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Automatic Segmentation of Drosophila Neural Compartments Using GAL4 Expression Data Reveals Novel Visual Pathways.

Published version
Peer-reviewed

Type

Article

Change log

Authors

Panser, Karin 
Tirian, Laszlo 
Schulze, Florian 
Villalba, Santiago 
Jefferis, Gregory SXE 

Abstract

Identifying distinct anatomical structures within the brain and developing genetic tools to target them are fundamental steps for understanding brain function. We hypothesize that enhancer expression patterns can be used to automatically identify functional units such as neuropils and fiber tracts. We used two recent, genome-scale Drosophila GAL4 libraries and associated confocal image datasets to segment large brain regions into smaller subvolumes. Our results (available at https://strawlab.org/braincode) support this hypothesis because regions with well-known anatomy, namely the antennal lobes and central complex, were automatically segmented into familiar compartments. The basis for the structural assignment is clustering of voxels based on patterns of enhancer expression. These initial clusters are agglomerated to make hierarchical predictions of structure. We applied the algorithm to central brain regions receiving input from the optic lobes. Based on the automated segmentation and manual validation, we can identify and provide promising driver lines for 11 previously identified and 14 novel types of visual projection neurons and their associated optic glomeruli. The same strategy can be used in other brain regions and likely other species, including vertebrates.

Description

Keywords

clustering, enhancers, neuroanatomy, vision, Animals, Drosophila, Drosophila Proteins, Neurons, Optic Lobe, Nonmammalian, Transcription Factors, Visual Pathways

Journal Title

Curr Biol

Conference Name

Journal ISSN

0960-9822
1879-0445

Volume Title

26

Publisher

Elsevier BV
Sponsorship
European Research Council (649111)