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Analysis of T cell receptor repertoire in monozygotic twins concordant and discordant for chronic hepatitis B infection
Highlights: • TCR Repertoire features from twin pairs show heritable bias. • TCR repertoires show antigen biases in chronic viral infection patients. • TCR repertoires show a high level of concordance among HBeAg-positive HBV twins. • Shared clonotypes in identical cured twins show therapeutic potential. Due to their identical inheritance and shared surroundings, identical twins have been the recommended group for studying the susceptibility and prognosis of diseases. Here, CD8{sup +} T cell receptor beta (TCRβ) chains were analyzed by high-throughput sequencing in three pairs of healthy identical twins and chronic hepatitis B patients. The data showed a high level of similarity in the TCR repertoire of each pair in terms of average TCR Vβ segment expression and frequency of the complementary determining region 3 (CDR3) pattern and skewed or oligoclonal clonotypes. Notably, the level of similarity in TCR Vβ expression between the twins appeared to be independent of the consistency or inconsistency of chronic HBV infection, although the detailed CDR3 pattern and frequency were related to disease prognosis. There were more immunodominant clonotypes in patients with HBV antigen seroconversion, which showed an increased abundance. These immunodominant clonotypes may be used as favorable prognostic biomarkers and potential targets for immunotherapy. Thus, delineating the CD8{sup +} T cell repertoire of identical twins with concordant chronic viral infections provides a promising means to screen protective TCR genes for immunotherapy.
Exploring thermal runaway propagation in Li-ion batteries through high-speed X-ray imaging and thermal analysis: Impact of cell chemistry and electrical connections
Battery safety design is important to consider from the individual Li-ion cell to the level of the macro-system. On the macro-level, failure in one single cell can lead to propagation of the thermal runaway and rapidly set a whole battery pack on fire. Factors that can impact the propagation outcome, such as cell model/chemistry and electrical connection are here investigated using a combination of measurements. Several abusive tests were conducted, combining two different cell models (Molicel P42A and LG M50, both 21700s) in series and parallel connections (16 tests per configuration). Overall, a propagation outcome of 56% was measured from the 32 conducted tests, a minimum temperature of 150 °C was required to initiate propagation, and the fastest propagation occurred in 123 s. Temperature measurements were higher in series connected cells, initiating the discussion of cell chemistry and internal resistance on this effect. The difference in current-flow during thermal runaway in series and parallel connections, and how this can affect the temperature evolution is further discussed. Spatio-temporal mapping of X-ray radiography allowed us to derive the speed of thermal runaway evolution inside the battery and has shown that series connected cells, in particular P42A, occur faster. It was further observed that deviant sidewall behaviors such as temperature-induced breaches and pressure-induced ruptures occurred in P42As only respective nail-penetrated cells only.
Proteomics analysis of dendritic cell activation by contact allergens reveals possible biomarkers regulated by Nrf2
Allergic contact dermatitis is a widespread disease with high clinical relevance affecting approximately 20% of the general population. Typically, contact allergens are low molecular weight electrophilic compounds which can activate the Keap1/Nrf2 pathway. We performed a proteomics study to reveal possible biomarkers for dendritic cell (DC) activation by contact allergens and to further elucidate the role of Keap1/Nrf2 signaling in this process. We used bone marrow derived dendritic cells (BMDCs) of wild-type (nrf2{sup +/+}) and Nrf2 knockout (nrf2{sup −/−}) mice and studied their response against the model contact sensitizers 2,4-dinitrochlorobenzene (DNCB), cinnamaldehyde (CA) and nickel(II) sulfate by 2-dimensional polyacrylamide gel electrophoresis (2D-PAGE) in combination with electrospray ionization tandem mass spectrometry (ESI-MS/MS). Sodium dodecyl sulfate (SDS, 100 μM) served as irritant control. While treatment with nickel(II) sulfate and SDS had only little effects, CA and DNCB led to significant changes in protein expression. We found 18 and 30 protein spots up-regulated in wild-type cells treated with 50 and 100 μM CA, respectively. For 5 and 10 μM DNCB, 32 and 37 spots were up-regulated, respectively. Almost all of these proteins were not differentially expressed in nrf2{sup −/−} BMDCs, indicating an Nrf2-dependent regulation. Among them proteins were detected which are involved in oxidative stress and heat shock responses, as well as in signal transduction or basic cellular pathways. The applied approach allowed us to differentiate between Nrf2-dependent and Nrf2-independent cellular biomarkers differentially regulated upon allergen-induced DC activation. The data presented might contribute to the further development of suitable in vitro testing methods for chemical-mediated sensitization. - Highlights: • Contact allergens induce proteins involved in DC maturation Nrf2-dependently. • Induction of these proteins points to a functional role of Nrf2 in DC maturation. • Evidence that metabolic reprogramming enables DC activation by contact allergens. • Identification of biomarker candidates for development of in vitro testing methods.
Computational design and analysis of modular cells for large libraries of exchangeable product synthesis modules
Microbial metabolism can be harnessed to produce a large library of useful chemicals from renewable resources such as plant biomass. However, it is laborious and expensive to create microbial biocatalysts to produce each new product. To tackle this challenge, we have recently developed modular cell (ModCell) design principles that enable rapid generation of production strains by assembling a modular (chassis) cell with exchangeable production modules to achieve overproduction of target molecules. Previous computational ModCell design methods are limited to analyze small libraries of around 20 products. In this study, we developed a new computational method, named ModCell-HPC, that can design modular cells for large libraries with hundreds of products with a highly-parallel and multi-objective evolutionary algorithm and enable us to elucidate modular design properties. We demonstrated ModCell-HPC to design Escherichia coli modular cells towards a library of 161 endogenous production modules. From these simulations, we identified E. coli modular cells with few genetic manipulations that can produce dozens of molecules in a growth-coupled manner with different types of fermentable sugars. These designs revealed key genetic manipulations at the chassis and module levels to accomplish versatile modular cells, involving not only in the removal of major by-products but also modification of branch points in the central metabolism. We further found that the effect of various sugar degradation on redox metabolism results in lower compatibility between a modular cell and production modules for growth on pentoses than hexoses. To better characterize the degree of compatibility, we developed a method to calculate the minimal set cover, identifying that only three modular cells are all needed to couple with up 85 compatible production modules. By determining the unknown compatibility contribution metric, we further elucidated the design features that allow an existing modular cell to be re-purposed towards production of new molecules. Altogether, ModCell-HPC is a useful tool for understanding modularity of biological systems and guiding more efficient and generalizable design of modular cells that help reduce research and development cost in biocatalysis.
3D Analysis of Fuel Cell Electrocatalyst Degradation on Alternate Carbon Supports
Understanding the mechanisms associated with Pt/C electrocatalyst degradation in proton exchange membrane fuel cell (PEMFC) cathodes is critical for the future development of higher-performing materials; however, there is a lack of information regarding Pt coarsening under PEMFC operating conditions within the cathode catalyst layer. We report a direct and quantitative 3D study of Pt dispersions on carbon supports (high surface area carbon (HSAC), Vulcan XC-72, and graphitized carbon) with varied surface areas, graphitic character, and Pt loadings ranging from 5 to 40 wt %. This is accomplished both before and after catalyst-cycling accelerated stress tests (ASTs) through observations of the cathode catalyst layer of membrane electrode assemblies. Electron tomography results show Pt nanoparticle agglomeration occurs predominantly at junctions and edges of aggregated graphitized carbon particles, leading to poor Pt dispersion in the as-prepared catalysts and increased coalescence during ASTs. Tomographic reconstructions of Pt/HSAC show much better initial Pt dispersions, less agglomeration, and less coarsening during ASTs in the cathode. However, a large loss of the electrochemically active surface area (ECSA) is still observed and is attributed to accelerated Pt dissolution and nanoparticle coalescence. Moreover, a strong correlation between Pt particle/agglomerate size and measured ECSA is established and is proposed as a more useful metric than average crystallite size in predicting degradation behavior across different catalyst systems.
Technical and Economic Analysis of Fuel Cells for Forklift Applications
Not Available
Guided construction of single cell reference for human and mouse lung
Accurate cell type identification is a key and rate-limiting step in single-cell data analysis. Single-cell references with comprehensive cell types, reproducible and functionally validated cell identities, and common nomenclatures are much needed by the research community for automated cell type annotation, data integration, and data sharing. Here, we develop a computational pipeline utilizing the LungMAP CellCards as a dictionary to consolidate single-cell transcriptomic datasets of 104 human lungs and 17 mouse lung samples to construct LungMAP single-cell reference (CellRef) for both normal human and mouse lungs. CellRefs define 48 human and 40 mouse lung cell types catalogued from diverse anatomic locations and developmental time points. We demonstrate the accuracy and stability of LungMAP CellRefs and their utility for automated cell type annotation of both normal and diseased lungs using multiple independent methods and testing data. We develop user-friendly web interfaces for easy access and maximal utilization of the LungMAP CellRefs.
Ultrastructural and SINS analysis of the cell wall integrity response of Aspergillus nidulans to the absence of galactofuranose
With lethal opportunistic fungal infections on the rise, it is imperative to explore new methods to examine virulence mechanisms. The fungal cell wall is crucial for both the virulence and viability of Aspergillus nidulans. One wall component, Galf, has been shown to contribute to important fungal processes, integrity of the cell wall and pathogenesis. Here in this paper, we explore gene deletion strains lacking the penultimate enzyme in Galf biosynthesis (ugmAΔ) and the protein that transports Galf for incorporation into the cell wall (ugtAΔ). In applying gene deletion technology to the problem of cell wall integrity, we have employed multiple micro- and nano-scale imaging tools, including confocal fluorescence microscopy, electron microscopy, X-Ray fluorescence and atomic force microscopy. Atomic force microscopy allows quantification of ultrastructural cell wall architecture while near-field infrared spectroscopy provides spatially resolved chemical signatures, both at the nanoscale. Here, for the first time, we demonstrate correlative data collection with these two emerging modalities for the multiplexed in situ study of the nanoscale architecture and chemical composition of fungal cell walls.
Analysis of individual cells identifies cell-to-cell variability following induction of cellular senescence
Senescent cells play important roles in both physiological and pathological processes, including cancer and aging. In all cases, however, senescent cells comprise only a small fraction of tissues. Senescent phenotypes have been studied largely in relatively homogeneous populations of cultured cells. In vivo, senescent cells are generally identified by a small number of markers, but whether and how these markers vary among individual cells is unknown. We therefore utilized a combination of single-cell isolation and a nanofluidic PCR platform to determine the contributions of individual cells to the overall gene expression profile of senescent human fibroblast populations. Individual senescent cells were surprisingly heterogeneous in their gene expression signatures. This cell-to-cell variability resulted in a loss of correlation among the expression of several senescence associated genes. Many genes encoding senescence-associated secretory phenotype (SASP) factors, a major contributor to the effects of senescent cells in vivo, showed marked variability with a subset of highly induced genes accounting for the increases observed at the population level. Inflammatory genes in clustered genomic loci showed a greater correlation with senescence compared to nonclustered loci, suggesting that these genes are coregulated by genomic location. Together, these data offer new insights into how genes are regulated in senescent cells and suggest that single markers are inadequate to identify senescent cells in vivo.
Ion Chromatography and Combustion Ion Chromatography Analysis of Fuel Cell Effluent Water During Open Circuit Voltage
Open circuit voltage tests were conducted on sixteen 3M Ionomer and eight Nafion™ NR211 membranes. Lifetime distributions were determined, and ion chromatography (IC) techniques were used to measure fluoride, sulfate, trifluoro acetate, and oxalate ions. Combustion Ion Chromatography (CIC) was used to determine the total organic fluoride (TOF) associated with water soluble membrane degradation fragments. The ratio of these products relative to the fluoride release rates were used to infer the likely degradation mechanism for each membrane. Peroxide attack at the sulfonic acid side chain was determined to be the least relevant reaction pathway while the long-proposed polymer chain end unzipping appears to be the dominant mechanism. Abstraction of the tertiary fluorine in the NR211 backbone and side chain is evident by organic fluoride release rates higher than can be explained by unzipping alone.
Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments
Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a microscope image correspond to which individual cells. Current approaches require many hours of manual curation and depend on approaches that are difficult to share between labs. They are also unable to robustly segment the cytoplasms of mammalian cells. Here, we show that deep convolutional neural networks, a supervised machine learning method, can solve this challenge for multiple cell types across the domains of life. We demonstrate that this approach can robustly segment fluorescent images of cell nuclei as well as phase images of the cytoplasms of individual bacterial and mammalian cells from phase contrast images without the need for a fluorescent cytoplasmic marker. These networks also enable the simultaneous segmentation and identification of different mammalian cell types grown in co-culture. A quantitative comparison with prior methods demonstrates that convolutional neural networks have improved accuracy and lead to a significant reduction in curation time. We relay our experience in designing and optimizing deep convolutional neural networks for this task and outline several design rules that we found led to robust performance. We conclude that deep convolutional neural networks are an accurate method that require less curation time, are generalizable to a multiplicity of cell types, from bacteria to mammalian cells, and expand live-cell imaging capabilities to include multi-cell type systems.
High throughput analysis of B cell dynamics and neutralizing antibody development during immunization with a novel clade C HIV-1 envelope
A protective HIV-1 vaccine has been hampered by a limited understanding of how B cells acquire neutralizing activity. Our previous vaccines expressing two different HIV-1 envelopes elicited robust antigen specific serum IgG titers in 20 rhesus macaques; yet serum from only two animals neutralized the autologous virus. Here, we used high throughput immunoglobulin receptor and single cell RNA sequencing to characterize the overall expansion, recall, and maturation of antigen specific B cells longitudinally over 90 weeks. Diversification and expansion of many B cell clonotypes occurred broadly in the absence of serum neutralization. However, in one animal that developed neutralization, two neutralizing B cell clonotypes arose from the same immunoglobulin germline and were tracked longitudinally. Early antibody variants with high identity to germline neutralized the autologous virus while later variants acquired somatic hypermutation and increased neutralization potency. The early engagement of precursors capable of neutralization with little to no SHM followed by prolonged affinity maturation allowed the two neutralizing lineages to successfully persist despite many other antigen specific B cells. The findings provide new insight into B cells responding to HIV-1 envelope during heterologous prime and boost immunization in rhesus macaques and the development of selected autologous neutralizing antibody lineages.
Discovering Innovations in Stress Tolerance through Comparative Gene Regulatory Network Analysis and Cell-Type Specific Expression Maps (Final Technical Report with Cover Page)
Through this grant, we developed a comparative framework to elucidate the mechanisms behind variations in environmental stress responses among a diverse group of species within the Brassicaceae family. Our focus was on the differences in physiological and transcriptomic responses to abscisic acid (ABA), a hormone associated with water stress. We examined the differential growth responses of four Brassicaceae species, finding that most exhibited reduced root growth correlated with smaller meristem size. In contrast, Schrenkiella parvula showed accelerated growth due to increased root cell elongation. We employed RNA sequencing to analyze the transcriptional responses to ABA across these species, and innovative bioinformatics techniques were used to pinpoint biological pathways with significant divergence. Additionally, we utilized DAP-seq to map the gene regulatory networks associated with ABAresponsive transcription factors, revealing that variations in the regulation of growth hormone biosynthesis play a critical role in the distinct ABA effects on root growth among the species. This research sets a new standard for comparative physiology by integrating comparative genomics and transcriptomics to uncover pathway divergences.
Analysis of secondary cells with lithium anodes and immobilized fused-salt electrolytes
Secondary cells with liquid lithium anodes, liquid bismuth or tellurium cathodes, and fused lithium halide electrolytes immobilized as rigid pastes operate between 380 and 485 degrees. Applications include power sources in space, military vehicle propulsion and special commercial vehicle propulsion.
Nickel-cadmium battery test project - Relationship between operation, life, and failure mechanism. Volume 3 - Analysis of the cells and their components
Nickel cadmium batteries operated in related cycling schemes with resulting dry separators
Thermoelastic analysis of solar cell arrays and their material properties
Announced report discusses experimental test program in which five different solar cell array designs were evaluated by subjecting them to 60 thermal cycles from minus 190 deg to 0.0 deg. Results indicate that solder-coated cells combined with Kovar n-interconnectors and p-interconnectors are more durable under thermal loading than other configurations.