Single-Cell Multi-Epigenomics Sequencing Service
Need to determine how chromatin accessibility, nucleosome organization, and DNA methylation coexist in the same selected cells? CD Genomics provides single-cell multi-epigenomics sequencing as a single-cell epigenomics approach for integrated profiling of chromatin accessibility, nucleosome organization, endogenous DNA methylation, CNV, and ploidy within the same selected cells.
Why CD Genomics for single-cell multi-epigenomics:
- Five readout types — accessibility, nucleosome organization, methylation, CNV, and ploidy from one library
- Feasibility-first planning — review cell source, viability, group design, controls, and expected heterogeneity before submission
- Coverage-aware interpretation — separate observed open or closed states from regions without sufficient information
- Integrated deliverables — connect chromatin state with endogenous methylation and genomic context in accepted cells
What Does Single-Cell Multi-Epigenomics Measure?
This single-cell epigenomics approach integrates multiple molecular readouts within the same cell. Accessible chromatin is labeled in vitro, while bisulfite sequencing distinguishes accessibility-associated GpC methylation from endogenous CpG methylation. The same library can also support nucleosome organization, CNV, and ploidy analysis when data quality and controls are sufficient.
Quick Answer
This approach helps answer, "Which accessibility, nucleosome, and DNA methylation states occur together in the same cells, and how do they vary across stages, lineages, or experimental groups?"
Why the Same Cell Matters
Separate scATAC-seq and methylation experiments can characterize matched populations but cannot directly pair both regulatory layers in one cell. Single-cell multi-epigenomics preserves that pairing for selected cells.
What It Does Not Establish
Missing coverage is not automatically closed chromatin, CNV resolution is coverage dependent, and statistical association does not prove that methylation causes an accessibility change. Orthogonal validation remains important.
Five Readout Types From One Single-Cell Multi-Epigenomics Library
The first five cards show the qualified readout types separately. Same-cell integration shows how those readouts are interpreted together within accepted cells; it is an interpretation step, not a sixth molecular readout.
Chromatin Accessibility
Profile accessibility-associated GpC signal, nucleosome-depleted regions, promoter accessibility, and cell-to-cell chromatin-state variation.
Nucleosome Organization
Evaluate aggregate protection patterns and nucleosome positioning around transcription start sites and other approved genomic features.
DNA Methylation
Summarize endogenous CpG methylation across genomic tiles, promoters, gene bodies, and differential regions.
Copy-Number Variation
Generate binned copy-number profiles, identify supported gains and losses, and group cells by genomic state where coverage permits.
Chromosome Ploidy
Estimate relative ploidy with suitable reference cells or controls and report the evidence supporting each interpretation.
Same-Cell Integration
Connect the five qualified readouts across cells, stages, lineages, or experimental groups without treating missing coverage as a biological state.
How the Workflow Separates Accessibility From Endogenous Methylation
Intact chromatin is exposed to an exogenous GpC methyltransferase. Accessible GpC sites can be labeled, whereas nucleosome-protected DNA is less accessible to the enzyme. After bisulfite conversion and sequencing, GpC methylation is interpreted as an accessibility-associated signal and CpG methylation as the endogenous methylome.
GpC Signal
Indicates in vitro labeling at enzyme-accessible chromatin under the assay conditions.
CpG Signal
Reports endogenous DNA methylation only in genomic regions with sufficient coverage.
Protection Pattern
Supports aggregate nucleosome occupancy and positioning analysis where coverage permits.
Read-Depth Pattern
Supports CNV and ploidy inference at a resolution justified by data quality and controls.
Single-Cell Multi-Epigenomics Workflow and Control Strategy
The study plan is reviewed before processing because this selected-cell workflow is plate-based, relatively low throughput, and sensitive to cell quality. Species, cell source, experimental groups, biological replication, target cell number, ploidy controls, and the primary readout are considered together.
- Feasibility Review
Confirm sample source, groups, replicates, target cell number, controls, and the priority readout.
- Single-Cell Deposition
Prepare a viable suspension and deposit one selected cell into each labeled analytical tube.
- GpC Labeling
Label enzyme-accessible GpC sites while retaining nucleosome protection patterns.
- Bisulfite Library
Convert DNA, construct low-input libraries, and verify library quality.
- Sequencing and QC
Assess mapping, conversion, coverage, and GpC/CpG signals for each cell.
- Integrated Analysis
Report qualified accessibility, nucleosome, methylation, CNV, and ploidy results.
Sample Requirements and Project Planning
The criteria below are initial planning baselines from the supplied technical specification, not universal acceptance guarantees. Final requirements depend on species, cell type, dissociation method, viability, experimental groups, expected heterogeneity, and the number of libraries likely to pass all analysis modules.
| Item | Recommended Requirement | Handling Note |
|---|---|---|
| Input | Fresh, unfixed cells isolated from fresh tissue or culture | Previously frozen or fixed cells are not recommended for the standard workflow. |
| Cell quality | Maintain viability and intact morphology; avoid damaged cells and excess debris | Low-quality cells can reduce usable library yield and distort cell-level comparisons. |
| Collection format | Use 200 µL PCR tubes with 3.5 µL of prepared lysis buffer; carryover buffer no more than 1 µL | Keep the deposited cell at the bottom of the tube and minimize dilution. |
| Cell number | Deposit one selected cell per analytical PCR tube; plan 5–8 single-cell replicates for each cell type as a starting recommendation | Final cell number should reflect heterogeneity, comparison groups, and expected assay attrition. |
| Storage | Briefly centrifuge after deposition, record tube identity, and store at −80°C | Avoid repeated freezing and thawing. |
| Shipping | Package tubes securely and ship on sufficient dry ice | Protect tubes from physical damage and temperature fluctuation. |
Bioinformatics From Single-Cell Libraries to Multi-Epigenomic Features
Analysis retains cell identity, experimental group, biological replicate, and module-specific quality status. Researchers receive coverage and inclusion evidence with the final feature tables so open, closed, and insufficiently covered regions are not conflated.
Library and Cell QC
Report mapping, duplication, conversion performance, covered cytosines, GpC/CpG context, usable genomic coverage, and the reason each cell is retained or excluded.
Chromatin Accessibility
Generate GpC-derived accessibility profiles, nucleosome-depleted regions, promoter-state summaries, cell-to-cell comparisons, and motif enrichment where supported.
Nucleosome Organization
Evaluate aggregate protection and positioning patterns around transcription start sites and approved genomic features, with coverage thresholds reported.
DNA Methylation
Summarize global CpG methylation, genomic tiles, promoters, gene bodies, group differences, and differentially methylated regions.
CNV and Ploidy
Generate binned read-depth profiles, supported gains and losses, genomic-state groupings, and ploidy estimates only when coverage and controls justify inference.
Cross-Layer Integration
Compare accessibility with endogenous methylation within cells, stages, lineages, or experimental groups and prioritize relationships for orthogonal validation.
Results and Deliverables
Deliverables are configured around the approved modules and include module-specific quality status, enabling each accessibility, methylation, nucleosome, CNV, or ploidy result to be traced to supporting coverage.
Sequencing Data
Demultiplexed sequence files for accepted libraries.
Quality Reports
Library and cell-level metrics with inclusion status.
Accessibility Results
Regional matrices, accessible regions, promoter states, and motif results.
Methylation Results
Cell-level methylation matrices, genomic summaries, and differential regions.
Genomic Results
Copy-number profiles and ploidy estimates where technically supported.
Integrated Report
Figures, methods, analysis notes, and cross-layer interpretation.
Research Applications of Single-Cell Multi-Epigenomics
This single-cell multi-epigenomics approach is best suited to focused studies in which the same-cell relationship among regulatory layers matters more than maximum cell throughput. A simpler assay may be preferable when only accessibility, methylation, or CNV is required.
Developmental and Reproductive Biology
Compare selected oocytes, pronuclei, blastomeres, embryonic stem cells, or germ cells to study coordinated accessibility, methylation, nucleosome, CNV, and ploidy changes.
Tumor Heterogeneity
Group cancer cells by supported CNV state and test whether lineages carry distinct accessibility and methylation programs. Candidate mechanisms still require independent validation.
Rare-Cell Epigenomics
Profile selected cells that are difficult to obtain in bulk quantities. Plate-based throughput, biological replication, and expected library attrition should be planned before collection.
How Single-Cell Multi-Epigenomics Compares With Related Epigenomic Assays
Single-cell multi-omics sequencing strategies differ in the molecular layers they capture, throughput, resolution, and sample requirements. Choose the most appropriate approach according to the primary biological question and whether same-cell integration is required.
| Approach | Core Readouts | Best Fit | Main Boundary |
|---|---|---|---|
| Single-Cell Multi-Epigenomics | Accessibility, nucleosome organization, DNA methylation, CNV, ploidy | Same-cell multi-epigenomic relationships in selected cells | Plate-based and low throughput; requires fresh cells |
| Single-cell ATAC-seq | Chromatin accessibility | Higher-throughput regulatory landscape studies | No direct DNA methylation readout |
| Single-cell WGBS | DNA methylation | Genome-wide methylome profiling | No direct accessibility or nucleosome readout |
| Single-cell CNV detection | Copy-number variation | Genomic clone and aneuploidy studies | No direct multi-epigenomic context |
| scTrio-seq | CNV, DNA methylation, transcriptome | Genetic–epigenetic–transcriptional relationships | Does not make nucleosome organization the core output |
Case Study: Resolving Genomic and Epigenomic Heterogeneity in Pancreatic Ductal Adenocarcinoma
This published study illustrates the use of a modified single-cell multiomics workflow for separating genomic status from regulatory state in heterogeneous pancreatic tumor samples.
Fan et al. · Cell Discovery · 2022
Background
Pancreatic ductal adenocarcinoma contains substantial inter- and intratumoral heterogeneity, while stromal-rich tumor samples can obscure epithelial-cell states.
Methods
The investigators used a modified single-cell multiomics workflow to profile CNV, DNA methylation, chromatin accessibility, and transcription in the same individual cells from primary pancreatic tumors and adjacent tissues across 13 patients.
Results
Same-cell genomic information distinguished euploid epithelial cells within tumor lesions from cancer cells. Methylation and accessibility analysis then revealed regulatory differences and supported candidate marker prioritization.
Conclusion
Same-cell multiomics can help separate genomic identity from epigenomic state in heterogeneous tumors. The published modified workflow also included transcriptome data, which is beyond the standard five-readout core described on this service page and would require separate feasibility review.
View DOI →Frequently Asked Questions (FAQ)
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References
- Guo F, Li L, Li J, et al. Single-cell multi-omics sequencing of mouse early embryos and embryonic stem cells. Cell Research. 2017;27(8):967–988. DOI: 10.1038/cr.2017.82.
- Li L, Guo F, Gao Y, et al. Single-cell multi-omics sequencing of human early embryos. Nature Cell Biology. 2018;20(7):847–858. DOI: 10.1038/s41556-018-0123-2.
- Fan X, Lu P, Wang H, et al. Integrated single-cell multiomics analysis reveals novel candidate markers for prognosis in human pancreatic ductal adenocarcinoma. Cell Discovery. 2022;8:13. DOI: 10.1038/s41421-021-00366-y.
- Huang Y, Li L, An G, et al. Single-cell multi-omics sequencing of human spermatogenesis reveals a DNA demethylation event associated with male meiotic recombination. Nature Cell Biology. 2023;25(10):1520–1534. DOI: 10.1038/s41556-023-01232-7.
Review Whether Single-Cell Multi-Epigenomics Fits Your Study
Share the organism, tissue or cell source, dissociation method, viability estimate, biological groups, replicates, target cell number, expected ploidy or CNV state, and primary epigenomic question. We will assess feasibility and determine whether this multi-epigenomics workflow or a more focused assay provides the most direct evidence.