Extracting fluorescent reporter time courses of cell lineages from high-throughput microscopy at low temporal resolution

Abstract

Live Cell Imaging and High Throughput Screening are rapidly evolving techniques and have found many applications in recent years. Modern microscopy enables the visualisation of internal changes in the cell through the use of fluorescently tagged proteins which can be targeted to specific cellular components. A system is presented here which is designed to track cells at low temporal resolution within large populations, and to extract fluorescence data which allows relative expression rates of tagged proteins to be monitored. Cell detection and tracking are performed as separate steps, and several methods are evaluated for suitability using timeseries images of Hoechst-stained C2C12 mouse mesenchymal stem cells. The use of Hoechst staining ensures cell nuclei are visible throughout a time-series. Dynamic features, including a characteristic change in Hoechst fluorescence intensity during chromosome condensation, are used to identify cell divisions and resulting daughter cells. The ability to detect cell division is integrated into the tracking, aiding lineage construction. To establish the efficiency of the method, synthetic cell images have been produced and used to evaluate cell detection accuracy. A validation framework is created which allows the accuracy of the automatic segmentation and tracking systems to be measured and compared against existing state of the art software, such as CellProfiler. Basic tracking methods, including nearest-neighbour and cell-overlap, are provided as a baseline to evaluate the performance of more sophisticated methods. The software is demonstrated on a number of biological systems, starting with a study of different control elements of the Msx1 gene, which regulates differentiation of mesenchymal stem cells. Expression is followed through multiple lineages to identify asymmetric divisions which may be due to cell differentiation. The lineage construction methods are applied to Schizosaccharomyces pombe time-series image data, allowing the extraction of generation lengths for individual cells. Finally a study is presented which examines correlations between the circadian and cell cycles. This makes use of the recently developed FUCCI cell cycle markers which, when used in conjunction with a circadian indicator such as Rev-erbα-Venus, allow simultaneous measurements of both cycles