Synaptic architecture of leg and wing premotor control networks in Drosophila

This work was supported by 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 no. U19NS104655 to J.C.T. and M.D.; 1RF1NS128785-01 to J.C.T.; and NIH no. R01MH117808 to J.C.T. and W.-C.A.L. J.C.T. is a New York Stem Cell Foundation – Robertson Investigator. We thank J. Truman, D. Shepherd and E. Marin for assistance with hemilineage identification. We thank H. Lacin, L. Marin, G. Jefferis amd G. Card for helpful discussions, and for their laboratory’s contributions to proofreading in the FANC dataset, in particular K. Eichler, P. Brooks, T. Stürner, M. Costa and G. Jefferis for sharing comprehensive proofreading and annotation of neck connective neurons including descending and ascending neurons in the FANC dataset (supported by Wellcome award 221300/Z/20/Z). We thank members of the Tuthill and Dickinson Laboratories, S. Ahmed, B. Brunton and J. Truman for comments on the manuscript.

The data presented in the paper were analysed from CAVE materialization version 840 (v.840). Annotated connectivity matrices (Fig. 2) are available as Python Pandas data frames (https://pandas.pydata.org/) at GitHub (https://github.com/tuthill-lab/Lesser_Azevedo_2023). Links to public preMN and MN segmentations are available throughout the text, as well as in Supplementary Tables 1–3.

Extended Data Fig. 1 Detailed properties of individual leg and wing MNs.

Scripts to recreate the analyses and figures in the paper, as well as scripts to recreate the connectivity matrices, are available at GitHub (https://github.com/tuthill-lab/Lesser_Azevedo_2023) for users authorized to interact with the CAVEclient. All analysis was performed in Python 3.9 using custom code, making extensive use of CAVEclient (https://github.com/seung-lab/CAVEclient)⁶⁵ and CloudVolume to interact with data infrastructure, and the libraries Matplotlib, Numpy, Pandas, Scikit-learn, Scipy, stats-models and VTK for general computation, machine learning and data visualization. Additional code is available at https://github.com/htem/FANC_auto_recon, providing additional tutorials, documentation for interaction with FANC and instructions for joining the FANC community.

The authors declare no competing interests.