Measuring Cell Dimensions in Fission Yeast Using Machine Learning.

In fission yeast (Schizosaccharomyces pombe), cell length is a crucial indicator of cell cycle progression. Microscopy screens that examine the effect of agents or genotypes suspected of altering genomic or metabolic stability and thus cell size are crucial for studying disruptions to cell cycle dynamics. This method is based on using an automated cell segmentation algorithm to measure S. pombe cells imaged by brightfield (BF) microscopy methods. PhotoPhenosizer (PP) is a machine learning-based tool designed for automated cell measuring and dimensional analysis of morphology frequency distributions. Integration of this method into large-scale pipelines for tracking cell dimension change streamlines morphological measurements, which facilitates the examination of cellular responses to genomic and metabolic stresses. In this protocol, we use PP to observe the effect of genomic instability on cell size dynamics over a 12-day chronological lifespan assay. Our results show that relative to wild-type cells, a replication stress mutant shows larger cells during chronological aging in excess glucose media. Our results are consistent with activation of checkpoints that regulate cell morphology in response to DNA damage. This method's application highlights the relevance of its incorporation in experimental routines that require large-scale image processing and its adoption by users with routine needs in S. pombe molecular research projects.