Supervised classification of etoposide-treated in vitro adherent cells based on noninvasive imaging morphology

Single-cell studies using noninvasive imaging is a challenging, yet appealing way to study cellular characteristics over extended periods of time, for instance to follow cell interactions and the behavior of different cell types within the same sample. In some cases, e.g., transplantation culturing, real-time cellular monitoring, stem cell studies, in vivo studies, and embryo growth studies, it is also crucial to keep the sample intact and invasive imaging using fluorophores or dyes is not an option. Computerized methods are needed to improve throughput of image-based analysis and for use with noninvasive microscopy such methods are poorly developed. By combining a set of well-documented image analysis and classification tools with noninvasive microscopy, we demonstrate the ability for long-term image-based analysis of morphological changes in single cells as induced by a toxin, and show how these changes can be used to indicate changes in biological function. In this study, adherent cell cultures of DU-145 treated with low-concentration (LC) etoposide were imaged during 3 days. Single cells were identified by image segmentation and subsequently classified on image features, extracted for each cell. In parallel with image analysis, an MTS assay was performed to allow comparison between metabolic activity and morphological changes after long-term low-level drug response. Results show a decrease in proliferation rate for LC etoposide, accompanied by changes in cell morphology, primarily leading to an increase in cell area and textural changes. It is shown that changes detected by image analysis are already visible on day 1 for [Formula: see text] etoposide, whereas effects on MTS and viability are detected only on day 3 for [Formula: see text] etoposide concentration, leading to the conclusion that the morphological changes observed occur before and at lower concentrations than a reduction in cell metabolic activity or viability. Three classifiers are compared and we report a best case sensitivity of 88% and specificity of 94% for classification of cells as treated/untreated.