Our companion paper (Takemura et al., 2023) introduces the first completely proofread connectome of the nerve cord of an animal that can walk or fly. The base connectome consists of neuronal morphologies and the connections between them. However, in order to efficiently navigate and understand this connectome, it is crucial to have a system of annotations that systematically categorises and names neurons, linking them to the existing literature. In this paper we describe the comprehensive annotation of the VNC connectome, first by a system of hierarchical coarse annotations, then by grouping left-right and serially homologous neurons and eventually by defining systematic cell types for the intrinsic interneurons and sensory neurons of the VNC; descending and motor neurons are typed in (Cheong et al., 2023). We assign a sensory modality to over 5000 sensory neurons, cluster them by connectivity, and identify serially homologous cell types and a layered organisation likely corresponding to peripheral topography. We identify the developmental neuroblast of origin of the large majority of VNC neurons and confirm that (in most cases) all secondary neurons of each hemilineage express a single neurotransmitter. Neuroblast hemilineages are serially repeated along the segments of the nerve cord and generally exhibit consistent hemilineage-to-hemilineage connectivity across neuromeres, supporting the idea that hemilineages are a major organisational feature of the VNC. We also find that more than a third of individual neurons belong to serially homologous cell types, which were crucial for identifying motor neurons and sensory neurons across leg neuropils. Categorising interneurons by their neuropil innervation patterns provides an additional organisation axis. Over half of the intrinsic neurons of the VNC appear dedicated to the legs, with the majority restricted to single leg neuropils; in contrast, inhibitory interneurons connecting different leg neuropils, especially those crossing the midline, appear rarer than anticipated by standard models of locomotor circuitry. Our annotations are being released as part of the neuprint.janelia.org web application and also serve as the basis of programmatic analysis of the connectome through dedicated tools that we describe in this paper.

Animal behavior is principally expressed through neural control of muscles. Therefore understanding how the brain controls behavior requires mapping neuronal circuits all the way to motor neurons. We have previously established technology to collect large-volume electron microscopy data sets of neural tissue and fully reconstruct the morphology of the neurons and their chemical synaptic connections throughout the volume. Using these tools we generated a dense wiring diagram, or connectome, for a large portion of theDrosophilacentral brain. However, in most animals, including the fly, the majority of motor neurons are located outside the brain in a neural center closer to the body, i.e. the mammalian spinal cord or insect ventral nerve cord (VNC). In this paper, we extend our effort to map full neural circuits for behavior by generating a connectome of the VNC of a male fly.

The synapse is a central player in the nervous system serving as the key structure that permits the relay of electrical and chemical signals from one neuron to another. The anatomy of the synapse contains important information about the signals and the strength of signal it transmits. Because of their small size, however, electron microscopy (EM) is the only method capable of directly visualizing synapse morphology and remains the gold standard for studying synapse morphology. Historically, EM has been limited to small fields of view and often only in 2D, but recent advances in automated serial EM (i.e. connectomics) have enabled collecting large EM volumes that capture significant fractions of neurons and the different classes of synapses they receive (i.e. shaft, spine, soma, axon). However, even with recent advances in automatic segmentation methods, extracting neuronal and synaptic profiles from these connectomics datasets are difficult to scale over large EM volumes. Without methods that speed up automatic segmentation over large volumes, the full potential of utilizing these new EM methods to advance studies related to synapse morphologies will never be fully realized. To solve this problem, we describe our work to leverage Argonne leadership-scale supercomputers for segmentation of a 0.6 terabyte dataset using state of the art machine learning-based segmentation methods on a significant fraction of the 11.69 petaFLOPs supercomputer Theta at Argonne National Laboratory. We describe an iterative pipeline that couples human and machine feedback to produce accurate segmentation results in time frames that will make connectomics a more routine method for exploring how synapse biology changes across a number of biological conditions. Finally, we demonstrate how dendritic spines can be algorithmically extracted from the segmentation dataset for analysis of spine morphologies. Advancing this effort at large compute scale is expected to yield benefits in turnaround time for segmentation of individual datasets, accelerating the path to biology results and providing population-level insight into how thousands of synapses originate from different neurons; we expect to also reap benefits in terms of greater accuracy from the more compute-intensive algorithms these systems enable.