MitoLatentProfiler: ROI-constrained deep learning for mitochondrial phenotyping in human myotubes

Mitochondrial network organization in skeletal muscle reflects metabolic health and is disrupted in primary mitochondrial myopathies, type 2 diabetes/insulin resistance, and age-related functional decline. Studying these disruptions in vitro using mature human myotubes is, however, complicated by the dense, anisotropic architecture of mitochondria, which poses fundamental challenges for automated segmentation and phenotypic classification. Here we present MitoLatentProfiler, a deep learning framework that combines images of micropatterned primary human myotubes with a topology-preserving U-Net and a Classifier U-Net that jointly optimizes segmentation and phenotype classification, structuring the encoder latent space for downstream analysis. Constraining analysis to troponin-positive myotubes excludes confounding signals from neighboring cells. The resulting embeddings organize along two main axes: a morphological axis (fusion/fission balance, shared by TOMM20 and MitoTracker) and a marker-specific axis (associated with bioenergetic insult, MitoTracker only), enabling hierarchical marker-adaptive classification. The segmentation model achieves Dice =0.82 and clDice =0.86. A hierarchical support vector machine classifier reaches macro F₁ up to 0.93, outperforming classical morphological descriptors across donors (n=2), markers, and plates not seen during training. Fragmentation, Hypertubulation, and two Damaged phenotype-scores (induced by oligomycin/antimycin and carbonyl cyanide m-chlorophenyl hydrazone) yield Z^' factors of 0.54, 0.38, 0.87, and 0.96 respectively, confirming screening applicability for damage and fragmentation readouts. The modular design allows the encoder to serve as a fixed feature extractor: adapting to new phenotypes requires only retraining the lightweight classifier. This pipeline provides a scalable tool for mechanistic studies and compound screening targeting mitochondrial dysfunction in muscle disease.