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基于光谱库的单细胞蛋白质组学解析细胞异质性。

Spectral Library-Based Single-Cell Proteomics Resolves Cellular Heterogeneity.

机构信息

The Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX 77030, USA.

Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA.

出版信息

Cells. 2022 Aug 7;11(15):2450. doi: 10.3390/cells11152450.

Abstract

Dissecting the proteome of cell types and states at single-cell resolution, while being highly challenging, has significant implications in basic science and biomedicine. Mass spectrometry (MS)-based single-cell proteomics represents an emerging technology for system-wide, unbiased profiling of proteins in single cells. However, significant challenges remain in analyzing an extremely small amount of proteins collected from a single cell, as a proteome-wide amplification of proteins is not currently feasible. Here, we report an integrated spectral library-based single-cell proteomics (SLB-SCP) platform that is ultrasensitive and well suited for a large-scale analysis. To overcome the low MS/MS signal intensity intrinsically associated with a single-cell analysis, this approach takes an alternative approach by extracting a breadth of information that specifically defines the physicochemical characteristics of a peptide from MS1 spectra, including monoisotopic mass, isotopic distribution, and retention time (hydrophobicity), and uses a spectral library for proteomic identification. This conceptually unique MS platform, coupled with the DIRECT sample preparation method, enabled identification of more than 2000 proteins in a single cell to distinguish different proteome landscapes associated with cellular types and heterogeneity. We characterized individual normal and cancerous pancreatic ductal cells (HPDE and PANC-1, respectively) and demonstrated the substantial difference in the proteomes between HPDE and PANC-1 at the single-cell level. A significant upregulation of multiple protein networks in cancer hallmarks was identified in the PANC-1 cells, functionally discriminating the PANC-1 cells from the HPDE cells. This integrated platform can be built on high-resolution MS and widely accepted proteomic software, making it possible for community-wide applications.

摘要

以单细胞分辨率解析细胞类型和状态的蛋白质组,虽然极具挑战性,但在基础科学和生物医学方面具有重要意义。基于质谱(MS)的单细胞蛋白质组学代表了一种新兴的技术,可用于系统、无偏地分析单细胞中的蛋白质。然而,从单个细胞中收集到的极少量蛋白质进行分析仍然存在重大挑战,因为目前还不可能对蛋白质组进行广泛的扩增。在这里,我们报告了一种基于集成光谱库的单细胞蛋白质组学(SLB-SCP)平台,该平台具有超灵敏性,非常适合大规模分析。为了克服与单细胞分析内在相关的低 MS/MS 信号强度,该方法采用了一种替代方法,从 MS1 光谱中提取广泛的信息,这些信息专门定义了肽的物理化学特性,包括单一同位素质量、同位素分布和保留时间(疏水性),并使用光谱库进行蛋白质组鉴定。这种概念独特的 MS 平台,加上 DIRECT 样品制备方法,使我们能够在单个细胞中鉴定出 2000 多种蛋白质,从而区分与细胞类型和异质性相关的不同蛋白质组景观。我们对单个正常和癌变的胰腺导管细胞(HPDE 和 PANC-1)进行了特征描述,并在单细胞水平上证明了 HPDE 和 PANC-1 之间蛋白质组的实质性差异。在 PANC-1 细胞中鉴定到多个与癌症标志相关的蛋白质网络的显著上调,这些功能区分了 PANC-1 细胞和 HPDE 细胞。这个集成平台可以建立在高分辨率 MS 和广泛接受的蛋白质组学软件之上,从而使其有可能在整个社区中应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/24cd/9368228/baf08bd4be45/cells-11-02450-g001.jpg

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