Exploring Immune Cell Heterogeneity Through Single-Cell RNA Sequencing Analysis

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

Advances in AI for Protein Structure Prediction: Implications for Cancer Drug Discovery and Development.

Recent advancements in AI-driven technologies, particularly in protein structure prediction, are significantly reshaping the landscape of drug discovery and development. This review focuses on the question of how these technological breakthroughs, exemplified by AlphaFold2, are revolutionizing our understanding of protein structure and function changes underlying cancer and improve our approaches to counter them. By enhancing the precision and speed at which drug targets are identified and drug candidates can be designed and optimized, these technologies are streamlining the entire drug development process. We explore the use of AlphaFold2 in cancer drug development, scrutinizing its efficacy, limitations, and potential challenges. We also compare AlphaFold2 with other algorithms like ESMFold, explaining the diverse methodologies employed in this field and the practical effects of these differences for the application of specific algorithms. Additionally, we discuss the broader applications of these technologies, including the prediction of protein complex structures and the generative AI-driven design of novel proteins

Deciphering Abnormal Platelet Subpopulations in COVID-19, Sepsis and Systemic Lupus Erythematosus through Machine Learning and Single-Cell Transcriptomics

This study focuses on understanding the transcriptional heterogeneity of activated platelets and its impact on diseases such as sepsis, COVID-19, and systemic lupus erythematosus (SLE). Recognizing the limited knowledge in this area, our research aims to dissect the complex transcriptional profiles of activated platelets to aid in developing targeted therapies for abnormal and pathogenic platelet subtypes. We analyzed single-cell transcriptional profiles from 47,977 platelets derived from 413 samples of patients with these diseases, utilizing Deep Neural Network (DNN) and eXtreme Gradient Boosting (XGB) to distinguish transcriptomic signatures predictive of fatal or survival outcomes. Our approach included source data annotations and platelet markers, along with SingleR and Seurat for comprehensive profiling. Additionally, we employed Uniform Manifold Approximation and Projection (UMAP) for effective dimensionality reduction and visualization, aiding in the identification of various platelet subtypes and their relation to disease severity and patient outcomes. Our results highlighted distinct platelet subpopulations that correlate with disease severity, revealing that changes in platelet transcription patterns can intensify endotheliopathy, increasing the risk of coagulation in fatal cases. Moreover, these changes may impact lymphocyte function, indicating a more extensive role for platelets in inflammatory and immune responses. This study identifies crucial biomarkers of platelet heterogeneity in serious health conditions, paving the way for innovative therapeutic approaches targeting platelet activation, which could improve patient outcomes in diseases characterized by altered platelet function

Shallow Water Bathymetry through Two-medium Photogrammetry Using High Resolution Satellite Imagery

This paper develops an automated shallow water bathymetry procedure based on two-medium photogrammetry using high resolution satellite multispectral imagery. In this method, near-infrared band were used for sunglint elimination and rational function model (RFM) was applied for raw DEM generation. By extracting the water-land edge and interpolating edge elevation, water surface position could be determined. An approximation refraction correction model, in which all homonymy lights were regarded as intersect to the same observed point, was adopted to correct the vertical offsets. Experimental results indicate that DEM accuracy of satellite two-medium photogrammetry is better than 20% of the average depth under the circumstance of relatively calm water and rich bottom texture

Enhancement of nutrient bioaccessibility and functional property of chicken bone powder through steam explosion

There are technological and nutritional challenges to efficient use of chicken bone in foods, due to its low bioaccessibility and techno-functionality. The impacts of steam explosion (SE), autoclave, steam treatment on the powder characteristics and the effect of SE on the physicochemical properties and bioaccessibility of chicken bone powders were investigated. X-ray diffraction and Fourier transform infrared spectroscopy indicated that SE treatment might increase crystallinity, partial loss of organic substance, but not completely destroyed its crystal type. SE had a smaller particle size of chicken bone powder with time and energy saving compared with other treatments, and it was conducive to improving the color acceptability. Compared with native chicken bone powder, SE significantly decreased the average particle size of powder by 79.23 %, whereas its specific surface of unit volume increased by 119.98 %. And SE improved the techno-functionality which evaluated by water holding capacity (4.19 %), oil holding capacity (10.53 %), water solubility index (117.64 %) and ABTS radical scavenging activity (14.05 %). The higher protein digestibility, calcium and major amino acids contents in chicken bone powder during digestion showed SE upgraded the bioaccessibility. The results were suggestive that steam explosion was an efficient process for preparing bone powder with improved techno-functionality and noticeable bioaccessibility

Abnormal topological organization of structural covariance networks in amyotrophic lateral sclerosis

Neuroimaging studies of patients with amyotrophic lateral sclerosis (ALS) have shown widespread alterations in structure, function, and connectivity in both motor and non-motor brain regions, suggesting multi-systemic neurobiological abnormalities that might impact large-scale brain networks. Here, we examined the alterations in the topological organization of structural covariance networks of ALS patients (N = 60) compared with normal controls (N = 60). We found that structural covariance networks of ALS patients showed a consistent rearrangement towards a regularized architecture evidenced by increased path length, clustering coefficient, small-world index, and modularity, as well as decreased global efficiency, suggesting inefficient global integration and increased local segregation. Locally, ALS patients showed decreased nodal degree and betweenness in the gyrus rectus and/or Heschl's gyrus, and increased betweenness in the supplementary motor area, triangular part of the inferior frontal gyrus, supramarginal gyrus and posterior cingulate cortex. In addition, we identified a different number and distribution of hubs in ALS patients, showing more frontal and subcortical hubs than in normal controls. In conclusion, we reveal abnormal topological organization of structural covariance networks in ALS patients, and provide network-level evidence for the concept that ALS is a multisystem disorder with a cerebral involvement extending beyond the motor areas. Keywords: Amyotrophic lateral sclerosis, Structural covariance network, Gray matter volume, Small-world, Modularit

Dynamic changes in human single‐cell transcriptional signatures during fatal sepsis

Systemic infections, especially in patients with chronic diseases, may result in sepsis: an explosive, uncoordinated immune response that can lead to multisystem organ failure with a high mortality rate. Patients with similar clinical phenotypes or sepsis biomarker expression upon diagnosis may have different outcomes, suggesting that the dynamics of sepsis is critical in disease progression. A within-subject study of patients with Gram-negative bacterial sepsis with surviving and fatal outcomes was designed and single-cell transcriptomic analyses of peripheral blood mononuclear cells (PBMC) collected during the critical period between sepsis diagnosis and 6 h were performed. The single-cell observations in the study are consistent with trends from public datasets but also identify dynamic effects in individual cell subsets that change within hours. It is shown that platelet and erythroid precursor responses are drivers of fatal sepsis, with transcriptional signatures that are shared with severe COVID-19 disease. It is also shown that hypoxic stress is a driving factor in immune and metabolic dysfunction of monocytes and erythroid precursors. Last, the data support CD52 as a prognostic biomarker and therapeutic target for sepsis as its expression dynamically increases in lymphocytes and correlates with improved sepsis outcomes. In conclusion, this study describes the first single-cell study that analyzed short-term temporal changes in the immune cell populations and their characteristics in surviving or fatal sepsis. Tracking temporal expression changes in specific cell types could lead to more accurate predictions of sepsis outcomes and identify molecular biomarkers and pathways that could be therapeutically controlled to improve the sepsis trajectory toward better outcomes

Cytochrome P450 3A1 mediates 2,2',4,4'-tetrabromodiphenyl ether-induced reduction of spermatogenesis in adult rats.

BACKGROUND: 2,2',4,4'-tetrabromodiphenyl ether (BDE47) is the dominant PBDE congener in humans, wildlife, and the environment. It has been reported to be metabolized by cytochrome P450 (CYP) enzymes. Still, the effects of BDE47 on spermatogenesis failure are attracting an increasing amount of attention. However, it is unclear whether CYP-mediated metabolism contributes to BDE47-induced reproductive toxicity. METHODOLOGY AND PRINCIPAL FINDINGS: The role of cytochrome P450 3A1 (CYP3A1) in the formation of oxidative metabolites of BDE47 and its induced spermatogenesis failure was investigated in SD rats. BDE47 significantly increased the expression and activity of CYP3A1 in rat liver, and 3-OH-BDE47, the major oxidative metabolite of BDE47, dose-dependently increased in rat liver, serum, and testis, which was aggravated by dexamethasone (DEX), an inducer of CYP3A1. Additionally, testicular 3-OH-BDE47 and reactive oxygen species (ROS) in seminiferous tubules increased especially when BDE47 was administered in combination with DEX, which was confirmed in GC-1 and GC-2 cells that 3-OH-BDE47 induced more ROS production and cell apoptosis via the upregulation of FAS/FASL, p-p53 and caspase 3. As a result, daily sperm production dose-dependently decreased, consistent with histological observations in giant cells and vacuolar spaces and increase in TUNEL-positive apoptotic germ cells. CONCLUSION: CYP3A1-mediated metabolic activation of BDE47 and the active metabolite 3-OH-BDE47 and consequent ROS played an important role in reduction of spermatogenesis by germ cell apoptosis. Our study helps provide new insights into the mechanism of reproductive toxicity of environmental chemicals