ArticleNAR genomics and bioinformatics2020
Joint single cell DNA-seq and RNA-seq of gastric cancer cell lines reveals rules of
Article in NAR genomics and bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers.
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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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Who cites it
68 citing papers in PubMed.
- Review
- Robust and generalizable CNV detection for single-cell sequencing assays.Nucleic acids research · 2026Article
- Gastric Cancer Organoids: Mechanistic Insights, Drug Discovery, and Translational Advances in Precision Medicine.Journal of gastric cancer · 2026Review
- Benchmarking scRNA-seq Copy Number Inference: A Comprehensive Evaluation and Practitioner's Guide.bioRxiv : the preprint server for biology · 2026Article
- CNAScope: pan-cancer copy number aberration database with functional annotation and interactive visualization.Nucleic acids research · 2026Article
- Single-cell aneuploidy and chromosomal arm imbalances define subclones with divergent transcriptomic phenotypes.NAR genomics and bioinformatics · 2025Article
- Benchmarking scRNA-seq copy number variation callers.Nature communications · 2025Article
- Hybrid BAG-seq: DNA and RNA from the same single nucleus reveals interactions between genomic and transcriptomic landscapes in human tumor samples.Genome biology · 2025Article
- SNPmanifold: detecting single-cell clonality and lineages from single-nucleotide variants using binomial variational autoencoder.Genome biology · 2025Article
- Cancer subclone detection based on DNA copy number in single-cell and spatial omic sequencing data.Nature methods · 2025Article
- ChromoPattern: characterizing chromosomal rearrangement patterns of copy number alteration in heterogeneous tumor cell populations at single-cell resolution.Briefings in bioinformatics · 2025Article
- Harnessing technologies to unravel gastric cancer heterogeneity.Trends in cancer · 2025Review
- CLONEID: A Framework for Longitudinal Integration of Phenotypic and Genotypic Data to Monitor and Steer Subclonal Dynamics.bioRxiv : the preprint server for biology · 2025Article
- CNRein: an evolution-aware deep reinforcement learning algorithm for single-cell DNA copy number calling.Genome biology · 2025Article
- High-resolution detection of copy number alterations in single cells with HiScanner.bioRxiv : the preprint server for biology · 2025Article
- Multimodal hierarchical classification of CITE-seq data delineates immune cell states across lineages and tissues.Cell reports methods · 2025Article
- Characterizing the evolutionary dynamics of cancer proliferation in single-cell clones with SPRINTER.Nature genetics · 2025Article
- Inferring Phenotypes of Copy Number Clones in Cancer Populations Using TreeAlign.Methods in molecular biology (Clifton, N.J.) · 2025Article
- The therapeutic potential of RNA m(6)A in lung cancer.Cell communication and signaling : CCS · 2024Review
- Single-cell multi-omics in the study of digestive system cancers.Computational and structural biotechnology journal · 2024Review
8 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Cancer cell lines are not homogeneous nor are they static in their genetic state and biological properties. Genetic, transcriptional and phenotypic diversity within cell lines contributes to the lack of experimental reproducibility frequently observed in tissue-culture-based studies. While cancer cell line heterogeneity has been generally recognized, there are no studies which quantify the number of clones that coexist within cell lines and their distinguishing characteristics. We used a single-cell DNA sequencing approach to characterize the cellular diversity within nine gastric cancer cell lines and integrated this information with single-cell RNA sequencing. Overall, we sequenced the genomes of 8824 cells, identifying between 2 and 12 clones per cell line. Using the transcriptomes of more than 28 000 single cells from the same cell lines, we independently corroborated 88% of the clonal structure determined from single cell DNA analysis. For one of these cell lines, we identified cell surface markers that distinguished two subpopulations and used flow cytometry to sort these two clones. We identified substantial proportions of replicating cells in each cell line, assigned these cells to subclones detected among the G0/G1 population and used the proportion of replicating cells per subclone as a surrogate of each subclone's growth rate.
Identifiers
What OpenQuestion holds
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.