Department of Biological & Biomedical Sciences, Rowan University, Glassboro, NJ 08028, USA.
College of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, China.
Brief Bioinform. 2022 Nov 19;23(6). doi: 10.1093/bib/bbac412.
To understand how distinct memories are formed and stored in the brain is an important and fundamental question in neuroscience and computational biology. A population of neurons, termed engram cells, represents the physiological manifestation of a specific memory trace and is characterized by dynamic changes in gene expression, which in turn alters the synaptic connectivity and excitability of these cells. Recent applications of single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) are promising approaches for delineating the dynamic expression profiles in these subsets of neurons, and thus understanding memory-specific genes, their combinatorial patterns and regulatory networks. The aim of this article is to review and discuss the experimental and computational procedures of sc/snRNA-seq, new studies of molecular mechanisms of memory aided by sc/snRNA-seq in human brain diseases and related mouse models, and computational challenges in understanding the regulatory mechanisms underlying long-term memory formation.
理解不同记忆是如何在大脑中形成和存储的，是神经科学和计算生物学中的一个重要和基本问题。一群神经元，称为记忆细胞，代表特定记忆痕迹的生理表现，其特征是基因表达的动态变化，进而改变这些细胞的突触连接和兴奋性。单细胞 RNA 测序（scRNA-seq）和单核 RNA 测序（snRNA-seq）的最近应用是描绘这些神经元亚群中动态表达谱的有前途的方法，从而理解记忆特异性基因、它们的组合模式和调控网络。本文的目的是回顾和讨论 sc/snRNA-seq 的实验和计算程序、sc/snRNA-seq 辅助人类大脑疾病和相关小鼠模型中记忆分子机制的新研究，以及理解长时记忆形成背后调控机制的计算挑战。
