Microbiology

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.

Microbial biofilms are structured communities of microorganisms embedded in a self-produced extracellular matrix, adhering to surfaces. These biofilms enhance bacterial resistance to antibiotics, immune responses, and environmental stress. Current microscopy techniques, such as scanning electron microscopy (SEM), confocal laser scanning microscopy (CLSM), and fluorescence microscopy, are commonly used to visualize and differentiate biofilms. However, their high cost and complexity often render them impractical. In contrast, simpler methods like crystal violet and Congo red staining are limited in distinguishing bacterial cells from the biofilm matrix. This study introduces a cost-effective, dual-staining method using Maneval’s stain to visualize and differentiate microbial biofilms. It requires only basic equipment and minimal reagents, making it ideal for routine use in clinical diagnosis and microbial research.