論文 - 詳細
| RRC ID | 82096 |
|---|---|
| 著者 | Suga S, Nakamura K, Nakanishi Y, Humbel BM, Kawai H, Hirabayashi Y. |
| タイトル | An interactive deep learning-based approach reveals mitochondrial cristae topologies. |
| ジャーナル | PLoS Biol |
| Abstract |
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. Here, we developed a novel, publicly available, deep learning (DL)-based image analysis platform called Python-based human-in-the-loop workflow (PHILOW) implemented with a human-in-the-loop (HITL) algorithm. 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. This nanometer-scale analysis in micrometer-scale cellular contexts uncovers fundamental parameters of cristae, such as total surface area, orientation, tubular/lamellar cristae ratio, and crista junction density in individual mitochondria. Unbiased clustering analysis of our structural data unraveled a new function for the dynamin-related GTPase Optic Atrophy 1 (OPA1) in regulating the balance between lamellar versus tubular cristae subdomains. |
| 巻・号 | 21(8) |
| ページ | e3002246 |
| 公開日 | 2023-8-1 |
| DOI | 10.1371/journal.pbio.3002246 |
| PII | PBIOLOGY-D-22-01998 |
| PMID | 37651352 |
| PMC | PMC10470929 |
| MeSH | Acclimatization Algorithms Deep Learning* Humans Mitochondria Mitochondrial Membranes* |
| IF | 7.076 |
| オルトメトリクス指標 |
オルトメトリクス指標項目
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| 最多言及媒体 | X(Twitter) |
| 各媒体での言及数の合計 | 97 |
| 過去6か月間でのオルトメトリクス指標の変動値 | 0.0 |
| リソース情報 | |
| ヒト・動物細胞 | 293T(RCB2202) NIH/3T3(RCB2767) |