<> "The repository administrator has not yet configured an RDF license."^^ . <> . . "Comprehensive characterization of gene expression response to type-I and type-II interferon in healthy and diseased conditions"^^ . "Interferons (IFNs) are critical regulators of the immune system, with special importance for \r\nviral infections, autoimmune diseases, and cancer. However, there are challenges in distinguishing the effects of different IFN types and understanding their cell-type-specific responses. Single-cell sequencing can address these gaps but requires robust tools for cell-type classification and precise analysis of IFN-mediated effects. To better characterize IFN-\r\nand cell-type-specific gene expression responses, novel bioinformatics approaches are needed. \r\nOne of the main challenges in single-cell analysis is cell-type classification. Most approaches \r\nuse expression information of all expressed genes to provide cell type labels. However, they often introduce bias into statistical procedures when testing the same genes for differential \r\nexpression that were already used for cell typing. In the first chapter of my thesis, I addressed \r\nthis issue by using only small sets of robust immune cell-type-specific genes for cell typing \r\nwithin a random forest model. While most studies have generated such gene expression \r\nsignatures (GESs) from single gene expression datasets, I developed a novel GES discovery \r\nworkflow based on similarities in gene expression across seven single-cell cancer datasets. \r\nCompared to existing algorithms and published GESs, my approach showed superior or \r\ncomparable performance, significantly improved the classification of myeloid cells and \r\nenhanced downstream analysis of peripheral blood mononuclear cell (PBMC) datasets. \r\nThereby, I establish an unbiased method for classifying immune cells and statistical \r\ninvestigation of expression differences between cell types.\r\nIn the second part, I dissected distinct effects of IFN-I and IFN-II across different cellular, \r\nexperimental, and disease contexts. Published IFN GESs have been purely generated from \r\nonly single gene expression datasets within specific cellular contexts and primarily comprise \r\ngenes induced by IFN-I. I used five different bulk tissue RNA-sequencing datasets of IFN \r\nstimulation and applied a novel meta-analysis workflow to resolve GESs with specificity for \r\nIFN-I and IFN-II response. My IFN GESs had greater functional relevance to IFN-type-specific \r\nresponse and higher coherence than most published signatures. My IFN-II GES detected IFN-\r\nII response of not only myeloid cells but also B cells, hematopoietic cells, and naïve T cells. \r\nFurther, I demonstrated the relevance of IFN-I GES in disease severity of lupus nephritis, and \r\nIFN-II GES as predictive biomarker for immune checkpoint inhibitor response. I provide a more \r\nprecise distinction between IFN-I and IFN-II responses at the cell type level, as well as their \r\nrelevance to disease progression and therapy outcomes. \r\nIn the final part, I characterized immune cell-type-specific responses to IFNs as \r\ncomprehensive and objective studies of these responses are still lacking. Previous studies do \r\nnot provide cellular or temporal resolution, nor compare responses across different IFNs within \r\na single study. To address this gap, I applied the tool sets established in Chapter 1 and 2 on \r\na novel temporal CITE-Seq dataset of IFN-I and IFN-II stimulation of human PBMCs. I showed \r\nthat all immune cell types exhibited transient responses to IFN-I stimulation while only myeloid \r\nand B cells responded to IFN-II, with distinct dynamic patterns. Furthermore, I identified five \r\nunique temporal gene groups specific to monocyte responses to IFN-I or IFN-II. Those groups \r\nconsist of genes that play key roles in distinct immunological pathways. My findings enable a \r\nmore detailed characterization of IFN-mediated responses in distinct immune cell populations\r\n compared to those provided by published datasets or GESs. \r\nIn this thesis, I introduce novel bioinformatics tools that address key challenges in immune \r\ncell classification and the study of IFN responses. These methods advance the resolution of \r\nimmune profiling in single-cell data and provide more precise insights into IFN-mediated immune cell type responses in both health and disease. My approaches overcome the \r\nlimitations of existing workflows, offering new insights into IFN biology and its relevance to \r\ndisease mechanisms, as well as potential applications in biomarker research and therapy."^^ . "2025" . . . . . . . "Bogac"^^ . "Aybey"^^ . "Bogac Aybey"^^ . . . . . . "Comprehensive characterization of gene expression response to type-I and type-II interferon in healthy and diseased conditions (PDF)"^^ . . . "PhD_Thesis_Aybey_2025_final.pdf"^^ . . . "Comprehensive characterization of gene expression response to type-I and type-II interferon in healthy and diseased conditions (Other)"^^ . . . . . . "indexcodes.txt"^^ . . . "Comprehensive characterization of gene expression response to type-I and type-II interferon in healthy and diseased conditions (Other)"^^ . . . . . . "lightbox.jpg"^^ . . . "Comprehensive characterization of gene expression response to type-I and type-II interferon in healthy and diseased conditions (Other)"^^ . . . . . . "preview.jpg"^^ . . . "Comprehensive characterization of gene expression response to type-I and type-II interferon in healthy and diseased conditions (Other)"^^ . . . . . . "medium.jpg"^^ . . . "Comprehensive characterization of gene expression response to type-I and type-II interferon in healthy and diseased conditions (Other)"^^ . . . . . . "small.jpg"^^ . . "HTML Summary of #36647 \n\nComprehensive characterization of gene expression response to type-I and type-II interferon in healthy and diseased conditions\n\n" . "text/html" . . . "570 Biowissenschaften, Biologie"@de . "570 Life sciences"@en . .