Back to Search Start Over

A novel technology for in vivo detection of cell type-specific neural connection with AQP1-encoding rAAV2-retro vector and metal-free MRI

Authors :
Ning Zheng
Mei Li
Yang Wu
Challika Kaewborisuth
Zhen Li
Zhu Gui
Jinfeng Wu
Aoling Cai
Kangguang Lin
Kuan-Pin Su
Hongbing Xiang
Xuebi Tian
Anne Manyande
Fuqiang Xu
Jie Wang
Source :
NeuroImage, Vol 258, Iss , Pp 119402- (2022)
Publication Year :
2022
Publisher :
Elsevier, 2022.

Abstract

A mammalian brain contains numerous neurons with distinct cell types for complex neural circuits. Virus-based circuit tracing tools are powerful in tracking the interaction among the different brain regions. However, detecting brain-wide neural networks in vivo remains challenging since most viral tracing systems rely on postmortem optical imaging. We developed a novel approach that enables in vivo detection of brain-wide neural connections based on metal-free magnetic resonance imaging (MRI). The recombinant adeno-associated virus (rAAV) with retrograde ability, the rAAV2-retro, encoding the human water channel aquaporin 1 (AQP1) MRI reporter gene was generated to label neural connections. The mouse was micro-injected with the virus at the Caudate Putamen (CPU) region and subjected to detection with Diffusion-weighted MRI (DWI). The prominent structure of the CPU-connected network was clearly defined. In combination with a Cre-loxP system, rAAV2-retro expressing Cre-dependent AQP1 provides a CPU-connected network of specific type neurons. Here, we established a sensitive, metal-free MRI-based strategy for in vivo detection of cell type-specific neural connections in the whole brain, which could visualize the dynamic changes of neural networks in rodents and potentially in non-human primates.

Details

Language :
English
ISSN :
10959572
Volume :
258
Issue :
119402-
Database :
Directory of Open Access Journals
Journal :
NeuroImage
Publication Type :
Academic Journal
Accession number :
edsdoj.4a359631c677443aa9e8f2157d18c54d
Document Type :
article
Full Text :
https://doi.org/10.1016/j.neuroimage.2022.119402