Barteneva Natasha S, Vorobjev Ivan A
PCMM-Harvard Medical School, Boston Children's Hospital, Boston, MA, USA.
Department of Biology, School of Sciences and Technology, Nazarbayev University, Astana, Kazakhstan.
Methods Mol Biol. 2018;1745:3-23. doi: 10.1007/978-1-4939-7680-5_1.
In this paper, we review some of the recent advances in cellular heterogeneity and single-cell analysis methods. In modern research of cellular heterogeneity, there are four major approaches: analysis of pooled samples, single-cell analysis, high-throughput single-cell analysis, and lately integrated analysis of cellular population at a single-cell level. Recently developed high-throughput single-cell genetic analysis methods such as RNA-Seq require purification step and destruction of an analyzed cell often are providing a snapshot of the investigated cell without spatiotemporal context. Correlative analysis of multiparameter morphological, functional, and molecular information is important for differentiation of more uniform groups in the spectrum of different cell types. Simplified distributions (histograms and 2D plots) can underrepresent biologically significant subpopulations. Future directions may include the development of nondestructive methods for dissecting molecular events in intact cells, simultaneous correlative cellular analysis of phenotypic and molecular features by hybrid technologies such as imaging flow cytometry, and further progress in supervised and non-supervised statistical analysis algorithms.
在本文中,我们回顾了细胞异质性和单细胞分析方法的一些最新进展。在现代细胞异质性研究中,有四种主要方法:混合样本分析、单细胞分析、高通量单细胞分析以及最近的单细胞水平细胞群体综合分析。最近开发的高通量单细胞遗传分析方法,如RNA测序,通常需要纯化步骤且会破坏被分析细胞,这往往只能提供所研究细胞的一个时空无关联的快照。多参数形态、功能和分子信息的相关分析对于区分不同细胞类型谱中更均匀的群体很重要。简化的分布(直方图和二维图)可能无法充分代表具有生物学意义的亚群。未来的方向可能包括开发用于剖析完整细胞中分子事件的非破坏性方法,通过成像流式细胞术等混合技术对表型和分子特征进行同步相关细胞分析,以及监督和非监督统计分析算法的进一步发展。