Whole-cell organelle segmentation in volume electron microscopy

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, Jennifer Lippincott-Schwartz, Stephan Saalfeld () and Aubrey V. Weigel ()
Additional contact information
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
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

Nature, 2021, vol. 599, issue 7883, 141-146

Abstract: 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.

Date: 2021
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DOI: 10.1038/s41586-021-03977-3

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