ArticleScientific reports2018
Analysis of public RNA-sequencing data reveals biological consequences of genetic heterogeneity in cell line populations.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pooled it
- Robustness of Clonogenic Assays as a Biomarker for Cancer Cell Radiosensitivity.International journal of molecular sciences · 2019Pooled it
- Genomic Variability of the HCT116 Cell Line Identified Using Oxford Nanopore Sequencing.International journal of molecular sciences · 2026Article
- Article
- Refined variant calling pipeline on RNA-seq data of breast cancer cell lines without matched-normal samples.BMC research notes · 2025Article
- EGAsubmitter: A software to automate submission of nucleic acid sequencing data to the European Genome-phenome Archive.Frontiers in bioinformatics · 2023Article
- Estimating intraclonal heterogeneity and subpopulation changes from bulk expression profiles in CMap.Life science alliance · 2022Article
- Computational estimation of quality and clinical relevance of cancer cell lines.Molecular systems biology · 2022Review
- Identification of pathological transcription in autosomal dominant polycystic kidney disease epithelia.Scientific reports · 2021Article
- Large-scale labeling and assessment of sex bias in publicly available expression data.BMC bioinformatics · 2021Article
- Authentication, characterization and contamination detection of cell lines, xenografts and organoids by barcode deep NGS sequencing.NAR genomics and bioinformatics · 2020Article
- Single-cell RNA-seq variant analysis for exploration of genetic heterogeneity in cancer.Scientific reports · 2019Article
- Genomic evolution of cancer models: perils and opportunities.Nature reviews. Cancer · 2019Review
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Abstract
Meta-analysis of datasets available in public repositories are used to gather and summarise experiments performed across laboratories, as well as to explore consistency of scientific findings. As data quality and biological equivalency across samples may obscure such analyses and consequently their conclusions, we investigated the comparability of 85 public RNA-seq cell line datasets. Thousands of pairwise comparisons of single nucleotide variants in 139 samples revealed variable genetic heterogeneity of the eight cell line populations analysed as well as variable data quality. The H9 and HCT116 cell lines were found to be remarkably stable across laboratories (with median concordances of 99.2% and 98.5%, respectively), in contrast to the highly variable HeLa cells (89.3%). We show that the genetic heterogeneity encountered greatly affects gene expression between same-cell comparisons, highlighting the importance of interrogating the biological equivalency of samples when comparing experimental datasets. Both the number of differentially expressed genes and the expression levels negatively correlate with the genetic heterogeneity. Finally, we demonstrate how comparing genetically heterogeneous datasets affect gene expression analyses and that high dissimilarity between same-cell datasets alters the expression of more than 300 cancer-related genes, which are often the focus of studies using cell lines.
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