Through innovative single-cell and spatial omics, cell-to-cell heterogeneity can be mapped today at unprecedented molecular detail, profiling thousands of analytes per cell, offering a promising avenue towards personalized medicine. Albeit still at an early stage, unbiased (i.e. untargeted) liquid chromatography mass spectrometry (LC-MS) based spatial proteomics approaches promise to revolutionize our understanding of cell heterogeneity from a global, functional and phenotype-centric perspective. Here, we recently co-developed deep visual proteomics (DVP) that combines whole-slide tissue imaging, machine-learning based image analysis and ultrasensitive LC-MS based proteomics to analyze cell type and spatially resolved proteomes at unprecedented biological resolution.
In my presentation, I will give an overview of state-of-the-art spatial proteomics approaches, highlight our previous and ongoing work to integrate targeted and untargeted methods and show several examples how we apply such approach for translational cancer research. Examples include the deep proteomic profiling of cancer precursor lesions, the spatially resolved analysis of melanoma progression obtained from a 17-years old archival specimen, and how the integration with spatial transcriptomics can prioritize actionable and niche-specific drug targets in an aggressive lung adenocarcinoma. Finally, I will give an outlook on promising new directions to improve throughput, sensitivity and accessibility of multimodal spatial proteomics in general, and the DVP pipeline in particular.