Automatic analysis of dividing cells in live cell movies to detect mitotic delays and correlate phenotypes in time

Abstract

Live-cell imaging allows detailed dynamic cellular phenotyping for cell biology and, in combination with small molecule or drug libraries, for high-content screening. Fully automated analysis of live cell movies has been hampered by the lack of computational approaches that allow tracking and recognition of individual cell fates over time in a precise manner. Here, we present a fully automated approach to analyze time-lapse movies of dividing cells. Our method dynamically categorizes cells into seven phases of the cell cycle and five aberrant morphological phenotypes over time. It reliably tracks cells and their progeny and can thus measure the length of mitotic phases and detect cause and effect if mitosis goes awry. We applied our computational scheme to annotate mitotic phenotypes induced by RNAi gene knockdown of CKAP5 (also known as ch-TOG) or by treatment with the drug nocodazole. Our approach can be readily applied to comparable assays aiming at uncovering the dynamic cause of cell division phenotypes.[Supplemental material is available online at http://www.genome.org.]High-content image-based screening is a powerful technology for gene function studies or drug profiling. This technology is characterized by the combination of automated microscopy to rapidly acquire high-content images of treated cells and sophisticated computational methods to extract quantitative information in an automatic and unbiased way.Quantitative studies have been performed based on populations of cells to analyze high-throughput RNAi (Wheeler et al. 2004;Neumann et al. 2006;Goshima et al. 2007), protein overexpression (Harada et al. 2005), or drug profiling screens (Perlman et al. 2004;Loo et al. 2007). Such studies require methods for segmentation and feature extraction, and classification if different object classes are considered. Publicly available software platforms like CellProfiler (Carpenter et al. 2006) can be applied. For population-based studies, however, results are often limited to general features of entire cell populations at certain time points.By contrast, following single cells over time allows studying the inherent dynamics of cellular and molecular processes more accurately and is consequently widely used in state-of-the-art cell biology. To make time-lapse imaging of single cells applicable for high content screening, additional methods for tracking of cells throughout image sequences and recognition of their phenotypic changes are required. Tracking approaches have been used, e.g., to quantify the level of fluorescently tagged proteins over time (e.g., Sigal et al. 2006; or to quantify cell-cell interactions and cell migration dynamics (e.g., Chen et al. 2009).Automated classification methods have also been used on static images to distinguish different phenotype classes, providing information on the structure and location of subcellular phenotypes at a single cell level (e.g., Conrad et al. 2004;Huang and Murphy 2004;Chen et al. 2007;Hamilton et al. 2007).Combining classification and tracking methods to stu...

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