Digital holographic cytometry (DHC) permits label-free visualization of adherent cells. Dozens of cellular features can be derived from segmentation of hologram-derived images. However, the accuracy of single cell classification by these features remains limited for most applications, and lack of standardization metrics has hindered independent experimental comparison and validation. Here we identify twenty-six DHC-derived features that provide biologically independent information across a variety of mammalian cell state transitions. When trained on these features, machine-learning algorithms achieve blind single cell classification with up to 95% accuracy. Using classification accuracy to guide platform optimization, we develop methods to standardize holograms for the purpose of kinetic single cell cytometry. Applying our approach to human melanoma cells treated with a panel of cancer therapeutics, we track dynamic changes in cellular behavior and cell state over time. We provide the methods and computational tools for optimizing DHC for kinetic single adherent cell classification.
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