Semi-automated detection of single cell signatures from a dielectrophoretic cytometer

We present a semi-automated event identification method for collecting the dielectrophoretic signatures of cells flowing through a dieletrophoretic cytometer. The marker free dielectrophoresis (DEP) cytometer presented in this study is capable of detecting electronic signatures of cells which identifies Claussius-Mossotti factor (CMF). The CMF can in turn be used to determine properties of the cell such as the viability. In past work the DEP cytometer signals were manually sorted by going through the entire recorded signals, which is very time-consuming. In the semi-automated method of collection, events are identified and displayed in the user interface to be accepted or rejected. We present results using semi-automated method on Hamster Chinese Ovary (CHO) cells in a batch culture and compared them with the manual analysis. The automated approach identified over 80% of the events identified manually and produced event histogram distributions nearly identical to the manual method.