Standard Single Cell Research Articles

Single-Cell Patch-Clamp/Proteomics of Human Alzheimer's Disease iPSC-Derived Excitatory Neurons Versus Isogenic Wild-Type Controls Suggests Novel Causation and Therapeutic Targets.

Standard single-cell (sc) proteomics of disease states inferred from multicellular organs or organoids cannot currently be related to single-cell physiology. Here, a scPatch-Clamp/Proteomics platform is developed on single neurons generated from hiPSCs bearing an Alzheimer's disease (AD) genetic mutation and compares them to isogenic wild-type controls. This approach provides both current and voltage electrophysiological data plus detailed proteomics information on single-cells. With this new method, the authors are able to observe hyperelectrical activity in the AD hiPSC-neurons, similar to that observed in the human AD brain, and correlate it to ≈1400 proteins detected at the single neuron level. Using linear regression and mediation analyses to explore the relationship between the abundance of individual proteins and the neuron's mutational and electrophysiological status, this approach yields new information on therapeutic targets in excitatory neurons not attainable by traditional methods. This combined patch-proteomics technique creates a new proteogenetic-therapeutic strategy to correlate genotypic alterations to physiology with protein expression in single-cells.

ScPathoQuant: a tool for efficient alignment and quantification of pathogen sequence reads from 10× single cell sequencing datasets.

Currently there is a lack of efficient computational pipelines/tools for conducting simultaneous genome mapping of pathogen-derived and host reads from single cell RNA sequencing (scRNAseq) output from pathogen-infected cells. Contemporary options include processes involving multiple steps and/or running multiple computational tools, increasing user operations time. To address the need for new tools to directly map and quantify pathogen and host sequence reads from within an infected cell from scRNAseq datasets in a single operation, we have built a python package, called scPathoQuant. scPathoQuant extracts sequences that were not aligned to the primary host genome, maps them to a pathogen genome of interest (here as demonstrated for viral pathogens), quantifies total reads mapping to the entire pathogen, quantifies reads mapping to individual pathogen genes, and finally integrates pathogen sequence counts into matrix files that are used by standard single cell pipelines for downstream analyses with only one command. We demonstrate that scPathoQuant provides a scRNAseq viral and host genome-wide sequence read abundance analysis that can differentiate and define multiple viruses in a single sample scRNAseq output. The SPQ package is available software accessible at https://github.com/galelab/scPathoQuant (DOI 10.5281/zenodo.10463670) with test codes and datasets available https://github.com/galelab/Whitmore_scPathoQuant_testSets (DOI 10.5281/zenodo.10463677) to serve as a resource for the community.

The Contribution of Multiplexing Single Cell RNA Sequencing in Acute Myeloid Leukemia.

Decades ago, the treatment for acute myeloid leukemia relied on cytarabine and anthracycline. However, advancements in medical research have introduced targeted therapies, initially employing monoclonal antibodies such as ant-CD52 and anti-CD123, and subsequently utilizing specific inhibitors that target molecular mutations like anti-IDH1, IDH2, or FLT3. The challenge lies in determining the role of these therapeutic options, considering the inherent tumor heterogeneity associated with leukemia diagnosis and the clonal drift that this type of tumor can undergo. Targeted drugs necessitate an examination of various therapeutic targets at the individual cell level rather than assessing the entire population. It is crucial to differentiate between the prognostic value and therapeutic potential of a specific molecular target, depending on whether it is found in a terminally differentiated cell with limited proliferative potential or a stem cell with robust capabilities for both proliferation and self-renewal. However, this cell-by-cell analysis is accompanied by several challenges. Firstly, the scientific aspect poses difficulties in comparing different single cell analysis experiments despite efforts to standardize the results through various techniques. Secondly, there are practical obstacles as each individual cell experiment incurs significant financial costs and consumes a substantial amount of time. A viable solution lies in the ability to process multiple samples simultaneously, which is a distinctive feature of the cell hashing technique. In this study, we demonstrate the applicability of the cell hashing technique for analyzing acute myeloid leukemia cells. By comparing it to standard single cell analysis, we establish a strong correlation in various parameters such as quality control, gene expression, and the analysis of leukemic blast markers in patients. Consequently, this technique holds the potential to become an integral part of the biological assessment of acute myeloid leukemia, contributing to the personalized and optimized management of the disease, particularly in the context of employing targeted therapies.

Effect of Thioflavin T on the Elongation Rate of Bacteria.

The growing field of bacterial electrophysiology examines the relationship between bacterial membrane potential and cell division, growth, sporulation, and biofilm formation. These experiments require Nernstian fluorescent dyes to monitor membrane potential. Our research uses single cell imaging to determine if a common fluorescent dye, Thioflavin T (ThT), affects the growth of bacteria. We use a combination of standard growth curve measurements and single cell imaging, both brightfield and fluorescence microscopy, to monitor the growth of Bacillus subtilis and Escherichia coli as a function of ThT concentration. Increased membrane potential (hyperpolarization) leads to increased intracellular accumulation of ThT: High fluorescence intensity is an indicator of hyperpolarization. Blue light is used to hyperpolarize a subpopulation of cells to monitor cellular elongation in response to increased cellular internalization of ThT. Single cell imaging shows that the elongation rates of B. subtilis and E. coli are decreased when these cells are incubated with ThT. At micromolar concentrations of ThT, this effect may be masked in standard growth curves, but is visible with single cell measurements on agarose pads. The increased cellular accumulation of ThT is a standard measure of hyperpolarization in bacterial electrophysiology. Growth curves, a bulk measurement, are typically used to determine suitable concentrations of ThT for use in experiments. Single cell measurements show that cells incubated with ThT have decreased elongation rates. This creates a potential experimental artifact that could lead to misinterpretation of data. Hyperpolarized cells internalize more ThT. This increased intracellular concentration of ThT, rather than the change in membrane potential, could lead to decreased growth. These experiments point toward the importance of single cell measurements to detect subtle changes in cell growth. We hope this research will be useful for other researchers in their choice of dye for the detection of membrane potential.

Abstract 188: Deep learning enables label-free profiling of the tumor microenvironment and enrichment of rare cancer cells

Abstract Understanding the tumor microenvironment and detecting rare circulating tumor cells from blood are two major challenges faced by cancer biologists and oncologists. Both require high sensitivity and accuracy in classifying and isolating single cells in a complex and heterogeneous environment. Classical cell classification and sorting techniques are limited by their reliance on pre-selected cell biomarkers or physical characteristics. Recent breakthroughs in machine learning have achieved unprecedented accuracy across a wide range of image classification problems. We have developed a platform that combines high-resolution imaging of unlabeled cells in microfluidic flow with real-time deep neural network (DNN) based classification and sorting. The DNN classifier was trained on more than 25 million high-resolution cell images of multiple types imaged on the platform. Our model was trained to discriminate among multiple cell classes, including immune cell subtypes, non-small-cell lung cancer cells (NSCLC), hepatocellular carcinomas (HCC), and stromal cells (including endothelial, epithelial, fibroblasts, smooth muscle cells). We then assessed model performance on a separate validation set of cell images, including cell lines not used in the training data. Our classifier accurately identifies NSCLC and HCC against a background of blood cells with an area under the ROC curve (AUC) of > 0.999. In addition we demonstrate the enrichment of NSCLC cells from spike-in mixtures with WBCs or whole blood at concentrations as low as 1:100,000, achieving an enrichment of > 25,000x on multiple cell lines. Using dissociated lung cancer tissue, we demonstrate that our label-free classification of tumor cells closely matches results from both standard flow cytometer analysis and single cell RNA sequencing. Additionally we were able to enrich the tumor cell fraction from dissociated tumor tissue thereby improving the sensitivity of mutation detection and enabling refined downstream single-cell genomic analysis. This work demonstrates that deep learning using high-resolution cell images collected at scale can achieve a high classification accuracy and can enable the label-free isolation of rare cells of interest for a wide range of applications. This system can be used to analyze tumor biopsies and liquid biopsies and has the potential to enable the study of tumor cells and tumor microenvironment with novel dimension and insight. Citation Format: Mahyar Salek, Hou-pu Chou, Prashast Khandelwal, Krishna P. Pant, Thomas J. Musci, Nianzhen Li, Christina Chang, Andreja Jovic, Esther Lee, Stephanie Huang, Jeff Walker, Phuc Nguyen, Kiran Saini, Jeanette Mei, Quillan F. Smith, Maddison Masaeli. Deep learning enables label-free profiling of the tumor microenvironment and enrichment of rare cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 188.

Abstract 198: Quantifying miRNA activity in single cell clusters

Abstract Background: MicroRNAs are small noncoding RNAs that mediate gene regulation at the post-transcriptional level via multiple mechanisms such as mRNA degradation, translational inhibition, and mRNA stabilization. They are involved in several cellular processes from development to homeostasis, and their deregulation is implicated in several diseases, including cancer. Since miRNA lacks the polyA tail, the standard single cell RNAseq protocols do not capture miRNAs, thus severely limiting our understanding of miRNA functions at cellular resolution. To overcome this limitation, we develop a novel machine learning method to infer the miRNA activity in a sample given its RNAseq profile. Methods: We develop a model using XGBoost, to predict miRNA profile in a sample from its global mRNA profile. We train and test the model using cross validation in the CCLE collection, as well as a number of healthy and cancer human tissue data obtained from GTEx and TCGA. We quantify the method's performance as the correlation between actual and predicted miRNA expression values across the test samples. We validate our model in multiple single cell datasets where miRNA and mRNA profiles are available for the same cell types by assessing the model's ability to identify cell type specific miRNAs based on a model trained on independent bulk datasets. Results: First, we show in CCLE collection, and multiple TCGA tissues, that a model based on all genes was far more accurate than the model based only on known targets. Our mean cross validation model accuracy across 10 tissues having greater than 100 paired miRNA and mRNA samples (in terms of Spearman correlation between predicted and actual expression of a miRNA) is 0.45 (min 0.39 in Pancreas to a max of 0.51 in Brain). In comparison to the normal tissues in GTEx, in the malignant counterpart in TCGA, due to greater heterogeneity, therefore greater variability in gene expression, our model performs significantly better (average cross validation accuracy improvement of 0.19). We have validated our model in independent single cell data. Using a model trained in bulk tissue data, we predict microRNA expression levels in a single cell based on the single cell RNA and compare our predicted fold difference by a miRNA's expression between two cell types with the actual fold difference. We quantify the prediction accuracy as the correlation between the predicted and actual fold differences across all miRNAs. In a total of 4 cell type pair comparisons (different sets of kidney, brain, breast, and skin), our model achieves an average accuracy of 0.81 (ranging from 0.73 to 0.89), thus strongly validating our model. Our next step is to apply our model to study miRNA activities during T cell development, Pancreatic Ductal Adenocarcinoma, and Glioblastoma, in collaboration with experimentalists. Conclusions: Our method addresses a major bottleneck in studying miRNA activities at a cellular resolution and can be applied to any scRNA data to infer miRNA activity. Citation Format: Gulden Olgun, Vishaka Gopalan, Sridhar Hannenhalli. Quantifying miRNA activity in single cell clusters [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 198.

Integration of Transcriptome and Cell Surface Protein Expression by LinQ-View Identifies Unique Immune Subpopulations

Single cell RNA sequencing is a powerful tool for characterizing the molecular and cellular heterogeneity of immune cells during their development and activation. Multimodal advances in single cell sequencing have enabled the simultaneous quantification of cell surface protein expression alongside unbiased transcriptional profiling. Here we present LinQ-View, a toolkit designed for multimodal single cell data visualization and analysis that links transcriptional and cell surface protein expression profiling data. Further, we propose a quantitative metric for cluster purity of CITE-seq data, enabling effective determination of clustering algorithms and their parameters, and finally demonstrate the utility of our toolkit through seamless integration with standard single cell analysis workflows on several public datasets. Through comparison to existing multimodal methods, we demonstrate that LinQ-View generates more accurate cell clusters and is highly efficient, even with massive datasets. LinQ-View is specialized in handling CITE-seq data with routine numbers of surface protein features (e.g. less than 50), by preventing variations in a single surface protein feature from affecting results. Finally, we utilized this method to integrate single cell transcriptional and protein expression data from COVID-19-infected patients and influenza-immunized subjects, revealing antigen-specific B cell subsets and previously unknown T cell subsets post-infection and vaccination.

Effects of Environmental Conditions on Cathode Degradation of Polymer Electrolyte Fuel Cell during Potential Cycle

We investigated the effects of cell temperature and the humidity of gas supplied to the cell during the load cycle durability test protocol recommended by The Fuel Cell Commercialization Conference of Japan (FCCJ). Changes in the electrochemically active surface area (ECA) and in the amount of carbon support corrosion were examined by using the JARI standard single cell. The ECA declined more quickly when the gas humidity was raised, and the carbon corrosion was at the same level. These results suggest that the agglomeration of platinum was accelerated by the same agglomeration mechanism, i.e., by raising the humidity of the gas supplied to the cell.

Design and Development of Methanol Sensing MEMS Sensor for DMFC Technology

The sensors can be developed based on ampherometric principle using Direct Methanol Fuel Cell (DMFC) technology. The synthesis of cost effective electrocatalyst materials for improved oxygen reduction reaction (ORR) and preparing electrodes by using suitable methods to reduce the cost of the sensing electrode is the major objective of the present work. Pt-Sn alloy exhibits high sensitivity in ORR among other Pt alloys and hence will be used as the ORR catalysts. For various concentration of methanol at different temperature, the current density of the chemically synthesised and characterized Pt-Sn/C was analysed. The accuracy will be determined by the detection of electric current or changes in electric current to design the sensor. To analyse the sensor accuracy, we have used passive mode design protocol in COMSOL Multiphysics. From the constructed cell of 1.0 cm cell area, we can optimize the overall power density and hence the sensitivity of the sensor by the modification of the cell parameters and interfacing it with Darcy’s law of fluidic flow through porous electrode medium. The micro electro mechanical systems (MEMS) technology was used for the design and fabrication of sensor with the electrochemical inputs from a standard DMFC single cell arrangement. The cell structure has been fabricated using a 3D printing technology and the output of the cell was optimized for ampherometric detection. The sensor with microfludic interconnects were fabricated by MEMS technology. The sensor response characteristics were studied and will be presented.

A novel clustering method to identify cell types from single cell transcriptional profiles

With the advancement of the high throughput single cell techniques, transcriptomics data generation at the single cell level becomes very easy. Analysis of this single cell expression values can reveal lots of unprecedented information about complex cellular heterogeneity and tissue composition. To date, different statistical methods are applied to analyze expression data at the cellular level, but there is still pretty much scope for the development of new bioinformatics tools. In this article, a graph theoretic clustering algorithm is proposed to identify cellular states from single cell gene expression data. The proposed algorithm first generates a shared nearest neighbor graph from the single cell RNA-seq dataset and then applies minimum spanning tree based clustering method to cluster the graph nodes. To compare our proposed algorithm’s performance with other unsupervised clustering methods, we used three real scRNA-seq datasets (human cancer cells, human embryonic cells and mouse embryonic cells). From the comparison result, it is evident that the proposed algorithm outperforms other standard single cell analysis methods.

3D optical lattices cooling of ultracold Cs atoms

Laser cooling of atoms is the basis of researches on ultracold atoms and molecules. It is of great importance in the study of precision measurement and quantum simulation, etc. In order to obtain ultracold cesium atomic sample with a lower temperature and a higher density, as the starting point to create ultracold cesium ground state molecules, we demonstrate in this article a method which effectively overcomes the heating from the reabsorption of scattered light in magneto-optical trap to further reduce the ultracold atomic temperature by loading atoms into a 3D optical lattices. Based on obtaining the ultracold Cs atoms in a standard magneto-optical trap, we use the compressed magneto-optical trap technology to increase ultracold Cs atomic density. Efficient loading of ultracold Cs atoms is achieved by 3D optical lattices which constructed by four lasers; the temperature of the atoms is measured by time-of-flight method. We observed the cooling result that the temperature drops from ~60.0 μK to ~11.6 μK. We also explore the dependence of the atom numbers in the lattice on the frequency of lattice lasers, and lead to the conclusion that the loading rate of the optical lattices reaches its maximum when the lattices laser’s detuning is –15.5 GHz. In the meantime, this paper investigates the use of optical lattice to suppress the heating of the ultracold atoms due to the radiation photons, in this sense, our research has far-reaching significance. Our scheme is advantageous due to its relaxed starting temperature requirement, meaning that 3D DRSC can be directly accomplished in a standard single cell vapour-loaded MOT without reliance on any additional devices such as a Zeeman slower.

Self-Standing Carbonaceous Sheets Composed of Micro and Nanometer Fibers Derived Bamboo and Application to PEFC and Edlc

Bamboo is a sustainable natural resource and the forests are considered to act as large carbon sinks, giving bamboo an advantage over other sustainable bioresources. However, it concerned that the rapid growth of bamboo may lead to biodiversity loss. This could be considered a serious disadvantage of bamboo in terms of its environmental impact. Bamboo has a fibrous microstructure and is composed mainly of cellulose, lignin, and hemicellulose, giving the isolation of cellulose fiber with relative ease. Owing to the unique microstructure of bamboo, we attempted to prepare self-standing carbonaceous fiber sheet by using bamboo as a starting material and have been investigated its application to polymer electrolyte fuel cells (PEFC) as the gas diffusion layer (GDL) [1].The Bamboo (Phyllostachys edulis, moso bamboo) cut in Japan was employed in this study. The basic preparation procedure of the self-standing carbonaceous bamboo fiber sheet (CBFS) was reported in [1]. In brief, CBFS was prepared viadelignification, defibration, molding, and carbonization. The bamboo fiber was obtained after the defibration process and then it was subjected to molding by using a plastic mold. After molding, the resultant fiber sheet was immersed in polyvinyl alcohol (PVA) aqueous solution for addition of PVA into the sheet. We also prepared CBFS without PVA. Finally, the sheet was subjected to carbonization under argon atmosphere. Here, the CBFS added PVA is denoted as CBFS w/ PVA. The microstructure of the CBFS was observed by field emission scanning electron microscope (FE-SEM) and was analyzed by X-ray diffraction (XRD) and Raman spectroscopy to elucidate the carbonization degree. The both of the in-plane and the through-plane of electrical conductivity were measured and the contact angle for a water droplet was measured to evaluate hydrophobicity of CBFS.A gas diffusion electrode was prepared by using CBFS, CBFS w/ PVA, the commercially available Pt-loaded carbon catalyst (Pt/C, TEC10E50E, Tanaka Kikinzoku Kogyo K. K., Japan) and Nafion ionomer solution by general manner. A single PEFC operation test was carried out by using Nafion117 membrane as the electrolyte and JARI’s standard PEFC single cell (the geometric electrode surface area was restricted to 1 cm × 1 cm).FE-SEM observation image shows CBFS is composed by aggregation of the carbonaceous fibers. The average diameter of fiber in the CBFS is ca. 6 μm irrespective to PVA addition, which is very close to that in commercially available carbon paper-based GDL as TGP-H-120 (Toray). In the case of CBFS w/ PVA, thin films derived from PVA were observed between each fibers and the surface of the fiber was seemed to be smooth, suggesting that PVA was partially covered with the bamboo fiber. The electrical conductivity was surely increased by addition of PVA.Figure 1 shows PEFC operation test results by using TGP-H-120, CBFS, and CBFS w/ PVA as GDL. The maximum power of CBFS was 135 mW cm-2, which is lower than that of TGP-H-120. On the other hand, the value was increased by addition of PVA and reached at 155 mW cm-2. Thus, the bamboo is expected as a potential GDL material. We also prepared cellulose nanofibrils (cellulose nanofibers; named as NFC or CFS) from the bamboo through chemical and physical treatments. Then, self-standing carbonaceous nanofiber sheet have been already obtained. The preparation procedure and its application to electric double layer capacitor (EDLC) will be also presented.Reference[1] T. Kinumoto et al., ACS Sustainable Chemistry & Engineering, Vol. 3(7), 1374−1380 (2015). AcknowledgementThis study was supported by The Environment Research and Technology Development Fund (ERTDF) from The Ministry of Environment, JAPAN, and the Venture Business Support Program from Oita University. The authors express gratitude to Mr. Yasuji Imahori Mr. Hideki Nakamizo, Mrs. Mayumi Nakamizo, and Mr. Yasuki Imahori of Silverloy Co. Ltd for supplying the bamboo strips. Figure 1

Study of Alcohol Sensing Devices Using DMFC Technology

Sensors for volatile organic compounds like alcohols (methanol and ethanol) can be fabricated using Direct Methanol Fuel Cell (DMFC) technology. The main problem is such a device is alcohol crossover. The alcohol diffuses from the anode through the electrolyte (perfluoro sulphonic acid membrane) to the cathode, where it reacts directly with the oxygen and produces no electrical current from the cell. This also results in poisoning of the cathode catalysts. The designed and fabrication of the sensor is by means of micro electro mechanical systems (MEMS) fabrication technology with the electrochemical inputs using a standard DMFC single cell arrangement. An ampherometric detection technique will be employed because of the redox reaction involved. The accuracy will be determined by the accurate detection of electric current or changes in electric current We have used a passive mode design protocol using COMSOL Multiphysics in the simulation. The design and simulation would involve optimization of various parameters, in the construction of the cell. We can optimize the overall power density and hence the sensitivity of the sensor by the modification of various parameters like the area of the working electrodes, separation distance and the electrode-electrolyte interface. A passive mode design protocol, for a cm cell area, using various parametric functions, and interfacing Darcy’s law of fluidic flow through a porous medium, under specific pressure and temperature, was applied. The designing involves the construction of gas diffusion layers using carbon matrix for electrodes with various parametric variations. Platinum and various alloy catalysts like Pt-Ru, Pt-Fe, Pt-Sn and Pt-Mo was chosen as the Oxygen Reduction Reaction (ORR) catalysts. The output of the cell was optimized for ampherometric detection. A MEMS based sensor with microfludic interconnects and its response characteristics will be presented.

Correlation between the Swelling Characteristic and Humidity Cycle Durability of a Polymer Electrolyte Membrane

Introduction Polymer electrolyte fuel cells (PEFCs) for automotive applications must operate over a wide range of operation conditions. Humidity cycle tests that simulate the operation of fuel cell under dry and wet conditions are widely used to test the mechanical durability of polymer electrolyte membrane of PEFCs in the form of membrane electrode assembly (MEA). The degradation phenomenon of membrane under conditions of cycling humidity has been studied by many researchers[1-5], but the mechanism is not clear enough. In this study, correlation between the humidity cycle durability of MEAs equipped with different type of membranes and their mechanical properties obtained by dynamic mechanical analysis (DMA) and thermal mechanical analysis (TMA) was investigated. Experimental MEAs of different catalyst layer thickness were prepared using NR-211(25μm) membranes or sulfonated polyethersulfone (SPES) membranes (32μm). Humidity cycle tests using a rectangular waveform of 0-150% RH (4min per cycle) were carried out on a JARI standard single cell (25cm2 electrode area, one serpentine flow channel). The cell temperature was set at 80°C. Linear sweep voltammetry was used to measure the hydrogen crossover rate through membrane as a diagnostic of the degradation status of the membrane in the humidity cycle tests. The cycle tests were finished when the hydrogen crossover rate through the membrane reached 10 times the initial value. To consider the reason of difference of the cycle life of MEAs, swelling tests in water was conducted at 80°C．Moreover, dynamic mechanical analysis (DMA), thermal mechanical analysis (TMA) of MEAs were carried out by changing the humidity of atmospheric gas. Result and Discussion The cycle life, which was defined as the terminated cycle, by humidity cycle tests were shown in Fig.1 The cycle life of SPES MEA was greatly shorter than that of NR-211 MEA at the same catalyst layer thickness (10μm). To consider the difference of humidity cycle durability of both membranes, DMA of membrane in dry nitrogen gas was carried out. Fig.2 shows the relationship between temperature and storage elastic modulus of membranes measured under the condition of 0%RH of relative humidity. Storage elastic modulus of SPES membrane was higher than that of NR-211 membrane. This result shows that the membrane stiffness of SPES was higher than that of NR-211 membrane at 0%RH. The difference of humidity cycle durability of both membranes was not explained by the result of DMA. Swelling tests of membranes were conducted to consider the reason of difference of cycle life of both MEAs. Swelling ratio of NR-211 and SPES were shown in Fig.1. Swelling ratio of SPES were greatly larger than that of NR-211. Our previous research revealed that the mechanical durability in humidity cycle test was influenced by the catalyst layer thickness of MEA[6]. In the case of this study, the thickness of both MEAs were same(10μm). The difference of cycle life of both MEAs was thought to be influenced by the difference of swelling ratio of both MEAs.These results indicate that it is important to prevent membrane swelling in order to improve the mechanical durability under humidity cycle . A CKNOWLEDGMENTS This work was supported by the New Energy and Industrial Technology Organization (NEDO). REFERENCES [1] A. Kusoglu, A. M. Karlsson, M. H. Santare, S. Cleghorn, and W. B. Johnson, J. Electrochem. Soc. 157, B705-B713 (2010).[2] Y.-H. Lai, Y. Li, and J. A. Rock, J. Power Sources, 195, 3215-3223 (2010).[3] F. E. Hizir, S. O. Ural, E. C. Kumbur, and M. M. Mench, J. Power Sources, 195, 3463-3471 (2010).[4] M. N. Silberstein and M. C. Boyce, J. Power Sources, 196, 3452-3460 (2011).[5] T. T. Aindow and J. O'Neill, J. Power Sources, 196, 3851-3854 (2011). [6] Y. Hashimasa, T. Numata, N. Yoshimura, J. Power Sources, 265, 30-35 (2014). Figure 1

Effect of Carbon Monoxide on the Performance of Polymer Electrolyte Fuel Cell with Hydrogen Circulation System

Introduction Carbon monoxide (CO) in hydrogen fuel is known to degrade a polymer electrolyte fuel cell (PEFC) performance [1-5]. The maximum allowable concentration of CO is 0.2 ppm, according to the quality standards for hydrogen fuel (ISO14687-2) in fuel cell vehicles (FCVs) [6]. Normally, a single-cell evaluation system is a hydrogen one-way pass system, which allows fuel unused by the cell to be released into the atmosphere. However, FCVs usually have a hydrogen circulation system, which returns the fuel from the anode outlet to the inlet. A comparison between the hydrogen one-way pass system and hydrogen circulation system has not been reported with respect to the effect of CO on hydrogen fuel. Our previous study showed that CO is not accumulated in hydrogen fuel when using a hydrogen circulation system [2]. However, the effect of CO on the actual FCV condition has not yet been fully understood because the experimental conditions in our previous study (CO concentration was 4.8 ppm and anode platinum loading was 0.4 mg cm−2) were far from the actual FCV conditions. In this study, using a hydrogen circulation system, the effect of CO on the PEFC’s performance degradation is investigated and is compared to that using a hydrogen one-way pass system under the conditions of low platinum loading at the anode and a low CO concentration. Experimental A JARI standard single cell (25 cm2 electrode area), whose temperature was controlled by a heating medium, and commercially available membrane electrode assemblies (MEA) were used for the single-cell tests. The platinum loading on the anode and the cathode were 0.1 and 0.4 mg cm−2, respectively. The electrolyte membrane thickness was 15 μm. The cell temperature was 60 ºC, the anode was non-humidified, and the cathode dew point was 40 ºC. The single-cell operation tests were conducted using both a conventional hydrogen one-way pass system and the hydrogen circulation system (Fig. 1). The cell was operated at a constant current density of 1000 mA cm−2. The anode gas was hydrogen mixed with CO (up to 1.0 ppm), and the cathode gas was a mixture of N2 (79%) and O2 (21%). The stoichiometry of the fuel and the oxidant was 2 and 2.5, respectively. The CO and CO2 in the purge gas in the hydrogen circulation system were sampled at a 1% purge rate and measured by gas chromatography coupled with pulsed discharge helium ionization detector. Results and Discussion Figure 2 shows, for comparison, the voltage change by adding CO at a steady state in the hydrogen one-way pass system and in the hydrogen circulation system and the CO concentration in the hydrogen circulation system. The voltage change in the hydrogen circulation system is smaller than that in the hydrogen one-way pass system. At 0.2 ppm CO concentration in the hydrogen circulation system, the voltage change induced by adding CO is about 1/10 of that incurred when the hydrogen one-way pass system is used. The CO concentration in the purge gas in the hydrogen circulation system is lower than that in the supplied CO/H2 mixture. Most of the CO in the hydrogen fuel is oxidized to CO2 and gets accumulated in the hydrogen circulation system when a low concentration of CO is supplied to the cell. When the hydrogen circulation system is used, the exhaust gas is sent to the anode inlet again. Consequently, the CO concentration at the anode inlet decreases because the gas in the cylinder, which contains CO, is diluted by the gas from the hydrogen circulation system. In addition, our previous study of the hydrogen one-way pass system revealed that a large excess of O2, compared with CO, existed in the anode [3]. O2 in the hydrogen circulation system is probably circulated and consumed by CO oxidation in the anode electrocatalyst via a non-electrochemical reaction [4]. Consequently, the voltage change in the hydrogen circulation system is smaller than that in the hydrogen one-way pass system. Acknowledgements This work was supported by the New Energy and Industrial Technology Development Organization (NEDO).

Adsorption behavior of low concentration carbon monoxide on polymer electrolyte fuel cell anodes for automotive applications

The adsorption behavior of CO on the anode around the concentration of 0.2 ppm allowed by ISO 14687-2 is investigated in polymer electrolyte fuel cells (PEFCs). CO and CO2 concentrations in the anode exhaust are measured during the operation of a JARI standard single cell at 60 °C cell temperature and 1000 mA cm−2 current density. CO coverage is estimated from the gas analysis and CO stripping voltammetry. The cell voltage decrease as a result of 0.2 ppm CO is 29 mV and the CO coverage is 0.6 at the steady state with 0.11 mg cm−2 of anode platinum loading. The CO coverage as a function of CO concentration approximately follows a Temkin-type isotherm. Oxygen permeated to the anode through a membrane is also measured during fuel cell operation. The exhaust velocity of oxygen from the anode was shown to be much higher than the CO supply velocity. Permeated oxygen should play an important role in CO oxidation under low CO concentration conditions.

Linking the T cell receptor to the single cell transcriptome in antigen-specific human T cells.

Heterogeneity of T cells is a hallmark of a successful adaptive immune response, harnessing the vast diversity of antigen-specific T cells into a coordinated evolution of effector and memory outcomes. The T cell receptor (TCR) repertoire is highly diverse to account for the highly heterogeneous antigenic world. During the response to a virus multiple individual clones of antigen specific CD8+ (Ag-specific) T cells can be identified against a single epitope and multiple epitopes are recognised. Advances in single-cell technologies have provided the potential to study Ag-specific T cell heterogeneity at both surface phenotype and transcriptome levels, thereby allowing investigation of the diversity within the same apparent sub-population. We propose a new method (VDJPuzzle) to reconstruct the native TCRαβ from single cell RNA-seq data of Ag-specific T cells and then to link these with the gene expression profile of individual cells. We applied this method using rare Ag-specific T cells isolated from peripheral blood of a subject who cleared hepatitis C virus infection. We successfully reconstructed productive TCRαβ in 56 of a total of 63 cells (89%), with double α and double β in 18, and 7% respectively, and double TCRαβ in 2 cells. The method was validated via standard single cell PCR sequencing of the TCR. We demonstrate that single-cell transcriptome analysis can successfully distinguish Ag-specific T cell populations sorted directly from resting memory cells in peripheral blood and sorted after ex vivo stimulation. This approach allows a detailed analysis of the TCR diversity and its relationship with the transcriptional profile of different clones.

Research on the performance differences between a standard PEMFC single cell and transparent PEMFC single cells using optimized transparent flow field unit–Part I: Design optimization of a transparent flow field unit

In this article, the optimization design steps of a transparent flow field unit, which is composed of a graphite pierced plate and a hollow transparent end plate, are introduced. Four graphite end plates of different gas channel depths (i.e., 0.6 mm, 1.1 mm, 2 mm and 2.5 mm) are firstly designed. Four standard single cells are assembled, which employ the four graphite end plates. I–V curves and Electrochemical Impedance Spectra (EIS) of these four standard single cells are then tested. Results show that the standard single cell with a gas channel depth of 2 mm presents the best performance when the current density is within 1200 mAcm−2. This is because the total resistance of this single cell always shows a minimum value. According to the analysis of the gas channel depth and considering trying to choose a larger thickness in order to avoid being broken during assembling a transparent single cell, 2 mm thick is determined to be the suitable thickness of the graphite pierced plate. Furthermore, a hollow transparent end plate is designed, i.e., a polycarbonate frame of 25 mm thick is assembled between two polycarbonate sheets of 6 mm thick to form a hollow transparent end plate. Glycerol, which is firstly heated to a temperature three degrees higher than the cell temperature, is pumped into the glycerol cavity of the hollow transparent end plate. No fog is formed on the inner surface of the hollow transparent end plate when the new design is employed in a transparent single cell.

Research on the performance differences between a standard PEMFC single cell and transparent PEMFC single cells using optimized transparent flow field unit–Part II: Performance comparison and explanation

Three transparent single cells, i.e., a cathode transparent single cell, an anode transparent single cell and an overall transparent single cell are tested and compared with a standard single cell. Results show the standard single cell presents the best performance and the overall transparent single cell shows the worst performance. This is because a large contact resistance exists between the gas diffusion layer and the graphite pierced flow field plate of a transparent single cell due to the deformation of the transparent flow field unit, which is caused by the bolt preload. Based on the testing data, four deformation process models are proposed to reveal how the structure differences affect the performance differences between the standard single cell and the transparent single cells.

Comparison of test results on load cycle durability of polymer electrolyte fuel cell cathode catalysts

The Fuel Cell Commercialization Conference of Japan (FCCJ) has proposed methodologies for testing MEAs focusing on the evaluation of the durability of electrode materials. In this study, we applied a load cycle durability test protocol proposed by FCCJ in 2011 to a standard and a highly durable catalyst, and verified whether the difference in the durability of the two catalysts could be estimated appropriately by the protocol. The highly durable catalyst consists of the same carbon support as the standard catalyst, and its durability was increased by heat treatment of the carbon-supported platinum. We examined the durability of each catalyst according to the conditions specified in the protocol using a JARI standard single cell. Cyclic voltammetry for electrochemical surface area measurement and I–V measurement were conducted as a diagnostics of degradation condition. During the durability test, CO2 concentration in the gas exhausted from the cathode was measured with a non-dispersive infrared analyzer. The highly durable catalyst needed larger number of cycles until the ECA maintenance ratio falls to 50% as initial than that of the standard catalyst. The release rate of CO2 from carbon support was equivalent for both catalysts. We have demonstrated that the protocol effectively differentiated degradation of the platinum from that of carbon support.