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Connectomic reconstruction of a female Drosophila ventral nerve cord


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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Fig. 1: Connectomic reconstruction of neural circuits in the Drosophila VNC.
Fig. 2: Validation of automated methods for segmentation and synapse prediction.
Fig. 3: Matching MNs in the connectome to leg muscle targets.
Fig. 4: Identification of the specific muscle innervated by each left front leg MN in FANC.
Fig. 5: Identification of wing MNs in FANC.
Fig. 6: Circuits that coordinate the wings and legs during escape take-off.

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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 (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 (ref. 80). Code for analysis and figures is available at (ref. 70).


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The authors thank R. Wilson for financial support of S.G. during development of (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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Authors and Affiliations



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 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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Correspondence to Wei-Chung Allen Lee or John C. Tuthill.

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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).

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