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.

SwiftReg cluster registration automatically reduces flow cytometry data variability including batch effects.

Biological differences of interest in large, high-dimensional flow cytometry datasets are often obscured by undesired variations caused by differences in cytometers, reagents, or operators. Each variation type requires a different correction strategy, and their unknown contributions to overall variability hinder automated correction. We now describe swiftReg, an automated method that reduces undesired sources of variability between samples and particularly between batches. A high-resolution cluster map representing the multidimensional data is generated using the SWIFT algorithm, and shifts in cluster positions between samples are measured. Subpopulations are aligned between samples by displacing cell parameter values according to registration vectors derived from independent or locally-averaged cluster shifts. Batch variation is addressed by registering batch control or consensus samples, and applying the resulting shifts to individual samples. swiftReg selectively reduces batch variation, enhancing detection of biological differences. swiftReg outputs registered datasets as standard .FCS files to facilitate further analysis by other tools.

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.

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.

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.

Microbubble array diffusion assay for the detection of cell secreted factors.

The therapeutic potential of monoclonal antibodies (mAbs) makes them an ideal tool in both clinical and research applications due to their ability to recognize and bind specific epitopes with high affinity and selectivity. While mAbs offer significant therapeutic potential, their utility is overshadowed by the cost associated with their production, which often relies on the ability to identify minor antigen-specific cells out of a heterogeneous population. To address concerns with suboptimal methods for screening cells, we have developed a cell-sorting array composed of nanoliter spherical cell culture compartments termed microbubble (MB) wells. We demonstrate a proof-of-concept system for the detection of cell secreted factors from both immortalized cell lines and primary B cell samples. Exploiting the unique ability of the MB well architecture to accumulate cell secreted factors as well as affinity capture coatings, we demonstrate on-chip detection and recovery of antibody-secreting cells for sequencing of immunoglobin genes. Furthermore, rapid image capture and analysis capabilities were developed for the processing of large MB arrays, thus facilitating the ability to conduct high-throughput screening of heterogeneous cell samples faster and more efficiently than ever before. The proof-of-concept assays presented herein lay the groundwork for the progression of MB well arrays as an advanced on-chip cell sorting technology.

Modeling the dynamics and migratory pathways of virus-specific antibody-secreting cell populations in primary influenza infection.

The B cell response to influenza infection of the respiratory tract contributes to viral clearance and establishes profound resistance to reinfection by related viruses. Numerous studies have measured virus-specific antibody-secreting cell (ASC) frequencies in different anatomical compartments after influenza infection and provided a general picture of the kinetics of ASC formation and dispersion. However, the dynamics of ASC populations are difficult to determine experimentally and have received little attention. Here, we applied mathematical modeling to investigate the dynamics of ASC growth, death, and migration over the 2-week period following primary influenza infection in mice. Experimental data for model fitting came from high frequency measurements of virus-specific IgM, IgG, and IgA ASCs in the mediastinal lymph node (MLN), spleen, and lung. Model construction was based on a set of assumptions about ASC gain and loss from the sampled sites, and also on the directionality of ASC trafficking pathways. Most notably, modeling results suggest that differences in ASC fate and trafficking patterns reflect the site of formation and the expressed antibody class. Essentially all early IgA ASCs in the MLN migrated to spleen or lung, whereas cell death was likely the major reason for IgM and IgG ASC loss from the MLN. In contrast, the spleen contributed most of the IgM and IgG ASCs that migrated to the lung, but essentially none of the IgA ASCs. This finding points to a critical role for regional lymph nodes such as the MLN in the rapid generation of IgA ASCs that seed the lung. Results for the MLN also suggest that ASC death is a significant early feature of the B cell response. Overall, our analysis is consistent with accepted concepts in many regards, but it also indicates novel features of the B cell response to influenza that warrant further investigation.

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.

Cytokine diversity in the Th1-dominated human anti-influenza response caused by variable cytokine expression by Th1 cells, and a minor population of uncommitted IL-2+IFNγ- Thpp cells.

Within overall Th1-like human memory T cell responses, individual T cells may express only some of the characteristic Th1 cytokines when reactivated. In the Th1-oriented memory response to influenza, we have tested the contributions of two potential mechanisms for this diversity: variable expression of cytokines by a uniform population during activation, or different stable subsets that consistently expressed subsets of the Th1 cytokine pattern. To test for short-term variability, in vitro-stimulated influenza-specific human memory CD4+ T cells were sorted according to IL-2 and IFNγ expression, cultured briefly in vitro, and cytokine patterns measured after restimulation. Cells that were initially IFNγ+ and either IL-2+ or IL-2- converged rapidly, containing similar proportions of IL-2-IFNγ+ and IL-2+IFNγ+ cells after culture and restimulation. Both phenotypes expressed Tbet, and similar patterns of mRNA. Thus variability of IL-2 expression in IFNγ+ cells appeared to be regulated more by short-term variability than by stable differentiated subsets. In contrast, heterogeneous expression of IFNγ in IL-2+ influenza-specific T cells appeared to be due partly to stable T cell subsets. After sorting, culture and restimulation, influenza-specific IL-2+IFNγ- and IL-2+IFNγ+ cells maintained significantly biased ratios of IFNγ+ and IFNγ- cells. IL-2+IFNγ- cells included both Tbetlo and Tbethi cells, and showed more mRNA expression differences with either of the IFNγ+ populations. To test whether IL-2+IFNγ-Tbetlo cells were Thpp cells (primed but uncommitted memory cells, predominant in responses to protein vaccines), influenza-specific IL-2+IFNγ- and IL-2+IFNγ+ T cells were sorted and cultured in Th1- or Th2-generating conditions. Both cell types yielded IFNγ-secreting cells in Th1 conditions, but only IL-2+IFNγ- cells were able to differentiate into IL-4-producing cells. Thus expression of IL-2 in the anti-influenza response may be regulated mainly by short term variability, whereas different T cell subsets, Th1 and Thpp, may contribute to variability in IFNγ expression.

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.

CD4+ T-cell responses among adults and young children in response to Streptococcus pneumoniae and Haemophilus influenzae vaccine candidate protein antigens.

We characterized cytokine profiles of CD4(+) T-helper (h) cells in adults and young children to ascertain if responses occur to next-generation candidate vaccine antigens PspA, PcpA, PhtD, PhtE, Ply, LytB of Streptococcus pneumonia (Spn) and protein D and OMP26 of non-typeable Haemophilus influenzae (NTHi). Adults had vaccine antigen-specific Th1 and Th2 cells responsive to all antigens evaluated whereas young children had significant numbers of vaccine antigen-specific CD4(+) T cells producing IL-2, (p=0.004). Vaccine antigen-specific CD4(+) T-cell populations in adults were largely of effector (TEM) and/or central memory (TCM) phenotypes as defined by CD45RA(-)CCR7(+) or CD45RA(-)CCR7(-) respectively; however among young children antigen-specific IL-2 producing CD4(+) T cells demonstrated CD45RA(+) expression (non-memory cells). We conclude that adults have circulating memory CD4(+) T cells (CD45RA(-)) that can be stimulated by all the tested Spn and NTHi protein vaccine candidate antigens, whereas young children have a more limited response.

Increase in IFNγ(-)IL-2(+) cells in recent human CD4 T cell responses to 2009 pandemic H1N1 influenza.

Human CD4 T cell recall responses to influenza virus are strongly biased towards Type 1 cytokines, producing IFNγ, IL-2 and TNFα. We have now examined the effector phenotypes of CD4 T cells in more detail, particularly focusing on differences between recent versus long-term, multiply-boosted responses. Peptides spanning the proteome of temporally distinct influenza viruses were distributed into pools enriched for cross-reactivity to different influenza strains, and used to stimulate antigen-specific CD4 T cells representing recent or long-term memory. In the general population, peptides unique to the long-circulating influenza A/New Caledonia/20/99 (H1N1) induced Th1-like responses biased toward the expression of IFNγ(+)TNFα(+) CD4 T cells. In contrast, peptide pools enriched for non-cross-reactive peptides of the pandemic influenza A/California/04/09 (H1N1) induced more IFNγ(-)IL-2(+)TNFα(+) T cells, similar to the IFNγ(-)IL-2(+) non-polarized, primed precursor T cells (Thpp) that are a predominant response to protein vaccination. These results were confirmed in a second study that compared samples taken before the 2009 pandemic to samples taken one month after PCR-confirmed A/California/04/09 infection. There were striking increases in influenza-specific TNFα(+), IFNγ(+), and IL-2(+) cells in the post-infection samples. Importantly, peptides enriched for non-cross-reactive A/California/04/09 specificities induced a higher proportion of Thpp-like IFNγ(-)IL-2(+)TNFα(+) CD4 T cells than peptide pools cross-reactive with previous influenza strains, which induced more Th1 (IFNγ(+)TNFα(+)) responses. These IFNγ(-)IL-2(+)TNFα(+) CD4 T cells may be an important target population for vaccination regimens, as these cells are induced upon infection, may have high proliferative potential, and may play a role in providing future effector cells during subsequent infections.

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.

The acute environment, rather than T cell subset pre-commitment, regulates expression of the human T cell cytokine amphiregulin.

Cytokine expression patterns of T cells can be regulated by pre-commitment to stable effector phenotypes, further modification of moderately stable phenotypes, and quantitative changes in cytokine production in response to acute signals. We showed previously that the epidermal growth factor family member Amphiregulin is expressed by T cell receptor-activated mouse CD4 T cells, particularly Th2 cells, and helps eliminate helminth infection. Here we report a detailed analysis of the regulation of Amphiregulin expression by human T cell subsets. Signaling through the T cell receptor induced Amphiregulin expression by most or all T cell subsets in human peripheral blood, including naive and memory CD4 and CD8 T cells, Th1 and Th2 in vitro T cell lines, and subsets of memory CD4 T cells expressing several different chemokine receptors and cytokines. In these different T cell types, Amphiregulin synthesis was inhibited by an antagonist of protein kinase A, a downstream component of the cAMP signaling pathway, and enhanced by ligands that increased cAMP or directly activated protein kinase A. Prostaglandin E2 and adenosine, natural ligands that stimulate adenylyl cyclase activity, also enhanced Amphiregulin synthesis while reducing synthesis of most other cytokines. Thus, in contrast to mouse T cells, Amphiregulin synthesis by human T cells is regulated more by acute signals than pre-commitment of T cells to a particular cytokine pattern. This may be appropriate for a cytokine more involved in repair than attack functions during most inflammatory responses.

Ki-67 expression reveals strong, transient influenza specific CD4 T cell responses after adult vaccination.

Although previous studies have found minimal changes in CD4 T cell responses after vaccination of adults with trivalent inactivated influenza vaccine, daily sampling and monitoring of the proliferation marker Ki-67 have now been used to reveal that a substantial fraction of influenza-specific CD4 T cells respond to vaccination. At 4-6 days after vaccination, there is a sharp rise in the numbers of Ki-67-expressing PBMC that produce IFNγ, IL-2 and/or TNFα in vitro in response to influenza vaccine or peptide. Ki-67(+) cell numbers then decline rapidly, and 10 days after vaccination, both Ki-67(+) and overall influenza-specific cell numbers are similar to pre-vaccination levels. These results provide a tool for assessing the quality and quantity of CD4 T cell responses to different influenza vaccines, and raise the possibility that the anti-influenza T cell memory response may be qualitatively altered by vaccination, even if the overall memory cell numbers do not change significantly.

Label-free, arrayed sensing of immune response to influenza antigens.

Periodic outbreaks of pandemic influenza have been a devastating cause of human mortality over the past century. More recently, an avian influenza strain, designated H5N1, has been identified as having the potential to cause a zoogenic pandemic in humans, and a current outbreak of a new H1N1 influenza variant hypothesized to be of swine origin is of considerable concern. In order to facilitate surveillance and the rapid assessment and comparison of vaccination efforts, a high-throughput assay is highly desirable to supplement standard methods, which require high biosafety-level facilities. In this paper, we describe the design, production, and preliminary evaluation of an antigen array incorporating a panel of hemagglutinins as a platform for the detection and rapid quantification of influenza-specific antibodies in human serum by Arrayed Imaging Reflectometry (AIR), a label-free optical biosensor.

Human basophils express amphiregulin in response to T cell-derived IL-3.

BACKGROUND: Amphiregulin, a member of the epidermal growth factor family, is expressed by activated mouse T(H)2 cells. Amphiregulin produced by mouse hematopoietic cells contributes to the elimination of a nematode infection by a type 2 effector response. OBJECTIVE: To identify the human peripheral blood cell population expressing amphiregulin. METHODS: Amphiregulin-expressing cells were identified by flow cytometry of cell surface markers and histologic staining. Histamine and amphiregulin in supernatants were measured by enzyme immunoassay. Quantitative real-time PCR was used to measure mRNA expression. RESULTS: Stimulation of human PBMCs by anti-CD3 + anti-CD28 antibodies induced expression of amphiregulin mRNA and protein by a non-T-cell population. The amphiregulin-producing cells were basophils, as judged by morphology and expression of CD203c and CD123 (IL-3 receptor α chain). Activated mouse basophils also produced amphiregulin. Amphiregulin expression by basophils in response to anti-TCR stimulation required IL-3 produced by T cells, and IL-3 alone induced high levels of amphiregulin expression by purified basophils. Amphiregulin was expressed at much higher levels when human basophils were stimulated by IL-3 than by IgE cross-linking, whereas the opposite was true for IL-4 expression and histamine release. Heparin-binding epidermal growth factor-like growth factor was also expressed by IL-3-stimulated human basophils. PBMCs from human subjects with asthma contained significantly higher numbers of basophils able to produce amphiregulin compared with controls with or without allergy. CONCLUSION: IL-3 can induce basophils to express high levels of amphiregulin, which may contribute to tissue remodeling during type 2 immune responses such as asthma.