Spatial Proteomics of a Human Brain Tumour

The spatial organisation of Cellular protein expression profiles within tissues determines cellular function and are key to understanding disease pathology. To define molecular phenotypes in the spatial context of tissue, there is a need for unbiased, quantitative technology capable of mapping proteomes within tissue structures. Here, we present a workflow for spatially resolved, quantitative proteomics of tissue that generates maps of protein expression across a tissue slice derived from a human atypical teratoid-rhabdoid tumour (AT/RT). We employ spatially-aware statistical methods that do not require prior knowledge of the fine tissue structure to detect proteins and pathways with varying spatial abundance patterns. We identified PYGL, ASPH and CD45 as spatial markers for tumour boundary and reveal immune response driven, spatially organized protein networks of the extracellular tumour matrix. Overall, this work informs on methods for spatially resolved deep proteo-phenotyping of tissue heterogeneity, which will push the boundaries of understanding tissue biology and pathology at the molecular level.

组织内细胞蛋白质表达谱的空间排布，决定细胞功能，亦是解析疾病病理机制的核心关键。为在组织的空间语境下定义分子表型，亟需能够在组织结构内绘制蛋白质组（proteome）图谱的无偏倚、定量化技术。本研究报道一种适用于组织的空间分辨定量蛋白质组学工作流程，可基于人类非典型畸胎瘤样横纹肌样瘤（AT/RT）的组织切片，生成全组织切片的蛋白质表达图谱。我们采用无需预先知晓精细组织结构的空间感知统计方法，以此检测具有不同空间丰度模式的蛋白质及信号通路。本研究鉴定出PYGL、ASPH与CD45可作为肿瘤边界的空间标志物，并揭示了由免疫应答驱动的、细胞外肿瘤基质中空间有序的蛋白质网络。总体而言，本研究为组织异质性的空间分辨深度蛋白质组表型分型提供了方法学参考，将拓展人们在分子层面解析组织生物学与病理机制的研究边界。