Author
Listed:
- Larissa Heinrich
(Janelia Research Campus, Howard Hughes Medical Institute)
- Davis Bennett
(Janelia Research Campus, Howard Hughes Medical Institute)
- David Ackerman
(Janelia Research Campus, Howard Hughes Medical Institute)
- Woohyun Park
(Janelia Research Campus, Howard Hughes Medical Institute)
- John Bogovic
(Janelia Research Campus, Howard Hughes Medical Institute)
- Nils Eckstein
(Janelia Research Campus, Howard Hughes Medical Institute
Institute of Neuroinformatics UZH/ETHZ)
- Alyson Petruncio
(Janelia Research Campus, Howard Hughes Medical Institute)
- Jody Clements
(Janelia Research Campus, Howard Hughes Medical Institute)
- Song Pang
(Janelia Research Campus, Howard Hughes Medical Institute)
- C. Shan Xu
(Janelia Research Campus, Howard Hughes Medical Institute)
- Jan Funke
(Janelia Research Campus, Howard Hughes Medical Institute)
- Wyatt Korff
(Janelia Research Campus, Howard Hughes Medical Institute)
- Harald F. Hess
(Janelia Research Campus, Howard Hughes Medical Institute)
- Jennifer Lippincott-Schwartz
(Janelia Research Campus, Howard Hughes Medical Institute)
- Stephan Saalfeld
(Janelia Research Campus, Howard Hughes Medical Institute)
- Aubrey V. Weigel
(Janelia Research Campus, Howard Hughes Medical Institute)
Abstract
Cells contain hundreds of organelles and macromolecular assemblies. Obtaining a complete understanding of their intricate organization requires the nanometre-level, three-dimensional reconstruction of whole cells, which is only feasible with robust and scalable automatic methods. Here, to support the development of such methods, we annotated up to 35 different cellular organelle classes—ranging from endoplasmic reticulum to microtubules to ribosomes—in diverse sample volumes from multiple cell types imaged at a near-isotropic resolution of 4 nm per voxel with focused ion beam scanning electron microscopy (FIB-SEM)1. We trained deep learning architectures to segment these structures in 4 nm and 8 nm per voxel FIB-SEM volumes, validated their performance and showed that automatic reconstructions can be used to directly quantify previously inaccessible metrics including spatial interactions between cellular components. We also show that such reconstructions can be used to automatically register light and electron microscopy images for correlative studies. We have created an open data and open-source web repository, ‘OpenOrganelle’, to share the data, computer code and trained models, which will enable scientists everywhere to query and further improve automatic reconstruction of these datasets.
Suggested Citation
Larissa Heinrich & Davis Bennett & David Ackerman & Woohyun Park & John Bogovic & Nils Eckstein & Alyson Petruncio & Jody Clements & Song Pang & C. Shan Xu & Jan Funke & Wyatt Korff & Harald F. Hess &, 2021.
"Whole-cell organelle segmentation in volume electron microscopy,"
Nature, Nature, vol. 599(7883), pages 141-146, November.
Handle:
RePEc:nat:nature:v:599:y:2021:i:7883:d:10.1038_s41586-021-03977-3
DOI: 10.1038/s41586-021-03977-3
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Citations
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Cited by:
- Chixiang Lu & Kai Chen & Heng Qiu & Xiaojun Chen & Gu Chen & Xiaojuan Qi & Haibo Jiang, 2024.
"Diffusion-based deep learning method for augmenting ultrastructural imaging and volume electron microscopy,"
Nature Communications, Nature, vol. 15(1), pages 1-14, December.
- Weria Pezeshkian & John H. Ipsen, 2024.
"Mesoscale simulation of biomembranes with FreeDTS,"
Nature Communications, Nature, vol. 15(1), pages 1-10, December.
- Jianping Wu & Georg Kislinger & Jerome Duschek & Ayşe Damla Durmaz & Benedikt Wefers & Ruoqing Feng & Karsten Nalbach & Wolfgang Wurst & Christian Behrends & Martina Schifferer & Mikael Simons, 2024.
"Nonvesicular lipid transfer drives myelin growth in the central nervous system,"
Nature Communications, Nature, vol. 15(1), pages 1-15, December.
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