Inflammatory diseases often involve complex and dynamic responses of monocytes, crucial cells of the innate immune system. Understanding these responses, particularly to lipopolysaccharide (LPS), a key inflammatory stimulus, is vital yet remains challenging due to their heterogeneity and plasticity. Upon analyzing available single-cell RNA sequencing data sets, we defined key patterns of monocyte inflammatory responses challenged with varying LPS dosages. We found that high-dose LPS induced the generation of exhausted monocytes with elevated expression of genes associated with pathogenic inflammation and immune suppression.. In contrast, super-low-dose LPS led to a state of low-grade inflammation, characterized by enhanced chemotaxis; immune-enhancement; and adhesion.. Pseudo-time analysis revealed a potential bifurcation of monocytes, starting from a proliferative, less-differentiated and premature state into either the exhausted state (under prolonged high dose LPS challenge) or the low-grade inflammatory state (under the prolonged super-low dose LPS treatment). Complementing our analyses with in vitro cultured murine monocytes, we observed similar exhaustion of monocytes collected from septic murine hearts published in an independent study. Furthermore, we analyzed publicly available scRNAseq datasets regarding monocytes from septic and severe COVID human patients and revealed a similar exhaustion phenotype as we documented in murine exhausted monocytes. In contrast, our analyses of newly published scRNAseq data regarding monocytes from chronic autoimmune patients reveal key distinct low-grade inflammation features. With translational potential, we analyzed the scRNAseq datasets of monocytes trained with 4-PBA, a potent anti-inflammatory compound, and observed that 4-PBA can effectively arrest monocytes in an anti-inflammatory state. Together, our comparative analyses reveal a systems landscape of monocyte memory dynamics with distinct dosage and history of LPS challenges, and offer novel insights for potential therapeutic strategies for modulating both acute sepsis and chronic inflammatory diseases. Our studies also provide a foundation for guiding future mechanistic and translational studies regarding monocyte dynamics and their involvements in health and disease pathogenesis. / Doctor of Philosophy / Inflammation is the body's natural response to injury or infection. A key player in this process is a type of immune cell called monocyte. Monocytes are our body's first line of defense, rushing to the site of injury or infection. However, the way these cells respond can vary greatly, depending on the dosage and duration of external challenges. In our research, we analyzed data collected through an advanced technique called single-cell RNA sequencing, in order to take a detailed look at how an individual monocyte responds to different amounts of LPS, a key substance found in most bacteria. We found that when exposed to a prolonged challenge of higher dose LPS, monocytes become exhausted with pathogenic inflammation and immune suppression, as seen in sepsis. However, when the LPS dose is low, these cells enter a state of low-grade inflammation, responsible for chronic inflammation as seen in autoimmune diseases, atherosclerosis and other chronic diseases. We found that this paradigm of exhaustion and low-grade inflammation can be seen in data analyzed from either patients with severe infections such as sepsis or severe COVID-19, or patients with long-term autoimmune diseases. In simpler terms, our study provides a detailed road map regarding how our body's first responders, namely the monocytes, react under different levels of threat, and how we might be able to guide their responses toward a beneficial direction. Understanding these processes more clearly may lead to new ways to treat a range of infectious or inflammatory diseases.

Single-cell RNA sequencing (scRNA-seq) is the leading technique for characterizing cellular heterogeneity in biological samples. Various scRNA-seq protocols have been developed that can measure the transcriptome from thousands of cells in a single experiment. With these methods readily available, the ability to transform raw data into biological understanding of complex systems is now a rate-limiting step. In this dissertation, I introduce novel computational software and tools which enhance preprocessing and clustering of scRNA-seq data and evaluate their performance compared to existing methods. First, I present scruff, an R/Bioconductor package that preprocesses data generated from scRNA-seq protocols including CEL-Seq or CEL-Seq2 and reports comprehensive data quality metrics and visualizations. scruff rapidly demultiplexes, aligns, and counts the reads mapped to genomic features with deduplication of unique molecular identifier (UMI) tags and provides novel and extensive functions to visualize both pre- and post-alignment data quality metrics for cells from multiple experiments. Second, I present Celda, a novel Bayesian hierarchical model that can perform simultaneous co-clustering of genes into transcriptional modules and cells into subpopulations for scRNA-seq data. Celda identified novel cell subpopulations in a publicly available peripheral blood mononuclear cell (PBMC) dataset and outperformed a PCA-based approach for gene clustering on simulated data. Third, I extend the application of Celda by developing a multimodal clustering method that utilizes both mRNA and protein expression information generated from single-cell sequencing datasets with multiple modalities, and demonstrate that Celda multimodal clustering captured meaningful biological patterns which are missed by transcriptome- or protein-only clustering methods. Collectively, this work addresses limitations present in the computational analyses of scRNA-seq data by providing novel methods and solutions that enhance scRNA-seq data preprocessing and clustering.

No description available.

Indiana University-Purdue University Indianapolis (IUPUI) / Single-cell RNA-sequencing (scRNA-seq) has enabled researchers to study interindividual cellular heterogeneity, to explore disease impact on cellular composition of tissue, and to identify novel cell subtypes. However, a major challenge in scRNA-seq analysis is to identify the cell type of individual cells. Accurate cell type identification is crucial for any scRNA-seq analysis to be valid as incorrect cell type assignment will reduce statistical robustness and may lead to incorrect biological conclusions. Therefore, accurate and comprehensive cell type assignment is necessary for reliable biological insights into scRNA-seq datasets. With over 200 distinct cell types in humans alone, the concept of cell identity is large. Even within the same cell type there exists heterogeneity due to cell cycle phase, cell state, cell subtypes, cell health and the tissue microenvironment. This makes cell type classification a complicated biological problem requiring bioinformatics. One approach to classify cell type identity is using marker genes. Marker genes are genes specific for one or a few cell types. When coupled with bioinformatic methods, marker genes show promise of improving cell type classification. However, current scRNA-seq classification methods and databases use marker genes that are non-specific across sources, samples, and/or species leading to bias and errors. Furthermore, many existing tools require manual intervention by the user to provide training datasets or the expected number and name of cell types, which can introduce selection bias. The selection bias negatively impacts the accuracy of cell type classification methods as the model cannot extrapolate outside of the user inputs even when it is biologically meaningful to do so. In this dissertation I developed CellTypeR, a suite of tools to explore the biology governing cell identity in a “normal” state for humans and mice. The work presented here accomplishes three aims: 1. Develop an ontology standardized database of published marker gene literature; 2. Develop and apply a marker gene classification algorithm; and 3. Create user interface and input data structure for scRNA-seq cell type prediction.

Single-cell RNA sequencing (scRNA-seq) has been a powerful technique for characterizing transcriptional heterogeneity related to tumor development and disease pathogenesis. Despite the advances of technology, there is still a lack of software to systematically and easily assess the quality and different types of artifacts present in scRNA-seq data and a statistical framework for understanding heterogeneity in the gene programs of cancer cells. In this dissertation, I first introduced novel computational software to enhance and streamline the process of quality control for scRNA-seq data called SCTK-QC. SCTK-QC is a pipeline that performs comprehensive quality control (QC) of scRNA-seq data and runs a multitude of tools to assess various types of noise present in scRNA-seq data as well as quantification of general QC metrics. These metrics are displayed in a user-friendly HTML report and the pipeline has been implemented in two cloud-based platforms. Most scRNA-seq studies only profiled a small number of tumors and provided a narrow view of the transcriptome in tumor tissue. Next, I developed a novel framework to perform a large-scale meta-analysis of cancer cells from 12 studies with scRNA-seq data from patients with non-small-cell lung cancer (NSCLC). I discovered interpretable gene co-expression modules with celda and demonstrated that the activity of gene modules accounted for both inter- and intra-tumor heterogeneity of NSCLC samples. Furthermore, I used CaDRa to determine that the levels of some gene modules were significantly associated with combinations of underlying genetic alterations. I also showed that other gene modules are associated with immune cell signatures and may be important for communication with the cancer cells and the immune microenvironment. Finally, I presented a novel computational method to study the association between copy number variation (CNV) and gene expression at the single-cell level. The diversity of the CNV profile was identified in tumor subclones within each sample and I discovered cis and trans gene signatures which have expression values associated with specific somatic CNV status. This study helped us prioritize the potential cancer driver genes within each CNV region. Collectively, this work addressed the limitation in the quality control of scRNA-seq data and provided insights for understanding the heterogeneity of NSCLC samples.

From the discovery of developmental gradients to pioneering some of the first gene regulatory models, the sea urchin model has played a foundational role in deciphering the complex molecular mechanisms behind the phenomena that underlie pattern formation during embryonic development. Of particular interest to our lab, primary mesenchyme cells (PMCs), a skeletogenic lineage, provide an excellent system for understanding the mechanisms behind skeletal pattern formation. Sea urchin skeletal patterning is driven by ectodermal cues that are differentially expressed in space and time; these cues instruct the PMCs. Originating as a homogeneous population, PMCs diversify in response to patterning cue reception, then produce distinct skeletal elements as a function of the cues that they have received from the ectoderm. However, the exact mechanisms underpinning PMC diversification and the role that individual ectodermal cues play to mediate this diversification process is poorly understood. To bridge that knowledge gap, this work leverages multiple data modalities, including single-cell RNA sequencing (scRNA-seq) and 3D visualization of gene expression in normal and perturbed embryos to not only present an exhaustive description of PMC diversification, but also offers novel computational approaches and the development of resources necessary for these studies. First, we present the novel algorithm ICAT. Created to correctly identify cell states from mixedcondition scRNA-seq experiments, ICAT plays a necessary role in identifying PMC subpopulations affected by ectodermal cue disruption. Using simulated and real datasets, we benchmark ICAT against several state-of-the-art workflows, and find ICAT provides more robust and sensitive performance compared to current practices. We further validate ICAT in vivo using single molecule fluorescent in situ hybridization (FISH) and show that, compared to leading algorithms, ICAT uniquely and correctly characterizes the effects of patterning cue disruption on PMC subpopulation composition. Finally, by combining temporal scRNA-seq data throughout skeletal patterning with a newly generated spatial gene expression reference map, we not only identify distinct PMC subpopulations, but also provide spatial and temporal coherence to each of their developmental trajectories during skeletal pattern formation. We compliment this work by inferring the gene regulatory networks underlying PMC diversification and thereby identifying the transcriptional regulators that function as network hubs. We empirically demonstrate that these hubs are required for skeletal patterning, and spatially map their expression within the PMCs. Sequencing single PMCs isolated from embryos in which ectodermal cue function was inhibited, we show that functional loss of each cue uniquely disrupts the PMC gene regulatory network and characterize the subsequent compositional effects of PMC subpopulations. Taken together, this work defines the spatiotemporal details of PMC diversification in normal embryos as well as in embryos with individual cue losses, as well as offering numerous novel computational methods and resources necessary for these advances. / 2026-09-26T00:00:00Z

京都大学 / 新制・課程博士 / 博士(医学) / 甲第25495号 / 医博第5095号 / 新制||医||1073(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 溝脇 尚志, 教授 伊達 洋至, 教授 黒田 知宏 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

In most eukaryotes, alternative splicing is an important regulatory mechanism of gene expression that results in a single gene coding for multiple protein isoforms, thus largely increases the diversity of the proteome. RNA-seq is widely used for genome-wide splicing isoform quantification, and several effective and powerful methods have been developed for splicing analysis with RNA-seq data. However, it remains problematic for genes with low coverages or large number of isoforms. These difficulties may in principle be ameliorated by exploiting correlations encoded in the structured data sources. This thesis contributes to developments of Bayesian methods for splicing analysis by leveraging additional information in multiple datasets with structured prior distributions. First, we developed DICEseq, the first isoform quantification method tailored to time-series RNA-seq experiments. DICEseq explicitly models the correlations between experiments at different time points to aid the quantification of isoforms across experiments. Numerical experiments on both simulated and real datasets show that DICEseq yields more accurate results than state-of-the-art methods, an advantage that can become considerable at low coverage levels. Furthermore, DICEseq permits to quantify the trade-off between temporal sampling of RNA and depth of sequencing, frequently an important choice when planning experiments. Second, we developed BRIE (Bayesian Regression for Isoform Estimation), a Bayesian hierarchical model which resolves the difficulties in splicing analysis in single-cell RNA-seq (scRNA-seq) data by learning an informative prior distribution from sequence features. This method combines the quantification and imputation for splicing analysis via a Bayesian way, which is particularly useful in scRNA-seq data due to its extreme low coverages and high technical noises. We validated BRIE on several scRNA-seq data sets, showing that BRIE yields reproducible estimates of exon inclusion ratios in single cells. Third, we provided an effective tool by using Bayes factor to sensitively detect differential splicing between different single cells. When applying BRIE to a few real datasets, we found interesting heterogeneity patterns in splicing events across cell population, for example alternative exons in DNMT3B. In summary, this thesis proposes structured Bayesian methods to integrate multiple datasets to improve splicing analysis and study its biological functions.

The advent of scRNA-seq has rapidly advanced our understanding of complex systems by enabling the researcher to look at the full transcriptional profile within each cell, with the potential to reveal intercellular communications within a tissue. To map these communications, I created SignallingSingleCell, an R package that provides an end-to-end approach for the analysis of scRNA-seq data, with a particular focus on building ligand and receptor signaling networks. Using these powerful techniques, we sought to dissect the heterogenous population of cells recently reported within the BMDC culture system. From this data we were able to determine the cell type composition, identify the different myeloid responses to similar stimuli, and unify recent conflicting studies about the populations within this system. We then applied these tools to study vitiligo, an autoimmune disease of the skin, to answer fundamental questions about the initiation and progression of disease. We found signatures of increased antigen presentation through MHC-I, loss of immunotolerance cytokines such as TGFB1 and IL-10, and changes in the complex chemokine circuits that influence T cell localization, including an essential role for CCR5 in Treg function. In order to identify and characterize the autoreactive T cells that are responsible for the targeted destruction of melanocytes, we then paired scRNA-seq with TCR-seq and MHC-II complexes loaded with melanocyte antigen. From this data we contrast the transcriptional state of melanocyte specific T cells to bystanders found within the skin and circulation.

Recent research in bioinformatics has presented multiple cell type identification meth- dologies using single cell RNA sequence data (scRNA-seq). However, a consensus on which cell typing methodology consistently demonstrates superior performance remains absent. Additionally, very few studies approach cell type identification through a semi- supervised learning study, whereby the information in unlabeled data is leveraged to train an enhanced classifier. This paper presents cell annotation methodologies through self- learning and graph-based semi-supervised learning, in both raw count scRNA-seq data as well as in a latent embedding. I find that a self-learning framework enhances perfor- mance compared to a solely supervised learning classifier. Additionally, modelling on the latent data representations consistently outperforms modelling on the original data. The results show an overall accuracy of 96.12%, whereas additional models achieve an average precision rate of 95.12% and an average recall rate of 94.40%. The semi-supervised learn- ing approaches in this thesis compare favourable to scANVI in terms of accuracy, average precision rate, average recall rate and average f1-score. Moreover, results for alternative scenarios, in which cell types among training and test data do not perfectly overlap, are reported in this thesis.