http://hdl.handle.net/10453/19978 2026-04-05T20:58:59Z http://hdl.handle.net/10453/194464 Title: Spatial Transcriptomics in COPD: Insights into Smoking-Induced Pathophysiological Changes Authors: Chen, Hao Abstract: The spatial architecture of the lung is essential for maintaining homeostasis, enabling efficient gas exchange, and mounting immune responses to external respiratory challenges such as infections and pollutants. Chronic obstructive pulmonary disease (COPD) is a complex, progressive, and heterogeneous lung disease characterised by chronic inflammation, airway remodelling, and alveolar destruction (emphysema), leading to irreversible airflow limitation and breathing difficulties. COPD is the third leading cause of death globally, with cigarette smoking as the predominant risk factor. Current therapies alleviate symptoms but fail to halt disease progression, reflecting an incomplete understanding of disease mechanisms. Although single-cell transcriptomics has advanced our molecular understanding of COPD, its spatial context remains largely unexplored. Chapters 2 and 3 of this thesis examine the cellular and molecular dynamics across lung regions, revealing new insights into COPD pathogenesis. Using experimental mouse models of chronic cigarette smoke exposure, we applied spatial transcriptomics (ST) and single-cell RNA sequencing (scRNA-seq) to map cell-type-specific gene expression within the lung microenvironment. These studies identified alveolar macrophages migrating from pulmonary vasculature to distal parenchyma and the progressive enlargement of lymphoid follicles with ongoing smoke exposure. The distal parenchyma and lymphoid follicles emerged as previously underexplored sites of pathogenesis, offering new perspectives on disease progression. Chapter 4 investigated the epigenetic effects of cigarette smoking on human airways. A distinct “smoking signature” gene set was identified that robustly distinguished current smokers from non-smokers. Spatial and single-cell analyses revealed smoke-induced injury concentrated in the airway surface epithelium. Comparative studies between human and non-human primate lungs identified a “human lung evolution signature,” suggesting evolutionary adaptation to chronic smoke exposure. Transcription factors NRF2 and AhR were found to regulate this response via target genes NQO1 and ALDH3A1, whose knockout studies confirmed protective roles against smoke toxicity. Chapter 5 examined cellular senescence in COPD, demonstrating that senescent cells accumulate and contribute to pathology. Senolytic treatment eliminated these cells, reduced immune infiltration, and partially improved lung structure, though functional recovery was limited. These findings suggest senolytics may serve as a complementary therapy for COPD. Overall, this thesis integrates spatial and molecular insights to advance the understanding of COPD pathogenesis. By bridging experimental models and human studies, it provides a framework for developing precision medicine strategies to better manage and treat COPD. Description: University of Technology Sydney. Faculty of Science. 2025-01-01T00:00:00Z http://hdl.handle.net/10453/194448 Title: 3D Micro-Engineered Models to Study Cancer Biology Authors: Nazari, Hojjatollah Abstract: Breast carcinoma metastasis remains the primary cause of cancer-related mortality, with small extracellular vesicles (sEVs) emerging as key mediators of communication between tumors (seed) and metastatic niches (soil). This thesis investigates the distinct yet complementary roles of seed- and soil-derived sEVs in orchestrating critical steps of the metastatic cascade, including luminal-to-basal transformation, metastatic adaptation, vascular attachment, extravasation, and organotropism. To address this, advanced three-dimensional microfluidic platforms, known as microphysiological systems, were developed to replicate physiologically relevant environments of metastasis. In the bone-on-a-chip model, basal carcinoma cell-derived sEVs were shown to induce luminal-to-basal transformation and promote bone invasion. In subsequent studies, brain organoid-derived sEVs were examined using brain organoid-on-a-chip and vascular lumen-on-a-chip platforms to assess their impact on metastatic adaptation, vascular attachment, blood-brain barrier integrity, and extravasation. Building upon this, the first vascularized brain organoid-on-a-chip system was engineered, integrating functional vasculature with brain organoids to investigate metastatic colonization in a complex, dynamic microenvironment. Finally, a multi-compartment device was fabricated to evaluate organotropism, particularly in brain and bone contexts. Employing cellular and molecular assays, real-time imaging, confocal microscopy, and quantitative analyses, this thesis advances understanding of how seed- and soil-derived sEVs drive tumor progression, providing innovative platforms for therapeutic exploration. Description: University of Technology Sydney. Faculty of Engineering and Information Technology. 2025-01-01T00:00:00Z http://hdl.handle.net/10453/194447 Title: Understanding Bacterial Evolution and Adaptation in Natural Microbial Environments Authors: Krishnan, Sidaswar Abstract: Microbial evolution is underpinned by the processes of mutation and recombination, yet the dynamics of these processes within natural microbiomes remain poorly characterised. The growing application of metagenomics to study prokaryotes in their native habitats presents an excellent opportunity for learning more about recombination and mutation. However, a major bottleneck relates to the paucity of tools that can precisely measure recombination and mutation rates using next-generation sequencing datasets. This thesis has delivered a new tool for quantifying mutation and recombination rates from metagenomic datasets, which has opened the door to understanding these important evolutionary forces in natural microbiomes. Using this new tool, I was able to demonstrate the heterogeneous dynamics of recombination and mutation within the marine microbiome for key bacteria on both local and global scales. This work highlighted the complex interplay between evolutionary processes and environmental conditions that shape marine bacterioplankton communities. Description: University of Technology Sydney. Faculty of Science. 2025-01-01T00:00:00Z http://hdl.handle.net/10453/194445 Title: mmWave Sensing for Vital Sign Monitoring and User Identification: Enhancing Accuracy and Robustness via Deep Learning Authors: Wang, Yingqi Abstract: Millimeter-wave (mmWave) sensing has emerged as a powerful sensing modality for non-contact, privacy-preserving human monitoring applications. However, such technology could still lose the efficiency in real-world sensing scenarios. This thesis explores the integration of mmWave sensing for vital sign monitoring and user identification, leveraging deep learning and advanced signal processing to enhance accuracy and robustness in dynamic environments. The first focus of this research is on vital sign monitoring, where mmWave radar is employed to detect respiratory rate, heart rate, arterial pulse, and blood pressure (BP). A multi-modal sensing technology and a novel multi-channel variational mode decomposition (VMD) method are investigated to separate vital sign components from interference caused by body movements and environmental noise. Further, a robust heartbeat and wrist localization algorithm and a physics-driven deep learning approach are developed, incorporating Neural Ordinary Differential Equations (Neural ODEs) and Temporal Convolutional Networks (TCNs) to reconstruct high-level physiological signals from radar Doppler shifts. The system achieves high-accuracy estimation of physiological parameters, demonstrating its feasibility for continuous, non-invasive health monitoring. The second focus is user identification, aiming to associate the user’s ID with the previous vital signs sensing. A novel Inverse Synthetic Aperture Radar (ISAR) sensing is investigated to reconstruct body profiles of individuals walking past the radar. A ResNet-based deep learning model with Additive Angular Margin Loss is developed to enhance feature discrimination, enabling high-precision user identification in long-term. Unlike traditional biometric methods, this approach identifies individuals based on their radio imaging profiles, making it robust to occlusions and appearance variations. The contributions of this work include: (1) a novel multi-modal mmWave localization and signal decomposition is proposed and address the challenges of low accuracy in practical vital sign monitoring, (2) an adaptive mmWave heart and wrist detection, and the physics-embedded learning scheme is proposed to improve flexibility, robustness and explainability in ECG (Electrocardiogram), pulse and BP monitoring, and (3) an ISAR-based user identification method optimized with deep learning for real-world scenarios, significantly improved the performance in long-term mmWave user Re-ID. This research paves the way for future applications in smart healthcare, security, and human-computer interaction. Description: University of Technology Sydney. Faculty of Engineering and Information Technology. 2025-01-01T00:00:00Z