributes single nuclei into microwells containing barcoded capture beads. QC Checkpoint: Tagmentation efficiency and microwell occupancy rate.
- Library Preparation
Cell-barcoded CUT&Tag 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 — fragment profile varies by target mark; concentration and adapter-dimer levels assessed on Bioanalyzer.
- Sequencing
Libraries are sequenced on Illumina platforms (NovaSeq or NextSeq series) at the recommended depth of 25,000–50,000 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 data analysis pipeline: read quality filtering, bwa-mem2 alignment (MAPQ ≥30), sinto fragment generation, MACS2 or SEACR peak calling, and Signac cell-by-peak matrix construction. Sample demultiplexing is performed for multiplexed runs. QC Checkpoint: Fragment size distribution by target mark, fraction of reads in peaks (FRiP), 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 peak analysis between cell types or conditions, 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; nuclei-based protocol compatible with frozen tissue |
| 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) |
| Target antibody | Validated primary antibody against histone modification or transcription factor of interest; antibody qualification included in project workflow |
| 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 |
Common targets successfully profiled include histone modifications (H3K27ac, H3K4me3, H3K27me3, H3K4me1, H3K36me3, H3K9me3) and transcription factors across a range of tissue types including brain, spleen, lymph node, lung, liver, kidney, tumor biopsies, PBMCs, and sorted cell populations. Antibody suitability is evaluated during project planning — contact our team with your target of interest.
Bioinformatics Analysis
All BD Rhapsody scCUT&Tag projects include a standard bioinformatics pipeline. Analysis scope is matched to your experimental design, target mark, and biological question.
Standard Analysis (Included)
- Raw data QC and fragment-level quality assessment (fragment size distribution, FRiP, TSS enrichment for promoter-associated marks)
- Read alignment, cell barcode demultiplexing, and sample demultiplexing for multiplexed runs
- Peak calling (MACS2 or SEACR) 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 modification signatures
- Differential peak 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
- Peak-gene association and co-accessibility network construction
- Integration with scRNA-seq data for joint regulatory network inference
- Multi-omics integration: paired CUT&Tag + RNA from the same single nuclei
- Multi-target integration: joint analysis of multiple histone marks profiled from parallel samples
- Custom visualization — interactive HTML reports, publication-quality figures
Analysis deliverables are compatible with R (Signac, Seurat, chromVAR), Python (Scanpy, epiScanpy), 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 CUT&Tag 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 peak tables | Differentially enriched 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 |
| Multi-target integration report (if ordered) | Joint analysis of multiple histone marks — regulatory state annotation and combinatorial chromatin state maps |
| Trajectory analysis (if ordered) | Pseudotime ordering, 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
Map histone modification landscapes across intratumoral cell populations — identify epigenetic subclones distinguished by H3K27me3 silencing patterns or H3K27ac enhancer activation signatures that drive drug resistance or metastasis. scCUT&Tag resolves chromatin states that scATAC-seq cannot distinguish, such as poised enhancers and bivalent promoters in cancer stem cells.
Immunology and immune cell epigenomics
Profile histone modification landscapes across immune cell subsets — naive, effector, memory, and exhausted states — at single-cell resolution. H3K4me1+H3K27ac co-profiling distinguishes poised from active enhancers during T cell differentiation. Sample multiplexing enables batch-free comparison of multiple time points, treatment conditions, or donor samples in a single cartridge run.
Developmental biology and cell fate specification
Track histone modification dynamics along developmental trajectories. Resolve bivalent promoter states (H3K4me3+H3K27me3) that mark lineage-primed genes in stem and progenitor cells. Pseudotime analysis of scCUT&Tag data reveals the epigenetic sequence of cell fate commitment at the level of specific histone modifications.
Drug development and epigenetic drug target validation
Evaluate drug-induced chromatin remodeling at single-cell resolution — determine whether an HDAC inhibitor or EZH2 inhibitor affects all cells uniformly or targets specific subpopulations. scCUT&Tag directly measures the histone modification change induced by epigenetic drugs, providing mechanistic evidence for target engagement.
Neuroscience and brain cell atlas
Profile neuron- and glia-specific histone modification patterns from complex brain regions. scCUT&Tag resolves cell-type-specific enhancer and silencing landscapes that link noncoding GWAS variants to neurological disease risk. Nuclei-based protocols are compatible with frozen brain tissue banks — see snRNA Sequencing Services for matched transcriptomic profiling.
Multi-target chromatin state analysis
Profiling multiple histone marks from parallel samples of the same specimen enables combinatorial chromatin state annotation at single-cell resolution — distinguish active promoters (H3K4me3+H3K27ac), active enhancers (H3K4me1+H3K27ac), Polycomb-repressed regions (H3K27me3), and heterochromatin (H3K9me3) — providing a more complete regulatory annotation than any single mark alone. See Spatial Epigenomics for matched spatial epigenomic profiling.
BD Rhapsody scCUT&Tag vs. 10x Genomics scCUT&Tag — Platform Comparison
Both BD Rhapsody and 10x Genomics support single-cell CUT&Tag with antibody-guided chromatin profiling. The choice between platforms depends primarily on your study design structure, sample characteristics, and throughput requirements.
| Feature | BD Rhapsody scCUT&Tag | 10x Genomics scCUT&Tag |
|---|---|---|
| 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 | CUT&Tag + mRNA (WTA) from same nuclei | ATAC + mRNA (Single Cell Multiome ATAC + Gene Expression) |
| Target specificity | Antibody-dependent; target-agnostic for any validated antibody | Antibody-dependent (scCUT&Tag via third-party adaptation) |
| Cost per sample | Lower (shared cartridge runs via multiplexing) | Higher (dedicated lane per sample) |
| Best for | Multi-condition comparisons, time series, dose-response studies, fragile cell types, histone mark profiling | Standard single-sample scCUT&Tag, deep characterization of one condition, 10x software ecosystem users |
When to choose BD Rhapsody: Your study design involves multiple samples, conditions, or time points where batch effects would confound biological interpretation — and sample multiplexing eliminates this problem at the experimental level. The gentle gravity-based capture is also preferred for fragile primary cells or nuclei.
When to choose 10x Genomics: Your study requires deep characterization of a small number of samples, integration with the 10x multiome (ATAC + RNA) ecosystem, or use of the Cell Ranger ARC and Loupe Browser software suite.
Both platforms are available at CD Genomics. For BD Rhapsody chromatin accessibility profiling, see BD Rhapsody Single-Cell ATAC-seq Services. For 10x-based single-cell epigenomic services, see Single-Cell ATAC Sequencing Services. For spatial epigenomic profiling, see Spatial Epigenomics Services.
Frequently Asked Questions (FAQ)
References
- Kaya-Okur HS, Wu SJ, Codomo CA, et al. CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nature Communications. 2019;10:1930. DOI: 10.1038/s41467-019-09982-5.
- Bartosovic M, Kabbe M, Castelo-Branco G. Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues. Nature Biotechnology. 2021;39(7):825–835. DOI: 10.1038/s41587-021-00869-9.
- Wu SJ, Furlan SN, Mihalas AB, et al. Single-cell CUT&Tag analysis of chromatin modifications in differentiation and tumor progression. Nature Biotechnology. 2021;39(7):819–824. DOI: 10.1038/s41587-021-00865-z.
- Meers MP, Llagas G, Janssens DH, et al. Multifactorial profiling of epigenetic landscapes at single-cell resolution using MulTI-Tag. Nature Biotechnology. 2023;41(5):708–716. DOI: 10.1038/s41587-022-01522-9.
- Janssens DH, Otto DJ, Meers MP, et al. Scalable single-cell profiling of chromatin modifications with sciCUT&Tag. Nature Protocols. 2024;19(1):83–112. DOI: 10.1038/s41596-023-00905-9.
