Young Expansion of the MSTARS Consortium
The group "Spatial Proteomics" introduces a novel and unique concept to the MSTARS consortium.
In July 2021, Dr. Fabian Coscia started his junior research group “Spatial Proteomics” at the Max Delbrück Center for Molecular Medicine in the Helmholtz Association in Berlin-Buch. The group applies a unique combination of high-resolution microscopy, artificial-intelligence (AI)-guided image analysis and ultra-sensitive mass spectrometry (MS)-based proteomics to gain a deep insight in the spatial resolution of the proteome heterogeneity in healthy and diseased tissues. By using this approach, the group will uncover molecular and cellular disease patterns that are going to serve as markers in precision oncology.
During his dissertation at the Max Planck Institute for Biochemistry in Martinsried in the research department “Proteomics and Signal Transduction”, headed by Prof. Dr. Matthias Mann, Fabian Coscia analyzed the proteomic landscape of chemosensitive ovarian cancers and the role of the tumor microenvironment during metastasis. Building on that Fabian Coscia continued his research activity between 2017 and 2021 as a postdoc in the group “Clinical Proteomics”, which is similarly headed by Matthias Mann, at the University of Copenhagen. During this time, Fabian Coscia was awarded with the research price of the Walter Schulz Stiftung for his excellent work during his PhD, became a Marie Skłodowska-Curie fellow supporting his scientific work and career and co-developed the innovative concept Deep Visual Proteomics (DVP), which comprises a unique combination of high-resolution imaging, AI, and MS-based proteomics.
As part of the MSTARS consortium, Fabian Coscia and his Team will advance and apply DVP to uncover the basis of therapy resistances in cancers and identify new strategies for the treatment of therapy-resistant tumors.
© Coscia Lab, MDC
Mund, A.#, Coscia F.#, et al (2021). AI-driven Deep Visual Proteomics defines cell identity and heterogeneity. bioRxiv 2021.01.25.427969, https://doi.org/10.1101/2021.01.25.427969
Eckert, M. A.#, Coscia, F.#, et al. (2019). Proteomics reveals NNMT as a master metabolic regulator of cancer-associated fibroblasts. Nature. https://doi.org/10.1038/s41586-019-1173-8
Coscia, F. et al. (2018). Multi-level Proteomics Identifies CT45 as a Chemosensitivity Mediator and Immunotherapy Target in Ovarian Cancer. Cell, 159–170. https://doi.org/10.1016/j.cell.2018.08.065.
To increase the throughput: The group "Quantitative Proteomics" develops improved methods for the sample acquisition and analysis.
The junior research group “Quantitative Proteomics”, headed by Vadim Demichev, started its work in September 2021 at the Charité – Universitätsmedizin Berlin. The team of Vadim Demichev follows the vision of bringing MS-based proteomics and PTM-omics to the clinical environment as a routine measurement method. For this, the Demichev lab cooperates with industry partners to increase the speed, quantitative accuracy and proteomic depth of MS-based methods.
Vadim Demichev previously worked as a postdoctoral scientist in the laboratories of Markus Ralser (Francis Crick Institute, London, UK) and Kathryn Lilley (University of Cambridge, Cambridge, UK). His research focused on the development of methods for fast proteomics, to enable quantitatively accurate and cost-effective large-scale experiments. First, he introduced a computational method and a software solution, DIA-NN, which solved the problem of deconvolution for DIA data acquired with chromatographic gradients shorter than 30 minutes, and has now been widely adopted in the field. He then co-developed Scanning SWATH, a novel acquisition technique that combines the advantages of DIA and DDA proteomics and is particularly beneficial for ultra-fast methods with active gradient lengths down to 1 minute and throughput over 400 samples per day. The new methods were applied to biomarker discovery for COVID-19 patient stratification and disease prognosis using large-scale plasma proteome profiling.
Within MSTARS, the primary goal of the Demichev lab is to use high-throughput multi-omics methods to identify biomarkers as well as establish machine learning predictors of treatment success in cancer patients. For this, the group develops novel multiplexing technologies, acquisition techniques and data analysis strategies.
© Demichev Lab, Charité - Universitätsmedizin Berlin
Demichev, V.#, Tober-Lau, P.# et al (2021). A time-resolved proteomic and prognostic map of COVID-19. Cell systems. https://doi.org/10.1016/j.cels.2021.05.005
Messner, C.B.#, Demichev, V.# et al (2021). Ultra-fast proteomics with Scanning SWATH. Nature Biotechnology. https://doi.org/10.1038/s41587-021-00860-4
Demichev, V. et al (2020). DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nature Methods. https://doi.org/10.1038/s41592-019-0638-x