Approaching RNA-seq for Cell Line Identification

Abstract

Cancer cell lines serve as invaluable model systems for cancer biology research and help in evaluating the efficacy of new therapeutic agents. However, cell line contamination and misidentification have become one of the most pressing problems affecting biomedical research. Available methods of cell line authentication suffer from limited access, time-consuming and often costly for many researchers, hence a new and cost-effective approach for cell line authentication is needed. In this regard, we developed a new method called CeL-ID for cell line authentication using genomic variants as a byproduct derived from RNA-seq data. CeL-ID was trained and tested on publicly available more than 900 RNA-seq dataset derived from the Cancer Cell Line Encyclopedia (CCLE) project; including most frequently used adult and pediatric cancer cell lines. We generated cell line specific variant profiles from RNA-seq data using our in-house pipeline followed by pair-wise variant profile comparison between cell lines using allele frequencies and depth of coverage values of the entire variant set. Comparative analysis of variant profiles revealed that they differ significantly from cell line to cell line whereas identical, synonymous and derivative cell lines share high variant identity and their allelic fractions are highly correlated, which is the basis of this cell line authentication protocol. Additionally, CeL-ID also includes a method to estimate the possible cross-contamination using a linear mixture model with any possible CCLE cells in case no perfect match was detected.