Abstract
BackgroundCell 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.ResultsWe 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.ConclusionsIn 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.
Highlights
Cell lines form the cornerstone of cell-based experimentation studies into understanding the underlying mechanisms of normal and disease biology including cancer
We identify variants in each cell lines using RNA-seq data followed by pairwise variant profile comparison between cell lines using frequencies and depth of coverage (DP) values
We choose to explore the utility of RNA-seq data in cell line authentication because it is the most commonly used technique among the seq-based methods and relatively inexpensive, and we demonstrated the minimum sequence reads requirement for each RNA-seq to maintain the authentication accuracy using a series of subsampling BAM files at 1million up to 50 million reads
Summary
Cell lines form the cornerstone of cell-based experimentation studies into understanding the underlying mechanisms of normal and disease biology including cancer. Cell lines are an indispensable component of biomedical research and serve as excellent in vitro model systems in disease biology research including cancer. Cell lines are usually named by the researcher who developed them and till recently were lacking a standard nomenclature protocol [1,2,3]. This had led to cell line misidentification and poor annotation. Cell lines suffer from cross-contamination from other sources including other cell lines [1, 4] All these factors affect overall scientific reproducibility. Though cross contamination of cell lines have been acknowledged for almost 50 years [1,2,3,4, 9], very few researchers check for contaminations probably
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