Obesity typically precedes and accompanies the development of cardiometabolic diseases (CMD) that lead to increased morbidity and mortality. One of these disorders is non-alcoholic fatty liver disease (NAFLD), which encompasses a spectrum of varying degrees of fat accumulation and inflammation in the liver. More severe forms of NAFLD, such as non-alcoholic steatohepatitis (NASH), lead to a higher risk of developing hepatocellular carcinoma (HCC), the most prevalent form of liver cancer. Adipose tissue dysfunction in obesity can lead to increased circulating free fatty acids, and thus to ectopic lipid deposition in the liver. Left unchecked, lipotoxicity in the liver can result in inflammation, cell death, fibrosis, and ultimately the development of HCC. In both adipose and liver tissues, non-parenchymal cells, such as vascular and immune cell-types, play important roles in the normal function of these tissues and the pathophysiology of obesity, NAFLD, and HCC. A holistic approach to studying cell-types in a global manner would therefore greatly enhance our understanding of these common obesity-related diseases. Single-cell technologies, such as single-cell RNA-sequencing (scRNA-seq), assay individual cells and provide an excellent tool to study cell-type changes. While these approaches provide high resolution, they are currently costly and low-throughput. Traditional methods that measure molecular phenotypes at the tissue level are therefore still more practical. These assess a composite sum of cells present in the sample or biopsy, leading to inherent uncertainty in whether observed results are due to changes at the compositional level, cellular level, or both. Given these limitations, I aimed to integrate bulk-tissue RNA-sequencing (RNA-seq) and scRNA-seq data to leverage larger sample sizes in bulk RNA-seq and higher resolution in scRNA-seq. The application of single-cell technologies is especially promising for biobanks, as they can contain multiple levels of data on participants to uncover novel associations. Tissues are typically stored frozen, however, and this usually requires nuclei suspensions for single-nucleus RNA-seq (snRNA-seq), whereas whole cells would typically be used for scRNA-seq. This presents challenges for current droplet-based technologies. RNA from the ambient pool of lysed cells and nuclei can encapsulate into droplets, confounding results. In Chapter 2, I present a computational method to remove empty droplets from gene expression data (Alvarez et al. 2020). This allows for cleaner downstream data analysis by ensuring that only droplets with nuclei or cells are used. As current scRNA-seq technologies are low-throughput, their application to population-based studies and cohorts are limited. Present scRNA-seq technologies have lower throughput compared to bulk-tissue RNA-seq, which are typically available in higher sample sizes. In Chapter 3, I developed a method to help address this methodological gap. This approach, called Bisque (Jew et al. 2020), estimates cell-type composition in bulk RNA-seq data sets using single cell level reference data from the same tissue. The estimated cell-type proportions can be associated with sample-level data to uncover relevant cell-types, or they can be included as covariates in a model to reduce confounding caused by cell-type heterogeneity. One advantage of our method is that it requires only a minimum amount of information in the form of cell-type markers. This makes it attractive for existing data sets, which may not have accompanying single-cell level RNA-seq data. In the fourth chapter of this dissertation, I present our application of snRNA-seq to HCC. Carcinomas, such as HCC, are typically characterized by high amounts of tissue heterogeneity. Larger scale cancer cohorts usually lack single-cell level data, making interpretation of bulk-tissue results challenging. Here, I integrated HCC single-cell level experiments with relatively large HCC case-control bulk RNA-seq cohorts. The results from these analyses highlighted the role that proliferating cells play in HCC (Alvarez et al. 2022). These cycling cells were highly enriched in cancer tissue, as expected, and were prognostic of poor survival outcomes consistently in two independent cohorts. Furthermore, we observed that individuals with TP53 mutations have higher levels of these proliferating cells. Thus, our integration helped to interpret tumor gene expression changes as cell-type composition changes. In the fifth chapter, I present our human adipose tissue snRNA-seq results, showing changes in obesity and insulin resistance (Alvarez et al. manuscript in preparation). We applied multiplexing to increase our snRNA-seq sample size to roughly 100 subcutaneous adipose samples and over 100,000 nuclei, providing unprecedented resolution of human adipose tissue. This allowed us to identify finer resolution subcell-types, or cell states, which are more challenging to study as they are lower in frequency and exhibit more subtle differences. In addition to substantiating previous findings, we identified subcell-types associated with CMD. Then, we apply integrative approaches to corroborate these cell state changes in adipose bulk RNA-seq. Overall, our results show that both main cell-type and subcell-type variations are associated with metabolic traits. In summary, this dissertation presents my work on the integration of snRNA-seq and bulk- tissue RNA-seq to leverage distinct advantages provided by each. This has allowed us to gain a better understanding of the origin of gene expression changes in CMD.

The recognition of cell death as an active process has changed the way in which biologists view living things. Geneticists re-evaluate long known mutants, research strategies are redesigned, and new model systems are sought. This volume reviews our new understanding of programmed cell death as it applies to plants. The book draws comparisons with programmed cell death in animals and unicellular organisms. The book is directed at researchers and professionals in plant cell biology, biochemistry, physiology, developmental biology and genetics.

The Mouse Nervous System provides a comprehensive account of the central nervous system of the mouse. The book is aimed at molecular biologists who need a book that introduces them to the anatomy of the mouse brain and spinal cord, but also takes them into the relevant details of development and organization of the area they have chosen to study. The Mouse Nervous System offers a wealth of new information for experienced anatomists who work on mice. The book serves as a valuable resource for researchers and graduate students in neuroscience. Systematic consideration of the anatomy and connections of all regions of the brain and spinal cord by the authors of the most cited rodent brain atlases A major section (12 chapters) on functional systems related to motor control, sensation, and behavioral and emotional states A detailed analysis of gene expression during development of the forebrain by Luis Puelles, the leading researcher in this area Full coverage of the role of gene expression during development and the new field of genetic neuroanatomy using site-specific recombinases Examples of the use of mouse models in the study of neurological illness

This book evaluates and comprehensively summarizes the scientific findings that have been achieved through RNA-sequencing (RNA-Seq) technology. RNA-Seq transcriptome profiling of healthy and diseased tissues allows FOR understanding the alterations in cellular phenotypes through the expression of differentially spliced RNA isoforms. Assessment of gene expression by RNA-Seq provides new insight into host response to pathogens, drugs, allergens, and other environmental triggers. RNA-Seq allows us to accurately capture all subtypes of RNA molecules, in any sequenced organism or single-cell type, under different experimental conditions. Merging genomics and transcriptomic profiling provides novel information underlying causative DNA mutations. Combining RNA-Seq with immunoprecipitation and cross-linking techniques is a clever multi-omics strategy assessing transcriptional, post-transcriptional and post-translational levels of gene expression regulation.

With the rapid development of biotechnologies, single-cell sequencing has become an important tool for understanding the molecular mechanisms of diseases, defining cellular heterogeneities and characteristics, and identifying intercellular communications and single-cell-based biomarkers. Providing a clear overview of the clinical applications, the book presents state-of-the-art information on immune cell function, cancer progression, infection, and inflammation gained from single-cell DNA or RNA sequencing. Furthermore, it explores the role of target gene methylation in the pathogenesis of diseases, with a focus on respiratory cancer, infection and chronic diseases. As such it is a valuable resource for clinical researchers and physicians, allowing them to refresh their knowledge and improve early diagnosis and therapy for patients.

Since the invention of next-generation RNA sequencing (RNA-seq) technologies, they have become a powerful tool to study the presence and quantity of RNA molecules in biological samples and have revolutionized transcriptomic studies on bulk tissues. Recently, the emerging single-cell RNA sequencing (scRNA-seq) technologies enable the investigation of transcriptomic landscapes at a single-cell resolution, providing a chance to characterize stochastic heterogeneity within a cell population. The analysis of bulk and single-cell RNA-seq data at four different levels (samples, genes, transcripts, and exons) involves multiple statistical and computational questions, some of which remain challenging up to date. The first part of this dissertation focuses on the statistical challenges in the transcript-level analysis of bulk RNA-seq data. The next-generation RNA-seq technologies have been widely used to assess full-length RNA isoform structure and abundance in a high-throughput manner, enabling us to better understand the alternative splicing process and transcriptional regulation mechanism. However, accurate isoform identification and quantification from RNA-seq data are challenging due to the information loss in sequencing experiments. In Chapter 2, given the fast accumulation of multiple RNA-seq datasets from the same biological condition, we develop a statistical method, MSIQ, to achieve more accurate isoform quantification by integrating multiple RNA-seq samples under a Bayesian framework. The MSIQ method aims to (1) identify a consistent group of samples with homogeneous quality and (2) improve isoform quantification accuracy by jointly modeling multiple RNA-seq samples and allowing for higher weights on the consistent group. We show that MSIQ provides a consistent estimator of isoform abundance, and we demonstrate the accuracy of MSIQ compared with alternative methods through both simulation and real data studies. In Chapter 3, we introduce a novel method, AIDE, the first approach that directly controls false isoform discoveries by implementing the statistical model selection principle. Solving the isoform discovery problem in a stepwise manner, AIDE prioritizes the annotated isoforms and precisely identifies novel isoforms whose addition significantly improves the explanation of observed RNA-seq reads. Our results demonstrate that AIDE has the highest precision compared to the state-of-the-art methods, and it is able to identify isoforms with biological functions in pathological conditions. The second part of this dissertation discusses two statistical methods to improve scRNA-seq data analysis, which is complicated by the excess missing values, the so-called dropouts due to low amounts of mRNA sequenced within individual cells. In Chapter 5, we introduce scImpute, a statistical method to accurately and robustly impute the dropouts in scRNA-seq data. The scImpute method automatically identifies likely dropouts, and only performs imputation on these values by borrowing information across similar cells. Evaluation based on both simulated and real scRNA-seq data suggests that scImpute is an effective tool to recover transcriptome dynamics masked by dropouts, enhance the clustering of cell subpopulations, and improve the accuracy of differential expression analysis. In Chapter 6, we propose a flexible and robust simulator, scDesign, to optimize the choices of sequencing depth and cell number in designing scRNA-seq experiments, so as to balance the exploration of the depth and breadth of transcriptome information. It is the first statistical framework for researchers to quantitatively assess practical scRNA-seq experimental design in the context of differential gene expression analysis. In addition to experimental design, scDesign also assists computational method development by generating high-quality synthetic scRNA-seq datasets under customized experimental settings.

Tumor Immunology and Immunotherapy – Cellular Methods Part B, Volume 632, the latest release in the Methods in Enzymology series, continues the legacy of this premier serial with quality chapters authored by leaders in the field. Topics covered include Quantitation of calreticulin exposure associated with immunogenic cell death, Side-by-side comparisons of flow cytometry and immunohistochemistry for detection of calreticulin exposure in the course of immunogenic cell death, Quantitative determination of phagocytosis by bone marrow-derived dendritic cells via imaging flow cytometry, Cytofluorometric assessment of dendritic cell-mediated uptake of cancer cell apoptotic bodies, Methods to assess DC-dependent priming of T cell responses by dying cells, and more. Contains content written by authorities in the field Provides a comprehensive view on the topics covered Includes a high level of detail

RNA-Sequencing (RNA-Seq) has enabled detailed unbiased profiling of whole transcriptomes with incredible throughput. Recent technological breakthroughs have pushed back the frontiers of RNA expression measurement to single-cell level (scRNA-Seq). With both bulk and single-cell RNA-Seq analyses, modeling of the noise structure embedded in the data is crucial for drawing correct inference. In this dissertation, I developed a series of statistical methods to account for the technical variations specific in RNA-Seq experiments in the context of isoform- or gene- level differential expression analyses. In the first part of my dissertation, I developed MetaDiff (https://github.com/jiach/MetaDiff ), a random-effects meta-regression model, that allows the incorporation of uncertainty in isoform expression estimation in isoform differential expression analysis. This framework was further extended to detect splicing quantitative trait loci with RNA-Seq data. In the second part of my dissertation, I developed TASC (Toolkit for Analysis of Single-Cell data; https://github.com/scrna-seq/TASC), a hierarchical mixture model, to explicitly adjust for cell-to-cell technical differences in scRNA-Seq analysis using an empirical Bayes approach. This framework can be adapted to perform differential gene expression analysis. In the third part of my dissertation, I developed, TASC-B, a method extended from TASC to model transcriptional bursting- induced zero-inflation. This model can identify and test for the difference in the level of transcriptional bursting. Compared to existing methods, these new tools that I developed have been shown to better control the false discovery rate in situations where technical noise cannot be ignored. They also display superior power in both our simulation studies and real world applications.

This volume expands on statistical analysis of genomic data by discussing cross-cutting groundwork material, public data repositories, common applications, and representative tools for operating on genomic data. Statistical Genomics: Methods and Protocols is divided into four sections. The first section discusses overview material and resources that can be applied across topics mentioned throughout the book. The second section covers prominent public repositories for genomic data. The third section presents several different biological applications of statistical genomics, and the fourth section highlights software tools that can be used to facilitate ad-hoc analysis and data integration. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, step-by-step, readily reproducible analysis protocols, and tips on troubleshooting and avoiding known pitfalls. Through and practical, Statistical Genomics: Methods and Protocols, explores a range of both applications and tools and is ideal for anyone interested in the statistical analysis of genomic data.