Same-Cell Genome–Epigenome–Transcriptome Profiling
scTrio-seq Single-Cell Triple-Omics Sequencing Service
Need to determine whether a copy-number lineage, DNA methylation pattern, and gene-expression program occur in the same individual cells? CD Genomics' scTrio-seq service combines single-cell isolation, physical RNA/DNA separation, matched library preparation, sequencing, cell-level quality control, and clone-aware integration to profile genomic CNVs, the DNA methylome, and the transcriptome from the same cells.
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Matched Triple Readouts
Retain one cell identity across CNV, methylome, and transcriptome data.
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Feasibility-First Planning
Review cell source, viability, group design, controls, and expected clone frequency before submission.
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Cell-Level Quality Control
Qualify the DNA and RNA results separately before cross-omics interpretation.
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Clone-Aware Deliverables
Connect candidate genetic lineages with methylation and expression programs.
Overview
What Does scTrio-seq Measure?
scTrio-seq is a plate-based single-cell multi-omics method. Gentle lysis separates the RNA-containing cytoplasmic fraction from the nucleus, after which transcriptome and bisulfite-based DNA libraries are prepared with the original cell identity retained. This allows CNV, DNA methylation, and gene expression to be compared within the same accepted cells.
Quick Answer
scTrio-seq answers, "Which CNV-defined cell lineages carry particular DNA methylation states and transcriptional programs?" It is intended for same-cell association, not three unrelated assays performed on matched populations.
Why the Same Cell Matters
Bulk or separately profiled single-cell datasets can suggest cross-omics relationships, but they cannot assign all three layers to one cell. Same-cell pairing helps separate lineage-associated changes from variation among cells within a lineage.
What It Does Not Establish
The assay does not provide high-coverage whole-genome variant discovery, exact CNV breakpoints, or causal proof that methylation controls expression. Those questions require dedicated genomic or functional validation.
Principle and Scope
How scTrio-seq Generates Three Matched Data Layers
After a single cell is deposited into lysis buffer, the cytoplasmic RNA and nuclear DNA fractions are processed separately. The RNA fraction supports transcriptome profiling, while the bisulfite-based DNA fraction provides methylation measurements and read-depth information for CNV analysis.
1 Genetic Layer
Copy-Number Variation
- Genome-wide binned copy-number profiles
- Candidate gains and losses
- Genetic lineage and sublineage structure
- Clone-associated genomic regions
2 Epigenetic Layer
DNA Methylome
- Global and regional CpG methylation
- Differentially methylated regions
- Promoter and gene-body patterns
- Lineage-associated methylation states
3 Functional Layer
Transcriptome
- Gene expression profiles
- Cell-state and pathway programs
- Lineage-associated expression
- Methylation–expression relationships
Workflow and Controls
scTrio-seq Workflow and Control Strategy
The laboratory and bioinformatics plan is set before processing. Species, cell source, experimental groups, biological replication, expected clone frequency, reference quality, and primary readout are reviewed together so that cell numbers and sequencing allocation support the same research question.
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Feasibility Review
Confirm sample source, groups, replicates, target cells, and expected clone frequency. -
Single-Cell Deposition
Isolate one viable cell per labeled tube and include suitable process controls. -
Fraction Separation
Recover cytoplasmic RNA while preserving the matched nucleus. -
Matched Libraries
Build transcriptome and bisulfite DNA libraries with a shared cell identifier. -
Sequencing and QC
Assess mapping, conversion, DNA-window detection, transcript detection, and usable coverage. -
Clone-Aware Integration
Infer CNV lineages and compare methylation and expression in accepted cells.
Sample Requirements
Sample Requirements and Project Planning
The values below are initial planning baselines, not universal acceptance guarantees. Final requirements depend on cell type, viability, isolation method, experimental groups, expected subclone frequency, and the number of cells likely to remain usable after both DNA and RNA quality control.
| Item | Recommended Requirement | Why It Matters |
|---|---|---|
| Input | Fresh, unfixed mammalian cells isolated from cultured cells or freshly collected tissue | Fixation and uncontrolled freeze–thaw can compromise fraction separation and nucleic-acid recovery. |
| Cell condition | Intact morphology and high viability; minimize debris and damaged cells | Each reaction depends on the nucleic acids present in one cell. |
| Collection format | One isolated cell per 200 µL PCR tube containing 4 µL of prepared lysis buffer; carryover buffer no more than 1 µL | Excess liquid can dilute or interfere with the low-volume reaction. |
| Pilot and cohort size | A technical pilot may begin with 3–5 successfully deposited cells per cell type; biological studies generally require substantially more cells and independent biological replicates | A small pilot tests recovery and library feasibility. It is not sufficient by itself for robust clone-frequency or group-level inference. |
| Storage and shipping | Freeze promptly at −80°C, ship on dry ice, and avoid thawing after freezing | Temperature stability protects both RNA and genomic material. |
| Handling window | Keep the operation cold and transfer deposited cells to −80°C as soon as possible, preferably within 1 hour | Short handling reduces RNA degradation and cell damage. |
| Optional reference data | Matched bulk gDNA, RRBS/WGBS, or bulk/pooled RNA-seq data, when available | Reference datasets can support CNV, methylation-concordance, and sensitivity checks; they are not required for every project. |
Bioinformatics
Bioinformatics From Matched DNA/RNA Libraries to Clone-Aware Results
Analysis retains the cell identifier, DNA library, RNA library, experimental group, and biological replicate throughout processing. Researchers receive the evidence behind cell inclusion and lineage assignment rather than only a final integrated figure.
Matched-Cell Quality Control
Review mapping, library complexity, conversion, DNA-segment/window detection, detected transcripts, usable coverage, and the reason each cell is retained or excluded.
CNV and Lineage Analysis
Generate binned copy-number profiles, identify supported gains and losses, cluster candidate lineages, and compare with matched bulk gDNA profiles when provided.
DNA Methylation Analysis
Summarize CpG methylation across tiles, promoters, and gene bodies, identify differential regions, and assess concordance with RRBS/WGBS references when available.
Transcriptome Analysis
Report detected-gene sensitivity, quantify expression, inspect cell concordance, annotate cell states, compare approved groups, and perform supported enrichment.
Same-Cell Integration
Compare methylation and expression within CNV-defined lineages so between-lineage differences are separated from heterogeneity inside a lineage.
Candidate Prioritization
Rank clone-associated regions, methylation–expression relationships, and pathways for orthogonal genomic, methylation, expression, or functional validation.
Results and Deliverables
Results and Deliverables
Each molecular layer is delivered separately before the integrated report so CNV, methylation, and expression findings can be traced back to cell-level quality and coverage.
Sequencing Data
Demultiplexed sequence files for accepted libraries.
Quality Reports
Cell- and library-level metrics, DNA-window detection, transcript sensitivity, and inclusion status.
Genome Results
Binned copy-number matrices, lineage assignments, visualizations, and optional bulk-reference comparison.
Methylome Results
Methylation matrices, regional summaries, and differential results.
Transcriptome Results
Expression matrices, cell-state results, markers, and pathway summaries.
Integrated Report
Same-cell relationships, figures, methods, and interpretation notes.
Applications
Research Applications of scTrio-seq
The service is most useful when the study must connect genetic lineage with epigenetic and transcriptional state. It is not automatically the best choice when only one molecular layer is required.
Tumor Evolution and Treatment
Track CNV-defined cancer-cell lineages across primary, metastatic, regional, or treatment groups and compare their methylation and expression programs.
Developmental and Lineage Biology
Study selected developmental cells when genomic state, DNA methylation, and transcription must remain linked at single-cell resolution.
Selected Rare-Cell Studies
Profile manually selected or sorted low-abundance cells while planning for plate-based throughput and expected assay attrition.
Method Comparison
How scTrio-seq Compares With Related Single-Cell Assays
The appropriate assay is determined by the primary question, required molecular layers, available cell number, and resolution. A three-layer method adds value only when the same-cell relationship is essential.
| Approach | Core Readouts | Best Fit | Main Boundary |
|---|---|---|---|
| scTrio-seq | CNV, DNA methylation, transcriptome in the same cell | Genetic–epigenetic–transcriptional heterogeneity | Low throughput; CNV-focused genomic readout |
| Single-cell ATAC + RNA-seq | Chromatin accessibility and transcriptome | Regulatory element–expression relationships at larger cell scale | Does not directly profile DNA methylation |
| Single-cell WGBS | DNA methylation | Methylome-first studies | No same-cell transcriptome |
| Single-cell WGS | Genome variants and CNV | Genomic heterogeneity and variant discovery | No direct methylome or transcriptome |
Case Study
Case Study: Using scTrio-seq2 Data to Improve DNA–RNA Cell Matching
This published analysis illustrates how CNV information can support integration of genomic and transcriptional profiles after the primary scTrio-seq2 dataset has passed cell-level quality control.
Edrisi et al. · Nature Communications · 2023
Background
Single-cell DNA and RNA profiles may have different signal structures, making it difficult to match or integrate cells reliably across modalities.
Methods
The study evaluated MaCroDNA with a colorectal cancer dataset generated by scTrio-seq2 and compared DNA–RNA integration performance with other approaches.
Results
The analysis demonstrated that copy-number information can anchor cross-modality matching and improve integration of genomic and transcriptional profiles.
Conclusion
Triple-omics datasets can support both direct same-cell interpretation and downstream clone-aware integration. The example also shows why copy-number signal, cell-level data quality, and model assumptions must be reported with the biological conclusion.
View DOI →FAQ
Frequently Asked Questions
Related Services and Solutions
Choose the Assay Around the Primary Evidence Needed
Scientific Evidence
Reference
- Hou Y, Guo H, Cao C, et al. Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas. Cell Research. 2016;26(3):304–319. https://doi.org/10.1038/cr.2016.23
- Bian S, Hou Y, Zhou X, et al. Single-cell multiomics sequencing and analyses of human colorectal cancer. Science. 2018;362(6418):1060–1063. https://doi.org/10.1126/science.aao3791
- Edrisi M, Huang X, Ogilvie HA, Nakhleh L. Accurate integration of single-cell DNA and RNA for analyzing intratumor heterogeneity using MaCroDNA. Nature Communications. 2023;14:8262. https://doi.org/10.1038/s41467-023-44014-3
- Lim J, Park C, Kim M, et al. Advances in single-cell omics and multiomics for high-resolution molecular profiling. Experimental & Molecular Medicine. 2024;56(3):515–526. https://doi.org/10.1038/s12276-024-01186-2
Study Design Support
Review Whether scTrio-seq Fits Your Study
Share the organism, cell source, isolation method, viability estimate, biological groups, replicates, expected cell number, suspected clone frequency, and primary hypothesis. We will assess sample feasibility and whether scTrio-seq or a more focused single-cell assay provides the most direct evidence.