RESUMEN
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.
RESUMEN
Ciliary and rhabdomeric photoreceptor cells represent two main lines of photoreceptor-cell evolution in animals. The two cell types coexist in some animals, however how these cells functionally integrate is unknown. We used connectomics to map synaptic paths between ciliary and rhabdomeric photoreceptors in the planktonic larva of the annelid Platynereis and found that ciliary photoreceptors are presynaptic to the rhabdomeric circuit. The behaviors mediated by the ciliary and rhabdomeric cells also interact hierarchically. The ciliary photoreceptors are UV-sensitive and mediate downward swimming in non-directional UV light, a behavior absent in ciliary-opsin knockout larvae. UV avoidance overrides positive phototaxis mediated by the rhabdomeric eyes such that vertical swimming direction is determined by the ratio of blue/UV light. Since this ratio increases with depth, Platynereis larvae may use it as a depth gauge during vertical migration. Our results revealed a functional integration of ciliary and rhabdomeric photoreceptor cells in a zooplankton larva.