UC Riverside

Over the past decade, single-cell RNA sequencing (scRNA-seq) has revolutionized the field of transcriptomics, enabling the acquisition of unprecedented insights and fostering research that was previously unattainable. This advanced technology allows scientists to investigate the gene expression patterns of individual cells, providing unprecedented insight into cellular differences, changes, and functions. scRNA-seq enables researchers to examine the unique gene expression patterns of each cell, revealing the true extent of cellular heterogeneity. By revealing the molecular signatures of different cell types, scRNA-seq helps researchers understand the specific roles and functions of cells within a tissue or organism. This knowledge can be used to investigate how cells interact with each other, communicate, and influence their microenvironment. The technology has also shed light on identifying rare and previously unknown cell types. These rare cells could have crucial functional roles in development, tissue homeostasis, or disease progression, which has advanced our understanding into biological processes. Furthermore, scRNA-seq has provided valuable information about disease mechanisms by revealing the molecular underpinnings of complex diseases by investigating differences in gene expression between healthy and diseased cells. This information can be used to identify potential therapeutic targets, develop new treatments, and better understand disease progression. In cancer research, it has deepened our understanding of tumor heterogeneity, immune cell infiltration, and the discovery of new cellular subpopulations linked to drug resistance or metastasis. Additionally, scRNA-seq has been used to study individual cell responses to drug treatments, revealing molecular-level mechanisms of drug resistance and laying the groundwork for personalized medicine. Despite these advances, challenges remain with scRNA-seq, such as technical issues concerning sensitivity, scalability, and data analysis. However, as experimental techniques and computational methods continue to improve, scRNA-seq is expected to become even more powerful and useful in the future. In this dissertation, we discuss scRNA-seq analysis and its application in various contexts. Chapter 1 serves as an introduction to scRNA-seq analysis, detailing current protocols, technologies, and computational methods. Chapter 2 focuses on the use of scRNA-seq in studying the immune system, examining different types of immune cells and their roles in health and disease. Chapter 3 presents our findings on abnormal immune cell subsets, functional pathway changes, and molecular signatures associated with sepsis patient outcomes. In Chapter 4, we compare single-cell transcriptomics data from sepsis, COVID-19, and SLE patients, exploring molecular pathways and potential biomarkers related to disease outcomes. We also investigate platelet-immune cell interactions and their implications for disease severity. In Chapter 5, we examine the impact of smoking history on the tumor immune microenvironment (TIME) in lung cancer patients. Our findings reveal that smoking exacerbates T cell heterogeneity and alters gene expression patterns in immune cells, which may have implications for the efficacy of immune-based cancer treatments. In conclusion, this dissertation discusses the computational and statistical methods for scRNA-seq data analysis and its application in studying the immune system. Our research highlights the potential of scRNA-seq in understanding immune system diversity and its implications for patient prognosis, offering valuable insights that may lead to the development of new diagnostic tools and treatments.