Cliburn Chan

Inhibition of NF-κB and Oxidative Pathways in Human Dendritic Cells by Antioxidative Vitamins Generates Regulatory T Cells

Dendritic cells (DCs) are central to T cell immunity, and many strategies have been used to manipulate DCs to modify immune responses. We investigated the effects of antioxidants ascorbate (vitamin C) and α-tocopherol (vitamin E) on DC phenotype and function. Vitamins C and E are both antioxidants, and concurrent use results in a nonadditive activity. We have demonstrated that DC treated with these antioxidants are resistant to phenotypic and functional changes following stimulation with proinflammatory cytokines. Following treatment, the levels of intracellular oxygen radical species were reduced, and the protein kinase RNA-regulated, eukaryotic translation initiation factor 2α, NF-κB, protein kinase C, and p38 MAPK pathways could not be activated following inflammatory agent stimulation. We went on to show that allogeneic T cells (including CD4+CD45RO, CD4+CD45RA, and CD4+CD25− subsets) were anergized following exposure to vitamin-treated DCs, and secreted higher levels of Th2 cytokines and IL-10 than cells incubated with control DCs. These anergic T cells act as regulatory T cells in a contact-dependent manner that is not dependent on IL-4, IL-5, IL-10, IL-13, and TGF-β. These data indicate that vitamin C- and E-treated DC might be useful for the induction of tolerance to allo- or autoantigens.

Understanding GPU Programming for Statistical Computation: Studies in Massively Parallel Massive Mixtures

This article describes advances in statistical computation for large-scale data analysis in structured Bayesian mixture models via graphics processing unit (GPU) programming. The developments are partly motivated by computational challenges arising in fitting models of increasing heterogeneity to increasingly large datasets. An example context concerns common biological studies using high-throughput technologies generating many, very large datasets and requiring increasingly high-dimensional mixture models with large numbers of mixture components. We outline important strategies and processes for GPU computation in Bayesian simulation and optimization approaches, give examples of the benefits of GPU implementations in terms of processing speed and scale-up in ability to analyze large datasets, and provide a detailed, tutorial-style exposition that will benefit readers interested in developing GPU-based approaches in other statistical models. Novel, GPU-oriented approaches to modifying existing algorithms software design can lead to vast speed-up and, critically, enable statistical analyses that presently will not be performed due to compute time limitations in traditional computational environments. Supplemental materials are provided with all source code, example data, and details that will enable readers to implement and explore the GPU approach in this mixture modeling context.

Statistical mixture modeling for cell subtype identification in flow cytometry.

Statistical mixture modeling provides an opportunity for automated identification and resolution of cell subtypes in flow cytometric data. The configuration of cells as represented by multiple markers simultaneously can be modeled arbitrarily well as a mixture of Gaussian distributions in the dimension of the number of markers. Cellular subtypes may be related to one or multiple components of such mixtures, and fitted mixture models can be evaluated in the full set of markers as an alternative, or adjunct, to traditional subjective gating methods that rely on choosing one or two dimensions. Four color flow data from human blood cells labeled with FITC‐conjugated anti‐CD3, PE‐conjugated anti‐CD8, PE‐Cy5‐conjugated anti‐CD4, and APC‐conjugated anti‐CD19 Abs was acquired on a FACSCalibur. Cells from four murine cell lines, JAWS II, RAW 264.7, CTLL‐2, and A20, were also stained with FITC‐conjugated anti‐CD11c, PE‐conjugated anti‐CD11b, PE‐Cy5‐conjugated anti‐CD8a, and PE‐Cy7‐conjugated‐CD45R/B220 Abs, respectively, and single color flow data were collected on an LSRII. The data were fitted with a mixture of multivariate Gaussians using standard Bayesian statistical approaches and Markov chain Monte Carlo computations. Statistical mixture models were able to identify and purify major cell subsets in human peripheral blood, using an automated process that can be generalized to an arbitrary number of markers. Validation against both traditional expert gating and synthetic mixtures of murine cell lines with known mixing proportions was also performed. This article describes the studies of statistical mixture modeling of flow cytometric data, and demonstrates their utility in examples with four‐color flow data from human peripheral blood samples and synthetic mixtures of murine cell lines.

Cooperative enhancement of specificity in a lattice of T cell receptors

Two of the most important models to account for the specificity and sensitivity of the T cell receptor (TCR) are the kinetic proofreading and serial ligation models. However, although kinetic proofreading provides a means for individual TCRs to measure accurately the length of time they are engaged and signal appropriately, the stochastic nature of ligand dissociation means the kinetic proofreading model implies that at high concentrations the response of the cell will be relatively nonspecific. Recent ligand experiments have revealed the phenomenon of both negative and positive crosstalk among neighboring TCRs. By using a Monte Carlo simulation of a lattice of TCRs, we integrate receptor crosstalk with the kinetic proofreading and serial ligation models and discover that receptor cooperativity can enhance T cell specificity significantly at a very modest cost to the sensitivity of the response.

Hierarchical Modeling for Rare Event Detection and Cell Subset Alignment across Flow Cytometry Samples

Flow cytometry is the prototypical assay for multi-parameter single cell analysis, and is essential in vaccine and biomarker research for the enumeration of antigen-specific lymphocytes that are often found in extremely low frequencies (0.1% or less). Standard analysis of flow cytometry data relies on visual identification of cell subsets by experts, a process that is subjective and often difficult to reproduce. An alternative and more objective approach is the use of statistical models to identify cell subsets of interest in an automated fashion. Two specific challenges for automated analysis are to detect extremely low frequency event subsets without biasing the estimate by pre-processing enrichment, and the ability to align cell subsets across multiple data samples for comparative analysis. In this manuscript, we develop hierarchical modeling extensions to the Dirichlet Process Gaussian Mixture Model (DPGMM) approach we have previously described for cell subset identification, and show that the hierarchical DPGMM (HDPGMM) naturally generates an aligned data model that captures both commonalities and variations across multiple samples. HDPGMM also increases the sensitivity to extremely low frequency events by sharing information across multiple samples analyzed simultaneously. We validate the accuracy and reproducibility of HDPGMM estimates of antigen-specific T cells on clinically relevant reference peripheral blood mononuclear cell (PBMC) samples with known frequencies of antigen-specific T cells. These cell samples take advantage of retrovirally TCR-transduced T cells spiked into autologous PBMC samples to give a defined number of antigen-specific T cells detectable by HLA-peptide multimer binding. We provide open source software that can take advantage of both multiple processors and GPU-acceleration to perform the numerically-demanding computations. We show that hierarchical modeling is a useful probabilistic approach that can provide a consistent labeling of cell subsets and increase the sensitivity of rare event detection in the context of quantifying antigen-specific immune responses.

Oscillations in the immune system

Summary:  Oscillations are surprisingly common in the immune system, both in its healthy state and in disease. The most famous example is that of periodic fevers caused by the malaria parasite. A number of hereditary disorders, which also cause periodic fevers, have also been known for a long time. Various reports of oscillations in cytokine concentrations following antigen challenge have been published over at least the past three decades. Oscillations can also occur at the intracellular level. Calcium oscillations following T‐cell activation are familiar to all immunologists, and metabolic and reactive oxygen species oscillations in neutrophils have been well documented. More recently, oscillations in nuclear factor κB activity following stimulation by tumor necrosis factor α have received considerable publicity. However, despite all of these examples, oscillations in the immune system still tend to be considered mainly as pathological aberrations, and their causes and significance remained largely unknown. This is partly because of a lack of awareness within the immunological community of the appropriate theoretical frameworks for describing and analyzing such behavior. We provide an introduction to these frameworks and give a survey of the currently known oscillations that occur within the immune system.

Reconstruction of cell population dynamics using CFSE

BackgroundQuantifying cell division and death is central to many studies in the biological sciences. The fluorescent dye CFSE allows the tracking of cell division in vitro and in vivo and provides a rich source of information with which to test models of cell kinetics. Cell division and death have a stochastic component at the single-cell level, and the probabilities of these occurring in any given time interval may also undergo systematic variation at a population level. This gives rise to heterogeneity in proliferating cell populations. Branching processes provide a natural means of describing this behaviour.ResultsWe present a likelihood-based method for estimating the parameters of branching process models of cell kinetics using CFSE-labeling experiments, and demonstrate its validity using synthetic and experimental datasets. Performing inference and model comparison with real CFSE data presents some statistical problems and we suggest methods of dealing with them.ConclusionThe approach we describe here can be used to recover the (potentially variable) division and death rates of any cell population for which division tracking information is available.

Feedback control of T-cell receptor activation.

The specificity and sensitivity of T–cell recognition is vital to the immune response. Ligand engagement with the T–cell receptor (TCR) results in the activation of a complex sequence of signalling events, both on the cell membrane and intracellularly. Feedback is an integral part of these signalling pathways, yet is often ignored in standard accounts of T–cell signalling. Here we show, using a mathematical model, that these feedback loops can explain the ability of the TCR to discriminate between ligands with high specificity and sensitivity, as well as provide a mechanism for sustained signalling. The model also explains the recent counter–intuitive observation that endogenous ‘null’ ligands can significantly enhance T–cell signalling. Finally, the model may provide an archetype for receptor switching based on kinase–phosphatase switches, and thus be of interest to the wider signalling community.

Thinking Outside the Gate: Single-Cell Assessments in Multiple Dimensions

Present address: Immuno-Oncology & Combinations DPU, Oncology RDBendall et al., 2011), allowing an oppor-tunity to better understand the immuno-logical mechanisms underlying disease.Complex flow cytometry (FCM) data arenow surpassing our ability to fully analyzeand interpret all information via currentstandard approaches, such as 2D dotplots and Boolean gates. Indeed, thenumber of potential cell subpopulationsincreases exponentially with the numberof parameters assessed, making it diffi-cult to decipher all possible combina-tions included in the raw data (e.g., 512potential subsets with nine markers) viathe traditional approaches (Bendall andNolan, 2012). This could limit the transla-tion of technical advances into new diag-nostics or therapies. Newly developedbioinformatics tools that have the poten-tial to bridge this gap are now available.The aim of this letter is to foster theimplementation and adoption of thesenovel computational methodologies forunbiased analysis of complex cytometrydata.In recent years, a host of new data-analysis tools have emerged, creatingworkflows for processing and analyzingcomplex FCM datasets; however, thesehave gone mostly unnoticed by immunol-ogists. Table S1 provides an overview ofmany of the currently available tools andtheir specific applications. They can beassigned to specific categories arrangedin a ‘‘FCM data-analysis workflow’’ fromcompensated data as input to biologi-cally interpretable results as output. Thevast majority of the listed tools for FCMdata processing, analysis, and visualiza-tion are made available by the bio-informaticians at no cost and includeopen source code and unrestrictive soft-ware licensing, opening up these compu-tational approaches to broad use by theresearch community. Many of the toolshave been developed to address similaranalysis objectives via quite different ap-proaches. They might provide optimal re-sultsfordifferentdatasets,suchthatthereis no ‘‘right’’ or ‘‘best’’ tool, and usingseveral algorithms in combination mightyield even better results and exceedthe possibilities offered by manual anal-ysis.Comprehensivecomparativestudiesby the Flow Cytometry: Critical Assess-mentofPopulationIdentificationMethods(FlowCAP) project have shown that manyof these tools have reached a level ofmaturitythatmatches,orevensurpasses,the results produced by human experts(Aghaeepour et al., 2013).The development of computational ap-proaches addresses many needs asso-ciated with high-dimensional datasets.However,fortheimmunologycommunity,threemain challengeshave surfaced, andtackling them will facilitate a paradigmshift in the analysis of FCM data. First,despite the focused efforts by bio-informaticians to develop novel tools foranalyzingFCMdata,onlyaminorityofim-munologists are aware of the advantagesofferedtothefield.Thesetoolsneedtobepresented in immunology forums ratherthan limited to bioinformatics journalsand conferences. Second, even thoughthe vast majority of the computationalImmunity 42, April 21, 2015 a2015 Elsevier Inc. 591

Polyfunctional T-Cell Signatures to Predict Protection from Cytomegalovirus after Lung Transplantation

RATIONALE Cytomegalovirus (CMV), which is one of the most common infections after lung transplantation, is associated with chronic lung allograft dysfunction and worse post-transplantation survival. Current approaches for at-risk patients include a fixed duration of antiviral prophylaxis despite the associated cost and side effects. OBJECTIVES We sought to identify a specific immunologic signature that predicted protection from subsequent CMV. METHODS CMV-seropositive lung transplantation recipients were included in the discovery (n = 43) and validation (n = 28) cohorts. Polyfunctional CMV-specific immunity was assessed by stimulating peripheral blood mononuclear cells with CMV pp65 or IE-1 peptide pools and then by measuring T-cell expression of CD107a, IFN-γ, tumor necrosis factor-α (TNF-α), and IL-2. Recipients were prospectively monitored for subsequent viremia. A Cox proportional hazards regression model that considered cytokine responses individually and in combination was used to create a predictive model for protection from CMV reactivation. This model was then applied to the validation cohort. MEASUREMENTS AND MAIN RESULTS Using the discovery cohort, we identified a specific combination of polyfunctional T-cell subsets to pp65 that predicted protection from subsequent CMV viremia (concordance index 0.88 [SE, 0.087]). The model included both protective (CD107a(-)/IFN-γ(+)/IL-2(+)/TNF-α(+) CD4(+) T cells, CD107a(-)/IFN-γ(+)/IL-2(+)/TNF-α(+) CD8(+) T cells) and detrimental (CD107a(+)/IFN-γ(+)/IL-2(-)/TNF-α(-) CD8(+) T cells) subsets. The model was robust in the validation cohort (concordance index 0.81 [SE, 0.103]). CONCLUSIONS We identified and validated a specific T-cell polyfunctional response to CMV antigen stimulation that provides a clinically useful prediction of subsequent cytomegalovirus risk. This novel diagnostic approach could inform the optimal duration of individual prophylaxis.