@phdthesis{Imdahl2023,
  author    = {Imdahl, Fabian Dominik},
  title     = {Development of novel experimental approaches to decipher host-pathogen interaction at the single-cell level},
  doi       = {10.25972/OPUS-28943},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-289435},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2023},
  abstract  = {Abstract: COVID-19 has impressively shown how quickly an emerging pathogen can have a massive impact on our entire lives and show how infectious diseases spread regardless of national borders and economic stability. We find ourselves in a post-antibiotic era and have rested too long on the laurels of past research, so today more and more people are dying from infections with multi-resistant germs. Infections are highly plastic and heterogeneous processes that are strongly dependent on the individual, whether on the host or pathogen side. Improving our understanding of the pathogenicity of microorganisms and finding potential targets for a completely new class of drugs is a declared goal of current basic research. To tackle this challenge, single-cell RNA sequencing (scRNA-seq) is our most accurate tool. In this thesis we implemented different state of the art scRNA-seq technologies to better understand infectious diseases. Furthermore, we developed a new method which is capable to resolve the transcriptome of a single bacterium. Applying a poly(A)-independent scRNA-seq protocol to three different, infection relevant growth conditions we can report the faithful detection of growth-dependent gene expression patterns in individual Salmonella Typhimurium and Pseudomonas aeruginosa bacteria. The data analysis shows that this method not only allows the differentiation of various culture conditions but can also capture transcripts across different RNA species. Furthermore, using state of the art imaging and single-cell RNA sequencing technologies, we comprehensively characterized a human intestinal tissue model which in further course of the project was used as a Salmonella enterica serovar Typhimurium infection model. While most infection studies are conducted in mice, lacking a human intestinal physiology, the in vitro human tissue model allows us to directly infer in vivo pathogenesis. Combining immunofluorescent imaging, deep single-cell RNA sequencing and HCR-FISH, applied in time course experiments, allows an unseen resolution for studying heterogeneity and the dynamics of Salmonella infection which reveals details of pathogenicity contrary to the general scientific opinion.},
  subject      = {Salmonella},
  language  = {en}
}
@phdthesis{Dietrich2024,
  author    = {Dietrich, Oliver},
  title     = {Integrating single-cell multi-omics to decipher host-pathogen interactions},
  doi       = {10.25972/OPUS-36013},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-360138},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2024},
  abstract  = {Interactions between host and pathogen determine the development, progression and outcomes of disease. Medicine benefits from better descriptions of these interactions through increased precision of prevention, diagnosis and treatment of diseases. Single-cell genomics is a disruptive technology revolutionizing science by increasing the resolution with which we study diseases. Cell type specific changes in abundance or gene expression are now routinely investigated in diseases. Meanwhile, detecting cellular phenotypes across diseases can connect scientific fields and fuel discovery. Insights acquired through systematic analysis of high resolution data will soon be translated into clinical practice and improve decision making. Therefore, the continued use of single-cell technologies and their application towards clinical samples will improve molecular interpretation, patient stratification, and the prediction of outcomes. In the past years, I was fortunate to participate in interdisciplinary research groups bridging biology, clinical research and data science. I was able to contribute to diverse projects through computational analysis and biological interpretation of sequencing data. Together, we were able to discover cellular phenotypes that influence disease progression and outcomes as well as the response to treatment. Here, I will present four studies that I have conducted in my PhD. First, we performed a case study of relapse from cell-based immunotherapy in Multiple Myeloma. We identified genomic deletion of the epitope as mechanism of immune escape and implicate heterozygosity or monosomy of the genomic locus at baseline as a potential risk factor. Second, we investigated the pathomechanisms of severe COVID-19 at the earliest stage of the COVID- 19 pandemic in Germany in March 2020. We discovered that profibrotic macrophages and lung fibrosis can be caused by SARS-CoV-2 infection. Third, we used a mouse model of chronic infection with Staphylococcus aureus that causes Osteomyelitis similar to the human disease. We were able to identify dysregulated immunometabolism associated with the generation of myeloid-derived suppressor cells (MDSC). Fourth, we investigated Salmonella infection of the human small intestine in an in vitro model and describe features of pathogen invasion and host response. Overall, I have been able to successfully employ single-cell sequencing to discover important aspects of diseases ranging from development to treatment and outcome. I analyzed samples from the clinics, human donors, mouse models and organoid models to investigate different aspects of diseases and managed to integrate data across sample types, technologies and diseases. Based on successful studies, we increased our efforts to combine data from multiple sources to build comprehensive references for the integration of large collections of clinical samples. Our findings exemplify how single-cell sequencing can improve clinical research and highlights the potential of mechanistic discoveries to drive precision medicine.},
  subject      = {Einzelzellanalyse},
  language  = {en}
}