ArticleCell research2016
Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas.
Article in Cell research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 363 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
363 citing papers in PubMed, 2 syntheses or guidelines pooled it, 675 citations in OpenAlex.
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- A single-cell lens into the co-evolution of genotypes and phenotypes in cancer.Nature reviews. Cancer · 2026Review
- mmVelo: a deep generative model for estimating cell state-dependent dynamics across multiple modalities.Bioinformatics (Oxford, England) · 2026Article
- Unraveling Cell of Origin in Breast Cancer via Joint Profiling of Whole Genome and Transcriptome in Individual Cells.Genomics, proteomics & bioinformatics · 2026Article
- CNVeil resolves haplotype-specific copy number and uncovers subclonal architecture hidden from total copy number profiling in single-cell cancer genomes.bioRxiv : the preprint server for biology · 2026Article
- Data-intensive immune network modelling for One Health.Briefings in bioinformatics · 2026Review
- A systematic benchmarking framework and dual-view optimization strategy for single-cell DNA methylation imputation.Briefings in bioinformatics · 2026Article
- Inferring tumor absolute copy number and clonal substructure from single-cell chromatin accessibility.Briefings in bioinformatics · 2026Article
- DNA methylation heterogeneity in complex tumor microenvironment: Quantitative methods, influencing factors, and clinical implications.Genes & diseases · 2026Review
- Single-bacterium metabolome revealing heterogeneous cellular states in bacterial populations.Nature communications · 2026Article
- Decoding the CAF-TAM axis: multi-omics dissection and therapeutic targeting of stromal-immune crosstalk in the tumor microenvironment.Cell death & disease · 2026Review
- AutoGERN: single-cell RNA-seq gene regulatory network inference via explicit link modeling and adaptive architectures.Bioinformatics (Oxford, England) · 2026Article
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- Single-Cell Triomics Analysis of Tumor Cells Infiltrating Patient-Derived Breast Cancer Scaffolds.The American journal of pathology · 2026Article
- Integrative Multi-Omics Analysis Identifies NUP205 as a Candidate Prognostic Biomarker in Liver Hepatocellular Carcinoma.International journal of molecular sciences · 2026Article
- mist: a hierarchical Bayesian framework for detecting differential DNA methylation dynamics in single-cell data.Nature communications · 2026Article
- Exploring single-cell and multi-omics technologies and their role in unraveling tumor heterogeneity of hepatocellular carcinoma.Journal of liver cancer · 2026Review
- SCITO-seq2: ultra-high-throughput single-cell transcriptome and epitope sequencing.Genome biology · 2026Article
- Single-Cell and Spatial Multi-Omics in Small-Intestinal Homeostasis and Ischemia/Reperfusion Injury: Current Advances, Applications, and Future Perspectives.Journal of inflammation research · 2026Review
303 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single-cell genome, DNA methylome, and transcriptome sequencing methods have been separately developed. However, to accurately analyze the mechanism by which transcriptome, genome and DNA methylome regulate each other, these omic methods need to be performed in the same single cell. Here we demonstrate a single-cell triple omics sequencing technique, scTrio-seq, that can be used to simultaneously analyze the genomic copy-number variations (CNVs), DNA methylome, and transcriptome of an individual mammalian cell. We show that large-scale CNVs cause proportional changes in RNA expression of genes within the gained or lost genomic regions, whereas these CNVs generally do not affect DNA methylation in these regions. Furthermore, we applied scTrio-seq to 25 single cancer cells derived from a human hepatocellular carcinoma tissue sample. We identified two subpopulations within these cells based on CNVs, DNA methylome, or transcriptome of individual cells. Our work offers a new avenue of dissecting the complex contribution of genomic and epigenomic heterogeneities to the transcriptomic heterogeneity within a population of cells.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.