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The self-organisation of cells into complex tissues relies on the tight regulation of molecular processes governing their behaviour. Understanding these processes is a central questions in cell biology. In recent years, technological breakthroughs in single-cell sequencing experiments have enabled us to probe these processes with unprecedented molecular detail. However, biological function relies on collective processes on the mesoscopic and macroscopic scale, which do not necessarily obey the rules that govern it on the microscopic scale. Insights from these experiments on how collective processes determine cellular behaviour consequently remain severely limited. Methods from nonequilibrium statistical physics provide a rigorous framework to connect microscopic measurements to their mesoscopic or macroscopic consequences. In this thesis, by combining for the first time the possibilities of single-cell technologies and tools from nonequilbrium statistical physics, we develop theoretical frameworks that overcome these conceptual limitations. In particular, we derive a theory that maps measurements along the linear sequence of the DNA to mesoscopic processes in space and time in the cell nucleus. We demonstrate this approach in the context of the establishment of chemical modifications of the DNA (DNA methylation) during early embryonic development. Drawing on sequencing experiments both in vitro and in vivo, we find that the embryonic DNA methylome is established through the interplay between DNA methylation and 30-40 nm dynamic chromatin condensates. This interplay gives rise to hallmark scaling behaviour with an exponent of 5/2 in the time evolution of embryonic DNA methylation and time dependent, scale-free connected correlation functions, both of which are predicted by our theory. Using this theory, we successfully identify regions of the DNA that carry DNA methylation patterns anticipating cellular symmetry breaking in vivo. The primary layer determining cell identity is gene expression. However, read-outs of gene-expression profiling experiments are dominated by systematic technical noise and they do not provide “stochiometric” measurements that allow experimental data to be predicted by theories. Here, by developing effective spin glass methods, we show that the macroscopic propagation of fluctuations in the concentration of mRNA molecules gives direct information on the physical mechanisms governing cell states, independent of technical bias. We find that gene expression fluctuations may exhibit glassy behaviour such that they are long-lived and carry biological information. We demonstrate the biological relevance of glassy fluctuations by analysing single-cell RNA sequencing experiments of mouse neurogenesis. Taken together, we overcome important conceptual limitations of emerging technologies in biology and pioneer the application of methods from stochastic processes, spin glasses, field and renormalization group theories to single-cell genomics.

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京都大学 / 新制・課程博士 / 博士(医学) / 甲第24499号 / 医博第4941号 / 新制||医||1064(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 伊藤 貴浩, 教授 中島 貴子, 教授 藤田 恭之 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

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Chronic obstructive pulmonary disease (COPD) is a common respiratory disorder and the third leading cause of mortality. In this thesis we performed a clustering analysis of four specific immune cells in the GSE136831 dataset, using the default recommended parameters of the Seurat package in R, and obtained 16 subclasses with various COPD and cell-type proportions. Clusters 3, 7 and 9 had more pronounced independence and were all composed of macrophage-dominated control samples. The results of the pseudo-time analysis based on Monocle 3 package in R showed three different patterns of cell evolution. All started with a high percentage of COPD states, one ended with a high rate of Control states, and the other two still finished with a high percentage of COPD states. The results of differentially expressed gene analysis corroborated the existence of finer clusters and provided support for their rational categorization based on the similar marker genes. The gene ontology (GO) enrichment analysis for cluster 0 and cluster 6 provided feedback on enriched biological process terms with significant and unique characteristics, which could help explore latent novel COPD treatment directions. Finally, some top-ranked potential pharmaceutical molecules were searched via the connectivity map (cMAP) database. / Graduate / 2023-08-12

This Master’s Thesis project consists of the research, design, and fabrication of a system that could perform broadband impedance measurements (1kHz-20Mhz) of single cells using National Instruments Labview data acquisition and programming in coordination with a single cell capture device. Presented first is the background information on cells and their electrical properties, along with background in micro-total-analysis systems as well as impedance spectroscopy. Experimental Methods are then discussed for the electrode design, cellular modeling in COMSOL, fabrication methods, and Labview 8.0 Set-up and programming. Measurements were performed using the single-cell capture device on saline, yeast cells, and a polysterene bead. Analysis of the impedance data showed a clear visual and statistically significant difference between live yeast, the bead, and saline. A comparison of live yeast cells to nutrient-starved yeast cells was also performed and a distinct difference in spectra was observed.

Lung cancer is one of the most aggressive cancers and the leading cause of cancer mortality in the US, mainly due to the lack of early detection. Meanwhile, gene expression profiling can identify molecular responses to carcinogen exposure and tumorigenesis. We have previously identified lung cancer-associated gene expression alterations in the normal bronchial airway epithelium of ever smokers with and without lung cancer. These alterations are the basis of a diagnostic test that is useful in clinical decision-making in patients with suspect lung cancer. Despite this success, further improvements in early lung cancer diagnosis are needed, along with a better understanding of airway biology during the initiation and development of lung cancer. Towards these goals, for the first aim of my thesis, I explored whether normal-appearing bronchial airway gene expression reflects lung cancer histologic subtypes. Genes differentially expressed in the bronchial airway between patients with lung squamous cell carcinoma and lung adenocarcinoma were identified and confirmed in independent data. Using a method developed based on independent component analysis (ICA), cell type-specific gene modules were derived from airway single-cell RNA-sequencing data and shown to be altered between lung cancer subtypes. Secondly, I sought to investigate whether integrating the bronchial airway molecular biomarker with radiomic features (i.e., quantitative features derived from radiographic images) could yield a better diagnosis for lung cancer screening. Using clinical variables, molecular signatures, and radiomic imaging features, I built and tested an integrated biomarker to improve discrimination between malignant and benign Indeterminate Pulmonary Nodules (IPNs). Finally, as COVID-19 became a pandemic during my thesis work, I sought to utilize large-scale genomic data from multiple cohorts to investigate possible clinical risk factors related to SARS-CoV-2 entry and disease severity. My analysis showed that smoking affects the expression of host genes involved in SARS-CoV-2 entry differently in the nasal and bronchial airways. The work has implications about how smoking might modulate SARS-CoV-2 infection and COVID-19 disease severity. Collectively, this work leverages computational approaches to identify airway biology associated with lung cancer subtypes, improve the diagnosis of lung cancer in patients with IPNs, and reveal relationship between smoking and SARS-CoV-2 infection. / 2024-02-18T00:00:00Z