Rapid reduction in Staphylococcus aureus in atopic dermatitis subjects following dupilumab treatment.

BACKGROUND: Atopic dermatitis (AD) is an inflammatory disorder characterized by dominant type 2 inflammation leading to chronic pruritic skin lesions, allergic comorbidities, and Staphylococcus aureus skin colonization and infections. S aureus is thought to play a role in AD severity. OBJECTIVES: This study characterized the changes in the host-microbial interface in subjects with AD following type 2 blockade with dupilumab. METHODS: Participants (n = 71) with moderate-severe AD were enrolled in a randomized (dupilumab vs placebo; 2:1), double-blind study at Atopic Dermatitis Research Network centers. Bioassays were performed at multiple time points: S aureus and virulence factor quantification, 16s ribosomal RNA microbiome, serum biomarkers, skin transcriptomic analyses, and peripheral blood T-cell phenotyping. RESULTS: At baseline, 100% of participants were S aureus colonized on the skin surface. Dupilumab treatment resulted in significant reductions in S aureus after only 3 days (compared to placebo), which was 11 days before clinical improvement. Participants with the greatest S aureus reductions had the best clinical outcomes, and these reductions correlated with reductions in serum CCL17 and disease severity. Reductions (10-fold) in S aureus cytotoxins (day 7), perturbations in TH17-cell subsets (day 14), and increased expression of genes relevant for IL-17, neutrophil, and complement pathways (day 7) were also observed. CONCLUSIONS: Blockade of IL-4 and IL-13 signaling, very rapidly (day 3) reduces S aureus abundance in subjects with AD, and this reduction correlates with reductions in the type 2 biomarker, CCL17, and measures of AD severity (excluding itch). Immunoprofiling and/or transcriptomics suggest a role for TH17 cells, neutrophils, and complement activation as potential mechanisms to explain these findings.

Reduced T-Helper 17 Responses to Streptococcus pneumoniae in Infection-Prone Children Can Be Rescued by Addition of Innate Cytokines.

Background: T-helper (Th) 17 cells are important in the control of Streptococcus pneumoniae. We sought to understand the mechanism of failure of Th17 immunity resulting in S. pneumoniae infections in children <2 years old. Methods: Peripheral blood mononuclear cells (PBMCs) from infection-prone (IP) and non-IP (NIP) children 9-18 months old were examined for their responses to heat-killed S. Pneumoniae, using flow cytometry, reverse-transcription polymerase chain reaction, and enzyme-linked immunoassay. We measured cytokine production, proliferation, and differentiation of Th17 cells and the expression of transcription factors in response to S. pneumoniae. Results: PBMCs of IP children stimulated with heat-killed S. pneumoniae had significantly reduced percentages of CD4+ Th1 (interleukin2, tumor necrosis factor α) and Th17 (interleukin 17A) cells compared with NIP children. Addition of exogenous Th17-promoting cytokines (interleukin 6, 1ß, and 23 and transforming growth factor ß) restored CD4+ Th17 cell function in cells from IP children to levels measured in NIP children. Conclusions: Reduced Th17 responses to S. pneumoniae in PBMCs of IP children can be rescued by addition of Th17-promoting cytokines.

Functional Immune Cell Differences Associated With Low Vaccine Responses in Infants.

BACKGROUND: We sought to understand why some children respond poorly to vaccinations in the first year of life. METHODS: A total of 499 children (6-36 months old) provided serum and peripheral blood mononuclear cell samples after their primary and booster vaccination. Vaccine antigen-specific antibody levels were analyzed with enzyme-linked immunosorbent assay, and frequency of memory B cells, functional T-cell responses, and antigen-presenting cell responses were assessed in peripheral blood mononuclear cell samples with flow cytometric analysis. RESULTS: Eleven percent of children were low vaccine responders, defined a priori as those with subprotective immunoglobulin G antibody levels to ≥66% of vaccines tested. Low vaccine responders generated fewer memory B cells, had reduced activation by CD4(+) and CD8(+) T cells on polyclonal stimulation, and displayed lower major histocompatibility complex II expression by antigen-presenting cells. CONCLUSIONS: We conclude that subprotective vaccine responses in infants are associated with a distinct immunologic profile.

Critical assessment of automated flow cytometry data analysis techniques.

Traditional methods for flow cytometry (FCM) data processing rely on subjective manual gating. Recently, several groups have developed computational methods for identifying cell populations in multidimensional FCM data. The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of these methods on two tasks: (i) mammalian cell population identification, to determine whether automated algorithms can reproduce expert manual gating and (ii) sample classification, to determine whether analysis pipelines can identify characteristics that correlate with external variables (such as clinical outcome). This analysis presents the results of the first FlowCAP challenges. Several methods performed well as compared to manual gating or external variables using statistical performance measures, which suggests that automated methods have reached a sufficient level of maturity and accuracy for reliable use in FCM data analysis.

Competitive SWIFT cluster templates enhance detection of aging changes.

Clustering-based algorithms for automated analysis of flow cytometry datasets have achieved more efficient and objective analysis than manual processing. Clustering organizes flow cytometry data into subpopulations with substantially homogenous characteristics but does not directly address the important problem of identifying the salient differences in subpopulations between subjects and groups. Here, we address this problem by augmenting SWIFT--a mixture model based clustering algorithm reported previously. First, we show that SWIFT clustering using a "template" mixture model, in which all subpopulations are represented, identifies small differences in cell numbers per subpopulation between samples. Second, we demonstrate that resolution of inter-sample differences is increased by "competition" wherein a joint model is formed by combining the mixture model templates obtained from different groups. In the joint model, clusters from individual groups compete for the assignment of cells, sharpening differences between samples, particularly differences representing subpopulation shifts that are masked under clustering with a single template model. The benefit of competition was demonstrated first with a semisynthetic dataset obtained by deliberately shifting a known subpopulation within an actual flow cytometry sample. Single templates correctly identified changes in the number of cells in the subpopulation, but only the competition method detected small changes in median fluorescence. In further validation studies, competition identified a larger number of significantly altered subpopulations between young and elderly subjects. This enrichment was specific, because competition between templates from consensus male and female samples did not improve the detection of age-related differences. Several changes between the young and elderly identified by SWIFT template competition were consistent with known alterations in the elderly, and additional altered subpopulations were also identified. Alternative algorithms detected far fewer significantly altered clusters. Thus SWIFT template competition is a powerful approach to sharpen comparisons between selected groups in flow cytometry datasets.

A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes.

The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of computational methods for identifying cell populations in multidimensional flow cytometry data. Here we report the results of FlowCAP-IV where algorithms from seven different research groups predicted the time to progression to AIDS among a cohort of 384 HIV+ subjects, using antigen-stimulated peripheral blood mononuclear cell (PBMC) samples analyzed with a 14-color staining panel. Two approaches (FlowReMi.1 and flowDensity-flowType-RchyOptimyx) provided statistically significant predictive value in the blinded test set. Manual validation of submitted results indicated that unbiased analysis of single cell phenotypes could reveal unexpected cell types that correlated with outcomes of interest in high dimensional flow cytometry datasets.

An animated landscape representation of CD4+ T-cell differentiation, variability, and plasticity: insights into the behavior of populations versus cells.

Recent advances in understanding CD4(+) T-cell differentiation suggest that previous models of a few distinct, stable effector phenotypes were too simplistic. Although several well-characterized phenotypes are still recognized, some states display plasticity, and intermediate phenotypes exist. As a framework for reexamining these concepts, we use Waddington's landscape paradigm, augmented with explicit consideration of stochastic variations. Our animation program "LAVA" visualizes T-cell differentiation as cells moving across a landscape of hills and valleys, leading to attractor basins representing stable or semistable differentiation states. The model illustrates several principles, including: (i) cell populations may behave more predictably than individual cells; (ii) analogous to reticulate evolution, differentiation may proceed through a network of interconnected states, rather than a single well-defined pathway; (iii) relatively minor changes in the barriers between attractor basins can change the stability or plasticity of a population; (iv) intrapopulation variability of gene expression may be an important regulator of differentiation, rather than inconsequential noise; (v) the behavior of some populations may be defined mainly by the behavior of outlier cells. While not a quantitative representation of actual differentiation, our model is intended to provoke discussion of T-cell differentiation pathways, particularly highlighting a probabilistic view of transitions between states.

Optimization of the cytokine secretion assay for human IL-2 in single and combination assays.

The cytokine secretion assay identifies live cytokine-secreting cells by capturing the secreted cytokine on a surface-bound capture antibody in dilute suspension culture, followed by detection with a fluorescent anti-cytokine antibody. However, examining the kinetics of cytokine detection revealed that IL-2 staining reached a maximum at early times and then declined, whereas staining for other cytokines including interferon (IFNγ) increased for up to 90 min. The decline in IL-2 staining could have been due to rapid cessation of cytokine synthesis, coupled with internalization of cytokine/antibody complexes from the cell surface. Consistent with this model, addition of the anti-IL-2 detection antibody during the cytokine secretion step resulted in higher and more sustained staining. This modified method enhanced staining of IL-2 and IL-4, but not IFNγ, tumor necrosis factor alpha (TNFα), or IL-5. However, the longer secretion times possible in the modified assay also improved detection of other cytokines in multi-cytokine combinations.

SWIFT-scalable clustering for automated identification of rare cell populations in large, high-dimensional flow cytometry datasets, part 1: algorithm design.

We present a model-based clustering method, SWIFT (Scalable Weighted Iterative Flow-clustering Technique), for digesting high-dimensional large-sized datasets obtained via modern flow cytometry into more compact representations that are well-suited for further automated or manual analysis. Key attributes of the method include the following: (a) the analysis is conducted in the multidimensional space retaining the semantics of the data, (b) an iterative weighted sampling procedure is utilized to maintain modest computational complexity and to retain discrimination of extremely small subpopulations (hundreds of cells from datasets containing tens of millions), and (c) a splitting and merging procedure is incorporated in the algorithm to preserve distinguishability between biologically distinct populations, while still providing a significant compaction relative to the original data. This article presents a detailed algorithmic description of SWIFT, outlining the application-driven motivations for the different design choices, a discussion of computational complexity of the different steps, and results obtained with SWIFT for synthetic data and relatively simple experimental data that allow validation of the desirable attributes. A companion paper (Part 2) highlights the use of SWIFT, in combination with additional computational tools, for more challenging biological problems.

SWIFT-scalable clustering for automated identification of rare cell populations in large, high-dimensional flow cytometry datasets, part 2: biological evaluation.

A multistage clustering and data processing method, SWIFT (detailed in a companion manuscript), has been developed to detect rare subpopulations in large, high-dimensional flow cytometry datasets. An iterative sampling procedure initially fits the data to multidimensional Gaussian distributions, then splitting and merging stages use a criterion of unimodality to optimize the detection of rare subpopulations, to converge on a consistent cluster number, and to describe non-Gaussian distributions. Probabilistic assignment of cells to clusters, visualization, and manipulation of clusters by their cluster medians, facilitate application of expert knowledge using standard flow cytometry programs. The dual problems of rigorously comparing similar complex samples, and enumerating absent or very rare cell subpopulations in negative controls, were solved by assigning cells in multiple samples to a cluster template derived from a single or combined sample. Comparison of antigen-stimulated and control human peripheral blood cell samples demonstrated that SWIFT could identify biologically significant subpopulations, such as rare cytokine-producing influenza-specific T cells. A sensitivity of better than one part per million was attained in very large samples. Results were highly consistent on biological replicates, yet the analysis was sensitive enough to show that multiple samples from the same subject were more similar than samples from different subjects. A companion manuscript (Part 1) details the algorithmic development of SWIFT.

Opportunities and challenges for T cell-based influenza vaccines.

Vaccination remains our main defence against influenza, which causes substantial annual mortality and poses a serious pandemic threat. Influenza virus evades immunity by rapidly changing its surface antigens but, even when the vaccine is well matched to the current circulating virus strains, influenza vaccines are not as effective as many other vaccines. Influenza vaccine development has traditionally focused on the induction of protective antibodies, but there is mounting evidence that T cell responses are also protective against influenza. Thus, future vaccines designed to promote both broad T cell effector functions and antibodies may provide enhanced protection. As we discuss, such vaccines present several challenges that require new strategic and economic considerations. Vaccine-induced T cells relevant to protection may reside in the lungs or lymphoid tissues, requiring more invasive assays to assess the immunogenicity of vaccine candidates. T cell functions may contain and resolve infection rather than completely prevent infection and early illness, requiring vaccine effectiveness to be assessed based on the prevention of severe disease and death rather than symptomatic infection. It can be complex and costly to measure T cell responses and infrequent clinical outcomes, and thus innovations in clinical trial design are needed for economic reasons. Nevertheless, the goal of more effective influenza vaccines justifies renewed and intensive efforts.

SWIFT clustering analysis of intracellular cytokine staining flow cytometry data of the HVTN 105 vaccine trial reveals high frequencies of HIV-specific CD4+ T cell responses and associations with humoral responses.

Introduction: The HVTN 105 vaccine clinical trial tested four combinations of two immunogens - the DNA vaccine DNA-HIV-PT123, and the protein vaccine AIDSVAX B/E. All combinations induced substantial antibody and CD4+ T cell responses in many participants. We have now re-examined the intracellular cytokine staining flow cytometry data using the high-resolution SWIFT clustering algorithm, which is very effective for enumerating rare populations such as antigen-responsive T cells, and also determined correlations between the antibody and T cell responses. Methods: Flow cytometry samples across all the analysis batches were registered using the swiftReg registration tool, which reduces batch variation without compromising biological variation. Registered data were clustered using the SWIFT algorithm, and cluster template competition was used to identify clusters of antigen-responsive T cells and to separate these from constitutive cytokine producing cell clusters. Results: Registration strongly reduced batch variation among batches analyzed across several months. This in-depth clustering analysis identified a greater proportion of responders than the original analysis. A subset of antigen-responsive clusters producing IL-21 was identified. The cytokine patterns in each vaccine group were related to the type of vaccine - protein antigens tended to induce more cells producing IL-2 but not IFN-γ, whereas DNA vaccines tended to induce more IL-2+ IFN-γ+ CD4 T cells. Several significant correlations were identified between specific antibody responses and antigen-responsive T cell clusters. The best correlations were not necessarily observed with the strongest antibody or T cell responses. Conclusion: In the complex HVTN105 dataset, alternative analysis methods increased sensitivity of the detection of antigen-specific T cells; increased the number of identified vaccine responders; identified a small IL-21-producing T cell population; and demonstrated significant correlations between specific T cell populations and serum antibody responses. Multiple analysis strategies may be valuable for extracting the most information from large, complex studies.

Quantifying the early immune response and adaptive immune response kinetics in mice infected with influenza A virus.

Seasonal and pandemic influenza A virus (IAV) continues to be a public health threat. However, we lack a detailed and quantitative understanding of the immune response kinetics to IAV infection and which biological parameters most strongly influence infection outcomes. To address these issues, we use modeling approaches combined with experimental data to quantitatively investigate the innate and adaptive immune responses to primary IAV infection. Mathematical models were developed to describe the dynamic interactions between target (epithelial) cells, influenza virus, cytotoxic T lymphocytes (CTLs), and virus-specific IgG and IgM. IAV and immune kinetic parameters were estimated by fitting models to a large data set obtained from primary H3N2 IAV infection of 340 mice. Prior to a detectable virus-specific immune response (before day 5), the estimated half-life of infected epithelial cells is approximately 1.2 days, and the half-life of free infectious IAV is approximately 4 h. During the adaptive immune response (after day 5), the average half-life of infected epithelial cells is approximately 0.5 days, and the average half-life of free infectious virus is approximately 1.8 min. During the adaptive phase, model fitting confirms that CD8(+) CTLs are crucial for limiting infected cells, while virus-specific IgM regulates free IAV levels. This may imply that CD4 T cells and class-switched IgG antibodies are more relevant for generating IAV-specific memory and preventing future infection via a more rapid secondary immune response. Also, simulation studies were performed to understand the relative contributions of biological parameters to IAV clearance. This study provides a basis to better understand and predict influenza virus immunity.

Rituximab immunotherapy results in the induction of a lymphoma idiotype-specific T-cell response in patients with follicular lymphoma: support for a "vaccinal effect" of rituximab.

The incorporation of rituximab, a chimeric anti-CD20 monoclonal antibody, into the therapeutic armamentarium for patients with follicular lymphoma (FL) has significantly improved treatment outcome for such patients. Despite the almost universal application of this therapy, however, its exact mechanism of action has not been completely defined. One proposed mechanism is that of a "vaccinal" effect, whereby FL cell kill by rituximab results in the elicitation of an FL-specific T-cell response. The demonstration that rituximab can even elicit such a response in patients has, to our knowledge, never been shown. We analyzed the response against the immunoglobulin expressed by the FL before and after rituximab monotherapy in 5 FL patients and found an increase in FL idiotype-specific T cells after rituximab in 4 of 5 patients. Our data thus provide "proof of principle" for the ability of passive immunotherapy with rituximab to elicit an active FL-specific cellular response.

Human follicular lymphoma CD39+-infiltrating T cells contribute to adenosine-mediated T cell hyporesponsiveness.

Our previous work has demonstrated that human follicular lymphoma (FL) infiltrating T cells are anergic, in part due to suppression by regulatory T cells. In this study, we identify pericellular adenosine, interacting with T cell-associated G protein-coupled A(2A/B) adenosine receptors (AR), as contributing to FL T cell hyporesponsiveness. In a subset of FL patient samples, treatment of lymph node mononuclear cells (LNMC) with specific A(2A/B) AR antagonists results in an increase in IFN-gamma or IL-2 secretion upon anti-CD3/CD28 Ab stimulation, as compared with that seen without inhibitors. In contrast, treatment with an A(1) AR antagonist had no effect on cytokine secretion. As the rate limiting step for adenosine generation from pericellular ATP is the ecto-ATPase CD39, we next show that inhibition of CD39 activity using the inhibitor ARL 67156 partially overcomes T cell hyporesponsiveness in a subset of patient samples. Phenotypic characterization of LNMC demonstrates populations of CD39-expressing CD4(+) and CD8(+) T cells, which are overrepresented in FL as compared with that seen in normal or reactive nodes, or normal peripheral blood. Thirty percent of the FL CD4(+)CD39(+) T cells coexpress CD25(high) and FOXP3 (consistent with regulatory T cells). Finally, FL or normal LNMC hydrolyze ATP in vitro, in a dose- and time-dependent fashion, with the rate of ATP consumption being associated with the degree of CD39(+) T cell infiltration. Together, these results support the finding that the ATP-ectonucleotidase-adenosine system mediates T cell anergy in a human tumor. In addition, these studies suggest that the A(2A/B) AR as well as CD39 are novel pharmacological targets for augmenting cancer immunotherapy.

Modulation of single-cell IgG secretion frequency and rates in human memory B cells by CpG DNA, CD40L, IL-21, and cell division.

During the recall response by CD27(+) IgG class-switched human memory B cells, total IgG secreted is a function of the following: 1) the number of IgG-secreting cells (IgG-SC), and 2) the secretion rate of each cell. In this study, we report the quantitative ELISPOT method for simultaneous estimation of single-cell IgG secretion rates and secreting cell frequencies in human B cell populations. We found that CD27(+) IgM(-) memory B cells activated with CpG and cytokines had considerable heterogeneity in the IgG secretion rates, with two major secretion rate subpopulations. BCR cross-linking reduced the frequency of cells with high per-cell IgG secretion rates, with a parallel decrease in CD27(high) B cell blasts. Increased cell death may account for the BCR-stimulated reduction in high-rate IgG-SC CD27(high) B cell blasts. In contrast, the addition of IL-21 to CD40L plus IL-4-activated human memory B cells induced a high-rate IgG-SC population in B cells with otherwise low per-cell IgG secretion rates. The profiles of human B cell IgG secretion rates followed the same biphasic distribution and range irrespective of division class. This, along with the presence of non-IgG-producing, dividing B cells in CpG plus cytokine-activated B memory B cell populations, is suggestive of an on/off switch regulating IgG secretion. Finally, these data support a mixture model of IgG secretion in which IgG secreted over time is modulated by the frequency of IgG-SC and the distribution of their IgG secretion rates.

Measuring the frequency of mouse and human cytotoxic T cells by the Lysispot assay: independent regulation of cytokine secretion and short-term killing.

Antigen-specific T cells demonstrate several potent effector functions during immune responses. Direct killing of infected cells is crucial for clearing viruses and other intracellular pathogens, but it has been difficult to measure the frequency of cytolytic cells. We have now developed a single-cell assay to measure the number of cytotoxic cells in a population, using a herpes simplex virus amplicon vector to express Escherichia coli beta-galactosidase in mouse or human target cells, and an Elispot to detect release of beta-galactosidase from killed target cells. This antigen-specific, perforin-dependent Lysispot assay has been combined with a cytokine Elispot in a two-color assay to confirm that cytotoxicity and interferon-gamma secretion are regulated independently. The simultaneous enumeration of cytokine-secreting and cytotoxic cells should be invaluable for ex vivo analysis of immune responses during infection and autoimmunity.