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单细胞转位和分泌分析检测法(TransSeA)。

A single-cell translocation and secretion assay (TransSeA).

机构信息

Materials Science and Engineering Program, University of California at San Diego, La Jolla, California, USA.

出版信息

Lab Chip. 2018 Oct 9;18(20):3154-3162. doi: 10.1039/c8lc00821c.

Abstract

Understanding biological heterogeneity at the single cell level is required for advancing insights into the complexity of human physiology and diseases. While advances in technological and analytical methods have afforded unprecedented glimpses of this heterogeneity, the information captured to date largely represents one-time "snap" shots of single cell physiology. To address the limits of existing methods and to accelerate discoveries from single cell studies, we developed a single-cell translocation and secretion assay (TransSeA) that supports time lapse analysis, enables molecular cargo analysis of secretions such as extracellular vesicles (EVs) from single cells, allows massively parallel single cell transfer according to user-defined cell selection criteria, and supports tracking of phenotypes between parental and progeny cells derived from single cells. To demonstrate the unique capabilities and efficiencies of the assay, we present unprecedented single cell studies related to cell secretions, EV cargos and cell intrinsic properties. Although used as examples to demonstrate the feasibility and versatility of the technology, the studies already provided insights into key unanswered questions such as the microRNAs carried by EVs, the relationships between EV secretion rates and gene expressions, and the spontaneous, trans-generational phenotypic changes in EV secretion between parental and progeny cells.

摘要

要深入了解人类生理学和疾病的复杂性,就必须在单细胞水平上理解生物学异质性。虽然技术和分析方法的进步为研究这种异质性提供了前所未有的视角,但迄今为止所捕获的信息在很大程度上仅代表了单细胞生理学的一次性“快照”。为了克服现有方法的局限性并加速单细胞研究的发现,我们开发了一种单细胞转位和分泌分析(TransSeA),该分析支持时程分析,能够对细胞外囊泡(EV)等分泌分子进行货物分析,允许根据用户定义的细胞选择标准进行大规模平行的单细胞转移,并支持对单细胞衍生的亲本和后代细胞之间的表型进行跟踪。为了展示该测定法的独特功能和效率,我们提出了与细胞分泌、EV 货物和细胞内在特性相关的前所未有的单细胞研究。尽管这些研究被用作证明该技术可行性和多功能性的示例,但它们已经提供了对一些关键未解决问题的深入了解,例如 EV 携带的 microRNAs、EV 分泌率与基因表达之间的关系,以及亲本和后代细胞之间 EV 分泌的自发、跨代表型变化。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e699/6177299/dac3aa5fb0ca/nihms-987959-f0001.jpg

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