Automatic segmentation of organelles in isotropic electron microscopy data of mouse choroid plexus (jrc_choroid-plexus-2)

The cell interior contains hundreds of different organelle and macromolecular assemblies intricately organized relative to each other to meet any cellular demand. Obtaining a complete understanding of this organization is challenging and requires nanometer-level, three-dimensional reconstruction of whole cells. Even then, the immense size of datasets and large number of structures to be characterized requires generalizable, automatic methods. To overcome this challenge, we developed an analysis pipeline for comprehensively reconstructing and analyzing all known cellular organelles from entire cells imaged by focused ion beam scanning electron microscopy (FIB-SEM) at a near-isotropic size of 4 or 8 nm per voxel. The pipeline involved deep learning architectures trained on diverse samples for automatic reconstruction of 35 different cellular organelle classes - ranging from ER to microtubules to ribosomes - from multiple cell types. Automatic reconstructions were used to directly quantify various previously inaccessible metrics about these structures and their spatial interactions. We show that automatic organelle reconstructions can also be used to automatically register light and electron microscopy images for correlative studies. The data, computer code, and trained models are all shared through an open data and open source web platform OpenOrganelle, enabling scientists everywhere to query and further reconstruct the datasets. Sample: Mouse choroid plexusDataset ID: jrc_choroid-plexus-2EM Data DOI: 10.25378/janelia.13123427EM voxel size (nm): 8.0 x 8.0 x 8.0 (X, Y, Z)Segmentation voxel size (nm): 4.0 x 4.0 x 4.0 (X, Y, Z)Dataset URL: https://data.janelia.org/VroeHVisualization Website: https://openorganelle.janelia.org/datasets/jrc_choroid-plexus-2Publication: Heinrich et al., 2020Segmented Organelles: Endo (M625k), ER (M650k), Mito (M875k), Nucleus (M1100k), PM (M975k), Vesicle (M800k)Included in Dataset: predictions, segmentations