An interactive deep learning-based approach reveals mitochondrial cristae topologies

Abstract

Outer and inner mitochondrial membranes are highly specialized structures with distinct functional properties. Reconstructing complex 3D ultrastructural features of mitochondrial membranes at the nanoscale requires analysis of large volumes of serial scanning electron tomography data. While deep-learning-based methods improved in sophistication recently, time-consuming human intervention processes remain major roadblocks for efficient and accurate analysis of organelle ultrastructure. In order to overcome this limitation, we developed a deep-learning image analysis platform called Python-based Human-In-the-LOop Workflows (PHILOW). Our implementation of an iterative segmentation algorithm and Three-Axis-Prediction method not only improved segmentation speed, but also provided unprecedented ultrastructural detail of whole mitochondria and cristae. Using PHILOW, we found that 42% of cristae surface exhibits tubular structures that are not recognizable in light microscopy and 2D electron microscopy. Furthermore, we unraveled a fundamental new regulatory function for the dynamin-related GTPase Optic Atrophy 1 (OPA1) in controlling the balance between lamellar versus tubular cristae subdomains.