#  FANC Project 

 



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##  **Reconstruction of motor control circuits in adult Drosophila using automated transmission electron microscopy**

   
To investigate circuit mechanisms underlying locomotor behavior, we used serial-section electron microscopy (EM) to acquire a synapse-resolution dataset containing the ventral nerve cord (VNC) of an adult female Drosophila melanogaster. To generate this dataset, we developed GridTape, a technology that combines automated serial-section collection with automated high-throughput transmission EM. Using this dataset, we studied neuronal networks that control leg and wing movements by reconstructing all 507 motor neurons that control the limbs. We show that a specific class of leg sensory neurons synapses directly onto motor neurons with the largest-caliber axons on both sides of the body, representing a unique pathway for fast limb control. We provide open access to the dataset and reconstructions registered to a standard atlas to permit matching of cells between EM and light microscopy data. We also provide GridTape instrumentation designs and software to make large-scale EM more accessible and affordable to the scientific community.

 **Media:**  
[\[1\]](https://twitter.com/darbly/status/1346177280511528964) A brief summary of the paper is available on Twitter.  
[\[2\]](https://www.youtube.com/playlist?list=PLsZtzIoZ7Gm5gFALjVj2dPKH8ADPL8Bkt) Videos associated with this paper are available on YouTube.  
[\[3\]](https://hms.harvard.edu/news/motor-control) Research highlight available from HMS News.

 **GridTape resources:**  
GridTape stage design and microscope control software: <https://github.com/htem/GridTapeStage>  
GridTape is commercially available: <https://luxel.com/gridtape>

 **Female Adult Nerve Cord (FANC) EM dataset resources:**  
[Visit BossDB](https://neuroglancer.bossdb.io/#!%7B%22layers%22:%5B%7B%22source%22:%22boss://https://api.bossdb.io/phelps_hildebrand_graham2021/FANC/em%22,%22type%22:%22image%22,%22name%22:%22FANC%22%7D%5D,%22navigation%22:%7B%22pose%22:%7B%22position%22:%7B%22voxelSize%22:%5B4.300000190734863,4.300000190734863,45%5D,%22voxelCoordinates%22:%5B23697.609375,116496.1171875,1228%5D%7D%7D,%22zoomFactor%22:8.154868142736303%7D,%22showAxisLines%22:false,%22layout%22:%22xy%22%7D) to view the EM dataset using Neuroglancer (username: public-access | password: public).  
[Visit BossDB](https://bossdb.org/project/phelps_hildebrand_graham2021) to download the EM image data using Python.  
[Use gsutil](https://cloud.google.com/storage/docs/gsutil) to download the EM image data from Google Cloud as JPEG tiles formatted for CATMAID – files are located at [gs://vnc1\_r066/alignmentV3/jpgs\_for\_catmaid](https://console.cloud.google.com/storage/browser/vnc1_r066/alignmentV3/jpgs_for_catmaid?forceOnObjectsSortingFiltering=false&pageState=(%22StorageObjectListTable%22:(%22f%22:%22%5B%5D%22))&prefix=&project=prime-sunset-531)

 **View and download neuron reconstructions:**  
[Visit VirtualFlyBrain](https://fanc.catmaid.virtualflybrain.org/?help=true&pid=1&s0=8&sid0=1&tool=tracingtool&xp=173092.19999999998&yp=512482.5999999994&zp=55260) to view the dataset and neuron reconstructions using CATMAID.  
[Visit the paper’s GitHub repository](https://github.com/htem/GridTape_VNC_paper/tree/main/neuron_reconstructions) to download neuron reconstructions as .swc files.

 **Visit the paper’s [GitHub repository](https://github.com/htem/GridTape_VNC_paper) to:**  
[\[1\]](https://github.com/htem/GridTape_VNC_paper/tree/main/figures_and_analysis) access code (Python and MATLAB) used to perform analyses and generate figures for the paper.  
[\[2\]](https://github.com/htem/GridTape_VNC_paper/tree/main/template_registration_pipeline) access the command-line pipeline for elastically registering 3D image datasets, aimed at users wanting to register light microscopy stacks of VNC neurons to the VNC standard atlas.

 **Other:**  
We oversee a community of researchers collaboratively reconstructing neurons in the FANC dataset. Please contact [wei-chung\_lee@hms.harvard.edu](mailto:wei-chung_lee@hms.harvard.edu) for more information or to inquire about joining.