Xu Ziyan, Wang Yunzhi, Xie Tao, Luo Rongkui, Ni Heng-Li, Xiang Hang, Tang Shaoshuai, Tan Subei, Fang Rundong, Ran Peng, Zhang Qiao, Xu Xiaomeng, Tian Sha, He Fuchu, Yang Wenjun, Ding Chen
Clinical Research Center for Cell-based Immunotherapy of Shanghai Pudong Hospital, Fudan University Pudong Medical Center, State Key Laboratory of Genetics and Development of Complex Phenotypes, School of Life Sciences, Human Phenome Institute, Institutes of Biomedical Sciences, Fudan University, Shanghai, China.
Department of Neurosurgery, Zhongshan Hospital, Fudan University, Shanghai, China.
J Hematol Oncol. 2025 May 26;18(1):58. doi: 10.1186/s13045-025-01710-5.
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.
复杂组织的空间蛋白质组学分析对于研究生理和病理状态下的细胞功能至关重要。然而，分辨率、蛋白质覆盖范围和成本之间的不平衡阻碍了它们以无偏倚且高分辨率的方式系统地应用于分析整个组织切片。在此，我们介绍全景空间增强分辨率蛋白质组学（PSERP），这是一种将组织膨胀、自动样本分割和胰蛋白酶消化与高通量蛋白质组学分析相结合的方法。PSERP方法有助于在亚毫米分辨率下对整个组织切片中的蛋白质组空间变异性进行快速定量分析。我们展示了该方法在确定胶质瘤简化的大规模空间蛋白质组学特征方面的实用性。具体而言，我们在亚毫米分辨率下（对应总共2230个体素）对三种不同突变类型（IDH1-WT/EGFR突变型、IDH1突变型和IDH1/EGFR双野生型胶质瘤）的九个胶质瘤样本的空间蛋白质组学特征进行了分析。结果显示在一张载玻片上鉴定出了超过10000种蛋白质，这有助于我们在肿瘤异质性和细胞特征的背景下描绘具有空间丰度模式的多种蛋白质和信号通路。我们的空间蛋白质组学数据揭示了恶性和非恶性肿瘤区域独特的蛋白质组学特征，并描绘了从肿瘤中心到肿瘤边界以及非恶性肿瘤区域的蛋白质分布。通过与单细胞转录组数据的综合分析，我们在空间背景下阐明了细胞组成和细胞间通讯。我们的PSERP还包括一个空间分辨的肿瘤特异性肽组鉴定工作流程，这不仅使我们能够阐明不同基因组类型的胶质瘤样本中肿瘤特异性肽的空间表达模式，还为我们提供了选择肿瘤特异性突变肽组合的机会，这些肽的表达可以覆盖最大的肿瘤区域，用于未来的免疫治疗。我们进一步证明，在患者来源的细胞（PDC）和患者来源的异种移植（PDX）模型中，联合肿瘤特异性肽可能会提高免疫治疗的疗效。PSERP在组织背景下有效地保留了精确的空间蛋白质组学信息，并在分子水平上提供了对组织生物学和病理学的更深入理解。
