Understanding the circuit mechanisms behind motion detection is a long-standing question in visual neuroscience. In , recent synapse-level connectomes in the optic lobe, particularly in ON-pathway (T4) receptive-field circuits, in concert with physiological studies, suggest an increasingly intricate motion model compared with the ubiquitous Hassenstein-Reichardt model, while our knowledge of OFF-pathway (T5) has been incomplete. Here we present a conclusive and comprehensive connectome that for the first time integrates detailed connectivity information for inputs to both T4 and T5 pathways in a single EM dataset covering the entire optic lobe. With novel reconstruction methods using automated synapse prediction suited to such a large connectome, we successfully corroborate previous findings in the T4 pathway and comprehensively identify inputs and receptive fields for T5. While the two pathways are likely evolutionarily linked and indeed exhibit many similarities, we uncover interesting differences and interactions that may underlie their distinct functional properties.

An automated ultra-microtome capable of sectioning thousands of ultrathin sections onto standard TEM slot grids was developed and used to section: a complete Drosophila melanogaster first-instar larva, three sections per grid, into 4,866 34-nm-thick sections with a cutting and pickup success rate of 99.74%; 30 microns of mouse cortex measuring roughly 400 um x 2000 um at 40 nm per slice; and a full adult Drosophila brain and ventral nerve column into 9,300 sections with a pickup success rate of 99.95%. The apparatus uses optical interferometers to monitor a reference distance between the cutting knife and multiple sample blocks. Cut sections are picked up from the knife-boat water surface while they are still anchored to the cutting knife. Blocks without embedded tissue are used to displace tissue-containing sections away from the knife edge so that the tissue regions end up in the grid slot instead of on the grid rim.

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). 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.