Shin-ya Takemura

A visual motion detection circuit suggested by Drosophila connectomics

Animal behaviour arises from computations in neuronal circuits, but our understanding of these computations has been frustrated by the lack of detailed synaptic connection maps, or connectomes. For example, despite intensive investigations over half a century, the neuronal implementation of local motion detection in the insect visual system remains elusive. Here we develop a semi-automated pipeline using electron microscopy to reconstruct a connectome, containing 379 neurons and 8,637 chemical synaptic contacts, within the Drosophila optic medulla. By matching reconstructed neurons to examples from light microscopy, we assigned neurons to cell types and assembled a connectome of the repeating module of the medulla. Within this module, we identified cell types constituting a motion detection circuit, and showed that the connections onto individual motion-sensitive neurons in this circuit were consistent with their direction selectivity. Our results identify cellular targets for future functional investigations, and demonstrate that connectomes can provide key insights into neuronal computations.

The Neural Substrate of Spectral Preference in Drosophila

Drosophila vision is mediated by inputs from three types of photoreceptor neurons; R1-R6 mediate achromatic motion detection, while R7 and R8 constitute two chromatic channels. Neural circuits for processing chromatic information are not known. Here, we identified the first-order interneurons downstream of the chromatic channels. Serial EM revealed that small-field projection neurons Tm5 and Tm9 receive direct synaptic input from R7 and R8, respectively, and indirect input from R1-R6, qualifying them to function as color-opponent neurons. Wide-field Dm8 amacrine neurons receive input from 13-16 UV-sensing R7s and provide output to projection neurons. Using a combinatorial expression system to manipulate activity in different neuron subtypes, we determined that Dm8 neurons are necessary and sufficient for flies to exhibit phototaxis toward ultraviolet instead of green light. We propose that Dm8 sacrifices spatial resolution for sensitivity by relaying signals from multiple R7s to projection neurons, which then provide output to higher visual centers.

Synaptic circuits of the Drosophila optic lobe: the input terminals to the medulla.

Understanding the visual pathways of the flys compound eye has been blocked for decades at the second optic neuropil, the medulla, a two‐part relay comprising 10 strata (M1–M10), and the largest neuropil in the flys brain. Based on the modularity of its composition, and two previous reports, on Golgi‐impregnated cell types (Fischbach and Dittrich, Cell Tissue Res., 1989 ; 258:441–475) and their synaptic circuits in the first neuropil, the lamina, we used serial‐section electron microscopy to examine inputs to the distal strata M1–M6. We report the morphology of the reconstructed medulla terminals of five lamina cells, L1–L5, two photoreceptors, R7 and R8, and three neurons, medulla cell T1 and centrifugal cells C2 and C3. The morphology of these conforms closely to previous reports from Golgi impregnation. This fidelity provides assurance that our reconstructions are complete and accurate. Synapses of these terminals broadly localize to the terminal and provide contacts to unidentified targets, mostly medulla cells, as well as sites of connection between the terminals themselves. These reveal that R8 forms contacts upon R7 and thus between these two spectral inputs; that L3 provides input upon both pathways, adding an achromatic input; that the terminal of L5 reciprocally connects to that of L1, thus being synaptic in the medulla despite lacking synapses in the lamina; that the motion‐sensing input cells L1 and L2 lack direct interconnection but both receive input from C2 and C3, resembling lamina connections of these cells; and that, as in the lamina, T1 provides no output chemical synapses. J. Comp. Neurol. 509:493–513, 2008.

Large-Scale Automated Histology in the Pursuit of Connectomes

How does the brain compute? Answering this question necessitates neuronal connectomes, annotated graphs of all synaptic connections within defined brain areas. Further, understanding the energetics of the brains computations requires vascular graphs. The assembly of a connectome requires sensitive hardware tools to measure neuronal and neurovascular features in all three dimensions, as well as software and machine learning for data analysis and visualization. We present the state of the art on the reconstruction of circuits and vasculature that link brain anatomy and function. Analysis at the scale of tens of nanometers yields connections between identified neurons, while analysis at the micrometer scale yields probabilistic rules of connection between neurons and exact vascular connectivity.

Synaptic circuits and their variations within different columns in the visual system of Drosophila

Significance Circuit diagrams of brains are generally reported only as absolute or consensus networks; these diagrams fail to identify the accuracy of connections, however, for which multiple circuits of the same neurons must be documented. For this reason, the modular composition of the Drosophila visual system, with many identified neuron classes, is ideal. Using EM, we identified synaptic connections in the fly’s second visual relay neuropil, or medulla, in the 20 neuron classes in a so-called “core connectome,” those neurons present in seven neighboring columns. These connections identify circuits for motion. Their error rates for wiring reveal that <1% of contacts overall are not part of a consensus circuit but incorporate errors of either omission or commission. Autapses are occasionally seen. 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.

A connectome of a learning and memory center in the adult Drosophila brain

Understanding memory formation, storage and retrieval requires knowledge of the underlying neuronal circuits. In Drosophila, the mushroom body (MB) is the major site of associative learning. We reconstructed the morphologies and synaptic connections of all 983 neurons within the three functional units, or compartments, that compose the adult MB’s α lobe, using a dataset of isotropic 8 nm voxels collected by focused ion-beam milling scanning electron microscopy. We found that Kenyon cells (KCs), whose sparse activity encodes sensory information, each make multiple en passant synapses to MB output neurons (MBONs) in each compartment. Some MBONs have inputs from all KCs, while others differentially sample sensory modalities. Only 6% of KC>MBON synapses receive a direct synapse from a dopaminergic neuron (DAN). We identified two unanticipated classes of synapses, KC>DAN and DAN>MBON. DAN activation produces a slow depolarization of the MBON in these DAN>MBON synapses and can weaken memory recall. DOI: http://dx.doi.org/10.7554/eLife.26975.001

Ig Superfamily Ligand and Receptor Pairs Expressed in Synaptic Partners in Drosophila.

Information processing relies on precise patterns of synapses between neurons. The cellular recognition mechanisms regulating this specificity are poorly understood. In the medulla of the Drosophila visual system, different neurons form synaptic connections in different layers. Here, we sought to identify candidate cell recognition molecules underlying this specificity. Using RNA sequencing (RNA-seq), we show that neurons with different synaptic specificities express unique combinations of mRNAs encoding hundreds of cell surface and secreted proteins. Using RNA-seq and protein tagging, we demonstrate that 21 paralogs of the Dpr family, a subclass of immunoglobulin (Ig)-domain containing proteins, are expressed in unique combinations in homologous neurons with different layer-specific synaptic connections. Dpr interacting proteins (DIPs), comprising nine paralogs of another subclass of Ig-containing proteins, are expressed in a complementary layer-specific fashion in a subset of synaptic partners. We propose that pairs of Dpr/DIP paralogs contribute to layer-specific patterns of synaptic connectivity.

The comprehensive connectome of a neural substrate for 'ON' motion detection in Drosophila

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. DOI: http://dx.doi.org/10.7554/eLife.24394.001

From Form to Function: the Ways to Know a Neuron

Abstract: The shape of a neuron, its morphological signature, dictates the neurons function by establishing its synaptic partnerships. Here, we review various anatomical methods used to reveal neuron shape and the contributions these have made to our current understanding of neural function in the Drosophila brain, especially the optic lobe. These methods, including Golgi impregnation, genetic reporters, and electron microscopy (EM), necessarily incorporate biases of various sorts that are easy to overlook, but that filter the morphological signatures we see. Nonetheless, the application of these methods to the optic lobe has led to reassuringly congruent findings on the number and shapes of neurons and their connection patterns, indicating that morphological classes are actually genetic classes. Genetic methods using, especially, GAL4 drivers and associated reporters have largely superceded classical Golgi methods for cellular analyses and, moreover, allow the manipulation of neuronal activity, thus enabling us to establish a bridge between morphological studies and functional ones. While serial-EM reconstruction remains the only reliable, albeit labor-intensive, method to determine actual synaptic connections, genetic approaches in combination with EM or high-resolution light microscopic techniques are promising methods for the rapid determination of synaptic circuit function.

Connectome of the fly visual circuitry

Recent powerful tools for reconstructing connectomes using electron microscopy (EM) have made outstanding contributions to the field of neuroscience. As a prime example, the detection of visual motion is a classic problem of neural computation, yet our understanding of the exact mechanism has been frustrated by our incomplete knowledge of the relevant neurons and synapses. Recent connectomic studies have successfully identified the concrete neuronal circuit in the flys visual system that computes the motion signals. This identification was greatly aided by the comprehensiveness of the EM reconstruction. Compared with light microscopy, which gives estimated connections from arbor overlap, EM gives unequivocal connections with precise synaptic counts. This paper reviews the recent study of connectomics in a brain of the fruit fly Drosophila and highlights how connectomes can provide a foundation for understanding the mechanism of neuronal functions by identifying the underlying neural circuits.