Panoramic spatial enhanced resolution proteomics (PSERP) reveals tumor architecture and heterogeneity in gliomas

The spatial proteomic profiling of complex tissues is essential for investigating cellular function in physiological and pathological states. However, the imbalance among resolution, protein coverage, and expense precludes their systematic application to analyze whole tissue sections in an unbiased manner and with high resolution. Here, we introduce panoramic spatial enhanced resolution proteomics (PSERP), a method that combines tissue expansion, automated sample segmentation, and tryptic digestion with high-throughput proteomic profiling. The PSERP approach facilitates rapid quantitative profiling of proteomic spatial variability in whole tissue sections at sub-millimeter resolution. We demonstrated the utility of this method for determining the streamlined large-scale spatial proteomic features of gliomas. Specifically, we profiled spatial proteomic features for nine glioma samples across three different mutation types (IDH1-WT/EGFR-mutant, IDH1-mutant, and IDH1/EGFR-double-WT gliomas) at sub-millimeter resolution (corresponding to a total of 2,230 voxels). The results revealed over 10,000 proteins identified in a single slide, which helps us to portray the diverse proteins and pathways with spatial abundance patterns in the context of tumor heterogeneity and cellular features. Our spatial proteomic data revealed distinctive proteomic features of malignant and non-malignant tumor regions and depicted the distribution of proteins from tumor centers to tumor borders and non-malignant tumor regions. Through integrative analysis with single-cell transcriptomic data, we elucidated the cellular composition and cell-cell communications in a spatial context. Our PSERP also includes a spatially resolved tumor-specific peptidome identification workflow that not only enables us to elucidate the spatial expression patterns of tumor-specific peptides in glioma samples with different genomic types but also provides us with opportunities to select combinations of tumor-specific mutational peptides whose expression could cover the maximum tumor regions for future immune therapies. We further demonstrated that combining tumor-specific peptides might enhance the efficacy of immunotherapy in both patient-derived cell (PDC) and patient-derived xenograft (PDX) models. PSERP efficiently retains precise spatial proteomic information within the tissue context and provides a deeper understanding of tissue biology and pathology at the molecular level.