CeL-ID: cell line identification using RNA-seq data
Open accessAbstract
BACKGROUND: Cell lines form the cornerstone of cell-based experimentation studies into understanding the underlying mechanisms of normal and disease biology including cancer. However, it is commonly acknowledged that contamination of cell lines is a prevalent problem affecting biomedical science and available methods for cell line authentication suffer from limited access as well as being too daunting and time-consuming for many researchers. Therefore, a new and cost effective approach for authentication and quality control of cell lines is needed. RESULTS: We have developed a new RNA-seq based approach named CeL-ID for cell line authentication. CeL-ID uses RNA-seq data to identify variants and compare with variant profiles of other cell lines. RNA-seq data for 934 CCLE cell lines downloaded from NCI GDC were used to generate cell line specific variant profiles and pair-wise correlations were calculated using frequencies and depth of coverage values of all the variants. Comparative analysis of variant profiles revealed that variant profiles differ significantly from cell line to cell line whereas identical, synonymous and derivative cell lines share high variant identity and are highly correlated (ρ > 0.9). Our benchmarking studies revealed that CeL-ID method can identify a cell line with high accuracy and can be a valuable tool of cell line authentication in biomedical science. Finally, CeL-ID estimates the possible cross contamination using linear mixture model if no perfect match was detected. CONCLUSIONS: In this study, we show the utility of an RNA-seq based approach for cell line authentication. Our comparative analysis of variant profiles derived from RNA-seq data revealed that variant profiles of each cell line are distinct and overall share low variant identity with other cell lines whereas identical or synonymous cell lines show significantly high variant identity and hence variant profiles can be used as a discriminatory/identifying feature in cell authentication model.
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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 [128]
The Background reports accurate cell-line identification from RNA-seq data, and the Results section on sample mix-up and contamination estimation reports estimates from simulated mixtures. But the method uses RNA-seq-derived variant profiles and frequencies, not expression profiles; the paper also says contamination-estimation accuracy was not tested on a real mixed dataset. (LLM, conf=0.87)
Experimental — accuracy not yet measuredSTR profiling is the gold standard and authentication standard for cell line authentication.
“STR profiling remains the gold standard for cell line authentication.”
From The history, landscape, and outlook of human cell line authentication and security (2024) · cites this paper as [128]
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- Authors
- Mohammad, T. A., Tsai, Y. S., Ameer, S., Chen, H.-I. H., Chiu, Y.-C., & Chen, Y.
- Affiliations
- The University of Texas at San Antonio Health Science Center (US)
- Corresponding author
- Chen, Yidong
- Venue
- BMC Genomics
- Published
- 2019-02
- Volume
- 20
- Issue
- S1
- Pages
- 81-81
- ISSN
- 1471-2164 (ISSN-L)
- PMC
- PMC6360649
- OpenAlex
- W2913196556
- Cited by
- 19
- References
- 29
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 1 · 2023: 3 · 2022: 2 · 2021: 3 · 2020: 5 · 2019: 1
- FWCI
- 0.91
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Genomics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Cancer Prevention and Research Institute of Texas (RP160732, RP120685-C2)
- National Institutes of Health
- National Cancer Institute (P30CA054174, P20CA165589)
- National Center for Research Resources (UL1RR025767)
- Topics
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Keywords
- cell line authentication, cell line contamination, somatic mutations, RNA-seq, linear mixing model, cell line identification, quality control
- MeSH terms
- Algorithms, Cell Line, Humans, Mutation, Models, Statistical, Databases, Factual, Sequence Analysis, RNA, Polymorphism, Single Nucleotide, Gene Expression Profiling, Cell Line, Tumor, INDEL Mutation, DNA Barcoding, Taxonomic, High-Throughput Nucleotide Sequencing
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