Project description
Linking subcellar proteomes with cell function
Cells carry out biochemical reactions in distinct subcellular regions, ensuring efficiency and regulation of cellular functions. Understanding how these spatially defined interactions influence health and disease is crucial, since disruptions can drive pathological conditions like cancer. However, systematic analysis of these interactions at the proteome level remains challenging. The ERC-funded Spatial Proteomics project aims to develop a multimodal strategy that integrates immunofluorescence imaging, machine learning and single-cell mass spectrometry to delineate proteomic states in subcellular compartments. Researchers will employ this approach to investigate cells critical for immunotherapy response, uncover resistance mechanisms and identify predictive biomarkers. This technology has the potential to extend beyond cancer in other proteomics applications.
Objective
Health and disease states result from dynamic cellular interactions within spatially defined regions in tissues and organs. In diseases such as cancer, these interactions are often disturbed, but their systematic analysis with respect to their impact on the proteome, a close proxy for cellular function, has so far remained elusive. To overcome this major bottleneck in molecular biosciences, I propose to develop and apply Deep Spatial Proteomics (DSP), a multimodal strategy, which for the first time will link distinct cellular neighbourhoods within biological samples to functional proteome states. DSP will combine multiplex immunofluorescence imaging and machine-learning driven cellular neighbourhood profiling with single-cell sensitivity mass spectrometry (MS) based proteomics. Our preliminary data support the feasibility and strong potential of DSP to uncover novel disease mechanisms, drug targets and predictive biomarkers. After development and rigorous benchmarking, we will apply DSP to an already available retrospective cohort of advanced head and neck squamous cell carcinoma, where response rates for anti-cancer immunotherapy are only below twenty percent. The correlation of cell states and spatial neighbourhoods with clinical outcomes will allow us to identify cell communities of highest likelihood to be critical for treatment response and hence patient survival. Through their functional characterisation by deep MS based proteomics, we will not only gain unique biological insights into immunotherapy resistance and potential therapeutic targets, but also identify predictive candidate markers to improve patient stratification. This new concept will have strong implications for basic and translational research, far beyond the study of cancer immunotherapy. DSP could pave the way for a plethora of spatial proteomics applications with countless opportunities for discovery-driven biomedical research.
                                Fields of science (EuroSciVoc)
                                                                                                            
                                            
                                            
                                                CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See:   The European Science Vocabulary.
                                                
                                            
                                        
                                                                                                
                            CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences biological sciences biochemistry biomolecules proteins proteomics
 - medical and health sciences clinical medicine oncology
 - medical and health sciences basic medicine immunology immunotherapy
 
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                                        Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
                                        
                                    
                                
                            
                            
                        Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
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                  HORIZON.1.1 - European Research Council (ERC)
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                  Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
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(opens in new window) ERC-2023-STG
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13125 Berlin
Germany
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