使用多重成像驱动的深度视觉蛋白质组学对扁桃体癌微环境进行空间蛋白质组分析的方案

Protocol for spatial proteomic profiling of tonsil cancer microenvironments using multiplexed imaging-powered deep visual proteomics.

作者信息

Zheng Xiang, Mund Andreas, Mann Matthias

机构信息

Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen, Denmark; Department of Biomedicine, Aarhus University, 8000 Aarhus, Denmark.

Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen, Denmark; OmicVision Biosciences, BioInnovation Institute, 2200 Copenhagen, Denmark.

出版信息

STAR Protoc. 2025 Jun 18;6(3):103901. doi: 10.1016/j.xpro.2025.103901.

DOI:10.1016/j.xpro.2025.103901

PMID:40536875

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12221282/

Abstract

Here, we present a protocol for spatial proteomic profiling of the tumor microenvironment in tonsil cancer using multiplexed imaging-powered deep visual proteomics (mipDVP). We describe steps for automated 22-plex immunofluorescence staining and imaging on formalin-fixed paraffin-embedded (FFPE) tissue sections, automated single-cell laser microdissection, and single-cell-type mass spectrometry. This workflow enables the spatially resolved isolation of distinct cell populations for proteomic analysis. We optimized this protocol for studying tumor-immune interactions, where it facilitates the systematic identification of biomarkers and functional cellular networks. For complete details on the use and execution of this protocol, please refer to Zheng et al..

摘要

在此，我们展示了一种使用多重成像驱动的深度视觉蛋白质组学（mipDVP）对扁桃体癌肿瘤微环境进行空间蛋白质组学分析的方案。我们描述了在福尔马林固定石蜡包埋（FFPE）组织切片上进行自动22重免疫荧光染色和成像、自动单细胞激光显微切割以及单细胞类型质谱分析的步骤。该工作流程能够对不同细胞群体进行空间分辨分离以进行蛋白质组学分析。我们针对研究肿瘤 - 免疫相互作用对该方案进行了优化，它有助于系统地鉴定生物标志物和功能性细胞网络。有关该方案使用和执行的完整详细信息，请参考郑等人的研究。