Young Investigators

Coscia Lab

The young investigator group ‘Spatial Proteomics’ headed by Fabian Coscia supports the MSTARS research core with a highly innovative and multimodal analysis concept. By the combination of digital pathology workflows with highly sensitive MS techniques, Fabian Coscia’s team will generate proteome data with unprecedented biological resolution. This concept allows the spatially-resolved analysis of tumor cells in the complex tissue environment, considering tumor subpopulations and cells of the tumor microenvironment. These experimental data are then integrated with clinical data to shed light onto therapy resistance on the molecular and cellular level. This resource will help to make clinical predictions and to identify new therapeutic strategies.

The performed analyses open up a new perspective on therapy resistance addressing the interaction of tumor cells among themselves and with cells in their microenvironment, such as immune cells. The project will result in new hypotheses addressing the development of therapy resistance in solid cancers, which will be studied mechanistically in cooperation with other partners of the MSTARS consortium. The research approach of the Coscia lab is widely applicable, but will initially focus on HNSCC studies. A unique collection of clinical samples and patient-derived preclinical models, available to the research core, serves as a solid basis to strengthen the diverse research activities. The overall goal is to unite different MS expertise resulting in a sustained MS platform beneficial for future patients.

Demichev Lab

Recently, mass spectrometry (MS)-based proteomics has taken a major leap in terms of speed, sensitivity and depth of proteome coverage. Novel fast workflows can measure hundreds of proteomes per day and thus facilitate robust and cost-effective large-scale experiments, from perturbation screens in cell culture to biomarker discovery studies. At the same time, proteomic profiling of sub-nanogram sample amounts has become a reality, resulting in a rapid rise of single-cell proteomics. These advances have been driven by the introduction of fast and sensitive mass spectrometers, development of novel acquisition modes and the emergence of sophisticated data processing strategies. Proteomics can now facilitate large-scale studies of a wide range of clinical samples, allowing information-rich characterisation of disease trajectories and prediction of outcomes. Nevertheless, its speed and data quality can still be improved significantly, paving the road for new applications.

The Demichev lab works in collaborations with industry partners to increase the speed, quantitative accuracy and proteomic depth of MS-based methods, with the ultimate vision of bringing MS-based proteomics to the clinical environment as a routine measurement method. Our primary focus is on the development of novel acquisition techniques and data analysis methods, and their application to clinical samples profiling as well as basic science. We also place special emphasis on establishing fast and robust methods for proteome-wide profiling of phosphorylation and ubiquitination, and using these to take an in depth look at protein turnover, its regulation and its link to metabolism and ageing. Within MSTARS, our primary goal is to use high-throughput multi-omics methods to identify biomarkers as well as establish machine learning predictors of treatment success in cancer patients.