Box plots of all manual and FLOCK gated populations between HIV-infected and -uninfected subjects.

<p>Box plot representation of summary results of the two groups generated by the three methods used to investigate the same HIV immunopathogenesis dataset, manual data analysis (left), FLOCK data analysis using the single HIV reference (middle) and FLOCK data analysis using the artificial reference (right). The data is presented in a box and whisker plot where the horizontal line in the box is the median population occupation, the edges of the boxes are the 25th and 75th percentiles of the population occupation and the ‘whiskers’ represent the 10th and 90th percentiles of the population occupation, and the dots indicate outliers. The purple and grey boxes represent the HIV+ and healthy control group, respectively, where the green dots indicate outliers that are AIDS patients. Results of the multiple Mann-Whitney tests followed by Bonferroni adjustments between the HIV and healthy control group for each gated population is shown using P value significance codes found directly above: 0 *** 0.001 ** 0.01 * 0.05.</p

Summary of the HIV-infected cohort.

<p>Median (IQR) shown for all parameters; n: numbers.</p><p>Summary of the HIV-infected cohort.</p

Flow cytometry gating and FLOCK populations.

<p>The manual gating strategy used to gate for the CD4+ T cells is shown in the top panel (A). The CD4+ T cell events were uploaded to immPort (immport.niaid.nih.gov) for FLOCK analysis. The unique populations identified by FLOCK using the single HIV reference (bottom left) (B) and artificial reference (top right) (C) is shown. The artificial reference is made-up of 2% of the CD4+ T cells from each subject in the HIV cohort, where as the single HIV reference is made-up of the CD4+ T cells from a single individual from the HIV cohort that appeared to be biologically representative of the cohort.</p

Non-parametric Spearman rank tests correlation analysis of the manual, sFLOCK and aFLOCK immunopathological populations to the clinical parameters.

<p>^<b>a</b> Bonferroni corrections have been performed.</p><p>Non-parametric Spearman rank tests correlation analysis of the manual, sFLOCK and aFLOCK immunopathological populations to the clinical parameters.</p

Clustering of HIV-infected and -uninfected subjects with manual and FLOCK gating principles.

<p>The top panel shows the heat map representation of the matrices containing the cell population frequencies of the manual gating results (A), the FLOCK results using one HIV infected subject that identified biologically relevant cell populations (B) and the FLOCK results using an artificial of the HIV-infected subjects as a reference (C). The bottom panel shows the principle component analysis (PCA) was performed on the matrices illustrated in A-C to investigate whether there were difference between the control, HIV-infected and AIDS subjects. The results of the PCA performed on the manually determined population frequencies is shown in (D), the results of the K-S test that compared the HIV infected individuals to the healthy controls for PC1 (P value = 0.0009, D value = 0.495) and PC2 (P value = 0.3, D value = 0.236) are shown below the biplot. The FLOCK data using one HIV infected subject that identified biologically relevant cell populations is shown in (E), the results of the K-S test that compared the HIV infected individuals to the healthy controls for PC1 (P value = 0.04, D = 0.353) and PC2 (P value = 0.02, D value = 0.378) are shown below the biplot. The FLOCK data using an artificial of the HIV-infected subjects as a reference is shown in (F), the results of the K-S test that compared the HIV infected individuals to the healthy controls for PC1 (P value = 0.02, D value = 0.384) and PC2 (P value = 0.0008, D value = 0.497) are shown below the biplot. A detailed overview of the FLOCK populations in (B) and (C) can be seen in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0137635#pone.0137635.s001" target="_blank">S1 Table</a>.</p

Expression of effector molecules by Gag-specific CD4+ T cells from HIV elite controller LNs and blood.

<p><b>(A)</b> Flow cytometry plots (HIV elite controllers) illustrating the distribution of LN (red) and PB (blue) IFNγ+ Gag-specific CD4+ T cell responses between the T-bet^dimEomes- and T-bet^hiEomes+ compartment. Corresponding scatterplots showing the frequencies of T-bet^dimEomes- (left) and T-bet^hiEomes+ (right) cells of Gag-specific CD4+ T cells between LN and PB for HIV elite controllers. Flow plots (HIV elite controllers) of IFNγ+ Gag-specific CD4+ T cell response from LN and PB in relation to <b>(B)</b> perforin and <b>(C)</b> MIP-1α expression. Graphs represent the frequency of <b>(B)</b> perforin+ and <b>(C)</b> MIP-1α+ Gag-specific CD4+ T cells between LN and PB. <b>(D)</b> Flow plots and graphs demonstrating the expression pattern of perforin+Granzyme B+ memory CD4+ T cells in acute/early seroconverters (left graph). Right graphs show the frequency perforin+Granzyme B+ or T-bet expression out of total memory Ki-67+ CD4+ T cells. Median and IQR are shown for all scatter plots. Mann-Whitney tests were performed to compare differences between groups; *<i>P</i> < 0.05, **<i>P</i> < 0.01 and ***<i>P</i> < 0.001. All data-points are derived from the North-American cohort.</p

Functional characteristics of polyclonal and virus-specific effector CD4+ T cell responses in HIV-infected LNs and blood.

<p><b>(A)</b> Flow cytometry plots (HIV ART+ subject) and plots for matched HIV-Gag or–Env-specific CD4+ T cell responses in HIV-infected LN and PB. <b>(B)</b> Flow cytometry plots (HIV-infected CP) showing the negative control (NC) and Gag-specific response of LN CD4+ T cell response. The high abundance of CD107a (red) that is not co-expressed with IFNγ or TNF is illustrated in this example. SPICE analysis of functional combination between LN (red) and PB (red-gray) Gag-specific CD4+ T cell responses for HIV-infected CPs and ART+ subjects. <b>(C)</b> Flow plots (HIV-infected CP) of Gag-specific CD4+ T cell response (red) from LN and PB in relation to CD27 and perforin expression. Graphs represent the frequency of <b>(C)</b> perforin+ and <b>(D)</b> T-bet^hi cells between LN and PB Gag-specific CD4+ T cells. <b>(E)</b> Flow plots (HIV-infected CP) of MIP-1α <i>versus</i> IFNγ and TNF production for LN and PB SEB stimulated CD4+ T cells. Corresponding plots showing the frequency of MIP-1α+ SEB stimulated CD4+ T cells (top) and MIP-1α+ of IFNγ/TNF/CD107a/MIP-1α+ SEB stimulated CD4+ T cells (bottom). <b>(F)</b> Flow plots (HIV-infected CP) of MIP-1α <i>versus</i> IFNγ and TNF production for LN and PB CMV-specific CD4+ T cells and corresponding graphs showing the frequency of MIP-1α+ CMV-specific CD4+ T cells (top) and MIP-1α+ of IFNγ/TNF/CD107a/MIP-1α+ CMV-specific CD4+ T cells (bottom). Median and IQR are shown for all bar plots. Permutation test was performed between the pie charts. Wilcoxon matched-pairs single rank tests were performed to compare differences between two matched groups; *<i>P</i> < 0.05, **<i>P</i> < 0.01 and ***<i>P</i> < 0.001. All data-points are derived from the Mexico cohort.</p

Phenotypic, cytolytic and transcriptional differences between LN and blood CD4+ T cells in HIV-infected and -uninfected individuals.

<p><b>(A)</b> Flow cytometry plots (HIV-infected CP) and scatter plots for naïve and memory subsets of LN and peripheral blood (PB) CD4+ T cells in HIV-infected and -uninfected subjects. <b>(B)</b> Flow cytometry plots (HIV-infected CP) showing the lack of T-bet^hiEomes+ CD4+ T cells in LNs. Corresponding scatter plots demonstrating the frequency of T-bet^hi cells of memory (non-naïve) CD4+ T cells (top) and frequency of Eomes+ cells of T-bet^hi CD4+ T cells (bottom) for matched LN and PB. <b>(C)</b> Flow plots (HIV-infected CP) showing the lack of Granzyme B+perforin+ CD4+ T cells in LNs and scatter plots with the frequency of LN and PB perforin+ cells of memory CD4+ T cells (top). Frequencies of Granzyme B+ cells of perforin+ CD4+ T cells (bottom) for matched LN and PB. <b>(D)</b> Flow plots (HIV-infected CP) and scatter plots showing the distribution of CD27+ cells within the Granzyme B+ CD4+ T cell compartment for matched LN and PB. <b>(E)</b> The distribution of different populations in the tSNE space is based on 30.000 live CD4+ T cells that were merged from LN and PB from a HIV-infected CP with detectable levels of cytolytic cells in the PB and LN Tfh cells. The tSNE clustering is based on CD45RO, CD27, CCR7, T-bet, Eomes, Granzyme B, perforin, CXCR5 and PD-1 expression on gated bulk CD4+ T cells. The naïve cluster (green) is based on high CCR7 and low CD45RO intensity; the Tfh cluster (red) on high intensity of PD-1 and CXCR5; and the effector cluster (orange) on high T-bet and perforin expression intensity. After separating out the merged LN and PB single CD4+ T cell data, a lack of Tfh cells was apparent in PB and effector CD4+ T cells in the LN (lower right tSNE plots). Median and IQR are shown for all scatter plots. Mann-Whitney tests were performed to compare differences between two unmatched groups, and Wilcoxon matched-pairs single rank tests between matched samples; *<i>P</i> < 0.05, **<i>P</i> < 0.01 and ***<i>P</i> < 0.001. All data-points are derived from the North-American and Mexico cohort.</p

Cytolytic CD4+ T cells express high levels of T-bet and Eomes in blood.

<p><b>(A)</b> Representative flow cytometry plots of Granzyme B and perforin expression in CD4+ T cells for an HIV-infected and–uninfected subject. The distribution of Granzyme B+perforin+ (red) and Granzyme B-perforin- (blue) CD4+ T cells are shown for T-bet and Eomes expression. <b>(B)</b> Frequency of perforin+ CD4+ T cells within the T-bet^hiEomes+ and T-bet^dim/- population (left) and T-bet^hiEomes+ within the perforin+ or perforin- population for HIV-infected and–uninfected subjects. <b>(C)</b> Correlation between the frequency of perforin+ and T-bet^hi CD4+ T cells. <b>(D)</b> Imagestream analysis on T-bet^hi and T-bet^dim CD4+ T cells. Overlays of fluorescent channels for DAPI (nuclear) and T-bet, showing where in the cells T-bet are localized. The frequency of nuclear, nuclear/cytoplasmic and cytoplasmic localization for T-bet^hi and T-bet^dim CD4+ T cells are shown in the before-after graphs. <b>(E)</b> tSNE plots based on 30,000 live CD4+ T cells that were merged from three HIV-uninfected subjects with detectable cytolytic CD4+ T cells. The tSNE clustering is based on CD45RO, CD27, CCR7, T-bet, Eomes, Granzyme A, Granzyme B and perforin expression intensity. The red gate indicates the identified “effector” cluster with overlapped expression of cytolytic markers as well as T-bet and Eomes. <b>(F)</b> Flow plots of MIP-1α production using media (NC) and aCD3-CD28 stimulations for T-bet^hi and Eomes+ CD4+ T cells, as well as correlation between the frequency of T-bet^hiEomes+ and MIP-1α+ CD4+ T cells following aCD3-CD28 stimulations. Median and IQR are shown for all scatter plots and Mann-Whitney tests were performed to compare differences between groups; ***<i>P</i> < 0.001. A non-parametric Spearman test was used for the correlations analysis. All data are derived from the North-American cohort.</p

Functional and transcriptional differences between degranulating cells in LN and blood.

<p><b>(A)</b> Flow cytometry plots (HIV-uninfected subject) of matched LN and PB CD107a+ SEB stimulated CD4+ T cell responses. Graphs are showing the frequencies of LN (top) and PB (bottom) CD107a+ SEB stimulated CD4+ T cell responses for CXCR5-, CXCR5+ and CXCR5^hi cells. <b>(B)</b> Flow plots (HIV-infected CP) illustrating the expression of CD107a+ (red) Gag-specific CD4+ T cells in relation to CXCR5 between LN (top) and PB (bottom). <b>(C)</b> Corresponding plots from the same subject showing the expression of CD107a+ (red) Gag-specific CD4+ T cells in relation to CXCR5 and perforin for LN (top) and PB (bottom). Graphs represent the frequency of CD107a+ cells within the CXCR5+, CXCR5^hi and perforin+ compartment for LN (top) and PB (bottom) Gag-specific CD4+ T cells. <b>(D)</b> Biomark analysis illustrating the tSNE distribution of single SEB stimulated CD107+ CD4+ T cells from LN (black) and PB (gray). Individual graphs represent the relative Log2 expression of different markers being significantly different (P<0.05) between blood and LN CD107+ cells. Non-parametric Kruskal Wallis test with Dunn’s multiple comparison test was performed to determine significant differences between groups; *<i>P</i> < 0.05, **<i>P</i> < 0.01 and ***<i>P</i> < 0.001. All data-points are derived from the North-American and Mexico cohort.</p