Proteomics

A Deep Learning Strategy for High-throughput Proteomics of Blood Plasma and Single Cells (Heidelberg Explorer Call)

This project is a cooperation with the proteomics laboratory of Prof. Krijgsveld. Proteins are the main drivers of cell function and disease, and therefore proteomics is a highly suitable technique to characterize determinants of cell identity and to identify biomarkers. Current proteomic technology has the breadth to profile thousands of proteins and the sensitivity to access single cells, however it lacks the throughput for meaningful analysis of large sample cohorts both in basic research and the clinic. Therefore, we here develop novel artificial intelligence (AI)-based approaches for the analysis of mass spectrometric (MS) data, to assign plasma proteomic data to clinical status, and to correlate single-cell data to cell identity. In addition, we will perform proteomic experiments to show that this deep learning-driven approach enhances throughput of plasma and single-cell proteome analysis. Collectively, this project will introduce a disruptive approach in proteomics to enable patient stratification and biomarker discovery, and to advance the young field of single-cell proteomics.