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Elife. 2020 Jan 15;9. pii: e50901. doi: 10.7554/eLife.50901.

A genetic, genomic, and computational resource for exploring neural circuit function.

Author information

Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States.
Molecular Immunology and Inflammation Branch, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, United States.
Howard Hughes Medical Institute and Department of Molecular and Cellular Biology, Harvard University, Cambridge, United States.
John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, United States.
Cold Spring Harbor Laboratory, Cold Spring Harbor, United States.
Contributed equally


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 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.


In the brain, large numbers of different types of neurons connect with each other to form complex networks. In recent years, researchers have made great progress in mapping all the connections between these cells, creating ‘wiring diagrams’ known as connectomes. However, charting the connections between neurons does not give all the answers as to how the brain works; for example, it does not necessarily reveal the nature of the information two connected cells exchange. Assessing which genes are switched on in different neurons can give insight into neuronal properties that are not obvious from physical connections alone. To fill that knowledge gap, Davis, Nern et al. aimed to measure the genes expressed in a well-characterized network of neurons in the fruit fly visual system. First, 100 fly strains were established, each carrying a single type of neuron colored with a fluorescent marker. Then, a biochemical approach was developed to extract the part of the cell that contains the genetic code from the neurons with the marker. Finally, a statistical tool was used to assess which genes were on in each type of neurons. This led to the creation of a database that shows whether 15,000 genes in each neuron type across 100 fly strains were switched on. Combining this information with previous knowledge about the flies’ visual system revealed new information: for example, it helped to understand which chemicals the neurons use to communicate, and whether certain cells activate or inhibit each other. The work by Davis, Nern et al. demonstrates how genetic approaches can complement other methods, and it offers a new tool for other scientists to use in their work. With more advanced genetic methods, it may one day become possible to better grasp how complex brains in other organisms are organized, and how they are disrupted in disease.


D. melanogaster; gene expression; genetics; genomics; neural circuit; neuroscience; visual system

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