High-content imaging characterization of cell cycle therapeutics through in vitro and in vivo subpopulation analysis

Abstract

Although the cycling of eukaryotic cells has long been a primary focus for cancer therapeutics, recent advances in imaging and data analysis allow even further definition of cellular events as they occur in individual cells and cellular subpopulations in response to treatment. High-content imaging (HCI) has been an effective tool to elucidate cellular responses to a variety of agents; however, these data were most frequently observed as averages of the entire captured population, unnecessarily decreasing the resolution of each assay. Here, we dissect the eukaryotic cell cycle into individual cellular subpopulations using HCI in conjunction with unsupervised K-means clustering. We generate distinct phenotypic fingerprints for each major cell cycle and mitotic compartment and use those fingerprints to screen a library of 310 commercially available chemotherapeutic agents. We determine that the cell cycle arrest phenotypes caused by these agents are similar to, although distinct from, those found in untreated cells and that these distinctions frequently suggest the mechanism of action. We then show via subpopulation analysis that these arrest phenotypes are similar in both mouse models and in culture. HCI analysis of cell cycle using data obtained from individual cells under a broad range of research conditions and grouped into cellular subpopulations represents a powerful method to discern both cellular events and treatment effects. In particular, this technique allows for a more accurate means of assessing compound selectivity and leads to more meaningful comparisons between so-called targeted therapeutics.