Quantitative analysis of biological membrane morphology is essential for understanding fundamental cellular processes such as organelle biogenesis and remodeling. While manual annotation has been the standard for complex structures, it is laborious and subjective, and conventional automated methods often fail to accurately delineate overlapping objects in 2D projected microscopy images. This protocol provides a complete, step-by-step workflow for the quantitative analysis of overlapping prospore membranes (PSMs) in sporulating yeast. The procedure details the synchronous induction of sporulation, acquisition of 3D fluorescence images and their conversion to 2D maximum intensity projections (MIPs), and the generation of a custom-annotated dataset using a semi-automated pipeline. Finally, it outlines the training and application of our mask R-CNN-based model, DeMemSeg, for high-fidelity instance segmentation and the subsequent extraction of morphological parameters. The primary advantage of this protocol is its ability to enable accurate and reproducible segmentation of individual, overlapping membrane structures from widely used 2D MIP images. This framework offers an objective, efficient, and scalable solution for the detailed quantitative analysis of complex membrane morphologies. Key features • Provides a mask R-CNN-based pipeline to accurately segment individual, overlapping membrane structures resulting from 2D maximum intensity projections of 3D image stacks. • Optimized for quantifying the dynamic morphology of yeast prospore membranes (PSMs), a key model system for studying de novo membrane biogenesis. • Presents a complete workflow from single-cell isolation using a custom CellPose model to detailed manual annotation for creating high-quality training datasets. • Enables robust quantitative phenotyping by extracting morphological parameters (e.g., length, roundness) to distinguish subtle differences between wild-type and mutant strains.

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Journal Article

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Taguchi S, Chagi K, Kawai H, Irie K, Suda Y

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