Otto Warburg Medal (sponsored by Elsevier/BBA)
Investigating metabolic switches in cancer in situ with single cell tissue proteomics
Cancer is a complex disease with heterogenous molecular signatures evolving constantly with the disease. Understanding the disease at single cell resolution detailing the molecular features of the cells within the tumors provides a more accurate diagnosis and prognosis of the disease. Mass spectrometry (MS)-based proteomics has made significant technological advancements over the last decade, and machine learning and AI have also transformed the field of bioscience. In this talk, I will describe our efforts in recent years towards understanding the molecular pathways of cancer using Mass spectrometry-based proteomics. I will introduce our open source AlphaPept software suite for ultra-fast data analysis, and focus on recent developments in MS instruments for single cell analysis. We leveraged these technological breakthroughs to develop a new multidisciplinary workflow for the spatial analysis of tissues at the level of single cell types or states. In Deep Visual Proteomics, we use machine learning and deep learning algorithms to recognize and classify cells in high-resolution visual images, as well as extract them using a laser microscope, followed by ultrasensitive proteomics. This allows us to explore functional cellular heterogeneity in great detail directly in the tissue context. Befitting the theme of this lecture, I will discuss how the metabolic states of single cancer cells can be interrogated by Deep Visual Proteomics.