eLife (Feb 2023)

A searchable image resource of Drosophila GAL4 driver expression patterns with single neuron resolution

  • Geoffrey W Meissner,
  • Aljoscha Nern,
  • Zachary Dorman,
  • Gina M DePasquale,
  • Kaitlyn Forster,
  • Theresa Gibney,
  • Joanna H Hausenfluck,
  • Yisheng He,
  • Nirmala A Iyer,
  • Jennifer Jeter,
  • Lauren Johnson,
  • Rebecca M Johnston,
  • Kelley Lee,
  • Brian Melton,
  • Brianna Yarbrough,
  • Christopher T Zugates,
  • Jody Clements,
  • Cristian Goina,
  • Hideo Otsuna,
  • Konrad Rokicki,
  • Robert R Svirskas,
  • Yoshinori Aso,
  • Gwyneth M Card,
  • Barry J Dickson,
  • Erica Ehrhardt,
  • Jens Goldammer,
  • Masayoshi Ito,
  • Dagmar Kainmueller,
  • Wyatt Korff,
  • Lisa Mais,
  • Ryo Minegishi,
  • Shigehiro Namiki,
  • Gerald M Rubin,
  • Gabriella R Sterne,
  • Tanya Wolff,
  • Oz Malkesman,
  • FlyLight Project Team

DOI
https://doi.org/10.7554/eLife.80660
Journal volume & issue
Vol. 12

Abstract

Read online

Precise, repeatable genetic access to specific neurons via GAL4/UAS and related methods is a key advantage of Drosophila neuroscience. Neuronal targeting is typically documented using light microscopy of full GAL4 expression patterns, which generally lack the single-cell resolution required for reliable cell type identification. Here, we use stochastic GAL4 labeling with the MultiColor FlpOut approach to generate cellular resolution confocal images at large scale. We are releasing aligned images of 74,000 such adult central nervous systems. An anticipated use of this resource is to bridge the gap between neurons identified by electron or light microscopy. Identifying individual neurons that make up each GAL4 expression pattern improves the prediction of split-GAL4 combinations targeting particular neurons. To this end, we have made the images searchable on the NeuronBridge website. We demonstrate the potential of NeuronBridge to rapidly and effectively identify neuron matches based on morphology across imaging modalities and datasets.

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