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Landscape of ribosome-engaged transcript isoforms reveals extensive neuronal-cell-class-specific alternative splicing programs

Furlanis, Elisabetta and Traunmüller, Lisa and Fucile, Geoffrey and Scheiffele, Peter. (2019) Landscape of ribosome-engaged transcript isoforms reveals extensive neuronal-cell-class-specific alternative splicing programs. Nature neuroscience, 22 (10). pp. 1709-1717.

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Official URL: https://edoc.unibas.ch/71771/

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Abstract

Nervous system function relies on complex assemblies of distinct neuronal cell types that have unique anatomical and functional properties instructed by molecular programs. Alternative splicing is a key mechanism for the expansion of molecular repertoires, and protein splice isoforms shape neuronal cell surface recognition and function. However, the logic of how alternative splicing programs are arrayed across neuronal cells types is poorly understood. We systematically mapped ribosome-associated transcript isoforms in genetically defined neuron types of the mouse forebrain. Our dataset provides an extensive resource of transcript diversity across major neuron classes. We find that neuronal transcript isoform profiles reliably distinguish even closely related classes of pyramidal cells and inhibitory interneurons in the mouse hippocampus and neocortex. These highly specific alternative splicing programs selectively control synaptic proteins and intrinsic neuronal properties. Thus, transcript diversification via alternative splicing is a central mechanism for the functional specification of neuronal cell types and circuits.
Faculties and Departments:05 Faculty of Science > Departement Biozentrum
05 Faculty of Science > Departement Biozentrum > Neurobiology > Cell Biology (Scheiffele)
UniBasel Contributors:Scheiffele, Peter
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:Nature America
ISSN:1097-6256
e-ISSN:1546-1726
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:18 Aug 2020 12:02
Deposited On:18 Aug 2020 12:02

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