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.

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.

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.

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.

Automated analysis of two- and three-color fluorescent Elispot (Fluorospot) assays for cytokine secretion.

The Elispot effectively measures the frequencies of cells secreting particular molecules, especially low-frequency cells such as antigen-specific T cells. The Fluorospot assay adapted this analysis to two products per cell, and this has now been extended to three-color measurement of both mouse and human cytokine-secreting cells. Due to the increased data complexity, and particularly the need to define single-, double- and triple-producing cells, it is critical to objectively quantify spot number, size, intensity, and coincidence with other spots. An automated counting program, Exploraspot, was therefore developed to detect and quantify Fluorospots in automated fluorescence microscope images. Morphological parameters, including size, intensity, location, circularity and others are calculated for each spot, exported in FCS format, and further analyzed by gating and graphical display in popular flow cytometry analysis programs. The utility of Exploraspot is demonstrated by identification of single-, double- and triple-secreting T cells; tolerance of variable background fluorescence; and estimation of the numbers of genuine versus random multiple events.

T regulatory and primed uncommitted CD4 T cells express CD73, which suppresses effector CD4 T cells by converting 5'-adenosine monophosphate to adenosine.

CD73 (5'-ectonucleotidase) is expressed by two distinct mouse CD4 T cell populations: CD25+ (FoxP3+) T regulatory (Treg) cells that suppress T cell proliferation but do not secrete IL-2, and CD25- uncommitted primed precursor Th (Thpp) cells that secrete IL-2 but do not suppress in standard Treg suppressor assays. CD73 on both Treg and Thpp cells converted extracellular 5'-AMP to adenosine. Adenosine suppressed proliferation and cytokine secretion of Th1 and Th2 effector cells, even when target cells were activated by anti-CD3 and anti-CD28. This represents an additional suppressive mechanism of Treg cells and a previously unrecognized suppressive activity of Thpp cells. Infiltration of either Treg or Thpp cells at inflammatory sites could potentially convert 5'-AMP generated by neutrophils or dying cells into the anti-inflammatory mediator adenosine, thus dampening excessive immune reactions.