The convolution of membranes called cristae is a critical structural and functional feature of mitochondria. Crista structure is highly diverse between different cell types, reflecting their role in metabolic adaptation. However, their precise three-dimensional (3D) arrangement requires volumetric analysis of serial electron microscopy and has therefore been limiting for unbiased quantitative assessment. Suga, Nakamura et al. developed a novel, publicly available, deep learning (DL)-based image analysis platform called Python-based human-in-the-loop workflow (PHILOW). Analysis of dense, large, and isotropic volumes of focused ion beam-scanning electron microscopy (FIB-SEM) using PHILOW reveals the complex 3D nanostructure of both inner and outer mitochondrial membranes and provides deep, quantitative, structural features of cristae in a large number of individual mitochondria. The image shows the 3D reconstruction of mitochondria (white), lamellar cristae (yellow), and tubular cristae (cyan) in an NIH3T3 cell.
Image Credit: Shogo Suga and Koki Nakamura
Essays
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Short Reports
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