Ultra-High Sensitivity Proteomics for Digital Cancer Pathology

Great instrumental advances continue to be made in mass

 spectrometry-based proteomics and I will discuss a few of these, including
advances in algorithms and bioinformatics and focusing on the new tims-TOF
technology, which now allows ultra-sensitive analysis of single cells.

These and other experimental advances are applied in our group in a wide
range of biomedical settings. A major effort of our group is the analysis of
pathology samples, which can now be done in a streamlined way even from
archived material (FFPE samples). Together with the group of Peter Horvath, we
are now combining high content imaging, deep neural networks and single cell
proteomics to determine the proteome of specific subsets of cells in human
tissues. We call this technology ‘deep visual proteomics’ and it inherently
provides spatial information that can be mined for functional characterization
of cells. Furthermore, deep visual proteomics can be used to characterize
sub-cellular structures as well, which we demonstrated by capturing the cell
cycle state by deep visual proteomics on the imaging and proteomics levels. I
will describe a number of applications of this technology in common and rare
cancers.