MitoLandscape, a semi-automated pipeline for subcellular localization and quantification of mitochondria

The precise characterization of mitochondrial morphology and subcellular localization provides crucial insights into cellular metabolic states and developmental fates. However, accurately analyzing mitochondria in cells with complex morphologies remains challenging, particularly within intact tissues where current methodologies lack sufficient resolution and specificity. Here we introduce MitoLandscape, an innovative pipeline tailored for comprehensive mitochondrial analysis at single-cell resolution in the developing nervous system. MitoLandscape integrates Airyscan super-resolution microscopy, semi-automated segmentation (leveraging ImageJ and 3DSlicer), machine-learning-driven pixel classification (ilastik), and a modular custom Python script for robust and versatile analysis. Using graph-based representations derived from manual annotations and binary mitochondrial images, MitoLandscape efficiently extracts detailed morphological parameters from distinct subcellular compartments, applicable from cells with simple morphologies to complex neuronal architectures. Additionally, the pipeline quantifies mitochondrial distribution relative to specific cellular landmarks, such as nucleus or soma. We validated MitoLandscape <i>in vitro</i> and in neural tissue, demonstrating its capability to precisely and reliably map mitochondrial features in diverse biological contexts. MitoLandscape represents a powerful, user-friendly, and highly adaptable solution for investigating mitochondrial biology in cell and developmental research.