Kanazawa University, Kanazawa, Ishikawa, Japan.
Adv Exp Med Biol. 2019;1129:51-61. doi: 10.1007/978-981-13-6037-4_4.
Research on the hierarchical nature of cell differentiation and heterogeneity in tissues has been performed by isolating and identifying cells by the use of monoclonal antibodies, cell sorting, microdissection, and functional assays. However, it is difficult to analyze continuous changes in cell differentiation and the identification of cells for which cell markers are unclear. Furthermore, cell populations considered identical were shown to be diverse. Recently, single cell gene expression analysis was performed to help understand the complexity of cell populations. Single-cell analysis can analyze the diversity of individual cell populations as well as the tissue microenvironment, and is extremely useful for research on intercellular interactions in diseases and identifying specific marker genes. Recent advances in technology have made it possible to analyze hundreds of single cells. In this paper, we introduce our newly developed well-based single-cell transcriptome method, which includes other methods.
通过使用单克隆抗体、细胞分选、显微切割和功能测定等方法对组织中细胞分化的层次结构和异质性进行了研究。然而,分析细胞分化的连续变化以及鉴定细胞标记物不明确的细胞是困难的。此外,被认为是相同的细胞群体被证明是多样化的。最近,进行了单细胞基因表达分析以帮助理解细胞群体的复杂性。单细胞分析可以分析单个细胞群体以及组织微环境的多样性,对于研究疾病中的细胞间相互作用以及鉴定特定的标记基因非常有用。技术的最新进展使得分析数百个单细胞成为可能。在本文中,我们介绍了我们新开发的基于孔的单细胞转录组学方法,该方法包括其他方法。