| Summary: | In early drug discovery and the study of the effects of new chemical compounds on cancer cells, the change in cell shape over time provides vital information about cell health. Live-cell image analysis systems can be used to extract cell-shape describing parameters of individual cells during exposure to new drugs. Multivariate statistical analysis is then applied to understand cell morphology and the correlation between various shape descriptors. Principal component analysis integrated with histogram distribution analysis is a method to compress and summarize important cellular data features without loss of information about the individual cell shapes. A workflow for this kind of analysis is being developed at Sartorius and aims to aid in the biological interpretation of different experimental results. However, methods for exploring the time dimension in the experiments are not yet fully explored, and a temporal view of the data would increase understanding of the change in cell morphology metrics over time. In this study, we implement the workflow to a data set generated from the microscope IncuCyte and investigate a possible continuation of time-series analysis on the data. The results demonstrate how we can use principal component analysis in two steps together with histogram distributions of different experimental conditions to study cell shapes over time. Scores and loadings from the analysis are used as new observations representing the original data, and the evolution of score-value can be backtracked to cell morphology metrics changing in time. The results show a comprehensive way of studying how cells from all experimental conditions relate to each other during the course of an experiment.
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