Single-cell transcriptome analysis of low and high permissive TCR-activated CD4⁺ T cells.

<p><b>(A)</b> Susceptibility to HIV-GFP infection of cells isolated from a donor with high permissive cells (#42) and from a donor with low permissive cells (#123). FACS plot analysis shows forward scatter plot (FSC) on the y axis and GFP expression on the x axis. The region delineates GFP⁺ infected cells and the proportion of GFP⁺ cells is indicated. <b>(B)</b> Principal Component Analysis (PCA) of single-cells (dots) from the high permissive (black) and low permissive (red) donors as comparison to bulk populations (triangles). Bulk sequencing was performed from cells collected at resting (pink), and after TCR activation (8h, 24h and 72h post-activation, purple). PCA was performed on gene expression levels expressed as the log10 of the number of library size-normalized reads per kilobase of exonic sequence (<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1006678#sec010" target="_blank">Methods</a>).</p

HIV permissiveness of cell populations expressing high and low levels of the selected markers.

<p><b>(A)</b> Schematic representation of experimental procedure: CD4⁺ T cells were TCR-stimulated for 48h and FACS-sorted for each single marker in two populations, expressing high or low levels of the markers. These populations were then transduced with HIV-GFP and 48h post-transduction, GFP expression was evaluated by FACS. <b>(B)</b> HIV permissiveness in FACS sorted populations. Values correspond to GFP (%) fold increase in the high marker population (in red) and in the low marker population (in grey), as compared to unsorted population (dash line; % of unsorted cell population between 30–50%). Error bars indicate SEM and data shown is from 4 independent experiments with 4 different donors. ** corresponds to <i>P</i><0.01, *** corresponds to <i>P</i><0.001, from a paired t-test.</p

HIV permissiveness of CD25^highCD4⁺ T cells expressing an additional candidate biomarker.

<p><b>(A)</b> Schematic representation of experimental procedure: CD4⁺ T cells were TCR-stimulated for 48h and FACS sorted first for CD25 (high and low) and then the CD25^high population was further sorted for the expression of each of the other candidate markers (high expression <i>versus</i> low expression). These populations were then transduced with HIV-GFP and 48h post-transduction, GFP expression was evaluated by FACS. Gating for sorting of high and low expressing populations was always defined from the initial unsorted cell population. <b>(B)</b> HIV permissiveness in FACS sorted populations. The graph shows the permissiveness of the sorted population (fold increase from unsorted population). Sorted populations were CD25^low (light blue), CD25^high (dark blue), CD25^highMRK^low (light red) and CD25^highMRK^high (dark red). Values correspond to GFP (%) fold increase as compared to unsorted population (grey bar; unsorted population between 13–32% of GFP positive cells). Error bars indicate SEM and data shown is from 4 independent experiments with 4 different donors. * corresponds to <i>P</i><0.05, ** corresponds to <i>P</i><0.01, *** corresponds to <i>P</i><0.001, from a paired t-test.</p

Transcriptome analysis of sorted subpopulations of bulk CD4⁺ T cells.

<p><b>(A)</b> Principal Component Analysis of cell subpopulations of bulk CD4⁺ T cells at 48h after activation FACS sorted for: CD25^low, CD25^highMRK3^low (CD25^highCD298^lowCD63^lowCD317^low), CD25^high and CD25^highMRK3^high (CD25^highCD298^highCD63^highCD317^high). The unsorted reference bulks are also included. PC1 placed samples according to their permissiveness to HIV infection from left (low permissive) to right (high permissive). <b>(B)</b> Heatmap clustering of the 96 genes differentially expressed among sorted CD25^high and CD25^highMRK3^high (fold change higher or lower than 2 and adjusted p-value of < 0.001; DESeq2 test [<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1006678#ppat.1006678.ref032" target="_blank">32</a>]). Complete hierarchical clustering of genes and cell samples was based on Pearson correlation. Color scale indicated in the legend corresponds to z-scores of gene expression levels expressed as the log10 of the number of library size-normalized reads per kilobase of exonic sequence, ranging from green (low) to red (high) expression. Downregulated genes are enriched in the type I interferon pathway (GO:0060337) and the defense response to virus (GO.0051607; <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1006678#ppat.1006678.s020" target="_blank">S5 Table</a>), and include important effectors such as: IFIT2, IFIT3, IRF7, ISG15, ISG20, MX1, RSAD2, XAF1, IFI44L and IL23A.</p

Screening for cellular markers of HIV permissiveness and CD4⁺ T cell activation.

<p><b>(A)</b> Schematic representation of experimental procedure: CD4⁺ T cells were TCR-stimulated for 48h and transduced with HIV-GFP. After 24h, cells were stained with 332 PE-conjugated antibodies (LEGENDScreen) and analysed by FACS to evaluate the expression of each marker, as well as GFP expression (as a surrogate of successful viral infection). HIV permissiveness, i.e. GFP expression, was compared between cells expressing high levels of candidate marker (MRK⁺, red) and low levels of candidate marker (MRK^-, grey). In the top example, the proportion of GFP⁺ cells was enriched in the MRK⁺ cell population; this candidate marker was thus positively correlated with HIV permissiveness. In contrast, the example of the bottom panel showed that the proportion of GFP⁺ cells was similar in both MRK⁺ and MRK^- cell populations, suggesting a poor correlation between marker expression and HIV permissiveness. <b>(B)</b> Correlation between single-cell transcriptome analysis and cell surface protein expression screen. From single-cell RNA-seq, a correlation was performed between the mRNA expression and cell activation for each candidate marker (y-axis). In parallel, the antibody screen allowed to correlate marker expression with HIV permissiveness (x-axis). Correlation between these two analyses (adjusted R-squared: 0.20; p-value = 9.7e-16) allowed to identify markers associated with HIV permissiveness and activation. Top candidate markers are highlighted in red. <b>(C)</b> Validation of top candidates as markers of HIV permissiveness. Activated CD4⁺ T cells were transduced with HIV-GFP and after 24h, GFP and marker expression was measured by FACS. Values correspond to proportion of GFP expressing cells and are normalized to one in the unsorted population (dash line; % of unsorted cell population between 8–20%). The proportion of GFP expressing cells was then assessed in both, MRK⁺ (red) and MRK^- (grey), gated subpopulations. Error bars indicate SEM and data shown are from 4 independent experiments with 4 different donors. ** corresponds to <i>P</i><0.01; *** corresponds to <i>P</i><0.001, from a paired t-test.</p

Additional file 2: of Multimorbidity patterns in the elderly: a prospective cohort study with cluster analysis

Multimorbidity patterns in men 65–79 years across the period analysed. Patterns defined by prevalence >20% and ratio O/E > 2. (XLSX 32 kb

Additional file 1: of Multimorbidity patterns in the elderly: a prospective cohort study with cluster analysis

Multimorbidity patterns in women 65–79 years across the period analysed. Patterns defined by prevalence >20% and ratio O/E > 2. (XLSX 26 kb

Modeling of the viral life cycle.

<p>(<b>A</b>) Raw data of measured viral replication intermediates (mean [dots] with one standard error) and curves of fitted progression model (solid lines). The temporal dynamics of each step in the viral life cycle was generated individually by modeling the net effect of production, decay, initial viral input, and experimental noise of the corresponding marker intermediate (<b><a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003161#ppat.1003161.s001" target="_blank">Text S1</a></b> and <b>Figure S4</b> and <b>S5 in <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003161#ppat.1003161.s001" target="_blank">Text S1</a></b>). (<b>B</b>) Activity profile of individual steps of the viral life cycle estimated from the progression model. Each violin spans the 98% quantile of the viral step with width proportional to activity level at each given point in time. The plus symbol (‘+’) denotes the peak of the activity and the inner white violin its 95% bootstrap confidence interval. In the shaded area, expected values extrapolated beyond the last observed time point (24 h, dashed line) are shown.</p

Core gene validation.

<p>RT-qPCR was used to validate key patterns of expression using heat-inactivated virus, primary cells, and natural viral envelope. (<b>A</b>) Analysis of 14 representative genes using competent or heat-inactivated HIV-based vector. The graphs depict the 24 dynamics of expression (log₂ fold change of VSV.G pseudotyped HIV-infected over mock) of eight upregulated genes (red lines), five downregulated genes (blue), and one control (<i>RPL31</i>, black line) in SupT1 cells exposed to similar amount of viral particles, only competent HIV (top panel), 1∶10 competent HIV∶heat-inactivated HIV (middle panel), and only heat-inactivated HIV (bottom panel). (<b>B</b>) Analysis in primary CD4+ T cells isolated from two healthy blood donors. Depicted are the 24 dynamics of expression (log₂ fold change of VSV.G pseudotyped HIV-infected over mock) of the upregulated (red), downregulated (blue), and control (black) genes. (<b>C</b>) Correlation analysis of RT-qPCR for the 14 representative genes at all time points in primary cells (donor 1) infected by VSV.G or CXCR4 pseudotyped HIV. Log₂ fold change linear regression yielded <i>r²</i> = 0.22, <i>p</i><10⁻⁴.</p

Transcriptome changes upon exposure to infectious and non-infectious viral particles.

<p>Principal component analysis is used to explore the overall variance structure of the transcriptome datasets. With each point representing a whole transcriptome sample, the figure presents the transcriptome of cells that were universally infected (HIV), cells exposed to heat-inactivated virus (Heat-inactivated), cells exposed to a mixture of 1∶10 infectious/heat-inactivated virus (HIV[1/10]), and non-infected cells (Mock). One mock sample failed and is not plotted. The transcriptome of mock cells and that of cells exposed to heat-inactivated viruses clustered together across the top principal components. Infected cells, on the other hand, spread away from the mock space as infection progressed, with the most distant dot corresponding to the latest time point (24 h). The mixture 1/10 infectious/noninfectious material occupies the intermediate space. Clustering of the two hours samples corresponds to end of cell exposure to the virus or control materials.</p