Detection of viral contamination in cell lines using ViralCellDetector

Rama Shankar^*Shreya PaithankarSuchir GuptaBin Chen^*

• Michigan State University, East Lansing, United States

Cell lines are widely used in biomedical research to study various biological events, including gene expression, cancer progression, and drug responses. However, crosscontaminations with bacteria, mycoplasma, and viruses remains a persistent issue in experiments. Detection of bacteria and mycoplasma contaminations in cell lines is relatively easy, whereas identifying viral contamination in cell lines is challenging. To address this challenge, we developed a tool called ViralCellDetector that detects viral contamination(s) through mapping RNA-seq data to a comprehensive viral genome library. Using this tool, we observed that approximately 10% (110 samples) of the RNAseq experiments involving MCF7 cells were likely contaminated with viruses. Furthermore, to facilitate the detection of samples with unknown sources of viral contamination, we identified differentially expressed genes associated with viral infection across two different cell lines and used these genes to train a machine learning model. This model successfully classified infected and non-infected samples based on host gene expression, achieving an AUC of 0.91 and an accuracy of 0.93. Overall, our mapping and marker-based approaches can detect viral contaminations in any sample based on their RNA-seq data, allowing researchers to avoid the use of unintentionally infected cell lines in their studies.