Monosynaptic Tracing Success Depends Critically on Helper Virus Concentrations.

AAV (adeno-associated virus) circuit tracing monosynaptic tracing rabies virus

Journal

Frontiers in synaptic neuroscience
ISSN: 1663-3563
Titre abrégé: Front Synaptic Neurosci
Pays: Switzerland
ID NLM: 101548972

Informations de publication

Date de publication:
2020
Historique:
received: 20 08 2019
accepted: 23 01 2020
entrez: 3 3 2020
pubmed: 3 3 2020
medline: 3 3 2020
Statut: epublish

Résumé

Monosynaptically-restricted transsynaptic tracing using deletion-mutant rabies virus (RV) has become a widely used technique in neuroscience, allowing identification, imaging, and manipulation of neurons directly presynaptic to a starting neuronal population. Its most common implementation is to use Cre mouse lines in combination with Cre-dependent "helper" adeno-associated viral vectors (AAVs) to supply the required genes to the targeted population before subsequent injection of a first-generation (ΔG) rabies viral vector. Here we show that the efficiency of transsynaptic spread and the degree of nonspecific labeling in wild-type control animals depend strongly on the concentrations of these helper AAVs. Our results suggest practical guidelines for achieving good results.

Identifiants

pubmed: 32116642
doi: 10.3389/fnsyn.2020.00006
pmc: PMC7033752
doi:

Types de publication

Journal Article

Langues

eng

Pagination

6

Subventions

Organisme : NIMH NIH HHS
ID : RF1 MH120017
Pays : United States

Informations de copyright

Copyright © 2020 Lavin, Jin, Lea and Wickersham.

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Auteurs

Thomas K Lavin (TK)

McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States.

Lei Jin (L)

McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States.

Nicholas E Lea (NE)

McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States.

Ian R Wickersham (IR)

McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States.

Classifications MeSH