蛋白质组
蛋白质组学
激光捕获显微切割
计算生物学
背景(考古学)
质谱法
质谱成像
生物
生物信息学
化学
生物化学
基因表达
色谱法
古生物学
基因
作者
Florian A. Rosenberger,Marvin Thielert,Maximilian T. Strauss,Lisa Schweizer,Constantin Ammar,Sophia C. Mädler,Andreas Metousis,Patricia Skowronek,Maria Wahle,Katherine Madden,Janine Schniering,А. А. Семенова,Herbert B. Schiller,Edwin H. Rodriguez,Thierry M. Nordmann,Andreas Mund,Matthias Mann
出处
期刊:Nature Methods
[Springer Nature]
日期:2023-10-01
卷期号:20 (10): 1530-1536
被引量:57
标识
DOI:10.1038/s41592-023-02007-6
摘要
Single-cell proteomics by mass spectrometry is emerging as a powerful and unbiased method for the characterization of biological heterogeneity. So far, it has been limited to cultured cells, whereas an expansion of the method to complex tissues would greatly enhance biological insights. Here we describe single-cell Deep Visual Proteomics (scDVP), a technology that integrates high-content imaging, laser microdissection and multiplexed mass spectrometry. scDVP resolves the context-dependent, spatial proteome of murine hepatocytes at a current depth of 1,700 proteins from a cell slice. Half of the proteome was differentially regulated in a spatial manner, with protein levels changing dramatically in proximity to the central vein. We applied machine learning to proteome classes and images, which subsequently inferred the spatial proteome from imaging data alone. scDVP is applicable to healthy and diseased tissues and complements other spatial proteomics and spatial omics technologies.
科研通智能强力驱动
Strongly Powered by AbleSci AI