京都大学 / 新制・課程博士 / 博士(医学) / 甲第23572号 / 医博第4786号 / 新制||医||1054(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 妹尾 浩, 教授 武藤 学, 教授 小川 誠司 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

Single-cell RNA-sequencing (scRNA-seq) technology enables researchers to investigate a genome at the cellular level with unprecedented resolution. An organism consists of a heterogeneous collection of cell types, each of which plays a distinct role in various biological processes. Hence, the first step of scRNA-seq data analysis often is to distinguish cell types so that they can be investigated separately. Researchers have recently developed several automated cell type annotation tools based on supervised machine learning algorithms, requiring neither biological knowledge nor subjective human decisions. Dropout is a crucial characteristic of scRNA-seq data which is widely utilized in differential expression analysis but not by existing cell annotation methods. We present scAnnotate, a cell annotation tool that fully utilizes dropout information. We model every gene’s marginal distribution using a mixture model, which describes both the dropout proportion and the distribution of the non-dropout expression levels. Then, using an ensemble machine learning approach, we combine the mixture models of all genes into a single model for cell-type annotation. This combining approach can avoid estimating numerous parameters in the high-dimensional joint distribution of all genes. Using fourteen real scRNA-seq datasets, we demonstrate that scAnnotate is competitive against nine existing annotation methods, and that it accurately annotates cells when training and test data are (1) similar, (2) cross-platform, and (3) cross-species. Of the cells that are incorrectly annotated by scAnnotate, we find that a majority are different from those of other methods. / Graduate / 2023-07-27

Cyclic di-GMP is a second messenger used by bacteria to regulate motility, extracellular polysaccharide production, and the cell cycle. Recent advances in the measurement of real time cyclic di-GMP levels in single cells have uncovered significant dynamic heterogeneity of second messenger concentrations within bacterial populations. This heterogeneity results in a wide range of phenotypic outcomes within a single population, providing the potential for population survival and adaptability in response to rapidly changing environments. In this chapter, we discuss some of the measurement technologies available for single-cell measurement of cyclic di-GMP concentrations, the resulting discovery of heterogeneous cyclic di-GMP populations, the mechanisms bacteria use to generate this heterogeneity, and the biochemical and functional consequences of heterogeneity on cyclic di-GMP effector binding and the bacterial population.

Traditional proteomics studies can measure many protein biomarkers simultaneously from a single patient-derived sample, promising the possibility of syndromic diagnoses of multiple diseases sharing common symptoms. However, precious cellular-level information is lost in conventional bulk-scale studies that measure tissues comprising many types of cells. As single cells are the building blocks of organisms and are easier to biopsy than traditional bulk samples, performing proteomics on a single-cell level would benefit clinicians and patients. Single-cell proteomics, combined with mass spectrometry imaging, can be used to analyze cells in their microenvironment, preserving spatial information. We have previously used laser-capture microdissection to isolate single motor neurons from tissue and analyze them in our single-cell proteomics platform. However, our sampled population of cells was necessarily limited by the low throughput of the measurement platform, and by the sensitivity of our liquid chromatography-mass spectrometry system to debris introduced in the laser-capture microdissection isolation workflow. In the work described in this dissertation, we dramatically improved the throughput of single-cell proteomics, created a method for removing insoluble debris that clogged our liquid chromatography-mass spectrometry system, and developed a high-performance, low-cost method for nanoflow gradient formation. Together, these methodologies will increase the depth of information and the number of biological replicates that can measured in single-cell proteomics. We hope that these technologies will be applied to future liquid chromatography systems to enable large scale single-cell proteomics studies of tissues. This will reveal the cellular origins of disease on a multimolecular level, while keeping important spatial information. Thus, we expect the technologies and ideas developed here to play a key role in understanding the cellular proteomics in biomedical and clinical settings.

Single-Cell Proteomics (SCP) can provide unique insights into biological processes by resolving heterogeneity that is obscured by bulk measurements. Gains in the overall sensitivity and proteome coverage through improvements in sample processing and analysis increase the information content obtained from each cell, particularly for less abundant proteins. In addition to achieving in-depth proteome coverage from single cells, higher throughput measurements enable large-scale and statistically significant features within single cell populations. This dissertation focuses on method development to improve the sensitivity and throughput of SCP based on the nanoPOTS (nanodroplet Processing in One pot for Trace Samples) platform. The methods discussed here include miniaturization of bottom-up proteome sample preparation and liquid chromatography (LC) separations, implementation of an ultrasensitive latest-generation mass spectrometer, development of automated sample handling workflow, and combination of isotopic and isobaric labeling for higher order multiplexing. The miniaturization of sample preparation largely reduced protein loss during sample preparation and enabled in-depth single-cell proteomics. The sensitivity was further improved using a 20-μm-i.d. in-house-packed nanoLC column and the latest generation Orbitrap Eclipse Tribrid mass spectrometer. A >70% increase in proteome coverage was observed for single cells relative to previous efforts using a 30-μm-i.d. LC columns coupled to a previous-generation Orbitrap Fusion Lumos mass spectrometer. To make SCP and low-input proteome profiling accessible to more proteomics laboratories, a fully automated platform termed autoPOTS (automated Preparation in One pot for Trace Samples) was developed using only commercially available instrumentation for sample processing and analysis. AutoPOTS can be used to analyze 1–500 cells with a modest reduction in peptide coverage for 150 cells and a 24% reduction in coverage for single cells compared to the nanoliter preparation. To improve the throughput of SCP, a hyperplexing sample preparation and analysis method for Single-Cell Proteomics (hyperSCP) was developed using a combination of isotopic and isobaric labeling. This method can improve the throughput by at least 28 times with the same gradient compared to the label-free proteomics and can double or triple the throughput of standard tandem mass tag multiplexing.

Understanding how the human brain works can lead to new discoveries and improved treatments for brain related diseases and disabilities such as Alzheimer's and autism. One method for studying brain activity is through electrophysiological recordings, particularly through the use of in vivo recording techniques. While these techniques have advanced significantly over the years, data analysis tools have not kept pace, making it difficult to isolate the activity of individual neurons from the recordings. In this thesis, we propose a unified protocol for isolating the spike activity of a neuron from an electrophysiology recording. Additionally, we conducted customized spike train analysis on the recorded cells in a pattern separation task. Preliminary results suggest that changes in the neural activity of mossy cells was not significant. However, for granule cells and interneurons, responses to punishment and reward were observed.

京都大学 / 新制・課程博士 / 博士(工学) / 甲第24211号 / 工博第5039号 / 新制||工||1787(附属図書館) / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 中北 英一, 准教授 山口 弘誠, 教授 田中 賢治 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Squamous cell carcinoma of the bronchus is the second most common and fatal subtype of lung cancer. In the process of squamous carcinogenesis, the normal bronchial epithelium undergoes a series of histologic transformations known as the metaplasia-dysplasia-carcinoma sequence. These intermediate histologic patterns are called premalignant lesions, and occur prior to the development of cancer. Compared to early stage cancer, survival following resection of premalignant lesions approaches 100%, highlighting the promise of lung cancer interception. However, because of our lack of understanding of the molecular events during squamous carcinogenesis, we are currently unable to predict which lesions will progress to cancer, and we do not have molecular targets for noninvasive treatment. The work in this thesis seeks to improve our understanding of the changes associated with grades of premalignant histology and progression at the level of single cells. I analyzed single cell RNA sequencing data from a cohort of 41 lesions from 26 patients, encompassing the normal-appearing bronchus, premalignant lesions, and early stage carcinoma. I described histology-associated changes in basal cells. Basal cells from low grade lesions expressed genes related to the maintenance of the normal epithelium, while basal cells from high grade lesions expressed genes related to the cell cycle and detoxification of the airway from smoking toxicants. Secondly, I identified a high grade lesion undergoing the epithelial-to-mesenchymal transition. These cells transitioned from a high grade basal cell state, lost their expression of basal cell markers, and expressed canonical EMT genes, including SPARC, FN1, and MMP2. Finally, I identified shifts in T cell subtypes and widespread expression of exhaustion markers PD-1, CTLA4, LAG3, and TIGIT co-occurring with high grade basal cells. Secondly, I leveraged our single cell data to identify gene modules associated with histology and progression in bulk RNA sequencing data. I identified a module of genes expressed in B and dendritic cells involved in antigen presentation through the MHC II pathway whose expression was decreased in progressive lesions. I also identified a module of stromal-expressed genes that were less expressed in progressive lesions, which had previously been unidentified. Associations between module expression and progression were validated in a second data set. This work improves our understanding of the signaling and interactions between cell types associated with histology and progression of premalignant lesions. These findings may be used to improve our prognostication and treatment of premalignant lesions.

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