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Die angeborene Zwerchfellhernie (CDH) ist eine Fehlbildung der Lungen und des Zwerchfells die weltweit 1:2500 Neugeborene betrifft. Die Morbidität (100%) und Mortalität (ca. 30%) der CDH sind weiterhin hoch und Neugeborene werden direkt nach der Geburt intubiert und intensivmedizinisch behandelt. Insbesondere die Unterentwicklung der Lungen (pulmonale Hypoplasie) und das Remodelling der Pulmonalarterien (pulmonale Hypertension) ist für die hohe Sterblichkeit verantwortlich. Während der Zwerchfelldefekt sicher und minimalinvasiv verschlossen werden kann, gibt es aktuell keine medikamentöse pränatale Therapie zur Verbesserung des Lungenwachstums. Zur Entwicklung neuer therapeutischer Ansätze ist ein besseres Verständnis der zugrundeliegenden Pathophysiologie essentiell. Allerdings konnte bislang nicht etabliert werden, welche molekularen Signalwege in CDH-Lungen verändert sind, um diese als therapeutische Targets zu modulieren. In der vorliegenden Arbeit konnten wir mithilfe eines etablierten Tiermodels und humanen Stammzellen, welche aus Trachealaspiraten isoliert wurden, zeigen, dass in CDH-Lungen pro-inflammatorische Signalwege angereichert sind. Zudem waren Proteinprofile in CDH-Lungen im anerkannten „Nitrofen-Ratten“ Modell für CDH pro-inflammatorisch verändert. Im Epithel der Luftwege war vor allem STAT3 deutlich erhöht, und die Inhibition von STAT3 in fötalen hypoplastischen Lungen führte zu einer Korrektur des abnormalen Wachstums in vitro. Vergleichbare pro-inflammatorischen Veränderungen wurden auch in epithelialen Vorläuferzellen aus Trachealaspiraten von humanen CDH-Patienten nachgewiesen. Bei der transkriptionellen und epigenetischen Analyse dieser epithelialen Vorläuferzellen, welche die Luftwege auskleiden und in funktionelles Epithel differenzieren, wurden Anreicherungen im Interferon- und NF-kB-Signalweg beobachtet. Dieses pro-inflammatorische Programm der Vorläuferzellen war assoziiert mit einer defekten Differenzierung in spezialisierte Zellen des Bronchialepithels. Die Modulation dieser Signalwege führte zur Normalisierung der CDH charakteristischen epithelialen Defekte. Diese Epitheldefekte, welche bei der Differenzierung der Vorläuferzellen in vitro beobachtet wurde, konnten wir auch in histologischen Präparaten von verstorbenen CDH-Föten nachweisen und damit die Validität unserer Assays bestärken. Zusammenfassend konnten wir einen neuen und therapeutisch relevanten Signalweg der Lungenhypoplasie bei CDH aufzeigen.:1. Einleitung 2 1.1 Angeborene Zwerchfellhernie 2 1.2 Nitrofen-CDH-Modell 3 1.3 Pränatale Intervention 4 1.4 Proteomics-Analyse von CDH-Lungen 4 1.5 Tracheal-Aspirate basierte neonatale Lungenforschung 5 1.6 Tracheal-Aspirate basierte CDH-Forschung 6 1.7 Ziele der Arbeit 7 2. Publikationsmanuskripte 2.1 Arbeit 1: “Proteomic Profiling of Hypoplastic Lungs Suggests an Underlying 8 Inflammatory Response in the Pathogenesis of Abnormal Lung Development in Congenital Diaphragmatic Hernia.” Wagner et al., Annals of Surgery, 2022. Impact Factor: 13.9 2.2 Arbeit 2: “A tracheal aspirate-derived airway basal cell model reveals a 20 proinflammatory epithelial defect in congenital diaphragmatic hernia.” Wagner et al., American Journal of Respiratory and Critical Care Medicine, 2023. Impact Factor: 30.6 3. Zusammenfassung 34 4. Literaturverzeichnis 43 5. Anlagen 45 5.1 Erklärung über den wissenschaftlichen Beitrag des Promovenden zur Arbeit 45 5.2 Erklärung über die eigenständige Anfertigung der Arbeit 46 5.3 Lebenslauf 47 5.4 Danksagung 57

The capacity to flexibly combine words into a virtually infinite number of well-formed sentences is uniquely human and rests upon syntax (Berwick et al., 2013; Friederici, 2017). At the formal level, linguistic units are recursively combined into constituents according to syntactic rules and grammatical categorical information (Chomsky, 1995). At the neural level, neuroimaging studies support the role of Broca’s area in syntactic composition purely based on grammatical categorical information (Goucha & Friederici, 2015; Zaccarella & Friederici, 2015b). Furthermore, both the latency and attention-independent nature of the Early Left Anterior Negativity (ELAN) event-related potential (ERP) component elicited by syntactic categorical violations indicate that the analysis of syntactic categorical information is a fast and automatic process (Friederici, 2011). However, at present it remains unclear how syntactic composition takes place at the cognitive level. The present work examines how Broca’s area might incrementally build syntactic structures. Two hypotheses, namely categorical prediction or bottom-up integration, are investigated focusing on basic two-word structures, in order to highlight compositional processes while limiting extra-linguistic (e.g., working memory) demands (Pylkkänen, 2020; Zaccarella & Friederici, 2015b). Study 1 adapted a two-word ERP paradigm with syntactic categorical violations (Hasting & Kotz, 2008), shown to elicit a two-word equivalent of the ELAN component termed Early Syntactic Negativity (ESN). Study 1 successfully elicited an ESN effect with the stimulus list and timing of events employed. Study 2 tested the causal role of Broca’s area in syntactic categorical prediction, by simultaneously employing online transcranial magnetic stimulation (TMS) and ERP measures. Crucially, the ESN effect was not affected by the functional disruption of Broca’s area by TMS at the predictive stage. Study 3 tested whether evidence for syntactic categorical prediction can be found at the behavioral level, employing a two-word masked syntactic priming paradigm (Berkovitch & Dehaene, 2019; Pyatigorskaya et al., 2023) to focus on automatic linguistic processes. A significant masked syntactic priming effect was observed, which however did not stem from facilitation in processing well-formed structures compared to a neutral context but rather from inhibition of the ungrammatical structures compared to a neural condition. This result converges on earlier observations made at the agreement level (Friederici & Jacobsen, 1999) and is not compatible with a central role of predictive processes in automatic categorical analysis. Finally, a comprehensive Review of two-word studies on syntactic processing characterized syntactic composition as a two-step and rule-based combinatorial process, achieved with a central role of Broca’s area and the posterior temporal lobe in the representation and combination of syntactic features, relying on efficient bottom-up integration rather than top-down prediction in the formation of constituents. Building on the results of the three studies and the review work, the present thesis puts forward a neurocognitive model of two-word incremental syntactic composition upon which future research can be based.

The pancreas is a secretory organ composed of exocrine and endocrine compartments. During development, endocrine cells delaminate from the pancreatic epithelium to associate with the local vasculature where they will secrete hormones such as insulin into the blood stream. The exocrine pancreas is composed of ductal cells which form a network of tubes to secrete and transport fluid carrying the digestive enzymes secreted from acini located at the terminal ends of ductal branches. Unlike many branched epithelia, the pancreas does not exhibit a stereotypical branching pattern. The ductal network develops from a mesh of interconnected lumens which are eventually pruned and give rise to a final tree-like structure optimized for the most economical delivery of enzymes and fluid to the digestive tract. In silico modelling suggests that fluid flow plays a role in resolving the final structure of the ductal network during development, indicating that physical forces may play a role in this self-organization Recent work in the adult human pancreas has shown that the cells of the small ducts in the most distal parts of the ductal network do not express the same transcripts as the proximal large ducts. The pancreas derives this structure and function from the differentiation and self-organization of progenitors into terminally differentiated cells which, together with mesenchymal cells and vasculature, contribute to the tissue niche of the organ. Despite the importance of this process in development and disease, little is known about how pancreatic progenitors balance differentiation with morphogenesis. The goal of this project was to uncover niche components that influence the differentiation of pancreas progenitors, and understand how identity and morphogenesis are mediated by niche-driven changes in gene expression. This remains a challenging process to understand due to limited accessibility of the embryonic pancreas. Therefore, human sphere and organoid models represent a valuable tool to address this question and were used together with expression profiling and manipulation of the extracellular environment to understand this relationship during pancreas development. Time-course bulk RNA sequencing of human pancreas progenitor spheres at different days of culture revealed the sequential processes happening as the cells form their niche, and then start proliferating and forming lumen. Notably, at the stage of lumen expansion, we observed an upregulation of genes associated with ductal epithelia, such as CFTR, MUC1, MUC6, and CA2 in tandem with increased expression of genes encoding proteins for ion and fluid secretion. This suggested hydraulics may act to integrate ductal differentiation with luminogenesis, which is consistent with in silico modelling and the secretory function of the pancreatic epithelia. Indeed, driving chloride ion secretion with the CFTR activator forskolin resulted in inflation of the sphere lumen and increased the expression of the ductal genes identified above. Induction of CFTR and MUC1 can also be achieved by inflating the lumen in a CFTR-independent manner using prostaglandin E2. This revealed the changes in gene expression were not due to a small feedback loop under the control of CFTR, and maybe due to morphological changes related to lumen inflation. Importantly, it was revealed that the induction of Cftr expression upon lumen inflation also occurred in pancreas explants isolated from embryonic mice, which suggests the relationship between lumen inflation and ductal identity is conserved between mouse and human. Datamining of single cell RNA sequencing of adult and fetal pancreas samples identified novel marker genes for progenitor, acinar, and large- and small-ducts of the human fetal pancreas. Comparison of these marker genes with gene expression patterns of pancreas progenitor spheres revealed a shift to a small-duct-like identity when the lumen is inflated. This shift seemed to be dependent on inflation of the lumen rather than cAMP signalling, as it is not observed in pancreas progenitors grown in 2D and treated with forskolin. The above experiments suggest a link between lumen inflation and small duct identity but the exact mechanism remains unclear. Lumen inflation is likely driven by an increase in hydrostatic pressure that occurs downstream of changes in osmotic pressure due to ion channel activation. Independent manipulation of osmotic pressure, hydraulic pressure, tissue stretching, and fluid shear stress will be valuable to decipher the mechanisms of ductal gene regulation. Taken together these results support the hypothesis that differential gene expression in ducts of different sizes is regulated by mechanical forces in the pancreas, and 3D sphere culture represents a powerful model to investigate these processes in finer detail.

Tyrosine-type site-specific recombinases (Y-SSRs) are versatile tools for genome engineering due to their ability to mediate excision, integration, inversion and exchange of genomic DNA with single nucleotide precision. The ever-increasing need for sophisticated genome engineering is driving efforts to identify novel SSR systems with intrinsic properties more suitable for particular applications. In this work, we develop a systematic computational workflow for annotation of putative Y-SSR systems and apply this pipeline to identify and characterize eight new naturally occurring Cre-type SSR systems. We test their activity in bacterial and mammalian cells and establish selectivity profiles for the new and already established Cre-type SSRs with regard to their ability to mutually recombine their target sites. These data form the basis for sophisticated genome engineering experiments using combinations of Y-SSRs in research fields including advanced genomics and synthetic biology. Finally, we identify putative pseudo-sites and potential off-targets for Y-SSRs in the human and mouse genome. Together with established methods for altering the DNA-binding specificity of this class of enzymes, this work should facilitate the use of Y-SSRs for future genome surgery applications.

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Microtubules play a major role in intracellular trafficking of vesicles in endocrine cells. Detailed knowledge of microtubule organization and their relation to other cell constituents is crucial for understanding cell function. However, their role in insulin transport and secretion is currently under debate. Here, we use Fib-Sem to image islet beta cells in their entirety with unprecedented resolution. We reconstruct mitochondria, Golgi apparati, centrioles, insulin secretory granules and micro-tubules of seven beta cells, and generate a comprehensive spatial map of microtubule-organelle interactions. We find that micro-tubules form non-radial networks that are predominantly not connected to either centrioles or endomembranes. Microtubule number and length, but not microtubule polymer density, vary with glucose stimulation. Furthermore, insulin secretory granules are enriched near the plasma membrane where they associate with microtubules. In summary, we provide the first 3D reconstructions of complete microtubule networks in primary mammalian cells together with evidence regarding their importance for insulin secretory granule positioning and thus supportive role in insulin secretion.

Identification of different cell types is an indispensable task of biomedical research and clinical application. During the last decades, much attention was given to molecular characterization, and many cell types can now be identified using established markers that bind to cell-specific antigens. The required staining process is a lengthy and costly treatment, which can cause alterations of cellular properties, contaminate the sample and therefore limit its subsequent use. For example, for photoreceptor transplantations, highly pure samples of photoreceptor cells are required, which can currently only be obtained using molecular labelling, rendering the resulting sample incompatible for clinical application. A promising alternative to molecular markers is the label-free identification of cells using mechanical or morphological features. Real-time deformability cytometry (RT DC) is a microfluidic technique, which allows capturing both types of information simultaneously for single cells at high-throughput. In this thesis, I present machine learning methods which allow identifying different cell types, based on bright-field images from RT DC. In particular, I introduce algorithms that are fast enough to be applied in real-time during the measurement (at >1000 cells/s), which can be used for image-based cell sorting. The performance of the algorithms is shown for the identification of rod precursor cells in retina-samples, indicating that image-based sorting based on those algorithms would allow enriching photoreceptors to a final concentration, applicable for transplantation purposes.:Contents Abstract iii Kurzfassung iv List of figures viii List of tables x 1. Introduction 1 1.1. Texture and mechanical properties: label-free markers 4 1.2. The retina, diseases and cure by photoreceptor transplantation 5 1.3. Technologies for label-free assessment of cells 8 2. Materials and Methods 10 2.1. Experimental setup 10 2.1.1. Chip design for RT DC and RT-FDC 10 2.1.2. Chip design for soRT-FDC 11 2.1.3. Chip fabrication 13 2.1.4. RT-DC, RT-FDC and soRT FDC Setup 14 2.1.5. Physics of surface acoustic wave mediated sorting 16 2.1.6. Measurement buffer (MB) for RT DC 17 2.2. Online parameters 18 2.3. Offline parameters 21 2.4. Linear mixed models (LMM) 28 2.5. Normality test using probability plots 31 2.6. Gaussian mixture model (GMM) and Bayesian information criterion (BIC) 32 2.7. Random forests 33 2.8. Confusion matrix 34 2.9. Deep learning 36 2.10. Preparation of retina samples 43 2.11. Preparation of blood samples 44 2.12. Staining of neutrophils and monocytes 45 3. Results 46 3.1. Meta-analysis of RT-DC data 46 3.1.1. Correlations of area and volume 47 3.1.2. Correlations of deformation and inertia ratio 48 3.1.3. Further screening of correlations 50 3.1.4. Shape of distributions 52 3.1.5. Discussion 54 3.2. Characterization of retina cells in RT-DC 57 3.2.1. Maturation of retina cells 57 3.2.2. Comparing retina cell types using statistical tests 61 3.2.3. Discussion 63 3.3. Classification of retina cells using supervised machine learning 66 3.3.1. The dataset 66 3.3.2. Cell classification using optimized area gating 72 3.3.3. Cell classification using random forests 74 3.3.4. Cell classification using deep neural nets 80 3.3.5. Improving DNN accuracy using image augmentation 85 3.3.6. Tuning of final models and classification performance 93 3.3.7. Visualization of model attention 98 3.3.8. Discussion 100 3.4. Software tools to train and apply deep neural nets for sorting 105 3.4.1. AIDeveloper 105 3.4.2. Sorting Software 113 3.4.3. Discussion 114 3.5. Sorting experiments 117 3.5.1. Sorting of rod precursor cells 117 3.5.2. Sorting of neutrophils 120 3.5.3. Discussion 125 4. Conclusion and outlook 128 A. Appendix 131 I. Comparison of dense and convolutional layer 131 Bibliography 133 Acronyms 148 Acknowledgements 150 Erklärung 152

A new route to highly active hopcalite catalysts via flame spray pyrolysis of an inverse microemulsion precursor is reported. The nitrate derived nanoparticles are around 15 nm in diameter and show excellent conversion of CO under ambient conditions, outperforming commercial reference hopcalite materials produced by co-precipitation.

We report here the draft genome of Kretzschmaria (Ustulina) deusta, an ascomycetous fungus that colonizes and substantially degrades hardwood and can infest living broad-leaved trees. The genome was assembled into 858 contigs, with a total size of 46.5 Mb, and 11,074 protein-coding genes were predicted.