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Cited by 26Cancer Genomics and DiagnosticsGenomics and Phylogenetic StudiesSingle-cell and spatial transcriptomics

A novel RNA sequencing data analysis method for cell line authentication

Open access
Erik Fasterius, Cinzia Raso, Susan A. Kennedy, Nora Rauch, Pär Lundin, Walter Kölch, Mathias Uhlén, Cristina Al‐Khalili Szigyarto·2017·PLoS ONE
DOI 10.1371/journal.pone.0171435PMC PMC5305277
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Abstract

We have developed a novel analysis method that can interrogate the authenticity of biological samples used for generation of transcriptome profiles in public data repositories. The method uses RNA sequencing information to reveal mutations in expressed transcripts and subsequently confirms the identity of analysed cells by comparison with publicly available cell-specific mutational profiles. Cell lines constitute key model systems widely used within cancer research, but their identity needs to be confirmed in order to minimise the influence of cell contaminations and genetic drift on the analysis. Using both public and novel data, we demonstrate the use of RNA-sequencing data analysis for cell line authentication by examining the validity of COLO205, DLD1, HCT15, HCT116, HKE3, HT29 and RKO colorectal cancer cell lines. We successfully authenticate the studied cell lines and validate previous reports indicating that DLD1 and HCT15 are synonymous. We also show that the analysed HKE3 cells harbour an unexpected KRAS-G13D mutation and confirm that this cell line is a genuine KRAS dosage mutant, rather than a true isogenic derivative of HCT116 expressing only the wild type KRAS. This authentication method could be used to revisit the numerous cell line based RNA sequencing experiments available in public data repositories, analyse new experiments where whole genome sequencing is not available, as well as facilitate comparisons of data from different experiments, platforms and laboratories.

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  • RNA-seq expression profiling has been able to correctly identify cell lines and estimate cross-contamination.

    “Expression profiling by RNA-seq has been able to correctly identify cell lines and estimate cross-contamination [127,128].”

    From The history, landscape, and outlook of human cell line authentication and security (2024) · cites this paper as [127]

    The Results section ('SNV comparisons for cell line authentication'; page unavailable) reports high concordance between RNA-seq SNV profiles and known cell-line profiles, supporting identification. It also uses HeLa-specific SNVs to rule out HeLa contamination in the studied lines, but does not estimate the extent of cross-contamination. The method analyzes variants found in RNA-seq data, rather than expression levels. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

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Authors
Fasterius, E., Raso, C., Kennedy, S. A., Rauch, N., Lundin, P., Kölch, W., Uhlén, M., & Szigyarto, C. A.
Affiliations
  • KTH Royal Institute of Technology (SE)
  • University College Dublin (IE)
  • Stockholm University (SE)
  • Science for Life Laboratory (SE)
Corresponding author
Cristina Al‐Khalili Szigyarto
ORCID
Venue
PLoS ONE
Publisher
Public Library of Science (PLoS)
Published
2017-02-13
Volume
12
Issue
2
Pages
e0171435
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0171435
PMC
PMC5305277
OpenAlex
W2588484968
Cited by
26
References
7
Citations by year
2025: 1 · 2024: 2 · 2023: 3 · 2022: 2 · 2021: 1 · 2020: 3 · 2019: 6 · 2018: 8
FWCI
1.56
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Seventh Framework Programme (278568)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
Keywords
cell line authentication, RNA sequencing data analysis, cell line contamination, mutational profiling, colorectal cancer cell lines, KRAS G13D mutation, HCT116 cells, DLD-1 cells, transcriptome profiles
MeSH terms
DNA Mutational Analysis, Humans, Mutation, Colorectal Neoplasms, Reproducibility of Results, Gene Expression Regulation, Neoplastic, Proto-Oncogene Proteins p21(ras), Sequence Analysis, RNA, HT29 Cells, Genetic Predisposition to Disease, Gene Expression Profiling, HCT116 Cells, Cell Line, Tumor, Transcriptome

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