Spatial single-cell mass spectrometry defines zonation of the hepatocyte proteome

Single-cell proteomics by mass spectrometry (MS) 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 MS. scDVP resolves the context-dependent, spatial proteome of murine hepatocytes at a current depth of 1,700 proteins from a slice of a cell. 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 or spatial omics technologies.

基于质谱（Mass Spectrometry, MS）的单细胞蛋白质组学，正逐渐成为解析生物异质性的高效且无偏的研究方法。迄今为止，该技术仅局限于培养细胞的研究；若能将其拓展至复杂组织样本，则将极大地深化生物学认知。本文介绍了单细胞深度视觉蛋白质组学（single-cell Deep Visual Proteomics, scDVP）技术，该技术整合了高内涵成像、激光显微切割与多重质谱技术。scDVP可解析小鼠肝细胞的空间语境依赖性蛋白质组，当前可从单张细胞切片中鉴定出1700种蛋白质。该蛋白质组中有半数呈现空间依赖性差异调控，在中央静脉附近的蛋白质水平会发生显著变化。我们将机器学习方法应用于蛋白质组分类与成像数据，后续仅通过成像数据即可推断出样本的空间蛋白质组信息。scDVP可应用于健康与病变组织的研究，同时可作为其他空间蛋白质组学或空间组学技术的重要补充。