CLASSIFYING CELL CYCLE BY ELECTRICAL PROPERTIES USING MACHINE LEARNING

Mitotic cell proliferation undergoes precisely controlled functional phases and results in two daughter cells divided from one parental cell. Some drugs interact with cell proliferation progression and lead to mitosis arrest at a certain phase, or cell death eventually. Combination of machine learning with impedance flow cytometry (IFC), an efficient tool for high-speed and label-free single cell analysis, allows linking of the cell physiology states post drug treatment with impedance data in a more accurate and convenient fashion. This work presents the use of different models of machine learning for classifying cell states, i.e. mitotic arrest at G1/S or G2/M phases and apoptosis, from the impedance data measured for drug-treated cells using microfluidic IFC chip.