An artificial intelligent platform for live cell identification and the detection of cross-contamination.

ABSTRACT:

Background

About 30% of cell lines have been cellular cross-contaminated and misidentification, which can result in invalidated experimental results and unusable therapeutic products. Cell morphology under the microscope was observed routinely, and further DNA sequencing analysis was performed periodically to verify cell line identity, but the sequencing analysis was costly, time-consuming, and labor intensive. The purpose of this study was to construct a novel artificial intelligence (AI) technology for "cell face" recognition, in which can predict DNA-level identification labels only using cell images.

Methods

Seven commonly used cell lines were cultured and co-cultured in pairs (totally 8 categories) to simulated the situation of pure and cross-contaminated cells. The mic

SUBMITTER: Wang R 

PROVIDER: S-EPMC7327366 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature