A unique human cord blood CD8+CD45RA+CD27+CD161+ T-cell subset identified by flow cytometric data analysis using Seurat.

Advances in single-cell level analytical techniques, especially cytometric approaches, have led to profound innovation in biomedical research, particularly in the field of clinical immunology. This has resulted in an expansion of high-dimensional data, posing great challenges for comprehensive and unbiased analysis. Conventional manual analysis is thus becoming untenable to handle these challenges. Furthermore, most newly developed computational methods lack flexibility and interoperability, hampering their accessibility and usability. Here, we adapted Seurat, an R package originally developed for single-cell RNA sequencing (scRNA-seq) analysis, for high-dimensional flow cytometric data analysis. Based on a 20-marker antibody panel and analyses of T-cell profiles in both adult blood and cord blood (CB), we showcased the robust capacity of Seurat in flow cytometric data analysis, which was further validated by Spectre, another high-dimensional cytometric data analysis package, and conventional manual analysis. Importantly, we identified a unique CD8+ T-cell population defined as CD8+CD45RA+CD27+CD161+ T cell that was predominantly present in CB. We characterised its IFN-γ-producing and potential cytotoxic properties using flow cytometry experiments and scRNA-seq analysis from a published dataset. Collectively, we identified a unique human CB CD8+CD45RA+CD27+CD161+ T-cell subset and demonstrated that Seurat, a widely used package for scRNA-seq analysis, possesses great potential to be repurposed for cytometric data analysis. This facilitates an unbiased and thorough interpretation of complicated high-dimensional data using a single analytical pipeline and opens a novel avenue for data-driven investigation in clinical immunology.

Two routes to value-based decisions in Parkinson's disease: differentiating incremental reinforcement learning from episodic memory.

Patients with Parkinson's disease are impaired at incremental reward-based learning. It is typically assumed that this impairment reflects a loss of striatal dopamine. However, many open questions remain about the nature of reward-based learning deficits in Parkinson's. Recent studies have found that a combination of different cognitive and computational strategies contribute even to simple reward-based learning tasks, suggesting a possible role for episodic memory. These findings raise critical questions about how incremental learning and episodic memory interact to support learning from past experience and what their relative contributions are to impaired decision-making in Parkinson's disease. Here we addressed these questions by asking patients with Parkinson's disease (n=26) both on and off their dopamine replacement medication and age- and education-matched healthy controls (n=26) to complete a task designed to isolate the contributions of incremental learning and episodic memory to reward-based learning and decision-making. We found that Parkinson's patients performed as well as healthy controls when using episodic memory, but were impaired at incremental reward-based learning. Dopamine replacement medication remediated this deficit while enhancing subsequent episodic memory for the value of motivationally relevant stimuli. These results demonstrate that Parkinson's patients are impaired at learning about reward from trial-and-error when episodic memory is properly controlled for, and that learning based on the value of single experiences remains intact in patients with Parkinson's disease.

The Role of the Cerebellum in Learning to Predict Reward: Evidence from Cerebellar Ataxia.

Recent findings in animals have challenged the traditional view of the cerebellum solely as the site of motor control, suggesting that the cerebellum may also be important for learning to predict reward from trial-and-error feedback. Yet, evidence for the role of the cerebellum in reward learning in humans is lacking. Moreover, open questions remain about which specific aspects of reward learning the cerebellum may contribute to. Here we address this gap through an investigation of multiple forms of reward learning in individuals with cerebellum dysfunction, represented by cerebellar ataxia cases. Nineteen participants with cerebellar ataxia and 57 age- and sex-matched healthy controls completed two separate tasks that required learning about reward contingencies from trial-and-error. To probe the selectivity of reward learning processes, the tasks differed in their underlying structure: while one task measured incremental reward learning ability alone, the other allowed participants to use an alternative learning strategy based on episodic memory alongside incremental reward learning. We found that individuals with cerebellar ataxia were profoundly impaired at reward learning from trial-and-error feedback on both tasks, but retained the ability to learn to predict reward based on episodic memory. These findings provide evidence from humans for a specific and necessary role for the cerebellum in incremental learning of reward associations based on reinforcement. More broadly, the findings suggest that alongside its role in motor learning, the cerebellum likely operates in concert with the basal ganglia to support reinforcement learning from reward.

Proactive and reactive construction of memory-based preferences.

We are often faced with decisions we have never encountered before, requiring us to infer possible outcomes before making a choice. Computational theories suggest that one way to make these types of decisions is by accessing and linking related experiences stored in memory. Past work has shown that such memory-based preference construction can occur at a number of different timepoints relative to the moment a decision is made. Some studies have found that memories are integrated at the time a decision is faced (reactively) while others found that memory integration happens earlier, when memories are encoded (proactively). Here we offer a resolution to this inconsistency. We demonstrate behavioral and neural evidence for both strategies and for how they tradeoff rationally depending on the associative structure of memory. Using fMRI to decode patterns of brain responses unique to categories of images in memory, we found that proactive memory access is more common and allows more efficient inference. However, participants also use reactive access when choice options are linked to more numerous memory associations. Together, these results indicate that the brain judiciously conducts proactive inference by accessing memories ahead of time in conditions when this strategy is most favorable.

GPR109A expressed on medullary thymic epithelial cells affects thymic Treg development.

Regulatory T cells (Treg) maintain immune homeostasis due to their anti-inflammatory functions. They can be generated either centrally in the thymus or in peripheral organs. Metabolites such as short-chain fatty acids produced by intestinal microbiota can induce peripheral Treg differentiation, by activating G-protein-coupled-receptors like GPR109A. In this study, we identified a novel role for GPR109A in thymic Treg development. We found that Gpr109a-/- mice had increased Treg under basal conditions in multiple organs compared with WT mice. GPR109A was not expressed on T cells but on medullary thymic epithelial cells (mTECs), as revealed by single-cell RNA sequencing in both mice and humans and confirmed by flow cytometry in mice. mTECs isolated from Gpr109a-/- mice had higher expression of autoimmune regulator (AIRE), the key regulator of Treg development, while the subset of mTECs that did not express Gpr109a in the WT displayed increased Aire expression and also enhanced signaling related to mTEC functionality. Increased thymic Treg in Gpr109a-/- mice was associated with protection from experimental autoimmune encephalomyelitis, with ameliorated clinical signs and reduced inflammation. This work identifies a novel role for GPR109A and possibly the gut microbiota, on thymic Treg development via its regulation of mTECs.

Uncertainty alters the balance between incremental learning and episodic memory.

A key question in decision-making is how humans arbitrate between competing learning and memory systems to maximize reward. We address this question by probing the balance between the effects, on choice, of incremental trial-and-error learning versus episodic memories of individual events. Although a rich literature has studied incremental learning in isolation, the role of episodic memory in decision-making has only recently drawn focus, and little research disentangles their separate contributions. We hypothesized that the brain arbitrates rationally between these two systems, relying on each in circumstances to which it is most suited, as indicated by uncertainty. We tested this hypothesis by directly contrasting contributions of episodic and incremental influence to decisions, while manipulating the relative uncertainty of incremental learning using a well-established manipulation of reward volatility. Across two large, independent samples of young adults, participants traded these influences off rationally, depending more on episodic information when incremental summaries were more uncertain. These results support the proposal that the brain optimizes the balance between different forms of learning and memory according to their relative uncertainties and elucidate the circumstances under which episodic memory informs decisions.

Dysbiosis in imiquimod-induced psoriasis alters gut immunity and exacerbates colitis development.

Psoriasis has long been associated with inflammatory bowel disease (IBD); however, a causal link is yet to be established. Here, we demonstrate that imiquimod-induced psoriasis (IMQ-pso) in mice disrupts gut homeostasis, characterized by increased proportions of colonic CX3CR1hi macrophages, altered cytokine production, and bacterial dysbiosis. Gut microbiota from these mice produce higher levels of succinate, which induce de novo proliferation of CX3CR1hi macrophages ex vivo, while disrupted gut homeostasis primes IMQ-pso mice for more severe colitis with dextran sulfate sodium (DSS) challenge. These results demonstrate that changes in the gut environment in psoriasis lead to greater susceptibility to IBD in mice, suggesting a two-hit requirement, that is, psoriasis-induced altered gut homeostasis and a secondary environmental challenge. This may explain the increased prevalence of IBD in patients with psoriasis.

Targeting inflammatory monocytes by immune-modifying nanoparticles prevents acute kidney allograft rejection.

Inflammatory monocytes are a major component of the cellular infiltrate in acutely rejecting human kidney allografts. Since immune-modifying nanoparticles (IMPs) bind to circulating inflammatory monocytes via the specific scavenger receptor MARCO, causing diversion to the spleen and subsequent apoptosis, we investigated the therapeutic potential of negatively charged, 500-nm diameter polystyrene IMPs to prevent kidney allograft rejection. Kidney transplants were performed from BALB/c (H2d) to C57BL/6 (H2b) mice in two groups: controls (allo) and allo mice infused with IMPs. Groups were studied for 14 (acute rejection) or 100 (chronic rejection) days. Allo mice receiving IMPs exhibited superior survival and markedly less acute rejection, with better kidney function, less tubulitis, and diminished inflammatory cell density, cytokine and cytotoxic molecule expression in the allograft and lower titers of donor-specific IgG2c antibody in serum at day 14, as compared to allo mice. Cells isolated from kidneys from allo mice receiving IMPs showed reduced Ly6Chi monocytes, CD11b+ cells and NKT+ cells compared to allo mice. IMPs predominantly bound CD11b+ cells in the bloodstream and CD11b+ and CD11c-B220+ marginal zone B cells in the spleen. In the spleen, IMPs were found predominantly in red pulp, colocalized with MARCO and expression of cleaved caspase-3. At day 100, allo mice receiving IMPs exhibited reduced macrophage M1 responses but were not protected from chronic rejection. IMPs afforded significant protection from acute rejection, inhibiting both innate and adaptive alloimmunity. Thus, our current experimental findings, coupled with our earlier demonstration of IMP-induced protection in kidney ischemia-reperfusion injury, identify IMPs as a potential induction agent in kidney transplantation.

Dietary protein increases T-cell-independent sIgA production through changes in gut microbiota-derived extracellular vesicles.

Secretory IgA is a key mucosal component ensuring host-microbiota mutualism. Here we use nutritional geometry modelling in mice fed 10 different macronutrient-defined, isocaloric diets, and identify dietary protein as the major driver of secretory IgA production. Protein-driven secretory IgA induction is not mediated by T-cell-dependent pathways or changes in gut microbiota composition. Instead, the microbiota of high protein fed mice produces significantly higher quantities of extracellular vesicles, compared to those of mice fed high-carbohydrate or high-fat diets. These extracellular vesicles activate Toll-like receptor 4 to increase the epithelial expression of IgA-inducing cytokine, APRIL, B cell chemokine, CCL28, and the IgA transporter, PIGR. We show that succinate, produced in high concentrations by microbiota of high protein fed animals, increases generation of reactive oxygen species by bacteria, which in turn promotes extracellular vesicles production. Here we establish a link between dietary macronutrient composition, gut microbial extracellular vesicles release and host secretory IgA response.

Impact of Dietary Fiber on West Nile Virus Infection.

Dietary fiber supports healthy gut bacteria and their production of short-chain fatty acids (SCFA), which promote anti-inflammatory cell development, in particular, regulatory T cells. It is thus beneficial in many diseases, including influenza infection. While disruption of the gut microbiota by antibiotic treatment aggravates West Nile Virus (WNV) disease, whether dietary fiber is beneficial is unknown. WNV is a widely-distributed neurotropic flavivirus that recruits inflammatory monocytes into the brain, causing life-threatening encephalitis. To investigate the impact of dietary fiber on WNV encephalitis, mice were fed on diets deficient or enriched with dietary fiber for two weeks prior to inoculation with WNV. To induce encephalitis, mice were inoculated intranasally with WNV and maintained on these diets. Despite increased fecal SCFA acetate and changes in gut microbiota composition, dietary fiber did not affect clinical scores, leukocyte infiltration into the brain, or survival. After the brain, highest virus loads were measured in the colon in neurons of the submucosal and myenteric plexuses. Associated with this, there was disrupted gut homeostasis, with shorter colon length and higher local inflammatory cytokine levels, which were not affected by dietary fiber. Thus, fiber supplementation is not effective in WNV encephalitis.

Integration, exploration, and analysis of high-dimensional single-cell cytometry data using Spectre.

As the size and complexity of high-dimensional (HD) cytometry data continue to expand, comprehensive, scalable, and methodical computational analysis approaches are essential. Yet, contemporary clustering and dimensionality reduction tools alone are insufficient to analyze or reproduce analyses across large numbers of samples, batches, or experiments. Moreover, approaches that allow for the integration of data across batches or experiments are not well incorporated into computational toolkits to allow for streamlined workflows. Here we present Spectre, an R package that enables comprehensive end-to-end integration and analysis of HD cytometry data from different batches or experiments. Spectre streamlines the analytical stages of raw data pre-processing, batch alignment, data integration, clustering, dimensionality reduction, visualization, and population labelling, as well as quantitative and statistical analysis. Critically, the fundamental data structures used within Spectre, along with the implementation of machine learning classifiers, allow for the scalable analysis of very large HD datasets, generated by flow cytometry, mass cytometry, or spectral cytometry. Using open and flexible data structures, Spectre can also be used to analyze data generated by single-cell RNA sequencing or HD imaging technologies, such as Imaging Mass Cytometry. The simple, clear, and modular design of analysis workflows allow these tools to be used by bioinformaticians and laboratory scientists alike. Spectre is available as an R package or Docker container. R code is available on Github (https://github.com/immunedynamics/spectre).

Dietary carbohydrate, particularly glucose, drives B cell lymphopoiesis and function.

While diet modulates immunity, its impact on B cell ontogeny remains unclear. Using mixture modeling, a large-scale isocaloric dietary cohort mouse study identified carbohydrate as a major driver of B cell development and function. Increasing dietary carbohydrate increased B cell proportions in spleen, mesenteric lymph node and Peyer's patches, and increased antigen-specific immunoglobulin G production after immunization. This was linked to increased B lymphopoiesis in the bone marrow. Glucose promoted early B lymphopoiesis and higher total B lymphocyte numbers than fructose. It drove B cell development through glycolysis and oxidative phosphorylation, independently of fatty acid oxidation in vitro and reduced B cell apoptosis in early development via mTOR activation, independently of interleukin-7. Ours is the first comprehensive study showing the impact of macronutrients on B cell development and function. It shows the quantitative and qualitative interplay between dietary carbohydrate and B cells and argues for dietary modulation in B cell-targeting strategies.

Linear and nonlinear profiles of weak behavioral and neural differentiation between numerical operations in children with math learning difficulties.

Mathematical knowledge is constructed hierarchically during development from a basic understanding of addition and subtraction, two foundational and inter-related, but semantically distinct, numerical operations. Early in development, children show remarkable variability in their numerical problem-solving skills and difficulties in solving even simple addition and subtraction problems are a hallmark of math learning difficulties. Here, we use novel quantitative analyses to investigate whether less distinct representations are associated with poor problem-solving abilities in children during the early stages of math-skill acquisition. Crucially, we leverage dimensional and categorical analyses to identify linear and nonlinear neurobehavioral profiles of individual differences in math skills. Behaviorally, performance on the two different numerical operations was less differentiated in children with low math abilities, and lower problem-solving efficiency stemmed from weak evidence-accumulation during problem-solving. Children with low numerical abilities also showed less differentiated neural representations between addition and subtraction operations in multiple cortical areas, including the fusiform gyrus, intraparietal sulcus, anterior temporal cortex and insula. Furthermore, analysis of multi-regional neural representation patterns revealed significantly higher network similarity and aberrant integration of representations within a fusiform gyrus-intraparietal sulcus pathway important for manipulation of numerical quantity. These findings identify the lack of distinct neural representations as a novel neurobiological feature of individual differences in children's numerical problem-solving abilities, and an early developmental biomarker of low math skills. More generally, our approach combining dimensional and categorical analyses overcomes pitfalls associated with the use of arbitrary cutoffs for probing neurobehavioral profiles of individual differences in math abilities.

Evaluating spectral cytometry for immune profiling in viral disease.

In conventional fluorescence cytometry, each fluorophore present in a panel is measured in a target detector, through the use of wide band-pass optical filters. In contrast, spectral cytometry uses a large number of detectors with narrow band-pass filters to measure a fluorophore's signal across the spectrum, creating a more detailed fluorescent signature for each fluorophore. The spectral approach shows promise in adding flexibility to panel design and improving the measurement of fluorescent signal. However, few comparisons between conventional and spectral systems have been reported to date. We therefore sought to compare a modern conventional cytometry system with a modern spectral system, and to assess the quality of resulting datasets from the point of view of a flow cytometry user. Signal intensity, spread, and resolution were compared between the systems. Subsequently, the different methods of separating fluorophore signals were compared, where compensation mathematically separates multiple overlapping fluorophores and unmixing relies on creating a detailed fluorescent signature across the spectrum to separate the fluorophores. Within the spectral data set, signal spread and resolution were comparable between compensation and unmixing. However, for some highly overlapping fluorophores, unmixing resolved the two fluorescence signals where compensation did not. Finally, data from mid- to large-size panels were acquired and were found to have comparable resolution for many fluorophores on both instruments, but reduced levels of spreading error on our spectral system improved signal resolution for a number of fluorophores, compared with our conventional system. Furthermore, autofluorescence extraction on the spectral system allowed for greater population resolution in highly autofluorescent samples. Overall, the implementation of a spectral cytometry approach resulted in data that are comparable to that generated on conventional systems, with a number of potential advantages afforded by the larger number of detectors, and the integration of the spectral unmixing approach. © 2020 International Society for Advancement of Cytometry.

Premarital sex and condom use among trainee healthcare workers: an exploratory study of selected healthcare training institutions in Enugu State, Nigeria.

INTRODUCTION: To assess the prevalence and causes of premarital sex and condom use among trainee healthcare workers in selected healthcare institutions in Enugu State, Nigeria; and to proffer solution to challenges identified. METHODS: We used a mixed study approach with qualitative and quantitative components. Informed consent was obtained from participants and data collected using self-administered structured questionnaires. Epi info® was used for data analysis. RESULTS: A total of 362 respondents (309 unmarried) from four healthcare training institutions participated in the study. Among unmarried respondents, 141 (45.8%) were sexually active. Premarital sex was more common among Pentecostals and sexual activity increased with age (r=0.78; p <0.05). Premarital sexual activity was more common among males and trainee nurses (p <0.005). Although knowledge of condom use was high, actual use was poor (20.1%), with lowest rates among females, Catholics and age-group 30-35 years. Breakages, high failure rates and reduced sexual satisfaction were cited as major factors responsible for poor use. Use of non-specific terms such as "casual sex" and "casual or regular sex partners" hindered consistent, correct condom use. CONCLUSION: There is a significant gap between knowledge of and actual use of condoms, despite high premarital sexual activity amongst healthcare workers. Furthermore, non-specific terminologies hinders appropriate condom usage. We propose the term: Committed Spousal Partner (CSP) defined as "a sexual partner who commits to fidelity (one sexual partner per time) and whose current HIV status is known through medical testing and is properly documented" in place of all non-specific terminology.

Uncovering hidden brain state dynamics that regulate performance and decision-making during cognition.

Human cognition is influenced not only by external task demands but also latent mental processes and brain states that change over time. Here, we use novel Bayesian switching dynamical systems algorithm to identify hidden brain states and determine that these states are only weakly aligned with external task conditions. We compute state transition probabilities and demonstrate how dynamic transitions between hidden states allow flexible reconfiguration of functional brain circuits. Crucially, we identify latent transient brain states and dynamic functional circuits that are optimal for cognition and show that failure to engage these states in a timely manner is associated with poorer task performance and weaker decision-making dynamics. We replicate findings in a large sample (N = 122) and reveal a robust link between cognition and flexible latent brain state dynamics. Our study demonstrates the power of switching dynamical systems models for investigating hidden dynamic brain states and functional interactions underlying human cognition.

Health: redefined.

For many years the definition of 'health' has remained unchanged as a narrow concept, encompassing physical wellbeing from a medical context. This somewhat focused definition has attracted criticism from individuals and professional bodies alike. Recent attempts have been made to redefine health, each offering an alternative viewpoint from sociological, environmental, societal and economic standpoints. We summarize and contextualize these definitions and provide an alternative, new, all-encompassing definition of health.

High-Dimensional Fluorescence Cytometry.

The immune system consists of a complex network of cells, all expressing a wide range of surface and/or intracellular proteins. Using flow cytometry, these cells can be analyzed by labeling with fluorophore-conjugated antibodies. The recent expansion of fluorescence flow cytometry technology, in conjunction with the ever-expanding understanding of the complexity of the immune system, has led to the generation of larger high-dimensional fluorescence flow cytometry panels. However, as panel size and complexity increases, so too does the difficulty involved in constructing high-quality panels, in addition to the challenges of analyzing such high-dimensional datasets. As such, this unit seeks to review the key principles involved in building high-dimensional panels, as well as to guide users through the process of building and analyzing quality panels. Here, cytometer configuration, fluorophore brightness, spreading error, antigen density, choosing the best conjugates, titration, optimization, and data analysis will all be addressed. © 2017 by John Wiley & Sons, Inc.

Temporal Dynamics and Developmental Maturation of Salience, Default and Central-Executive Network Interactions Revealed by Variational Bayes Hidden Markov Modeling.

Little is currently known about dynamic brain networks involved in high-level cognition and their ontological basis. Here we develop a novel Variational Bayesian Hidden Markov Model (VB-HMM) to investigate dynamic temporal properties of interactions between salience (SN), default mode (DMN), and central executive (CEN) networks-three brain systems that play a critical role in human cognition. In contrast to conventional models, VB-HMM revealed multiple short-lived states characterized by rapid switching and transient connectivity between SN, CEN, and DMN. Furthermore, the three "static" networks occurred in a segregated state only intermittently. Findings were replicated in two adult cohorts from the Human Connectome Project. VB-HMM further revealed immature dynamic interactions between SN, CEN, and DMN in children, characterized by higher mean lifetimes in individual states, reduced switching probability between states and less differentiated connectivity across states. Our computational techniques provide new insights into human brain network dynamics and its maturation with development.