Deep learning-driven imaging of cell division and cell growth across an entire eukaryotic life cycle.

ABSTRACT: The life cycle of biomedical and agriculturally relevant eukaryotic microorganisms involves complex transitions between proliferative and non-proliferative states such as dormancy, mating, meiosis, and cell division. New drugs, pesticides, and vaccines can be created by targeting specific life cycle stages of parasites and pathogens. However, defining the structure of a microbial life cycle often relies on partial observations that are theoretically assembled in an ideal life cycle path. To create a more quantitative approach to studying complete eukaryotic life cycles, we generated a deep learning-driven imaging framework to track microorganisms across sexually reproducing generations. Our approach combines microfluidic culturing, life cycle stage-specific segmentation of microscopy image

SUBMITTER: Ramakanth S 

PROVIDER: S-EPMC11071524 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature