Single cell RNA sequencing allows expression profiles for individual cells to be obtained thus offering unprecedented insight into the behavior of individual cells. Insight gained from exploration of individual cells has implications in both cancer and developmental biology. Much of the power of these models is derived from the shear amount and granularity of the data that can be collected; however, with this power comes the deleterious introduction of batch effects. Samples sequenced on different days, by different technicians can show variance that cannot be attributed to biological condition, but rather is only due to the batch in which it was sequenced. These batch effects can cause alterations to the perceived relationships between the main effect and the outcome of interest, for instance cancer status, the main effect of cancer status may be hidden by the unwanted and unmodeled variance. Two known methods for the correction of batch effects in bulk RNA sequencing data are ComBat-Seq and Surrogate Variable Analysis; in this work, we demonstrate that when cell-type is known, inclusion of that covariate in the ComBat-Seq results in an appropriate correction of the batch effect. We also demonstrate that when cell-type is not known, SVA can be used to infer cell-type information form the latent structure of the count matrix with some loss of accuracy compared to the correction with cell type. This cell type information can be used in place of the actual cell-type covariate information to correct single cell RNA sequencing data with ComBat-Seq; inclusion of surrogate variables helps the accuracy of the correction in certain scenarios. Additionally, in the case where cell-type is not known, and the cell proportions are balanced between batches we demonstrate that ComBat-Seq can be used naive to cell-type information. The efficacy of this procedure is demonstrated with two simulated datasets and a dataset containing Jurkat and t293 cells. These results are then compared to Harmony, a recently reported batch correction algorithm. The procedure, herein reported, has benefits over harmony in certain situations such as when a counts matrix is needed for further analysis or when there is thought to be substantial intra-cell-type variability across different batches.

Following stroke there is a robust increase in the proliferation of neural stem and progenitor cells (NSPCs) that ectopically migrate from the subventricular zone (SVZ) to surround the site of damage induced by stroke (infarct). Previous in vivo studies by our lab and others have shown that a majority of migrating NSPCs when labelled prior to stroke become astrocytes surrounding the infarct. In contrast, our lab has shown that the majority of NSPCs when labelled after stroke become neurons surrounding the infarct. This thesis aims to elucidate the contributions of intrinsic changes that can alter the temporal fate of the NSPCs. The NSPCs were fate mapped in this study using the nestin-CreERT2 mouse model and strokes were induced using the photothrombosis model within the cortex. In alignment with our previous findings, fate-mapping the NSPCs using a single injection of tamoxifen treatment revealed a temporal-specific switch in neuronal fate when NSPCs were labeled at timepoints greater than 7 days following stroke. Single cell RNA sequencing and histological analysis identified significant differences in the proportion of populations of NSPCs and their progeny labeled at the SVZ in the absence or presence of a stroke. NSPCs labelled after stroke were comprised of a reduced proportion of quiescent neural stem cells alongside an accompanied increase in doublecortin-expressing neuroblasts. The RNA transcriptional profile of the NSPCs labelled also revealed NSPCs and their progeny labeled after stroke had an overall enrichment for a neuronal transcription profile in all of the labeled cells with a reduction in astrocytic gene expression in quiescent and activated neural stem cells. Furthermore, we highlight the presence of perturbed transcriptional dynamics of neuronal genes, such as doublecortin following stroke. Altogether, our study reveals following a stroke there is a sustained intrinsic regulated neuronal-fated response in the NSPCs that reside in the SVZ that may not be exclusive from extrinsic regulation. This work raises the challenge to learn how to harness the potential of this response to improve recovery following stroke through examining their contributions to recovery.

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

The recent technology of single-cell RNA sequencing can be used to investigate molecular, transcriptional, changes in cells as they develop. I reviewed the literature on the technology, and made a large scale quantitative comparison of the different implementations of single cell RNA sequencing to identify their technical limitations. I investigate how to model transcriptional changes during cellular development. The general forms of expression changes with respect to development leads to nonparametric regression models, in the forms of Gaussian Processes. I used Gaussian process models to investigate expression patterns in early embryonic development, and compared the development of mice and humans. When using in vivo systems, ground truth time for each cell cannot be known. Only a snapshot of cells, all being in different stages of development can be obtained. In an experiment measuring the transcriptome of zebrafish blood precursor cells undergoing the development from hematopoietic stem cells to thrombocytes, I used a Gaussian Process Latent Variable model to align the cells according to the developmental trajectory. This way I could investigate which genes were driving the development, and characterise the different patterns of expression. With the latent variable strategy in mind, I designed an experiment to study a rare event of murine embryonic stem cells entering a state similar to very early embryos. The GPLVM can take advantage of the nonlinear expression patterns involved with this process. The results showed multiple activation events of genes as cells progress towards the rare state. An essential feature of cellular biology is that precursor cells can give rise to multiple types of progenitor cells through differentiation. In the immune system, naive T-helper cells differentiate to different sub-types depending on the infection. For an experiment where mice were infected by malaria, the T-helper cells develop into two cell types, Th1 and Tfh. I model this branching development using an Overlapping Mixture of Gaussian Processes, which let me identify both which cells belong to which branch, and learn which genes are involved with the different branches. Researchers have now started performing high-throughput experiments where spatial context of gene expression is recorded. Similar to how I identify temporal expression patterns, spatial expression patterns can be identified nonparametrically. To enable researchers to make use of this technique, I developed a very fast method to perform a statistical test for spatial dependence, and illustrate the result on multiple data sets.

Colonic stromal cells provide critical structural support but also regulate immunity, tolerance and inflammatory responses in the mucosa. Substantial variability and plasticity of mucosal stromal cells has been reported but a paucity of distinct marker genes exist to identify distinct cell states. Here single-cell RNA-sequencing is used to document heterogeneity and subtype specific markers of individual colonic stromal cells in human and mouse. Marker-free transcriptional clustering of fibroblast-like cells derived from healthy human tissue reveals distinct populations corresponding to myofibroblasts and three transcriptionally and functionally dissimilar populations of fibroblasts. A SOX6 high fibroblast subset occupies a position adjacent to the epithelial basement membrane and expresses multiple epithelial morphogens including WNT5A and BMP2. Additional fibroblast subtypes show specific enrichment for chemokine signalling and prostaglandin E₂ synthesis respectively. In ulcerative colitis, substantial remodelling occurs with depletion of the SOX6 high population and emergence of an immune enriched population expressing genes associated with fibroblastic reticular cells including CCL19, CCL21 and IL33. A large murine dataset comprising over 7,000 colonic mesenchymal cells from an acute colitis model and matched healthy controls reveals strong preservation of the SOX6 high and myofibroblast transcriptional signatures. Unsupervised pseudotemporal ordering is used to relate fibroblast subsets to one another producing a branched developmental hierarchy that includes a potential progenitor population with mesothelial characteristics at its origin. This work provides a molecular basis for re-classification of colonic stromal cells and identifies pathological changes in these cells underpinning inflammation in UC.

The initiating events of ovarian cancer remain unknown, but an established risk factor is use of estrogen therapy by post-menopausal women where there is a positive correlation between duration of use and risk for disease. Mouse models of ovarian cancer have shown that exposure to exogenous 17β-estradiol (E2) accelerates tumour onset so this study aims to investigate the E2 signalling mechanisms responsible for sensitizing ovarian epithelial cells to transformation. By developing model systems that are responsive to E2 manipulation, we showed that E2 induces the formation of epithelial dysplasias both in vitro and in vivo. microRNA microarray was used to discover that E2 up-regulates microRNA-378 via the ESR1 pathway, resulting in the down-regulation of a tumour suppressor gene called Disabled-2 (Dab2). E2 suppression of Dab2 was found to result in increased proliferation, loss of contact inhibition, epithelial dysplasia, and increased sensitivity to transformation. This mechanism was also found to be active in mouse fallopian tube epithelium and human ovarian cancer cells. Single-cell RNA sequencing and trajectory analysis was subsequently used to explore additional signalling mechanisms that might contribute to the emergence of dysplastic lesions induced by E2. Multiple molecular signalling pathways dysregulated by E2 were identified and this revealed several possible biomarkers to be investigated for early detection of ovarian cancer. In the context of a current lack of strategies for ovarian cancer prevention or early detection, this work represents a significant advance in our understanding of how E2 promotes ovarian cancer initiation.

Indiana University-Purdue University Indianapolis (IUPUI) / Metabolic syndrome (MetS) affects 1/3 of all Americans and is the clustering of three or more of the following cardiometabolic risk factors: obesity, hypertension, dyslipidemia, glucose intolerance, and insulin resistance. MetS drastically increases the incidence of coronary artery disease (CAD), which is the leading cause of mortality globally. A cornerstone of CAD is arterial remodeling associated with coronary smooth muscle (CSM) cytodifferentiation from a contractile phenotype to proliferative and osteogenic phenotypes. This cytodifferentiation is tightly coupled to changes in intracellular Ca2+ handling that regulate several key cellular functions, including contraction, transcription, proliferation, and migration. Our group has recently elucidated the time course of Ca2+ dysregulation during MetS-induced CAD development. Ca2+ transport mechanisms, including voltage-gated calcium channels, sarcoplasmic reticulum (SR) Ca2+ store, and sarco-endoplasmic reticulum Ca2+ ATPase (SERCA), are enhanced in early, mild disease and diminished in late, severe disease in the Ossabaw miniature swine. Using this well-characterized large animal model, I tested the hypothesis that this Ca2+ dysregulation pattern occurs in multiple etiologies of CAD, including diabetes and aging. The fluorescent intracellular Ca2+ ([Ca2+]i) indicator fura-2 was utilized to measure [Ca2+]i handling in CSM from lean and diseased swine. I found that [Ca2+]i handling is enhanced in mild disease with minimal CSM phenotypic switching and diminished in severe disease with greater phenotypic switching, regardless of CAD etiology. We are confident of the translatability of this research, as the Ca2+ influx, SR Ca2+ store, and SERCA functional changes in CSM of humans with CAD are similar to those found in Ossabaw swine with MetS. Single-cell RNA sequencing revealed that CSM cells from an organ culture model of CAD exhibited many different phenotypes, indicating that phenotypic modulation is not a discreet event, but a continuum. Transcriptomic analysis revealed differential expression of many genes that are involved in the osteogenic signaling pathway and in cellular inflammatory responses across phenotypes. These genes may be another regulatory mechanism common to the different CAD etiologies. This study is the first to show that CSM Ca2+ dysregulation is common among different CAD etiologies in a clinically relevant animal model.

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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