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Transcriptome analysis for the development of cell-type specific labeling to study olfactory circuits

Authors :
Yu-Pei Huang
Hiroaki Matsunami
Fumihiro Sugiyama
Cary Zhang
Izumi Fukunaga
Anzhelika Koldaeva
Satoru Takahashi
Taha Soliman
Seiya Mizuno
Janine Kristin Reinert
Publication Year :
2020
Publisher :
Cold Spring Harbor Laboratory, 2020.

Abstract

In each sensory system of the brain, mechanisms exist to extract distinct features from stimuli to generate a variety of behavioural repertoires. These often correspond to different cell types at some stage in sensory processing. In the mammalian olfactory system, complex information processing starts in the olfactory bulb, whose output is conveyed by mitral and tufted cells (MCs and TCs). Despite many differences between them, and despite the crucial position they occupy in the information hierarchy, little is known how these two types of projection neurons differ at the mRNA level. Here, we sought to identify genes that are differentially expressed between MCs and TCs, with an ultimate goal to generate a cell-type specific Cre-driver line, starting from a transcriptome analysis using a large and publicly available single-cell RNA-seq dataset (Zeisel et al., 2018). Despite many genes showing differential expressions, we identified only a few that were abundantly and consistently expressed only in MCs. After further validating these putative markers usingin-situhybridization, two genes, namelyPkibandLbdh2, remained as promising candidates. Using CRISPR/Cas9-mediated gene editing, we generated Cre-driver lines and analysed the resulting recombination patterns. This analysis indicated that our new inducible Cre-driver line,Lbhd2-CreERT2, can be used to genetically label MCs in a tamoxifen dose-dependent manner, as assessed by soma locations, projection patterns and sensory-evoked responses. Hence this line is a promising tool for future investigations of cell-type specific contributions to olfactory processing and demonstrates the power of publicly accessible data in accelerating science.

Details

Database :
OpenAIRE
Accession number :
edsair.doi...........d964f8b8fa493d069cfc8c8f1a78f8e8
Full Text :
https://doi.org/10.1101/2020.11.30.403865