Department of Life Sciences, Pohang University of Science and Technology (POSTECH), Pohang, Gyeongbuk, 37673, Republic of Korea.
Institute for Convergence Research and Education in Advanced Technology, Yonsei University, Seoul, 03722, Republic of Korea.
Exp Mol Med. 2023 Oct;55(10):2105-2115. doi: 10.1038/s12276-023-01093-y. Epub 2023 Oct 2.
The brain is one of the most complex living tissue types and is composed of an exceptional diversity of cell types displaying unique functional connectivity. Single-cell RNA sequencing (scRNA-seq) can be used to efficiently map the molecular identities of the various cell types in the brain by providing the transcriptomic profiles of individual cells isolated from the tissue. However, the lack of spatial context in scRNA-seq prevents a comprehensive understanding of how different configurations of cell types give rise to specific functions in individual brain regions and how each distinct cell is connected to form a functional unit. To understand how the various cell types contribute to specific brain functions, it is crucial to correlate the identities of individual cells obtained through scRNA-seq with their spatial information in intact tissue. Spatial transcriptomics (ST) can resolve the complex spatial organization of cell types in the brain and their connectivity. Various ST tools developed during the past decade based on imaging and sequencing technology have permitted the creation of functional atlases of the brain and have pulled the properties of neural circuits into ever-sharper focus. In this review, we present a summary of several ST tools and their applications in neuroscience and discuss the unprecedented insights these tools have made possible.
大脑是最复杂的活体组织之一,由具有独特功能连接的异常多样化的细胞类型组成。单细胞 RNA 测序 (scRNA-seq) 可以通过提供从组织中分离的单个细胞的转录组谱,有效地绘制大脑中各种细胞类型的分子特征。然而,scRNA-seq 缺乏空间背景,无法全面了解不同类型的细胞配置如何在特定的脑区产生特定的功能,以及每个不同的细胞如何连接形成一个功能单元。为了了解各种细胞类型如何为特定的大脑功能做出贡献,关键是要将通过 scRNA-seq 获得的单个细胞的身份与其在完整组织中的空间信息相关联。空间转录组学 (ST) 可以解决大脑中细胞类型的复杂空间组织及其连接性。过去十年中基于成像和测序技术开发的各种 ST 工具允许创建大脑的功能图谱,并使神经回路的特性更加引人注目。在这篇综述中,我们总结了几种 ST 工具及其在神经科学中的应用,并讨论了这些工具带来的前所未有的见解。