Abstract
A deep understanding of how the brain controls behaviour requires mapping neural circuits down to the muscles that they control. Here, we apply automated tools to segment neurons and identify synapses in an electron microscopy dataset of an adult female Drosophila melanogaster ventral nerve cord (VNC)1, which functions like the vertebrate spinal cord to sense and control the body. We find that the fly VNC contains roughly 45 million synapses and 14,600 neuronal cell bodies. To interpret the output of the connectome, we mapped the muscle targets of leg and wing motor neurons using genetic driver lines2 and X-ray holographic nanotomography3. With this motor neuron atlas, we identified neural circuits that coordinate leg and wing movements during take-off. We provide the reconstruction of VNC circuits, the motor neuron atlas and tools for programmatic and interactive access as resources to support experimental and theoretical studies of how the nervous system controls behaviour.
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Data availability
The electron microscopy dataset and reconstructions are freely available1. The segmentation of the FANC dataset is available by joining the FANC community. Instructions on joining the FANC community can be found at https://github.com/htem/FANC_auto_recon/wiki#collaborative-community (ref. 80).
Code availability
Details on code for alignment, segmentation, automatic detection of synapses and cell nuclei, and annotation software (CAVE) can be found via the citations in Methods, ‘Electron microscopy dataset alignment, segmentation and annotation’. Code for interacting with FANC can be found at https://github.com/htem/FANC_auto_recon (ref. 80). Code for analysis and figures is available at https://github.com/EllenLesser/Azevedo_Lesser_Phelps_Mark_2023/ (ref. 70).
References
Phelps, J. S. et al. Reconstruction of motor control circuits in adult Drosophila using automated transmission electron microscopy. Cell 184, 759–774.e18 (2021).
Meissner, G. W. et al. A searchable image resource of Drosophila GAL4 driver expression patterns with single neuron resolution. eLife 12, e80660 (2023).
Kuan, A. T. et al. Dense neuronal reconstruction through X-ray holographic nano-tomography. Nat. Neurosci. 23, 1637–1643 (2020).
Tuthill, J. C. & Wilson, R. I. Mechanosensation and adaptive motor control in insects. Curr. Biol. 26, R1022–R1038 (2016).
Baek, M. & Mann, R. S. Lineage and birth date specify motor neuron targeting and dendritic architecture in adult Drosophila. J. Neurosci. 29, 6904–6916 (2009).
Brierley, D. J., Rathore, K., VijayRaghavan, K. & Williams, D. W. Developmental origins and architecture of Drosophila leg motoneurons. J. Comp. Neurol. 520, 1629–1649 (2012).
Burke, R. E. Motor units: anatomy, physiology, and functional organization. Compr. Physiol. https://doi.org/10.1002/cphy.cp010210 (2011).
Dickinson, M. H. & Tu, M. S. The function of dipteran flight muscle. Comp. Biochem. Physiol. A 116, 223–238 (1997).
O’Sullivan, A. et al. Multifunctional wing motor control of song and flight. Curr. Biol. 28, 2705–2717.e4 (2018).
Trimarchi, J. R. & Schneiderman, A. M. The motor neurons innervating the direct flight muscles of Drosophila melanogaster are morphologically specialized. J. Comp. Neurol. 340, 427–443 (1994).
Ikeda, K. & Koenig, J. H. Morphological identification of the motor neurons innervating the dorsal longitudinal flight muscle of Drosophila melanogaster. J. Comp. Neurol. 273, 436–444 (1988).
Schlurmann, M. & Hausen, K. Motoneurons of the flight power muscles of the blowfly Calliphora erythrocephala: structures and mutual dye coupling. J. Comp. Neurol. 500, 448–464 (2007).
Donovan, E. R. et al. Muscle activation patterns and motor anatomy of anna’s hummingbirds Calypte anna and zebra finches Taeniopygia guttata. Physiol. Biochem. Zool. 86, 27–46 (2013).
Takemura, S.-Y. et al. A connectome of the male Drosophila ventral nerve cord. eLife https://doi.org/10.7554/eLife.97769.1 (2024).
Marin, E. C. et al. Systematic annotation of a complete adult male Drosophila nerve cord connectome reveals principles of functional organisation. Preprint at bioRxiv https://doi.org/10.1101/2023.06.05.543407 (2023).
Dorkenwald, S. et al. Neuronal wiring diagram of an adult brain. Preprint at bioRxiv https://doi.org/10.1101/2023.06.27.546656 (2023).
Schlegel, P. et al. Whole-brain annotation and multi-connectome cell typing quantifies circuit stereotypy in Drosophila. Preprint at bioRxiv https://doi.org/10.1101/2023.06.27.546055 (2023).
Scheffer, L. K. et al. A connectome and analysis of the adult Drosophila central brain. eLife 9, e57443 (2020).
Cheong, H. S. J. et al. Transforming descending input into behavior: the organization of premotor circuits in the Drosophila Male Adult Nerve Cord connectome. eLife https://doi.org/10.7554/eLife.96084.1 (2024).
Buhmann, J. et al. Automatic detection of synaptic partners in a whole-brain Drosophila electron microscopy data set. Nat. Methods 18, 771–774 (2021).
Dorkenwald, S. et al. FlyWire: online community for whole-brain connectomics. Nat. Methods 19, 119–128 (2022).
Azevedo, A. W. et al. A size principle for recruitment of Drosophila leg motor neurons. eLife 9, e56754 (2020).
Enriquez, J. et al. Specification of individual adult motor neuron morphologies by combinatorial transcription factor codes. Neuron 86, 955–970 (2015).
Venkatasubramanian, L. et al. Stereotyped terminal axon branching of leg motor neurons mediated by IgSF proteins DIP-α and Dpr10. eLife 8, e42692 (2019).
Burrows, M. & Horridge, G. A. The organization of inputs to motoneurons of the locust metathoracic leg. Philos. Trans. R. Soc. Lond. B. 269, 49–94 (1974).
Karashchuk, P. et al. Anipose: a toolkit for robust markerless 3D pose estimation. Cell Rep. 36, 109730 (2021).
Dickinson, M. H. et al. How animals move: an integrative view. Science 288, 100–106 (2000).
Siegler, M. V. Electrical coupling between supernumerary motor neurones in the locust. J. Exp. Biol. 101, 105–119 (1982).
Hartenstein, V. in Muscle Development in Drosophila (ed. Sink, H.) 8–27 (Springer, 2006).
Hoyle, G. Muscles and Their Neural Control (Wiley, 1983).
Landgraf, M., Bossing, T., Technau, G. M. & Bate, M. The origin, location, and projections of the embryonic abdominal motorneurons of Drosophila. J. Neurosci. 17, 9642–9655 (1997).
Brown, M. C., Jansen, J. K. & Van Essen, D. Polyneuronal innervation of skeletal muscle in new-born rats and its elimination during maturation. J. Physiol. 261, 387–422 (1976).
Radnikow, G. & Bässler, U. Function of a muscle whose apodeme travels through a joint moved by other muscles: why the retractor unguis muscle in stick insects is tripartite and has no antagonist. J. Exp. Biol. 157, 87–99 (1991).
Wolf, H. Inhibitory motoneurons in arthropod motor control: organisation, function, evolution. J. Comp. Physiol. A 200, 693–710 (2014).
Burrows, M., Watson, A. H. D. & Brunn, D. Physiological and ultrastructural characterization of a central synaptic connection between identified motor neurons in the locust. Eur. J. Neurosci. 1, 111–126 (1989).
Schneider-Mizell, C. M. et al. Quantitative neuroanatomy for connectomics in Drosophila. eLife 5, e12059 (2016).
Mark, B. et al. A developmental framework linking neurogenesis and circuit formation in the Drosophila CNS. eLife. 10, e67510 (2021).
Matsunaga, T., Kohsaka, H. & Nose, A. Gap junction–mediated signaling from motor neurons regulates motor generation in the central circuits of larval Drosophila. J. Neurosci. 37, 2045–2060 (2017).
Marder, E., Gutierrez, G. J. & Nusbaum, M. P. Complicating connectomes: electrical coupling creates parallel pathways and degenerate circuit mechanisms. Dev. Neurobiol. 77, 597–609 (2017).
Pringle, J. W. S. Insect Flight (Cambridge Univ. Press, 1957).
Deora, T., Gundiah, N. & Sane, S. P. Mechanics of the thorax in flies. J. Exp. Biol. 220, 1382–1395 (2017).
Hürkey, S. et al. Gap junctions desynchronize a neural circuit to stabilize insect flight. Nature 618, 118–125 (2023).
King, D. G. & Wyman, R. J. Anatomy of the giant fibre pathway in Drosophila. I. Three thoracic components of the pathway. J. Neurocytol. 9, 753–770 (1980).
Kennedy, T. & Broadie, K. Newly identified electrically coupled neurons support development of the Drosophila Giant fiber model circuit. eNeuro 5, ENEURO.0346–18.2018 (2018).
von Reyn, C. R. et al. A spike-timing mechanism for action selection. Nat. Neurosci. 17, 962–970 (2014).
Lacin, H. et al. Neurotransmitter identity is acquired in a lineage-restricted manner in the Drosophila CNS. eLife 8, e43701 (2019).
Nachtigall, W. & Wilson, D. M. Neuro-muscular control of dipteran flight. J. Exp. Biol. 47, 77–97 (1967).
Cook, S. J. et al. Whole-animal connectomes of both Caenorhabditis elegans sexes. Nature 571, 63–71 (2019).
Winding, M. et al. The connectome of an insect brain. Science 379, eadd9330 (2023).
Eschbach, C. & Zlatic, M. Useful road maps: studying Drosophila larva’s central nervous system with the help of connectomics. Curr. Opin. Neurobiol. 65, 129–137 (2020).
Bargmann, C. I. & Marder, E. From the connectome to brain function. Nat. Methods 10, 483–490 (2013).
Eckstein, N. et al. Neurotransmitter classification from electron microscopy images at synaptic sites in Drosophila melanogaster. Cell 187, 2574–2594.e23 (2024).
Agrawal, S. et al. Central processing of leg proprioception in Drosophila. eLife 9, e60299 (2020).
Chen, C. et al. Functional architecture of neural circuits for leg proprioception in Drosophila. Curr. Biol. 31, 5163–5175.e7 (2021).
Howard, C. E. et al. Serotonergic modulation of walking in Drosophila. Curr. Biol. 29, 4218–4230.e8 (2019).
Lesser, E. et al. Synaptic architecture of leg and wing premotor control networks in Drosophila. Nature https://doi.org/10.1038/s41586-024-07600-z (2024).
Aymanns, F., Chen, C.-L. & Ramdya, P. Descending neuron population dynamics during odor-evoked and spontaneous limb-dependent behaviors. eLife 11, e81527 (2022).
Chen, C.-L. et al. Ascending neurons convey behavioral state to integrative sensory and action selection brain regions. Nat. Neurosci. 26, 682–695 (2023).
Lobato-Rios, V. et al. NeuroMechFly, a neuromechanical model of adult Drosophila melanogaster. Nat. Methods 19, 620–627 (2022).
Heckscher, E. S. et al. Even-skipped+ interneurons are core components of a sensorimotor circuit that maintains left–right symmetric muscle contraction amplitude. Neuron 88, 314–329 (2015).
Agrawal, S. & Tuthill, J. C. The two-body problem: proprioception and motor control across the metamorphic divide. Curr. Opin. Neurobiol. 74, 102546 (2022).
Barsotti, E., Correia, A. & Cardona, A. Neural architectures in the light of comparative connectomics. Curr. Opin. Neurobiol. 71, 139–149 (2021).
Popovych, S, et al.Petascale pipeline for precise alignment of images from serial section electron microscopy. Nat. Commun. 15, 289 (2024).
Macrina, T. et al. Petascale neural circuit reconstruction: automated methods. Preprint at bioRxiv https://doi.org/10.1101/2021.08.04.455162 (2021).
Dorkenwald, S. et al. CAVE: Connectome Annotation Versioning Engine. Preprint at bioRxiv https://doi.org/10.1101/2023.07.26.550598 (2023).
Otsuna, H., Ito, M. & Kawase, T. Color depth MIP mask search: a new tool to expedite Split-GAL4 creation. Preprint at biorXiv https://doi.org/10.1101/318006 (2018).
Maitin-Shepard, J. et al. google/neuroglancer:. Zenodo https://doi.org/10.5281/zenodo.5573294 (2021).
Mu, S. et al. 3D reconstruction of cell nuclei in a full Drosophila brain. Preprint at bioRxiv https://doi.org/10.1101/2021.11.04.467197 (2021).
Lee, K., Zung, J., Li, P., Jain, V. & Seung, H. S. Superhuman accuracy on the SNEMI3D connectomics challenge. Preprint at arXiv https://doi.org/10.48550/arXiv.1706.00120 (2017).
Lesser, E. & Azevedo, A. CSVs and scripts from Azevedo, Lesser, Phelps, Mark et. al., 2023, as well as a space to host json states of neuroglancer views linked in the paper. Github https://github.com/EllenLesser/Azevedo_Lesser_Phelps_Mark_2023/ (2023).
Azevedo, A., Sustar, A. & Tuthill, J. Confocal image stacks of GFP expression in Drosophila forelegs driven by Gal4 driver expression in foreleg motor neurons. Dryad https://doi.org/10.5061/dryad.k0p2ngfg0 (2024).
Costa, M., Manton, J. D., Ostrovsky, A. D., Prohaska, S. & Jefferis, G. S. X. E. NBLAST: Rapid, sensitive comparison of neuronal structure and construction of neuron family databases. Neuron 91, 293–311 (2016).
McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at arXiv https://doi.org/10.48550/arXiv.1802.03426 (2020).
Court, R. et al. A systematic nomenclature for the Drosophila ventral nerve cord. Neuron 107, 1071–1079.e2 (2020).
Heide, G. Neural mechanisms of flight control in Diptera. BIONA-Rep. 2, 35–52 (1983).
Ehrhardt, E. et al. Single-cell type analysis of wing premotor circuits in the ventral nerve cord of Drosophila melanogaster. Preprint at bioRxiv https://doi.org/10.1101/2023.05.31.542897 (2023).
Melis, J. M., Siwanowicz, I. & Dickinson, M. H. Machine learning reveals the control mechanics of an insect wing hinge. Nature 628, 795–803 (2024)
Bacon, J. P. & Strausfeld, N. J. The dipteran ‘giant fibre’ pathway: neurons and signals. J. Comp. Physiol. A 158, 529–548 (1986).
Miller, A. in Biology of Drosophila (ed. Demerec, M.) 420–534 (Wiley, 1950).
Phelps, J. et al. FANC_auto_recon. GitHub https://github.com/htem/FANC_auto_recon (2023).
Acknowledgements
The authors thank R. Wilson for financial support of S.G. during development of braincircuits.io (via U19NS104655 and R01NS129647 to R. Wilson); and R. Mann, H. Cheong, E. Ehrhardt, K. Eichler, G. Card and G. Jefferis for assistance with leg and wing MN identification. This work was supported by the beamtime LS3033 at ID16A beamline, ERC 852455 to A.P., a Searle Scholar Award, a Klingenstein-Simons Fellowship, a Pew Biomedical Scholar Award, a McKnight Scholar Award, a Sloan Research Fellowship, the New York Stem Cell Foundation, and a UW Innovation Award to J.C.T.; a Genise Goldenson Award to W.-C.A.L.; NIH U19NS104655 to J.C.T. and M.D.; NIH R01MH117808 to J.C.T., W.-C.A.L. and H.S.S. J.C.T. is a New York Stem Cell Foundation–Robertson Investigator.
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Contributions
J.S.P., H.S.S., W.-C.A.L. and J.C.T. conceived the project. H.S.S., W.-C.A.L. and J.C.T. acquired funding. R.L., N.K., K.L., A.H., M.C., D.I., J.G., M.T. and C.S.J. performed the segmentation of electron microscopy data and supported the proofreading of the segmentation. S.D., F.C., C.S.-M. and D.B. deployed and supported the annotation software, CAVE. S.G. developed visualizations and analysis tools at braincircuits.io. A.K. manually traced leg MNs and annotated synapses in CATMAID. J.S.P. and W.-C.A.L. established guidelines for collaborative proofreading efforts. J.S.P. organized and oversaw the proofreading community. S.K. deployed code to identify cell nuclei and performed quality checks. A.A., E.L., J.S.P., B.M., L.E., A.M., A.K., C.J.D., S.A., S.-Y.J.L., B.P., A.C. and K.S.-K. proofread neurons in FANC and edited the paper. J.S.P., A.P. and A.A. gathered and annotated the XNH volume of the fly leg. A.S. designed and performed light microscopic imaging. A.A. identified leg MNs in FANC. E.L. identified wing MNs in FANC. M.D. advised the project and edited the paper. A.A., E.L., J.S.P., B.M. and L.E. analysed data. A.A., E.L. and J.C.T. wrote the paper, with input from the other authors.
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H.S.S. declares financial interests in Zetta AI. The remaining authors declare no competing interests.
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Extended data figures and tables
Extended Data Fig. 1 Soma segmentation in FANC.
a, Size distribution of all 17,076 putative nuclei. We manually inspected each putative nucleus and found 14621 neurons (85.6%), 2030 glia (11.9%), 410 false positives (2.4%), and 15 fragments of neuron nuclei detected twice (Duplicated neurons, 0.1%). Volume size is calculated based on the number of voxels within each detected objects. Inset: example of a large diameter axons that was falsely predicted as a putative nucleus. b, Violin plot showing the size distribution of three major neuronal cell types that have cell bodies in VNC: interneurons (n���= 12,468), ascending neurons (n = 1,668), and motor neurons (n = 485). (χ2 = 1760.7, p < 0.001, Kruskal-Wallis test.) VNC neurons with arbors projecting to the neck connective were labeled as ascending neurons. Motor neurons include haltere motor neurons (n = 32), leg motor neurons (all T1, T2, and T3, n = 371), neck motor neurons (n = 24), and wing motor neurons (ADMN, PDMN, and MesoAN, n = 58). Haltere, leg, and neck motor neurons were identified based on their skeleton nodes previously reported in CATMAID1. c, Comparison of volume size between four motor neurons: haltere motor neurons, leg motor neurons, neck motor neurons, wing motor neurons. (χ2 = 84.816, p < 0.001, Kruskal-Wallis test, ***p < 0.001, post-hoc Benjamini–Hochberg procedure-corrected Dunn’s test for multiple comparisons).
Extended Data Fig. 2 Summary of FANC software tools for cell proofreading.
a, Proofreading of cell morphology in Neuroglancer via ChunkedGraph, b, cell type annotation, c, identification, d, cellular and circuit analysis, and e, identification of genetic driver lines.
Extended Data Fig. 3 Comparison of automatic synapse prediction with manually annotated ground truth.
a, Example synapses from the EM volume. Pink blobs indicate predicted postsynaptic sites. The arrowhead in the inset indicates the “T-bar” at the presynaptic site. b, Average distance manual to predicted synapses (M-P), and from predicted to manual synapses (P-M) for the four MNs shown in Fig. 2. The larger predicted-to-manual distances are consistent with larger numbers of predicted synapses (Fig. 2e). c, Distributions of manual vs. predicted synapse annotations along the medial/lateral axis (top) and the anterior/posterior axis (bottom) for the posterior rotator MN, shown in Fig. 2b. Note, ~50% more synapses are predicted compared to manual annotations for this neuron (Fig. 2e). d, Synapse distributions along the dorsal/ventral axis, aligned to the neuron and the synapses below. The distributions are significantly different (Mann-Whitney AUC = 0.56, p < 10−7), with a small increase in the peak of the distribution of predicted synapses. e, An example dendrite in the synapse rich region of the dorsal/ventral distribution in d. Five synapses are predicted, but only one annotated (#6). f, Predicted synapses appear in a region that was not manually traced. We conclude that comparing manual annotation with automatic synapse prediction in these synapse dense reconstructions includes four sources of noise36: 1) completeness of manual tracing of fine branches, 2) completeness of manual synapse annotation, 3) completeness/accuracy of segmentation, and 4) accuracy of synapse prediction. Here, we have ensured that all the manually traced dendrites are proofread and reattached to the segmented reconstructed, thus limiting the third source of noise. Thus, we reiterate our statement in the text to say that the appropriate level of proofreading is likely dictated by the degree to which conclusions based on connectomes would be affected by these sources of noise. In Fig. 2f, we show that for the four neurons we examine here, most of the synapses are from a small number of partners, and those partners are largely correctly identified from both the manual and predicted synapses.
Extended Data Fig. 4 MNs in T1R have half as many input sites as MNs in T1L, likely due to rough dissection of the right T1 leg nerves.
a, The number of synapses onto the right T1 MN (y-axis) vs. onto the paired left T1 MN (x-axis). Colors indicate MN pairs in b-d. The slope of the relationship is 0.51, with a Pearson’s correlation coefficient of 0.89, p < 10−22. b, Left and right SETi MNs. The right T1 neurons tend to appear smoother, with fewer fine twigs. c, Left and right main tibia flexor MNs (Fast flexor22). d, Left and right pleural coxa promotor MNs. Even though the axons exit the PrDN, rather than the ProLN, many of the dendrites of the right T1 neuron run through the damaged regions. Blebby boutons can be seen (white arrow). e, EM image of the damaged area of right T1. f, Magnified view of the damaged area. g, Magnified view of branches of MNs (colors) near the damaged area, including a bleb (arrow) in the pleural promotor MN (magenta). The bleb diameter is on the order of the primary neurite of the largest cell in the T1 neuromeres (blue). h, For comparison, the right side mesoAN is clearly damaged while the right side ADMN and PDMN are intact. Left and right pairs of wing MNs in the ADMN and PDMN have similar numbers of postsynaptic sites, but the right side mesoAN MNs have fewer synapses than the left side mesoAN MNs.
Extended Data Fig. 5 FANC efferent neurons with axons in the ADMN or PDMN that are not wing MNs.
a, MNs that innervate the T2 tergotrochanter leg muscle send axons through the PDMN. The two small neurons have not been identified previously. We identify them as TT MNs here based on their fasciculation with the main TT MN. We predict that they also innervate the main TT muscle, or the intracoxal depressor and levator, respectively (muscles 67 and 68 according to ref. 79). b, The peripherally synapsing interneuron (PSI) sends an axon into the PDMN and synapses onto DLM axons but does not innervate muscles43. c, Four other unidentified neurons have axons in the ADMN or PDMN. Their dendrites are thinner than any other motor neurons, and not like anything previously shown using light microscopy. One has an ascending process (indicated with an arrow), and its projection into PDMN does not travel to the end of the dissection so it is not an MN. One (right-most) shares a majority of its input with pleurosternal MNs, and is likely neuromodulatory or may play a similar role to the tpn MN. d, DLM MNs are the only MNs we observed with output synapses, and they synapse onto each other. e, Recently collected XNH images of the wing and wing hinge were used to help inform the anatomical cartoon schematics.
Extended Data Fig. 6 Proofreading of motor neurons.
a, Overview of how proofreading affected the number of input synapses to each MN. Many of the MN meshes changed substantially, some increased in size as objects were merged (# of synapses before vs. after <1), and some decreased in size as objects were split (# of synapses before vs. after >1). Leg MNs underwent larger changes than wing MNs, relative to starting volume. b, Example leg MNs before and after proofreading. Left) An example MN that was initially merged with glia and other neurons (blue, extra somas are visible). Right) An example MN that required branches to be merged across knife marks.
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Azevedo, A., Lesser, E., Phelps, J.S. et al. Connectomic reconstruction of a female Drosophila ventral nerve cord. Nature 631, 360–368 (2024). https://doi.org/10.1038/s41586-024-07389-x
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DOI: https://doi.org/10.1038/s41586-024-07389-x
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