Towards rapid prediction of drug-resistant cancer cell phenotypes: single cell mass spectrometry combined with machine learning

Abstract

Combined single cell mass spectrometry and machine learning methods is demonstrated for the first time to achieve rapid and reliable prediction of the phenotype of unknown single cells based on their metabolomic profiles, with experimental validation. This approach can be potentially applied towards prediction of drug-resistant phenotypes prior to chemotherapy.