InfPathMapper · Mapping path and fate diversity of molecular mechanisms of infection with smart microscopy

Pathogens like Yersinia pseudotuberculosis must rapidly adapt to environmental cues to progress through infection, manipulating host responses to survive and replicate. Understanding these adaptive host-pathogen interactions is key to developing targeted therapies, combating antibiotic resistance, and preventing chronic infections. However, current methods miss rare, transient infection stages, limiting our understanding of infection pathways. My project aims to address this by developing InfPathMapper, a data-driven super-resolution microscopy (SRM) platform to map infection pathways in living cells at molecular resolution.While advances in fluorescence microscopy provide detailed molecular insights, they often struggle with phototoxicity, limited spatiotemporal resolution, and reliance on fixed samples, missing dynamic information of infection processes. My approach will extend a data-driven microscopy platform by integrating adaptive SRM modalities for live-cell imaging of Y. pseudotuberculosis infection, a pathogen with virulence mechanisms conserved across many bacteria. This system will be able to map pathways of individual infection events at super-resolution level in a live-cell fluorescence-based infection assay supported by machine learning to classify pathogen invasion stages and host cell response patterns. Live-to-fix super-resolution mapping of the molecular architecture of critical infection stages will provide new insights into how Yersinia adapts to niche-specific environments and host-induced stress. By combining cutting-edge microscopy with infection biology, the project will not only set me on an excellent pathway toward achieving research independence, it is also expected to contribute valuable knowledge and tools for the broader research community, fostering innovation in the study of other pathogens and dynamic intracellular processes and the fight against infectious diseases.