A framework for image-based classification of mitotic cells in asynchronous populations.

High content screening (HCS) has emerged an important tool for drug discovery because it combines rich readouts of cellular responses in a single experiment. Inclusion of cell cycle analysis into HCS is essential to identify clinically suitable anticancer drugs that disrupt the aberrant mitotic activity of cells. One challenge for integration of cell cycle analysis into HCS is that cells must be chemically synchronized to specific phases, adding experimental complexity to high content screens. To address this issue, we have developed a rules-based method that utilizes mitotic phosphoprotein monoclonal 2 (MPM-2) marker and works consistently in different experimental conditions and in asynchronous populations. Further, the performance of the rules-based method is comparable to established machine learning approaches for classifying cell cycle data, indicating the robustness of the features we use in the framework. As such, we suggest the use of MPM-2 analysis and its associated expressive features for integration into HCS approaches.