Unraveling disease mechanisms of different lung pathologies with single-cell RNA sequencing

The respiratory system is composed of different tissues with their respective cell types that together work in concert to perform air conductance and gas exchange. With the advent of single-cell RNA-sequencing (scRNA-seq), it is now possible to comprehensively interrogate the function of each individual cell in homeostatic and diseased states. In this dissertation, various roles of epithelial, mesenchymal, and immune cell types of the respiratory system in idiopathic pulmonary fibrosis (IPF) and corona virus disease 2019 (COVID-19) were investigated with scRNA-seq. IPF is a chronic interstitial lung disease characterized by the progressive scarring of the lung parenchyma. Previous studies that surveyed the cellular landscape of IPF lungs utilized explant lungs that reflect end-stage fibrosis. To uncover disease mechanisms of airway cell types in early-stage fibrosis, air-liquid interface (ALI) cultures of primary cells taken from newly diagnosed IPF patients were used. This identified proinflammatory epithelial cells, profibrotic basal cells, and primed fibroblasts as early-stage drivers of IPF. Treatment with antifibrotic compounds nintedanib, pirfenidone, and saracatinib fail to completely ameliorate the identified signatures. With the emergence of the COVID-19 pandemic and its extensive public health burden, it was imperative to understand the molecular mechanisms of viral entry and disease pathology to identify potential risk factors and therapeutic targets. In the early stages of the pandemic, viral entry factors ACE2, TMPRSS2, and FURIN were found to be expressed by a transient secretory cell type (differentiating from secretory to ciliated cell) of the airway mucosa and by alveolar type 2 cells of the alveolar epithelium. With further investigation of severe COVID-19, the early-stage of COVID-19 infection characterized itself with a hyperactivated immune response mediated by proinflammatory macrophages. On the other hand, late-stage COVID-19, especially those with acute respiratory distress syndrome (ARDS), was characterized by an accumulation of profibrotic macrophages and activated myofibroblasts that drove pulmonary scarring and fibrosis. Although IPF and COVID-19 are different diseases by their own right, they share a commonality in aberrant wound healing responses. Both diseases are characterized by tissue inflammation that is followed by a profibrotic phase. Unlike in IPF where the tissue remodeling is progressive and chronic, COVID-19 ARDS-associated fibrosis undergoes a resolution phase. Future studies comparing the cellular and transcriptional landscape of both conditions in early and late stages of disease will uncover pathogenic mechanisms and therapeutic targets of lung fibrosis. The application of high-resolution transcriptomic profiling techniques such as scRNA-seq permits the interrogation of individual cell types and their direct contribution to the development of diseases. Moreover, it allows the comparison and transfer of identified pathomechanisms across different pulmonary diseases and, in doing so, provides deeper and generalizable insights. As this field continues to evolve, it will undoubtedly continue to provide a deeper understanding of respiratory diseases.Das respiratorische System setzt sich aus verschiedenen Geweben und ihren zugrundeliegenden Zelltypen zusammen, die gemeinsam Luftaufnahme und Gasaustausch gewährleisten. Mit dem Aufkommen der Einzelzell-RNA-Sequenzierung (scRNA-seq) ist es nun möglich, die Funktion jeder einzelnen Zelle in homöostatischen und kranken Zuständen umfassend zu untersuchen. In dieser Dissertation wurden verschiedene Rollen von Epithel-, Mesenchymal- und Immunzelltypen des Atmungssystems bei idiopathischer Lungenfibrose (IPF) und der Coronavirus-Krankheit-2019 (COVID-19) mit scRNA-seq untersucht. IPF ist eine chronische interstitielle Lungenerkrankung, die durch eine fortschreitende Vernarbung des Lungenparenchyms gekennzeichnet ist. Frühere Studien, die die Zellkomposition von IPF-Lungen untersuchten, verwendeten Lungenexplantate, die das Endstadium der Fibrose widerspiegeln. Um Krankheitsmechanismen von Atemwegszelltypen im Frühstadium der Fibrose aufzudecken, wurden Air-Liquid-Interface (ALI)-Kulturen von primären Zellen verwendet, die frisch diagnostizierten IPF-Patienten entnommen wurden. Dabei wurden proinflammatorische Epithelzellen, profibrotische Basalzellen und aktivierte Fibroblasten als treibende Kräfte im Frühstadium der IPF identifiziert. Die Behandlung mit den antifibrotischen Wirkstoffen Nintedanib, Pirfenidon und Saracatinib führte nicht zu einer vollständigen Verbesserung der identifizierten Signaturen. Mit dem Beginn der COVID-19-Pandemie und ihrer großen Belastung für die öffentliche Gesundheit war es unerlässlich, die molekularen Mechanismen des Viruseintritts und der Krankheitspathologie zu verstehen, um potenzielle Risikofaktoren und therapeutische Ansätze zu identifizieren. In den frühen Stadien der Pandemie wurde festgestellt, dass die viralen Eintrittsfaktoren ACE2, TMPRSS2 und FURIN von einem vorübergehenden sekretorischen Zelltyp (der sich von sekretorischen zu ziliierten Zellen differenziert) der Atemwegsschleimhaut und von Typ-2 -Pneumozyten des Alveolarepithels exprimiert werden. Bei der weiteren Untersuchung von schweren COVID-19 Verläufen zeigte sich, dass das Frühstadium der COVID-19-Infektion durch eine hyperaktivierte Immunantwort charakterisiert ist, die durch proinflammatorische Makrophagen vermittelt wird. Andererseits war das Spätstadium der COVID-19-Infektion, insbesondere bei Patienten mit akutem Atemnotsyndrom (ARDS), durch eine Anhäufung von profibrotischen Makrophagen und aktivierten Myofibroblasten gekennzeichnet, die die pulmonale Narbenbildung und Fibrose vorantrieben. Obwohl es sich bei IPF und COVID-19 um unterschiedliche Krankheiten handelt, ähneln sie sich in ihrer gestörten Wundheilung. Beide Krankheiten sindS durch eine Gewebeentzündung gekennzeichnet, auf die eine profibrotische Phase folgt. Im Gegensatz zur IPF, bei der die Gewebeveränderung fortschreitend und chronisch ist, durchläuft die COVID-19 ARDS-assoziierte Fibrose eine Reparationsphase. Zukünftige Studien, die die zelluläre und transkriptionelle Landschaft beider Erkrankungen in frühen und späten Stadien vergleichen, werden pathogene Mechanismen und therapeutische Ansätze der Lungenfibrose aufdecken können. Die Anwendung hochauflösender transkriptomischer Sequenzierung wie scRNA-seq ermöglicht die Untersuchung einzelner Zelltypen und ihren Beitrag zur Entstehung von Krankheiten. Darüber hinaus ermöglicht sie den Vergleich und die Übertragbarkeit identifizierter Pathomechanismen über verschiedene Lungenkrankheiten hinweg und liefert so tiefere und generalisierbare Erkenntnisse. Da sich dieses Feld stetig weiter entwickelt, wird es zweifellos auch weiterhin zu einem tieferen Verständnis von Atemwegserkrankungen beitragen

Evaluation of Generative Models for Predicting Microstructure Geometries in Laser Powder Bed Fusion Additive Manufacturing

In-situ process monitoring for metals additive manufacturing is paramount to the successful build of an object for application in extreme or high stress environments. In selective laser melting additive manufacturing, the process by which a laser melts metal powder during the build will dictate the internal microstructure of that object once the metal cools and solidifies. The difficulty lies in that obtaining enough variety of data to quantify the internal microstructures for the evaluation of its physical properties is problematic, as the laser passes at high speeds over powder grains at a micrometer scale. Imaging the process in-situ is complex and cost-prohibitive. However, generative modes can provide new artificially generated data. Generative adversarial networks synthesize new computationally derived data through a process that learns the underlying features corresponding to the different laser process parameters in a generator network, then improves upon those artificial renderings by evaluating through the discriminator network. While this technique was effective at delivering high-quality images, modifications to the network through conditions showed improved capabilities at creating these new images. Using multiple evaluation metrics, it has been shown that generative models can be used to create new data for various laser process parameter combinations, thereby allowing a more comprehensive evaluation of ideal laser conditions for any particular build

Evolutionary histories of breast cancer and related clones

乳がん発生の進化の歴史を解明 --ゲノム解析による発がんメカニズムの探索--. 京都大学プレスリリース. 2023-07-28.Tracking the ol' mutation trail: Unraveling the long history of breast cancer formation. 京都大学プレスリリース. 2023-08-31.Recent studies have documented frequent evolution of clones carrying common cancer mutations in apparently normal tissues, which are implicated in cancer development1, 2, 3. However, our knowledge is still missing with regard to what additional driver events take place in what order, before one or more of these clones in normal tissues ultimately evolve to cancer. Here, using phylogenetic analyses of multiple microdissected samples from both cancer and non-cancer lesions, we show unique evolutionary histories of breast cancers harbouring der(1;16), a common driver alteration found in roughly 20% of breast cancers. The approximate timing of early evolutionary events was estimated from the mutation rate measured in normal epithelial cells. In der(1;16)(+) cancers, the derivative chromosome was acquired from early puberty to late adolescence, followed by the emergence of a common ancestor by the patient’s early 30s, from which both cancer and non-cancer clones evolved. Replacing the pre-existing mammary epithelium in the following years, these clones occupied a large area within the premenopausal breast tissues by the time of cancer diagnosis. Evolution of multiple independent cancer founders from the non-cancer ancestors was common, contributing to intratumour heterogeneity. The number of driver events did not correlate with histology, suggesting the role of local microenvironments and/or epigenetic driver events. A similar evolutionary pattern was also observed in another case evolving from an AKT1-mutated founder. Taken together, our findings provide new insight into how breast cancer evolves

Targeted computational analysis of the C3HEB/FEJ mouse model for drug efficacy testing

2020 Spring.Includes bibliographical references.Efforts to develop effective and safe drugs for the treatment of tuberculosis (TB) require preclinical evaluation in animal models. Alongside efficacy testing of novel therapies, effects on pulmonary pathology and disease progression are monitored by using histopathology images from these infected animals. To compare the severity of disease across treatment cohorts, pathologists have historically assigned a semi-quantitative histopathology score that may be subjective in terms of their training, experience, and personal bias. Manual histopathology, therefore, has limitations regarding reproducibility between studies and pathologists, potentially masking successful treatments. This report describes a pathologist-assistive software tool that reduces these user limitations while providing a rapid, quantitative scoring system for digital histopathology image analysis. The software, called 'Lesion Image Recognition and Analysis' (LIRA), employs convolutional neural networks to classify seven different pathology features, including three different lesion types from pulmonary tissues of the C3HeB/FeJ tuberculosis mouse model. LIRA was developed to improve the efficiency of histopathology analysis for mouse tuberculosis infection models. The model approach also has broader applications to other diseases and tissues. This also includes animals that are undergoing anti-mycobacterial treatment and host immune system modulation. A complimentary software package called 'Mycobacterial Image Analysis' (MIA) had also been developed that characterizes the varying bacilli characteristics such as density, aggregate/planktonic bacilli size, fluorescent intensity, and total counts. This further groups the bacilli characteristic data depending on the seven different classifications that are selected by the user. Using this approach allows for an even more targeted analysis approach that can determine how therapy and microenvironments influence the Mtb response