单细胞蛋白质组学入门回顾与实用指南》。

IF 3.4 4区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Proteomics Pub Date : 2024-11-16 DOI:10.1002/pmic.202400021
Hsien-Jung L Lin, Kei G I Webber, Andikan J Nwosu, Ryan T Kelly
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引用次数: 0

摘要

近年来,单细胞蛋白质组学(Single-cell proteomics,SCP)取得了长足的进步,专门用于制备和分析单细胞的新工具现已在市场上销售,供研究人员使用。这一领域已经非常成熟,任何有能力分离单细胞并进行大规模蛋白质组学分析的实验室都可以广泛利用。在这篇综述中,我们重点介绍了 SCP 领域最近开展的工作,这些工作大大降低了进入该领域的门槛,从而为新进入 SCP 领域的人员提供了实用指南。我们概述了基本原理,并报告了完成成功 SCP 实验关键步骤的多种途径,包括样品制备、分离以及质谱数据采集和分析。我们建议研究人员从无标记 SCP 工作流程开始,因为与基于标记的多路复用策略相比,获得高质量和定量准确的结果更为直接。利用这些便捷的方法,研究人员可以自信地进行 SCP 实验,并在单细胞水平上做出有意义的发现。
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Review and Practical Guide for Getting Started With Single-Cell Proteomics.

Single-cell proteomics (SCP) has advanced significantly in recent years, with new tools specifically designed for the preparation and analysis of single cells now commercially available to researchers. The field is sufficiently mature to be broadly accessible to any lab capable of isolating single cells and performing bulk-scale proteomic analyses. In this review, we highlight recent work in the SCP field that has significantly lowered the barrier to entry, thus providing a practical guide for those who are newly entering the SCP field. We outline the fundamental principles and report multiple paths to accomplish the key steps of a successful SCP experiment including sample preparation, separation, and mass spectrometry data acquisition and analysis. We recommend that researchers start with a label-free SCP workflow, as achieving high-quality and quantitatively accurate results is more straightforward than label-based multiplexed strategies. By leveraging these accessible means, researchers can confidently perform SCP experiments and make meaningful discoveries at the single-cell level.

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来源期刊
Proteomics
Proteomics 生物-生化研究方法
CiteScore
6.30
自引率
5.90%
发文量
193
审稿时长
3 months
期刊介绍: PROTEOMICS is the premier international source for information on all aspects of applications and technologies, including software, in proteomics and other "omics". The journal includes but is not limited to proteomics, genomics, transcriptomics, metabolomics and lipidomics, and systems biology approaches. Papers describing novel applications of proteomics and integration of multi-omics data and approaches are especially welcome.
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