Proteins are the functional building blocks of life that determine health and disease states and constitute important drug targets. If a person gets sick and needs to see a doctor, it is because of a protein imbalance in our cells. The doctor then looks for irregularities in certain proteins to find out if there is inflammation in our body. In cancer for example, when chemotherapy does not work, it is because some of the cancer cells show changes in their protein repertoire, which can make them chemoresistant. The analysis of all proteins in a diseased tissue, the so-called proteome, is therefore of enormous value to understand the disease on a molecular level and to identify therapeutic vulnerabilities. The most widely used method to comprehensively study all the proteins in a biological system is mass spectrometry (MS) based proteomics. Using MS based proteomics to dissect a chemoresistant tumor and compare the sick tissue with healthy tissue from the same patient allows us to identify the proteins, which are most critical to the disease. However, the disease related proteome is complex, and analytical challenges exist due to the presence of different cell types and cellular states that can differentially promote disease progression or drug resistance. Classic approaches only provide average proteome descriptions of the disease as no methods exist to dissect and analyze the tissue with respect to cellular subsets.
The goal of the fellowship was therefore to develop an innovative MS based method that would address these limitations to obtain fine-resolved molecular maps of the disease related proteome. To achieve this, we married high-resolution microscopy with artificial intelligence guided image analysis and ultra-high sensitivity MS based proteomics. For the first time, this new ‘visual proteomics’ concept combines the visual dimension with the molecular phenotype and is generically applicable to cell cultures and patient biobank specimens. Our method represents an exciting new tool for biomedical research for biomarker discovery and next-level molecular disease profiling on the protein level.
