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An Optimized Mouse Brain Atlas for Automated Mapping and Quantification of Neuronal Activity Using iDISCO+ and Light Sheet Fluorescence Microscopy.

Authors :
Perens J
Salinas CG
Skytte JL
Roostalu U
Dahl AB
Dyrby TB
Wichern F
Barkholt P
Vrang N
Jelsing J
Hecksher-Sørensen J
Source :
Neuroinformatics [Neuroinformatics] 2021 Jul; Vol. 19 (3), pp. 433-446.
Publication Year :
2021

Abstract

In recent years, the combination of whole-brain immunolabelling, light sheet fluorescence microscopy (LSFM) and subsequent registration of data with a common reference atlas, has enabled 3D visualization and quantification of fluorescent markers or tracers in the adult mouse brain. Today, the common coordinate framework version 3 developed by the Allen's Institute of Brain Science (AIBS CCFv3), is widely used as the standard brain atlas for registration of LSFM data. However, the AIBS CCFv3 is based on histological processing and imaging modalities different from those used for LSFM imaging and consequently, the data differ in both tissue contrast and morphology. To improve the accuracy and speed by which LSFM-imaged whole-brain data can be registered and quantified, we have created an optimized digital mouse brain atlas based on immunolabelled and solvent-cleared brains. Compared to the AIBS CCFv3 atlas, our atlas resulted in faster and more accurate mapping of neuronal activity as measured by c-Fos expression, especially in the hindbrain. We further demonstrated utility of the LSFM atlas by comparing whole-brain quantitative changes in c-Fos expression following acute administration of semaglutide in lean and diet-induced obese mice. In combination with an improved algorithm for c-Fos detection, the LSFM atlas enables unbiased and computationally efficient characterization of drug effects on whole-brain neuronal activity patterns. In conclusion, we established an optimized reference atlas for more precise mapping of fluorescent markers, including c-Fos, in mouse brains processed for LSFM.

Details

Language :
English
ISSN :
1559-0089
Volume :
19
Issue :
3
Database :
MEDLINE
Journal :
Neuroinformatics
Publication Type :
Academic Journal
Accession number :
33063286
Full Text :
https://doi.org/10.1007/s12021-020-09490-8