Machine Learning with Optical Phase Signatures for Phenotypic Profiling of Cell Lines. Issue 7 (22nd April 2019) - Lam, Van K.
ABSTRACT: Robust and reproducible profiling of cell lines is essential for phenotypic screening assays. The goals of this study were to determine robust and reproducible optical phase signatures of cell lines for classification with machine learning and to correlate optical phase parameters to motile behavior. Digital holographic microscopy (DHM) reconstructed phase maps of cells from two pairs of cancer and non‐cancer cell lines. Seventeen image parameters were extracted from each cell's phase map, used for linear support vector machine learning, and correlated to scratch wound closure and Boyden chamber chemotaxis. The classification accuracy was between 90% and 100% for the six pairwise cell line comparisons. Several phase parameters correlated with wound closure rate and chemotaxis across the four cell lines. The level of cell confluence in culture affected phase parameters in all cell lines tested. Results indicate that optical phase features of cell lines are a robust set of quantitative data of potential utility for phenotypic screening and prediction of motile behavior. © 2019 International Society for Advancement of Cytometry Abstract : Quantitative phase imaging of adherent cells yields seventeen parameters that characterize the cell type as a phase signature. Differences in phase signatures 1) allow highly accurate classification of four cell lines, 2) assess effects of confluence on cell populations, and 3) correlate with motility assays. Results indicate that optical phase features of each cell line are robust and have potential for phenotypic screening and prediction of motile behavior.