Nature NeuroscienceNeurosciencePeer-reviewed journal article
Viral manipulation of functionally distinct neurons from mice to humans
Douglas Vormstein-SchneideriD, Jessica D. LiniD, Kenneth A. PelkeyiD, Ramesh ChittajalluiD, Baolin GuoiD, Mario A. Arias-GarcíaiD, Kathryn C. AllawayiD, Sofia Sakopoulos, Gates P. Schneider, Olivia Stevenson, Josselyn VergaraiD, Jitendra Sharma, Qiangge ZhangiD, Tom P. FrankeniD, Jared B. SmithiD, Leena Ali IbrahimiD, Kevin J. MastroiD, Ehsan SabriiD, Shuhan HuangiD, Emilia FavuzziiD, Timothy J. Burbridge, Qing XuiD, Lihua GuoiD, Ian A. VogeliD, Vanessa SáncheziD, , Jordane DimidschsteiniD
Abstract
Recent success in identifying gene regulatory elements in the context of recombinant adeno-associated virus vectors have enabled cell type-restricted gene expression. However, within the cerebral cortex these tools are presently limited to broad classes of neurons. To overcome this limitation, we developed a strategy that led to the identification of multiple novel enhancers to target functionally distinct neuronal subtypes. By investigating the regulatory landscape of the disease gene Scn1a, we identified enhancers that target the breadth of its expression, including two that are selective for parvalbumin and vasoactive intestinal peptide cortical interneurons. Demonstrating the functional utility of these elements, we found that the PV-specific enhancer allowed for the selective targeting and manipulation of these neurons across species, from mice to humans. Finally, we demonstrate that our selection method is generalizable to other genes and characterize four additional PV-specific enhancers with exquisite specificity for distinct regions of the brain. Altogether, these viral tools can be used for cell-type specific circuit manipulation and hold considerable promise for use in therapeutic interventions.
- organism: mouse
- organism: human
- assay: DNA methylation
- assay: CRISPR screen
- research focus: gene regulatory network inference
- research focus: cell type annotation
- approach: statistical model
- Citations
- 235
- FWCI
- 16.10
- Field percentile
- top 1%
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