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Somatosensory neuron types and their neural networks as revealed via single-cell transcriptomics.

Somatosensory neuron types and their neural networks as revealed via single-cell transcriptomics.

Authors :
Wang, Kaikai
Cai, Bing
Song, Yuran
Chen, Yan
Zhang, Xu
Source :
Trends in Neurosciences. Aug2023, Vol. 46 Issue 8, p654-666. 13p.
Publication Year :
2023

Abstract

Through multiple scRNA-seq approaches, mouse DRG neurons have been classified into multiple cell types. Across studies, 17 consistent cell types can be identified, each with distinct molecular markers. Most DRG neuron clusters are equivalent in mouse and human and have various subclusters. scRNA-seq analyses revealed transcriptomic changes in DRG neuron types after peripheral nerve injury and the induction of new neuron clusters. The neural networks of different DRG neuron types provide insights into the modality selectivity of each type and into the associated pathways for processing distinct sensory modalities in the brain. Single-cell RNA sequencing (scRNA-seq) has allowed profiling cell types of the dorsal root ganglia (DRG) and their transcriptional states in physiology and chronic pain. However, the evaluation criteria used in previous studies to classify DRG neurons varied, which presents difficulties in determining the various types of DRG neurons. In this review, we aim to integrate findings from previous transcriptomic studies of the DRG. We first briefly introduce the history of DRG-neuron cell-type profiling, and discuss the advantages and disadvantages of different scRNA-seq methods. We then examine the classification of DRG neurons based on single-cell profiling under physiological and pathological conditions. Finally, we propose further studies on the somatosensory system at the molecular, cellular, and neural network levels. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01662236
Volume :
46
Issue :
8
Database :
Academic Search Index
Journal :
Trends in Neurosciences
Publication Type :
Academic Journal
Accession number :
164854574
Full Text :
https://doi.org/10.1016/j.tins.2023.05.005