A genetic, genomic, and computational resource for exploring neural circuit function
- Janelia Research Campus, Howard Hughes Medical Institute, United States
Abstract
The anatomy of many neural circuits is being characterized with increasing resolution, but their molecular properties remain mostly unknown. Here, we characterize gene expression patterns in distinct neural cell types of the Drosophila visual system using genetic lines to access individual cell types, the TAPIN-seq method to measure their transcriptomes, and a probabilistic method to interpret these measurements. We used these tools to build a resource of high-resolution transcriptomes for 100 driver lines covering 67 cell types, available at http://www.opticlobe.com. Combining these transcriptomes with recently reported connectomes helps characterize how information is transmitted and processed across a range of scales, from individual synapses to circuit pathways. We describe examples that include identifying neurotransmitters, including cases of apparent co-release, generating functional hypotheses based on receptor expression, as well as identifying strong commonalities between different cell types.
Data availability
All raw and processed transcriptome data is available from NCBI GEO (accession GSE116969).
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A genetic, genomic, and computational resource for exploring neural circuit functionNCBI Gene Expression Omnibus, GSE116969.
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RNA sequencing of Drosophila melanogaster optic lobe cell typesNCBI Gene Expression Omnibus, GSE103772.
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Single-cell RNA sequencing of Drosophila melanogaster optic lobe cellsNCBI Gene Expression Omnibus, GSE103771.
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A single-cell transcriptome atlas of the ageing Drosophila brainNCBI Gene Expression Omnibus, GSE107451.
Article and author information
Author details
Funding
National Institute of Arthritis and Musculoskeletal and Skin Diseases (Intramural Research Program)
- Fred P Davis
Howard Hughes Medical Institute
- Fred P Davis
- Aljoscha Nern
- Serge Picard
- Michael B Reiser
- Gerald M Rubin
- Sean R Eddy
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Hugo J Bellen, Baylor College of Medicine, United States
Version history
- Received: August 6, 2019
- Accepted: January 14, 2020
- Accepted Manuscript published: January 15, 2020 (version 1)
- Version of Record published: February 21, 2020 (version 2)
Copyright
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
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A genetic, genomic, and computational resource for exploring neural circuit function
eLife 9:e50901.
https://doi.org/10.7554/eLife.50901
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