Single-Cell DNA Methylation and Transcriptome Sequencing Service
Need to determine whether epigenetic variation and gene-expression states occur in the same cells? Single-cell DNA methylation and RNA sequencing generates paired methylome and transcriptome readouts linked to the same cell or nucleus, supporting direct analysis of cell-resolved methylation-expression relationships.
Use paired profiling to:
- Connect DNA methylation patterns with transcriptional states without relying only on cross-sample matching
- Resolve cell populations whose epigenetic differences are hidden by bulk averages
- Test promoter, gene-body, or regulatory-region associations with gene expression
- Build one analysis framework for cell identity, methylation variation, and transcriptional response
Figure 1. Paired methylome and transcriptome readouts retain a shared cell identity.
What Is Single-Cell DNA Methylation and RNA Sequencing?
Single-cell DNA methylation and RNA sequencing is a multi-omics approach that measures DNA methylation and gene expression from the same cell or nucleus. Because the two readouts share cell identity, researchers can examine epigenetic and transcriptional variation together instead of inferring relationships between separate cell populations.
DNA Methylation Readout
- Cell-linked methylation calls and coverage summaries
- Regional profiles for promoters, gene bodies, or other annotated features
- Cell- or cluster-level methylation patterns
- Differential methylation analysis when data support the comparison
Transcriptome Readout
- Cell-by-gene expression matrix
- Dimensionality reduction and clustering
- Cell-type or cell-state annotation
- Differential expression and pathway analysis
The assay reports associations rather than automatic causation. A methylation change near a gene may accompany expression change, remain uncoupled, or reflect another regulatory process; paired data help distinguish these possibilities at cell resolution.
Why Pair Methylation With the Transcriptome?
Separate methylation and RNA experiments can identify broad trends, but they do not show whether both molecular states belong to the same cell. Same-cell pairing preserves that relationship, so heterogeneous populations can be analyzed without assuming that two independently measured clusters are equivalent.
| Research Question | Evidence From Paired Readouts | Interpretive Value |
|---|---|---|
| Which cell states differ epigenetically? | Cell annotation combined with regional methylation profiles | Places methylation heterogeneity within defined transcriptional populations |
| Does promoter methylation track expression? | Promoter-level methylation paired with gene-level expression | Tests the relationship within the same cells instead of across separate assays |
| How do cell states change after perturbation? | Joint methylation and expression comparisons across study groups | Separates coordinated remodeling from changes limited to one molecular layer |
| Are rare populations being averaged away? | Cell-resolved clustering and methylation summaries | Identifies subgroup-specific patterns that bulk profiles may dilute |
For methylation-only discovery, our Single-Cell Whole-Genome Bisulfite Sequencing Service may be sufficient. Paired profiling is most useful when the biological decision depends on the relationship between methylation and expression.
Technology Overview
The workflow retains a cell-specific molecular identity while generating separate RNA and DNA methylation libraries. This design allows each library to receive modality-specific processing and quality review, while matched cell labels support downstream integration.
- Cell or Nucleus Isolation
Qualified cells or nuclei are prepared for single-unit separation, reducing mixed-cell signals before molecular processing.
- Cell-Specific Labeling
Molecular labels preserve cell identity across the two readouts, enabling matched analysis after sequencing.
- Paired Library Construction
RNA-derived material and genomic DNA follow separate library routes, so expression and methylation evidence can be optimized and assessed independently.
- Methylation Conversion
A validated project-specific conversion workflow distinguishes methylation-sensitive sequence states. The exact chemistry and its ability to distinguish modified cytosine forms are confirmed before project start.
- Joint Data Reconstruction
Quality-filtered methylation and expression profiles are matched by cell identity, creating a paired dataset for integrated interpretation.
Technology selection remains platform-neutral until the study design and sample route are confirmed. This avoids forcing one workflow onto samples that differ in tissue origin, preservation, or cell-versus-nucleus requirements.
Service Workflow and Quality Review
Figure 2. End-to-end workflow with sample, modality, and paired-cell quality checkpoints.
- Study Design
We define the biological comparison, required molecular relationship, sample route, controls, and intended analyses.
- Sample Feasibility Review
Cell or nucleus quality, concentration, debris, aggregation, preservation history, and reference compatibility are assessed.
- Single-Cell Molecular Processing
Qualified material receives cell-specific labeling and coordinated processing for paired molecular readouts.
- Dual-Library Construction
RNA and DNA methylation libraries are built separately, allowing each modality to undergo appropriate library QC.
- Sequencing and Primary QC
Sequencing configuration is set around study objectives, while mapping, conversion, coverage, and expression metrics are reviewed separately.
- Cell Matching and Joint Analysis
Cells passing modality-specific and paired-data filters enter clustering, annotation, differential, and association analyses.
- Scientific Review and Delivery
Results are delivered with methods, parameters, QC findings, limitations, and interpretable figures and tables.
Sample Requirements
Sample acceptance depends on the validated cell or nucleus workflow selected for the project. We review material type, species, preservation, cell or nucleus quality, and experimental design before issuing collection and shipping instructions.
| Planning Item | Feasibility Review | Why It Matters |
|---|---|---|
| Single-cell suspension | Viability, concentration, debris, aggregation, and preparation history | Supports reliable partitioning and reduces avoidable cell loss or mixed-cell events |
| Isolated nuclei | Nuclear integrity, background material, concentration, and tissue origin | May provide a practical route for difficult or preserved tissues when validated |
| Tissue, organoid, or cultured cells | Dissociation or nuclei-isolation plan and expected cellular composition | Aligns preparation with the populations the study is intended to retain |
| Species and reference | Reference-genome and annotation compatibility | Determines whether both expression and methylation readouts can be interpreted reliably |
| Groups and replicates | Biological groups, covariates, controls, and batch structure | Separates biological differences from sample and processing variation |
| Collection and shipping | Instructions issued after the material and workflow are approved | Protects cell or nucleus quality and preserves the intended molecular signals |
Exact input amounts, cell or nucleus targets, preservation conditions, and sequencing depth are project-specific. They are confirmed in the technical plan rather than presented as universal thresholds.
Bioinformatics and Joint Analysis
The analysis keeps RNA and methylation quality decisions visible before integration. This prevents a strong readout from one modality from masking poor evidence in the other and makes the paired-cell set explicit.
Core Analysis
- Raw-read and library-level quality control
- RNA alignment, gene quantification, cell filtering, and normalization
- Methylation alignment, conversion review, calling, and coverage summaries
- Dimensionality reduction, clustering, and cell annotation
- Differential expression and region-level methylation comparisons when supported
- Matched-cell methylation and expression data integration
Optional Interpretation
- Promoter, gene-body, and regulatory-region association analysis
- Cell-state-specific methylation-expression relationships
- Trajectory or state-transition analysis
- Pathway and gene-set enrichment
- Regulatory-network analysis
- Copy-number inference when the data and study design support it
Figure 3. Illustrative paired-cell clustering, regional methylation, expression, and association outputs.
Analysis parameters, references, software versions, filtering decisions, and paired-cell retention are documented. Projects needing custom transcriptome interpretation can also use our Single-Cell RNA-Seq Data Analysis Service.
Project Deliverables
The final package is confirmed during project design. A typical paired multi-omics delivery can include the following data types, subject to the selected workflow and analysis scope.
| Deliverable | Content |
|---|---|
| Raw sequencing data | Demultiplexed sequence files for the RNA and methylation libraries |
| RNA expression data | Cell-by-gene matrices, cell metadata, and expression QC summaries |
| Methylation data | Cell-linked methylation calls or regional summaries with coverage and conversion QC |
| Cell annotations | Clusters, markers, cell-type or cell-state labels, and supporting plots |
| Integrated results | Matched-cell embeddings and methylation-expression association tables |
| Figures and report | Publication-oriented visualizations, methods, QC, findings, and limitation notes |
Research Applications
Paired methylome-transcriptome profiling measures DNA methylation and gene expression in the same cell, helping you connect epigenetic variation with cell identity and transcriptional state. It is most valuable when separate methylation and RNA datasets cannot resolve whether both signals originate from the same cellular population.
Cancer epigenetics and tumor heterogeneity
For tumor and cancer-model studies, this service links methylation states with the expression programs of malignant, immune, and stromal populations. It helps you identify epigenetically distinct subpopulations, determine whether transcriptional plasticity is associated with promoter or regulatory-region methylation, and prioritize candidate mechanisms underlying tumor evolution, lineage switching, or treatment resistance.
Development and lineage differentiation
In developmental and differentiation studies, paired measurements show how DNA methylation remodeling accompanies transcriptional transitions in individual cells. This helps you reconstruct cell-state trajectories, compare lineage branches, and identify regulatory regions whose methylation changes are associated with activation or silencing of lineage-specific gene programs.
Stem cell state and reprogramming
For pluripotency, reprogramming, or directed-differentiation projects, this service provides matched methylation and expression profiles for each analyzed cell. It helps you distinguish stable, transitional, and incompletely reprogrammed states and examine whether heterogeneous distal or gene-associated methylation patterns correspond to key pluripotency and differentiation programs.
Aging and cellular heterogeneity
In aging studies, the paired readout allows methylation-based age variation to be interpreted together with cell identity and functional gene expression. It helps you determine whether epigenetic heterogeneity is concentrated in particular blood or tissue-cell populations and identify transcriptional programs associated with cells that appear epigenetically younger or older than their peers.
Perturbation and epigenetic-response research
For genetic, pharmacologic, environmental, or time-course experiments, this service tracks methylation and transcriptional responses within the same cellular populations. It helps you separate changes in cell composition from coordinated or discordant molecular responses, identify persistent epigenetic states after treatment, and prioritize methylation–expression relationships for functional validation.
Interpretation boundary: Same-cell profiling identifies associations between methylation and gene expression, but it does not by itself establish that a methylation change directly causes a transcriptional change. Perturbation experiments or orthogonal assays are recommended when causality is central to the study.
Choose the Assay Around the Regulatory Question
| Approach | Epigenetic Readout | RNA Readout | Pairing Level |
|---|---|---|---|
| Methylation + RNA | DNA methylation | Gene expression | Same cell or nucleus |
| Single-cell methylation only | DNA methylation | None | Not applicable |
| ATAC + RNA | Chromatin accessibility | Gene expression | Same nucleus |
| Separate methylation and RNA datasets | DNA methylation | Gene expression | Group or computational alignment |
Figure 4. Method selection based on the epigenetic layer and required pairing level.
Best fit: choose methylation plus RNA when the study must test methylation-expression relationships in the same cells. Not the best fit: choose Single-Cell ATAC + RNA Sequencing when regulatory-element accessibility is the primary question, or consider Single-Cell Reduced Representation Bisulfite Sequencing when methylation-focused profiling is sufficient and an RNA readout is unnecessary.
Case Study: Resolving Epigenetic Age Heterogeneity in Mouse Blood
Source: Bonder and colleagues reported a 2024 Nature Communications study using paired single-cell methylome and transcriptome data from mouse peripheral blood. See the peer-reviewed article.
Background: Population-level methylation measurements can mix biological ageing effects with changes in blood-cell composition. The study asked whether epigenetic age varies between individual cells and cell types.
Methods: Researchers applied paired single-cell methylome and transcriptome sequencing to blood cells collected across four mouse ages. RNA profiles supported cell-type annotation, while methylation profiles were used for cell-level age modeling.
Results: The study generated paired data from 1,055 cells, with 823 cells passing both methylation and expression quality filters. The analysis identified broader-than-technical variation in predicted epigenetic age and cell-type-dependent heterogeneity.
Conclusion: The work shows how paired readouts can separate cell identity from methylation variation and support questions that either modality alone would address less directly. These published findings are scientific evidence and are not presented as CD Genomics performance data.
Figure 5. Original conceptual summary of the literature case; not a reproduced paper panel.
Frequently Asked Questions (FAQ)
References
- Angermueller C, Clark SJ, Lee HJ, et al. Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nature Methods. 2016;13:229–232. DOI: 10.1038/nmeth.3728.
- Hu Y, Huang K, An Q, et al. Simultaneous profiling of transcriptome and DNA methylome from a single cell. Genome Biology. 2016;17:88. DOI: 10.1186/s13059-016-0950-z.
- Clark SJ, Argelaguet R, Kapourani CA, et al. scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells. Nature Communications. 2018;9:781. DOI: 10.1038/s41467-018-03149-4.
- Xu X, Zeng X, Lin X, et al. DMF-scMT-seq linking methylome and transcriptome within single cells with digital microfluidics. Science China Chemistry. 2024;67:2070–2078. DOI: 10.1007/s11426-023-1934-2.
- Bonder MJ, Clark SJ, Krueger F, et al. scEpiAge: an age predictor highlighting single-cell ageing heterogeneity in mouse blood. Nature Communications. 2024;15:7567. DOI: 10.1038/s41467-024-51833-5.
This service and the information on this page are intended for Research Use Only. They are not intended for clinical diagnosis, treatment, disease monitoring, therapeutic decision-making, or individual health assessment.