A wide variety of biological experiments rely on the ability to express an exogenous gene in a transgenic animal at a defined level and in a spatially and temporally controlled pattern. We describe major improvements of the methods available for achieving this objective in Drosophila melanogaster. We have systematically varied core promoters, UTRs, operator sequences, and transcriptional activating domains used to direct gene expression with the GAL4, LexA, and Split GAL4 transcription factors and the GAL80 transcriptional repressor. The use of site-specific integration allowed us to make quantitative comparisons between different constructs inserted at the same genomic location. We also characterized a set of PhiC31 integration sites for their ability to support transgene expression of both drivers and responders in the nervous system. The increased strength and reliability of these optimized reagents overcome many of the previous limitations of these methods and will facilitate genetic manipulations of greater complexity and sophistication.

Reconstructing neuronal circuits at the level of synapses is a central problem in neuroscience, and the focus of the nascent field of connectomics. Previously used to reconstruct the C. elegans wiring diagram, serial-section transmission electron microscopy (ssTEM) is a proven technique for the task. However, to reconstruct more complex circuits, ssTEM will require the automation of image processing. We review progress in the processing of electron microscopy images and, in particular, a semi-automated reconstruction pipeline deployed at Janelia. Drosophila circuits underlying identified behaviors are being reconstructed in the pipeline with the goal of generating a complete Drosophila connectome.

Pixel and superpixel classifiers have become essential tools for EM segmentation algorithms. Training these classifiers remains a major bottleneck primarily due to the requirement of completely annotating the dataset which is tedious, error-prone and costly. In this paper, we propose an interactive learning scheme for the superpixel classifier for EM segmentation. Our algorithm is "active semi-supervised" because it requests the labels of a small number of examples from user and applies label propagation technique to generate these queries. Using only a small set (<20%) of all datapoints, the proposed algorithm consistently generates a classifier almost as accurate as that estimated from a complete groundtruth. We provide segmentation results on multiple datasets to show the strength of these classifiers.

Pixel and superpixel classifiers have become essential tools for EM segmentation algorithms. Training these classifiers remains a major bottleneck primarily due to the requirement of completely annotating the dataset which is tedious, error-prone and costly. In this paper, we propose an interactive learning scheme for the superpixel classifier for EM segmentation. Our algorithm is 'active semi-supervised' because it requests the labels of a small number of examples from user and applies label propagation technique to generate these queries. Using only a small set (< 20%) of all datapoints, the proposed algorithm consistently generates a classifier almost as accurate as that estimated from a complete groundtruth. We provide segmentation results on multiple datasets to show the strength of these classifiers.

A central problem in neuroscience is reconstructing neuronal circuits on the synapse level. Due to a wide range of scales in brain architecture such reconstruction requires imaging that is both high-resolution and high-throughput. Existing electron microscopy (EM) techniques possess required resolution in the lateral plane and either high-throughput or high depth resolution but not both. Here, we exploit recent advances in unsupervised learning and signal processing to obtain high depth-resolution EM images computationally without sacrificing throughput. First, we show that the brain tissue can be represented as a sparse linear combination of localized basis functions that are learned using high-resolution datasets. We then develop compressive sensing-inspired techniques that can reconstruct the brain tissue from very few (typically 5) tomographic views of each section. This enables tracing of neuronal processes and, hence, high throughput reconstruction of neural circuits on the level of individual synapses.

We reconstructed the synaptic circuits of seven columns in the second neuropil or medulla behind the fly's compound eye. These neurons embody some of the most stereotyped circuits in one of the most miniaturized of animal brains. The reconstructions allow us, for the first time to our knowledge, to study variations between circuits in the medulla's neighboring columns. This variation in the number of synapses and the types of their synaptic partners has previously been little addressed because methods that visualize multiple circuits have not resolved detailed connections, and existing connectomic studies, which can see such connections, have not so far examined multiple reconstructions of the same circuit. Here, we address the omission by comparing the circuits common to all seven columns to assess variation in their connection strengths and the resultant rates of several different and distinct types of connection error. Error rates reveal that, overall, <1% of contacts are not part of a consensus circuit, and we classify those contacts that supplement (E+) or are missing from it (E-). Autapses, in which the same cell is both presynaptic and postsynaptic at the same synapse, are occasionally seen; two cells in particular, Dm9 and Mi1, form ≥20-fold more autapses than do other neurons. These results delimit the accuracy of developmental events that establish and normally maintain synaptic circuits with such precision, and thereby address the operation of such circuits. They also establish a precedent for error rates that will be required in the new science of connectomics.

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.

Analysing computations in neural circuits often uses simplified models because the actual neuronal implementation is not known. For example, a problem in vision, how the eye detects image motion, has long been analysed using Hassenstein-Reichardt (HR) detector or Barlow-Levick (BL) models. These both simulate motion detection well, but the exact neuronal circuits undertaking these tasks remain elusive. We reconstructed a comprehensive connectome of the circuits of Drosophila's motion-sensing T4 cells using a novel EM technique. We uncover complex T4 inputs and reveal that putative excitatory inputs cluster at T4's dendrite shafts, while inhibitory inputs localize to the bases. Consistent with our previous study, we reveal that Mi1 and Tm3 cells provide most synaptic contacts onto T4. We are, however, unable to reproduce the spatial offset between these cells reported previously. Our comprehensive connectome reveals complex circuits that include candidate anatomical substrates for both HR and BL types of motion detectors.

Animal sounds are produced by patterned vibrations of specific organs, but the neural circuits that drive these vibrations are not well defined in any animal. Here we provide a functional and synaptic map of most of the neurons in the Drosophila male ventral nerve cord (the analog of the vertebrate spinal cord) that drive complex, patterned song during courtship. Male Drosophila vibrate their wings toward females during courtship to produce two distinct song modes – pulse and sine song – with characteristic features that signal species identity and male quality. We identified song-producing neural circuits by optogenetically activating and inhibiting identified cell types in the ventral nerve cord (VNC) and by tracing their patterns of synaptic connectivity in the male VNC connectome. The core song circuit consists of at least eight cell types organized into overlapping circuits, where all neurons are required for pulse song and a subset are required for sine song. The pulse and sine circuits each include a feed-forward pathway from brain descending neurons to wing motor neurons, with extensive reciprocal and feed-back connections. We also identify specific neurons that shape the individual features of each song mode. These results reveal commonalities amongst diverse animals in the neural mechanisms that generate diverse motor patterns from a single set of muscles.

Visual pathways from the compound eye of an insect relay to four neuropils, successively the lamina, medulla, lobula, and lobula plate in the underlying optic lobe. Among these neuropils, the medulla, lobula, and lobula plate are interconnected by the complex second optic chiasm, through which the anteroposterior axis undergoes an inversion between the medulla and lobula. Given their complex structure, the projection patterns through the second optic chiasm have so far lacked critical analysis. By densely reconstructing axon trajectories using a volumetric scanning electron microscopy (SEM) technique, we reveal the three-dimensional structure of the second optic chiasm of , which comprises interleaving bundles and sheets of axons insulated from each other by glial sheaths. These axon bundles invert their horizontal sequence in passing between the medulla and lobula. Axons connecting the medulla and lobula plate are also bundled together with them but do not decussate the sequence of their horizontal positions. They interleave with sheets of projection neuron axons between the lobula and lobula plate, which also lack decussations. We estimate that approximately 19,500 cells per hemisphere, about two thirds of the optic lobe neurons, contribute to the second chiasm, most being Tm cells, with an estimated additional 2,780 T4 and T5 cells each. The chiasm mostly comprises axons and cell body fibers, but also a few synaptic elements. Based on our anatomical findings, we propose that a chiasmal structure between the neuropils is potentially advantageous for processing complex visual information in parallel. The EM reconstruction shows not only the structure of the chiasm in the adult brain, the previously unreported main topic of our study, but also suggest that the projection patterns of the neurons comprising the chiasm may be determined by the proliferation centers from which the neurons develop. Such a complex wiring pattern could, we suggest, only have arisen in several evolutionary steps.