Segmentation of C. elegans germline nuclei.

Abstract

Immunofluorescence microscopy is a widely adopted method for studying meiotic prophase in the nematode model organism, Caenorhabditis elegans . An in-depth examination of specific meiotic processes requires the quantitative analysis of immunofluorescence images, which often involves the segmentation of individual cells or nuclei. Here, we introduce our image analysis pipeline to automate significant portions of this task. This pipeline relies on the powerful deep learning model Cellpose 2.0 to segment cellular structures. To further improve the segmentation accuracy for germline nuclei stained for chromatin or synaptonemal complexes, we retrained the generalist Cellpose model and integrated our data processing pipeline into the easy-to-use Cell-ACDC image analysis software. Our pipeline thus makes deep learning-based segmentation of nuclei in the distal germline of C. elegans accessible for users without coding experience.