Deep-sea anoxic brine pools are one of the most extreme ecosystems on Earth, which are characterized by drastic changes in salinity, temperature, and oxygen concentration. The interface between the brine and overlaying seawater represents a boundary of oxic-anoxic layer and a steep gradient of redox potential that would initiate favorable conditions for divergent metabolic activities, mainly methanogenesis and sulfate reduction. This study aimed to investigate the diversity of Bacteria, particularly sulfate-reducing communities, and their ecological roles in the interfaces of five geochemically distinct brine pools in the Red Sea. Performing a comprehensive study would enable us to understand the significant role of the microbial groups in local geochemical cycles. Therefore, we combined culture-dependent approach and molecular methods, such as 454 pyrosequencing of 16S rRNA gene, phylogenetic analysis of functional marker gene encoding for the alpha subunits of dissimilatory sulfite reductase (dsrA), and single-cell genomic analysis to address these issues. Community analysis based on 16S rRNA gene sequences demonstrated high bacterial diversity and domination of Bacteria over Archaea in most locations. In the hot and multilayered Atlantis II Deep, the bacterial communities were stratified and hardly overlapped. Meanwhile in the colder brine pools, sulfatereducing Deltaproteobacteria were the most prominent bacterial groups inhabiting the interfaces. Corresponding to the bacterial community profile, the analysis of dsrA gene sequences revealed collectively high diversity of sulfate-reducing communities. Desulfatiglans-like dsrA was the prevalent group and conserved across the Red Sea brine pools. In addition to the molecular studies, more than thirty bacterial strains were successfully isolated and remarkably were found to be cytotoxic against the cancer cell lines. However, none of them were sulfate reducers. Thus, a single-cell genomic analysis was used to study the metabolism of uncultured phyla without having them in culture. We analysed ten single-cell amplified genomes (SAGs) of the uncultivated euryarchaeal Marine Benthic Group E (MBGE), which contain a key enzyme for sulfate reduction. The results showed the possibility of MBGE to grow autotrophically only with carbon dioxide and hydrogen. In the absence of adenosine 5’-phosphosulfate reductase, we hypothesized that MBGE perform sulfite reduction rather than sulfate reduction to conserve energy.

Cells have been found to have an inherent heterogeneity that has led to an increase in the development of single-cell analysis methods to characterize the extent of heterogeneity that can be found in seemingly identical cells. With an understanding of normal cellular variability, the identification of disease induced cellular changes, known as biomarkers, may become more apparent and readily detectable. Biomarker discovery in single-cells is challenging and needs to focus on molecules that are abundant in cells. Lipids are widely abundant in cells and play active roles in cellular signaling, energy metabolism, and are the main component of cellular membranes. The regulation of lipid metabolism is often disrupted or lost during disease progression, especially in cancer, making them ideal candidates as biomarkers. Challenges exist in the analysis of lipids beyond those of single-cell analysis. Lipid extraction solvents must be compatible with the lipid or lipids of interest. Many lipids are isobaric making mass spectrometry analysis difficult without separations. Single-cell extractions using nanomanipulation coupled to mass spectrometry has shown to be an excellent method for lipid analysis of tissues and cell cultures. Extraction solvents are tunable for specific lipid classes, nanomanipulation prevents damage to neighboring cells, and lipid separations are possible through phase dispersion. The most important aspect of single-cell analysis is that it uncovers the extent of cellular heterogeneity that exists among cellular populations that remains undetected during averaged sampling.

Clustering of single-cell RNA sequencing data is often used to show what states and subtypes cells have. Using this technique, striatal cells were clustered into subtypes using different clustering algorithms. Previously known subtypes were confirmed and new subtypes were found. One of them is a third medium spiny neuron subtype. Using the observed heterogeneity, as a second task, this project questions whether or not differences in individual neurons have an impact on the network dynamics. By clustering spiking activity from a neural network model, inconclusive results were found. Both algorithms indicating low heterogeneity, but by altering the quantity of a subtype between a low and high number, and clustering the network activity in each case, results indicate that there is an increase in the heterogeneity. This project shows a list of potential striatal subtypes and gives reasons to keep giving attention to biologically observed heterogeneity.

The capability to characterize disease states by way of determining novel biomarkers has led to a high demand of single cell and organelle analytical methodologies due to the unexpected heterogeneity present in cells of the same type. Lipids are of particular interest in the search for biomarkers due to their active roles in cellular metabolism and energy storage. Analyzing localized lipid chemistry from individual cells and organelles is challenging however, due to low analyte volume, limited discriminate instrumentation, and common requirements of separation procedures and expenditure of cell sample. Using nanomanipulation in combination with mass spectrometry, individual cells and organelles can be extracted from tissues and cultures in vitro to determine if heterogeneity at the cellular level is present. The discriminate extraction of a single cell or organelle allows the remainder of cell culture or tissue to remain intact, while the high sensitivity and chemical specificity of mass spectrometry provides structural information for limited volumes without the need for chromatographic separation. Mass analysis of lipids extracted from individual cells can be carried out in multiple mass spectrometry platforms through direct-inject mass spectrometry using nanoelectrospray-ionization and through matrix-assisted laser/desorption ionization.

<p>The fast advancing of high-throughput technology has reinforced the biomedical research ecosystem with highly scaled and commercialized data acquisition standards, which provide us with unprecedented opportunity to interrogate biology in novel and creative ways. However, unraveling the high dimensional data in practice is difficult due to the following challenges: 1) how to handle outlier and data contaminations; 2) how to address the curse of dimensionality; 3) how to utilize occasionally provided auxiliary information such as an external phenotype observation or spatial coordinate; 4) how to derive the unknown non-linear relationship between observed data and underlying mechanisms in complex biological system such as human metabolic network. </p> <p><br></p> <p>In sight of the above challenges, this thesis majorly focused on two research directions, for which we have proposed a series of statistical learning and AI-empowered systems biology models. This thesis separates into two parts. The first part focuses on identifying latent low dimensional subspace in high dimensional biomedical data. Firstly, we proposed CAT method which is a robust mixture regression method to detect outliers and estimate parameter simultaneously. Then, we proposed CSMR method in studying the heterogeneous relationship between high dimensional genetic features and a phenotype with penalized mixture regression. At last, we proposed SRMR which investigate mixture linear relationship over spatial domain. The second part focuses on studying the non-linear relationship for human metabolic flux estimation in complex biological system.  We proposed the first method in this domain that can robustly estimate flux distribution of a metabolic network at the resolution of individual cells.</p>

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Indiana University-Purdue University Indianapolis (IUPUI) / Accurate estimation of State of Charge (SOC) is crucial. With the ever-increasing usage of batteries, especially in safety critical applications, the requirement of accurate estimation of SOC is paramount. Most current methods of SOC estimation rely on data collected and calibrated offline, which could lead to inaccuracies in SOC estimation as the battery ages or under different operating conditions. This work aims at exploring the real-time estimation and optimization of SOC by applying Particle Swarm Optimization (PSO) to a detailed electrochemical model of a single cell. The goal is to develop a single cell model and PSO algorithm which can run on an embedded device with reasonable utilization of CPU and memory resources and still be able to estimate SOC with acceptable accuracy. The scope is to demonstrate the accurate estimation of SOC for 1C charge and discharge for both healthy and aged cell.

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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