Values measured by digital PCR do not vary with cycle number.

<p>Plasmid templates were emulsified into droplets and thermally cycled for 30 to 50 cycles before analysis. No significant differences in measured <i>pol</i> (<b>A</b>) or 2-LTR (<b>B</b>) copy numbers were observed over this range of cycling times. No positive events were observed after 20 cycles (not shown). Similar results were obtained using dilutions of infected CD4+ T cells into uninfected PBMC (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055943#pone.0055943.s001" target="_blank">Fig. S.1</a>). Error bars indicate the observed standard deviation between wells.</p

Effect of sequence variation.

<p>Patient isolates with previously determined <i>pol</i> sequences that differ from the consensus primer/probe set in at least two positions were analyzed by ddPCR and by qPCR. Both assays were conducted in parallel using the mismatched consensus primer/probe set and a patient-specific matched primer/probe set. Use of consensus primers and probe resulted in an underestimate of copy number by one to two log₁₀ by qPCR, with complete loss of detection in the extreme case of 5 total mismatched bases. The underestimate was largely mitigated (mean 57% reduction in log₁₀ copy number change) by ddPCR in all cases. These 4 cases reflect the most extreme mismatches observed in 84 patients, suggesting that sequence variation is unlikely to significantly impact ddPCR assay results in clinical studies. All samples analyzed were HIV-1 subtype B.</p

Assay limit of detection and quantification.

<p>CD4 cells infected in vitro were serially diluted into a background of PBMC DNA. The number of replicate wells was increased from 3 to 36 in proportion to the dilution, allowing accurate measurement below one copy per 10⁶ cells. The shaded area indicates concentrations below the theoretical limit of detection for a single well.</p

Correlation of ddPCR and qPCR measurements.

<p>(<b>a</b>) Pol copy numbers measured by ddPCR and qPCR were significantly correlated (Pearson R² = 0.64, slope = 0.98±0.08). The correlation weakened at low copy numbers, primarily due to a rapid increase in the variance of the qPCR assay (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055943#pone.0055943.s003" target="_blank">Fig. S3</a> (a)). For copy numbers measured in the bottom tertile (<230 HIV DNA copies/10⁶ cells) by ddPCR, the correlation was not significant (R² = 0.08, P = 0.12). In the central tertile, the correlation was weak but significant (R² = 0.15, P = 0.03). In the top tertile, the correlation was strong (R² = 0.53, P<0.0001). (<b>b</b>) 2-LTR copy numbers measured by both methods were significantly but weakly (R² = 0.14, P = 0.002) correlated.</p

Graphic summary of the ability of each compound to activate HIV within each cell model: (A) primary CD4 T cell models and patient cell outgrowth assay (QVOA), and (B) J-Lat T cell line clones.

<p>Each compound and concentration tested is listed on the X-axis. In the primary CD4 cell models, each compound was tested using cells from 2, 3 or 4 different donors and in duplicate or triplicate with cells from each donor (See <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003834#s4" target="_blank">Methods</a> Section for details). For the QVOA, results from the limiting dilution cultures from 3 patients were pooled to calculate one common IUPM (infectious units per million cells) value which was then normalized to that obtained with PHA. With the J-Lat clones, experiments were performed in triplicate. Asterisks represent “not done”.</p

List of stimuli used in this study and their corresponding signaling pathways.

<p>List of stimuli used in this study and their corresponding signaling pathways.</p

Heatmap visualization of the ability of each compound to activate HIV within each model when excluding (A) and including (B) data from the QVOA model.

<p>A reduced set of compounds was analyzed in (<b>B</b>) since not every compound was run at every concentration in the QVOA. The clustergram at the left of each heatmap reflects the relationships between compounds based on their ability to activate HIV across compounds. Since cells in all models responded to PHA with high strength, ranking was normalized within each model to the response to PHA at 10 µg/mL and, therefore, all models display in the heatmap the same relative responsiveness to this treatment. The clustergram at the top of each heatmap reflects the relationship between each model based on their response to compounds. Clustergrams were created by calculating Euclidean distances and then clustering distances using the average linkage method. The numbers at the nodes of clusters are AU p-values where 95% represents a <i>p</i>-value cut-off of 0.05 and only values 95% or greater are depicted. Red cells in the heatmaps reflect HIV activation whereas blue or blank cells indicate that the compound did not effectively activate HIV.</p