Pulmonary arterial hypertension (PAH) is a devastating lung disease characterized by arterial pruning, occlusive vascular remodeling, and inflammation contributing to increased pulmonary vascular resistance with resultant right heart failure. Endothelial cell (EC) injury and apoptosis are commonly considered triggers for PAH, the mechanisms leading from injury to complex arterial remodeling are incompletely understood. While current therapies can improving symptoms, with the exception of parenteral prostacyclin, they do not significantly prolong transplant free survival. As well, there are no therapies that can regenerate the damaged lung short of transplantation. In this project, I sought to both advance the understanding of disease pathogenesis and explore regenerative therapeutic options for PAH. To this end, I first employed single cell RNA sequencing (scRNA-seq) at multiple time points during the Sugen 5416 (SU) – chronic hypoxia (CH) model of PAH, to provide new insights into PAH pathogenesis both during onset and progression of disease. We also employed microCT analysis to visualize and quantify the arterial pruning associated with PH and found significant loss up to 65% of the healthy arteriolar volume in this model. Through scRNA-seq analysis performed at four timepoints spanning the onset and progression of disease, two disease-specific EC cell types emerged as key drivers of PAH pathogenesis. The first was the emergence of capillary ECs with a de-differentiated gene expression profile, which we termed dedifferentiated capillary (dCap) ECs, with enrichment for the Cd74 gene. Interestingly, RNA velocity analysis suggested that these cells may be undergoing endothelial to mesenchymal transition during PAH development. At later times, a second arterial EC population became apparent, which we termed activated arterial ECs (aAECs), since it uniquely exhibited persistently elevated levels of differential gene expression consistent with a migratory, invasive and proliferative state. Interestingly, the aAECs together with the smooth muscle (SM)-like pericytes, a population which was also greatly expanded in PAH, expressed Tm4sf1, a gene previously associated with a number of cancers and abnormal cell growth. Furthermore, by immunostaining, TM4SF1 was found to be spatially localized to sites of complex and occlusive arterial remodeling, associated with both endothelial cells and pericytes in these lesions, suggesting an important role for the aAECs and SM-like pericytes in arterial remodeling and PH progression. Together, these findings suggest that aAECs, dCap ECs, and SM-like pericytes are emerging cell populations responsible for lung arterial remodeling in PAH, which drives disease progression, and that TM4SF1 may be a novel therapeutic target for this disease. As a first step in trying to develop approaches to regenerate lung arterial bed that is lost in PAH, we investigated the potential role of endothelial colony forming cells (ECFCs) and mesenchymal stromal cell (MSC) derived extracellular vesicles (EVs) as novel therapeutics, on the premise that these stem/progenitor cells would stimulate lung regeneration by mainly paracrine mechanisms. Additionally, we used biomaterials to microencapsulate cells and EVs to improve their local delivery and retention. While ECFCs were found to be ineffective in treating the monocrotaline model on their own, they were poorly retained in the lung and microencapsulation of ECFCs led to enhanced lung delivery within the first 72 hours, with resultant hemodynamic improvements in this model of PAH. MSCs are well known to be immunomodulatory and proangiogenic, largely acting through paracrine mechanisms, including by the release of EVs. Yet, following intravenous administration, nano sized EVs are rapidly cleared from circulation, potentially limiting their therapeutic potential. I adapted our microencapsulation strategy for EVs, and demonstrated significantly greater retention of microgel-loaded EVs were within the lung, resulting in enhanced local cell uptake. Interestingly, the hydrogel used for microencapsulation induced a local immune response which made it unsuitable for testing any potential therapeutic benefits of MSC-EVs in this study. Nonetheless, this work demonstrated proof-of-principle for the utility of microencapsulation as a strategy to enhance EV lung delivery. Overall, this work has identified novel lung cell populations (aAECs, dCap ECs, SM-like pericytes) driving arterial remodeling associated with PH progression, demonstrated the potential of ECFCs as a regenerative cell for the treatment of PAH, and illustrated the utility of microencapsulation as a tool to enhance lung targeting of both cells and EVs.

京都大学 / 新制・課程博士 / 博士(医学) / 甲第23750号 / 医博第4796号 / 新制||医||1055(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 村川 泰裕, 教授 斎藤 通紀, 教授 藤渕 航 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

This research investigates the application of machine learning techniques in computational genomics across two distinct domains: (1) the predicting the source of bacterial pathogen using whole genome sequencing data, and (2) the functional annotation of genes using single- cell RNA sequencing data. This work proposes the development of a bioinformatics pipeline tailored for identifying genomic variants, including gene presence/absence and single nu- cleotide polymorphism. This methodology is applied to specific strains such as Salmonella enterica serovar Typhimurium and the Ralstonia solanacearum species complex. Phylo- genetic analyses along with pan-genome and positive selection studiesshow that genomic variants and evolutionary patterns of S. Typhimurium vary across sources, which suggests that sources can be accurately attributed based on genomic variants empowered by machine learning. We benchmarked seven traditional machine learning algorithms, achieving a no- table accuracy of 94.6% in host prediction for S. Typhimurium using the Random Forest model, underscored by SHAP value analyses which elucidated key predictive features. Next, the focus is shifted to the prediction of Gene Ontology terms for Arabidopsis genes using single-cell RNA-seq data. This analysis offers a detailed comparison of gene expression in root versus shoot tissues, juxtaposed with insights from bulk RNA-seq data. The integration of regulatory network data from DAP-seq significantly enhances the prediction accuracy of gene functions. / Master of Science / This work applies machine learning techniques to two areas in computational biology: pre- dicting the hosts of bacterial pathogens based on their genome data, and predicting the func- tions of plant genes using single-cell gene expression data. The first part develops a method to analyze genome sequences from bacterial pathogens like Salmonella enterica serovar Ty- phimurium and the Ralstonia solanacearum species complex, identifying genomic variants, including gene presence/absence and single nucleotide polymorphism, which are variations in genetic code. By studying the evolutionary relationships and genetic diversity among dif- ferent strains, the motivation for using machine learning models to predict the sources (e.g., poultry, swine) of the pathogen genomes is established. Several machine learning models are then trained on these datasets, and the most important factors contributing to the predic- tions are identified. The second part focuses on predicting the functions of genes in the model plant species Arabidopsis thaliana using the gene expression data measured at the single-cell level to train machine learning models for identifying standardized gene function descrip- tions called Gene Ontology (GO) terms. By comparing results from single-cell and bulk tissue data, the study evaluates whether the higher resolution of single-cell data improves gene function prediction accuracy. Additionally, by incorporating information about gene regulation from a specialized experiment, the role of gene expression control in determining gene functions is explored.

A cell, the building block of all life, stores a plethora of information in its genome, epigenome, and transcriptome which needs to be analyzed via various Omic studies. The heterogeneity in a seemingly similar group of cells is an important factor to consider and it could lead us to better understand processes such as cancer development and resistance to treatment, fetal development, and immune response. There is an ever growing demand to be able to develop more sensitive, accurate and robust ways to study Omic information and to analyze subtle biological variation between samples even with limited starting material obtained from a single cell. Microfluidics has opened up new and exciting possibilities that have revolutionized how we study and manipulate the contents of the cell like the DNA, RNA, proteins, etc. Microfluidics in conjunction with Next Gen Sequencing has provided ground-breaking capabilities for handling small sample volumes and has also provided scope for automation and multiplexing. In this thesis, we discuss a number of platforms for developing low-input or single cell Omic technologies. The first part talks about the development of a novel microfluidic platform to carry out single-cell RNA-sequencing in a one-pot method with a diffusion-based reagent swapping scheme. This platform helps to overcome the limitations of conventional microfluidic RNA seq methods reported in literature that use complicated multiple-chambered devices. It also provides good quality data that is comparable to state-of-the-art scRNA-seq methods while implementing a simpler device design that permits multiplexing. The second part talks about studying the transcriptome of innate leukocytes treated with varying levels of LPS and using RNA-seq to observe how innate immune cells undergo epigenetic reprogramming to develop phenotypes of memory cells. The third part discusses a low-cost alternative to produce tn5 enzyme which low-cost NGS studies. And finally, we discuss a microfluidic approach to carrying out low-input epigenomic studies for studying transcription factors. Today, single-cell or low-input Omic studies are rapidly moving into the clinical setting to enable studies of patient samples for personalized medicine. Our approaches and platforms will no doubt be important for transcriptomic and epigenomic studies of scarce cell samples under such settings. / Doctor of Philosophy / This is the era of personalized medicine which means that we are no longer looking at one-size-fits-all therapies. We are rather focused on finding therapies that are tailormade to every individual’s personal needs. This has become more and more essential in the context of serious diseases like cancer where therapies have a lot of side-effects. To provide tailor-made therapy to patients, it is important to know how each patient is different from another. This difference can be found from studying how the individual is unique or different at the cellular level i.e. by looking into the contents of the cell like DNA, RNA, and chromatin. In this thesis, we discussed a number of projects which we can contribute to advancement in this field of personalized medicine. Our first project, MID-RNA-seq offers a new platform for studying the information contained in the RNA of a single cell. This platform has enough potential to be scaled up and automated into an excellent platform for studying the RNA of rare or limited patient samples. The second project discussed in this thesis involves studying the RNA of innate immune cells which defend our bodies against pathogens. The RNA data that we have unearthed in this project provides an immense scope for understanding innate immunity. This data provides our biologist collaborators the scope to test various pathways in innate immune cells and their roles in innate immune modulation. Our third project discusses a method to produce an enzyme called ‘Tn5’ which is necessary for studying the sequence of DNA. This enzyme which is commercially available has a very high cost associated with it but because we produced it in the lab, we were able to greatly reduce costs. The fourth project discussed involves the study of chromatin structure in cells and enables us to understand how our lifestyle choices change the expression or repression of genes in the cell, a study called epigenetics. The findings of this study would enable us to study epigenomic profiles from limited patient samples. Overall, our projects have enabled us to understand the information from cells especially when we have limited cell numbers. Once we have all this information we can compare how each patient is different from others. The future brings us closer to putting this into clinical practice and assigning different therapies to patients based on such data.

Solid oxide fuel cells (SOFCs) are promising devices to convert chemical energy to electrical energy due to their high efficiency, fuel flexibility, and low emissions. However, there are still some drawbacks hindering its wide application, such as high operative temperature, electrode degradation, chromium poisoning, oxidization of interconnect, and so on. Cathode plays a major role in determining the electrochemical performance of a single cell. In this dissertation, three perovskite cathode materials, La0.6Sr0.4Co0.2Fe0.8O3 (LSCF), Ba0.5Sr0.5Co0.2Fe0.8O3 (BSCF), and Sm0.5Sr0.5Co0.2Fe0.8O3 (SSCF), are comparatively studied through half-cells in the temperature range of 600-800 ºC. Sm0.2Ce0.8O1.9 (SDC) block layer on the yttria-stabilized zirconia (YSZ) electrolyte can lead to smaller polarization resistances of the three cathode materials through stopping the reaction between the cathodes and the YSZ electrolyte. SDC is also used as a catalyst to increase the oxygen reduction reaction (ORR) rate in the LSCF cathode. In addition, interconnect is protected by CoxFe1-x oxide and Co3O4/SDC/Co3O4 tri-layer coatings separately. These coatings are demonstrated to be effective in decreasing the area specific resistance (ASR) of the interconnect, inhibiting the Cr diffusion/evaporation, leading higher electrochemical performance of the SSCF-based half-cell. Only 1.54 at% of Cr is detected on the surface of the SSCF cathode with the Co0.8Fe0.2 oxide coated interconnect and no Cr is detected with the Co3O4/SDC/Co3O4 tri-layer coated interconnect. Finally, single cells with LSCF, BSCF, and SSCF as the cathodes are operated in the temperature range of 600-800 °C fueled by natural gas. BSCF has the highest power density of 39 mW cm-2 at 600 °C, 88 mW cm-2 at 650 °C, and 168 mW cm-2 at 700 °C; LSCF has the highest power density of 263 mW cm-2 at 750 °C and 456 mW cm-2 at 800 °C. Activation energies calculated from the cathode ASR are 0.44 eV, 0.38 eV, and 0.52 eV for the LSCF, BSCF, and SSCF cathodes respectively, which means the BSCF cathode is preferred. The stability test shows that the BSCF-based single cell is more stable at lower operative temperature (600 °C) while the LSCF-based single cell is more stable at higher operative temperature (800 °C). / Ph. D.

Microfluidics has revolutionized how molecular biology studies are conducted. It permits profiling of genomic and epigenomic features for a wide range of applications. Microfluidics has been proven to be highly complementary to NGS technology with its unique capabilities for handling small volumes of samples and providing platforms for automation, integration, and multiplexing. In this thesis, we focus on three projects (diffusion-based PCR, MID-RRBS, and SurfaceChIP-seq), which improved the sensitivities of conventional assays by coupling with microfluidic technology. MID-RRBS and SurfaceChIP-seq projects were designed to profiling genome-wide DNA methylation and histone modifications, respectively. These assays dramatically improved the sensitivities of conventional approaches over 1000 times without compromising genomic coverages. We applied these assays to examine the neuronal/glial nuclei isolated from mouse brain tissues. We successfully identified the distinctive epigenomic signatures from neurons and glia. Another focus of this thesis is applying electrical field to investigate the intracellular contents. We report two projects, drug delivery to encapsulated bacteria and mRNA extraction under ultra-high electrical field intensity. We envision rapid growth in these directions, driven by the needs for testing scarce primary cells samples from patients in the context of precision medicine. / Ph. D.

Autophagy is a conserved biological stress response in mammalian cells that is responsible for clearing damaged proteins and organelles from the cytoplasm and recycling their contents via the lysosomal pathway. In cases where the stress is not too severe, autophagy acts as a survival mechanism. In cases of severe stress, it may lead to programmed cell death. Autophagy is abnormally regulated in a wide-range of diseases, including cancer. To integrate the existing knowledge about this decision process into a rigorous, analytical framework, we built a mathematical model of cell fate decision mediated by autophagy. The model treats autophagy as a gradual response to stress that delays the initiation of apoptosis to give the cell an opportunity to survive. We show that our dynamical model is consistent with existing quantitative measurements of time courses of autophagic responses to cisplatin treatment. To understand the function of this response in cancer cells we have provided a systems biology experimental framework to study dynamical aspects of autophagy in single cancer cells using live-cell imaging and quantitative uorescence microscopy. This framework can provide new insights on function of autophagic response in cancer cells. / Ph. D.

I present novel solutions to two different classes of computational problems that arise in the study of complex cellular processes. The first problem arises in the context of planning large-scale genetic cross experiments that can be used to validate predictions of multigenic perturbations made by mathematical models. (i) I present CrossPlan, a novel methodology for systematically planning genetic crosses to make a set of target mutants from a set of source mutants. CrossPlan is based on a generic experimental workflow used in performing genetic crosses in budding yeast. CrossPlan uses an integer-linear-program (ILP) to maximize the number of target mutants that we can make under certain experimental constraints. I apply it to a comprehensive mathematical model of the protein regulatory network controlling cell division in budding yeast. (ii) I formulate several natural problems related to efficient synthesis of a target mutant from source mutants. These formulations capture experimentally-useful notions of verifiability (e.g., the need to confirm that a mutant contains mutations in the desired genes) and permissibility (e.g., the requirement that no intermediate mutants in the synthesis be inviable). I present several polynomial time or fixed-parameter tractable algorithms for optimal synthesis of a target mutant for special cases of the problem that arise in practice. The second problem I address is inferring gene regulatory networks (GRNs) from single cell transcriptomic (scRNA-seq) data. These GRNs can serve as starting points to build mathematical models. (iii) I present BEELINE, a comprehensive evaluation of state-of-the-art algorithms for inferring gene regulatory networks (GRNs) from single-cell gene expression data. The evaluations from BEELINE suggest that the area under the precision-recall curve and early precision of these algorithms are moderate. Techniques that do not require pseudotime-ordered cells are generally more accurate. Based on these results, I present recommendations to end users of GRN inference methods. BEELINE will aid the development of gene regulatory network inference algorithms. (iv) Based on the insights gained from BEELINE, I propose a novel graph convolutional neural network (GCN) based supervised algorithm for GRN inference form single-cell gene expression data. This GCN-based model has a considerably better accuracy than existing supervised learning algorithms for GRN inference from scRNA-seq data and can infer cell-type specific regulatory networks. / Doctor of Philosophy / A small number of key molecules can completely change the cell's state, for example, a stem cell differentiating into distinct types of blood cells or a healthy cell turning cancerous. How can we uncover the important cellular events that govern complex biological behavior? One approach to answering the question has been to elucidate the mechanisms by which genes and proteins control each other in a cell. These mechanisms are typically represented in the form of a gene or protein regulatory network. The resulting networks can be modeled as a system of mathematical equations, also known as a mathematical model. The advantage of such a model is that we can computationally simulate the time courses of various molecules. Moreover, we can use the model simulations to predict the effect of perturbations such as deleting one or more genes. A biologist can perform experiments to test these predictions. Subsequently, the model can be iteratively refined by reconciling any differences between the prediction and the experiment. In this thesis I present two novel solutions aimed at dramatically reducing the time and effort required for this build-simulate-test cycle. The first solution I propose is in prioritizing and planning large-scale gene perturbation experiments that can be used for validating existing models. I then focus on taking advantage of the recent advances in experimental techniques that enable us to measure gene activity at a single-cell resolution, known as scRNA-seq. This scRNA-seq data can be used to infer the interactions in gene regulatory networks. I perform a systematic evaluation of existing computational methods for building gene regulatory networks from scRNA-seq data. Based on the insights gained from this comprehensive evaluation, I propose novel algorithms that can take advantage of prior knowledge in building these regulatory networks. The results underscore the promise of my approach in identifying cell-type specific interactions. These context-specific interactions play a key role in building mathematical models to study complex cellular processes such as a developmental process that drives transitions from one cell type to another

No / The comet assay or single-cell gel electrophoresis assay is a relatively simple and sensitive technique for quantitatively measuring DNA damage and repair at the single-cell level in all types of tissue where a single-cell suspension can be obtained. Isolated cells are mixed with agarose, positioned on a glass slide, and then lysed in a high-salt solution which removes all cell contents except the nuclear matrix and DNA, which is finally subjected to electrophoresis. Damaged DNA is electrophoresed from the nuclear matrix into the agarose gel, resembling the appearance of a comet, while undamaged DNA remains largely within the proximity of the nuclear matrix. By choosing different pH conditions for electrophoresis, different damage types and levels of sensitivity are produced: a neutral (pH 8–9) electrophoresis mainly detects DNA double-strand breaks, while alkaline (pH ≥ 13) conditions detect double- and single-strand breaks as well as alkali-labile sites. This protocol describes a standard comet assay study for the analysis of DNA damage and outlines important variations of this protocol.

Single-cell proteomics is a useful tool for measuring differences in cell populations for clinical trials. In this report we have conducted a literary review where we looked at 23 different single-cell proteomics methods and their advantages and disadvantages. We have looked at both mass spectrometry-based and affinity-based methods to find upcoming methods in the field of single-cell proteomics. Our findings show that there are multiple promising techniques that can be applied in different contexts. Moreover, we recommended combining different protocols, for instance Capillary zone electrophoresis (CZE) with a microfluidic platform or Optidrop with one of the barcoding methods for better results. When conducting this review it became clear that most methods could be improved by implementing software programs such as PEPerMINT and Infinity flow. Therefore, we encourage that such data acquisition and analysis methods are implemented to yield more accurate characterization and quantification of the single-cell proteome.