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.

High-Content Phenotypic Profiling of Drug Response Signatures across Distinct Cancer Cells

“…As the Definiens algorithm is context based, every measurement can be related to each segmented cell. Due to the wide heterogeneity of cell phenotypes observed within cell populations especially following compound perturbation, a further level of subpopulation segmentation is beneficial (23,24).…”

Tasisulam Sodium, an Antitumor Agent That Inhibits Mitotic Progression and Induces Vascular Normalization

“…For HCI, all procedures are previously described (18). For cell viability, cellular ATP production was measured by Cell Titer Glo Assay (Promega).…”

“…The HCI assay associates multiple phenotypic readouts within the same cell, as opposed to population-based readouts that cannot associate concomitant biological events at the cellular level (18). Boundaries are established for different cell-cycle states on the basis of nuclear parameters, with an emphasis on DNA content.…”

“…The first involved induction of the intrinsic apoptosis pathway in a cell-cycle-dependent fashion. Using high content imaging (HCI)-based subpopulation analysis (17,18), we showed that G 2 -M accumulation leads to caspase-3 activity and subsequent cell death. The second involved tasisulam-mediated effects on tumor vasculature.…”

Multiparametric Cell-Based Assay for the Evaluation of Transcription Inhibition by High-Content Imaging

“…This technology might even prevent the advancement of molecules with undesirable off-target effects, by showing a phenotype that is not linked with the target for which they have been synthesized. 10 Although in recent years, several methods have been developed to precisely address which are the relevant cell cycle targets and how their inhibition results in specific cellular phenotypes, 11,12 there are no reported biological screenings to monitor compound effect on transcription inhibition. In the present study, we propose a new multiplexed cellbased assay to identify compounds involved in the inhibition of transcription, by measuring biological characteristics such as DNA content and cell number along with the phosphorylation of the carboxyl terminal domain of the large units of RNA polymerase II.…”