Q4ddPCR: a flexible, 4-target assay for high-resolution HIV reservoir profiling

Domain: chemistry
Published: 2026-02-20
Editorial outcome: minor_revision

Abstract
Precise and scalable quantification of the intact HIV reservoir is critical for advancing curative strategies. Current reservoir assays, such as the intact proviral DNA assay (IPDA), are limited by quantification failures or misclassification of defective proviruses due to HIV sequence heterogeneity. Q4ddPCR is a modular, droplet digital PCR simultaneously targeting four conserved regions in the HIV genome to improve specificity, reduce quantification gaps, and provide multi-layered readouts. It comprises two configurations: one fully based on Q4PCR primer/probes and one combining IPDA with gag and pol primer/probes from Q4PCR. We benchmark Q4ddPCR against 3650 near full-length proviral sequences from 13 virally suppressed people with HIV (PWH) generated by Q4PCR. Q4ddPCR closely matches sequence-confirmed reservoir measurements, and multi-probe readouts reveal clonal reservoir dynamics not detectable by IPDA. Q4ddPCR enables intact reservoir quantification in 95% of samples across four independent cohorts and in 16 PWH, strongly correlates with viral outgrowth. In longitudinal samples from 42 participants over the first 4.5 years on antiretroviral therapy (ART), Q4ddPCR reports lower proviral frequencies and a steeper decline in intact proviral DNA compared to IPDA. Collectively, our findings confirm key predictions from mathematical modeling, demonstrating that multi-target assays improves specificity and more accurately capture intact reservoir dynamics.

Introduction
The persistence of genetically intact HIV proviruses in long-lived, clonally expanded CD4+ T cells remains the principal barrier to a broadly applicable HIV cure1. Although the majority of integrated proviruses are genetically defective and incapable of reactivation, a small subset of genetically intact proviruses can reinitiate viral replication if antiretroviral therapy (ART) is interrupted1,2. Accurate quantification of this intact reservoir is essential for understanding the mechanisms of HIV persistence and evaluating curative strategies1.

A variety of assays have been developed to measure the HIV reservoir, each with distinct advantages and limitations. Quantitative viral outgrowth assays (QVOA) detect inducible, replication-competent virus, providing a functional measure and a minimal estimate of the reservoir size. However, QVOA is labor-intensive, costly, requires large cell inputs, and underestimates reservoir size due to limited viral inducibility with a single round of stimulation3. PCR-based approaches, by contrast, offer greater scalability and require fewer cells. These assays target conserved regions of the HIV genome to quantify proviral DNA4. However, single-target PCR methods substantially overestimate HIV reservoir size by including defective proviruses, as they cannot distinguish between intact sequences and defective sequences with deletions or mutations4.

To address this, the Intact Proviral DNA Assay (IPDA) was developed as a high-throughput droplet digital PCR (ddPCR) method to detect intact proviruses through amplification of two conserved regions of the HIV genome: the packaging signal (Ψ) and the Rev-responsive element (RRE) in env, which were strategically selected based on the degree of sequence conservation in near full-length proviral genome alignments. To further discriminate and exclude hypermutated proviruses, a non-fluorescent (“dark”) probe is included5. For IPDA, individual proviruses are encapsulated within droplets and classified as ‘intact’ if both targets are co-detected. In contrast, a provirus is classified as ‘defective’ if only one target is detected (with a correction applied for DNA shearing)5. While IPDA effectively excludes approximately 93-97% of defective proviruses, the accuracy is limited by misclassification of proviruses with internal deletions or mutations outside of the targeted regions. Only 51-70% of the detected proviruses using simultaneous detection of an env- and Ψ-target are truly intact by near full-length genome sequencing6,7. This is significant given the differential decay rates of intact and defective proviruses: the intact reservoir has a half-life of approximately one to four years over the first seven years of ART (and subsequently plateaus or even increases8–10), whereas the defective reservoir remains relatively stable over time7,11–14. As a result, the misclassification of defective genomes as “intact” proviruses can lead to an underestimation of reservoir decay and obscure the effects of clinical interventions aimed at reducing the HIV reservoir. Mathematical modeling highlighted that 3- or 4-target approaches in PCR-based reservoir-quantification methods may result in higher specificity of intact reservoir quantification7.

While early implementations of such multi-probe approaches were limited to quantitative PCR (qPCR) platforms or shared fluorophores with different signal intensities, advances in digital PCR (dPCR) technology now enable true (droplet) dPCR-multicolor detection, such as the Rainbow assay15–19.

Another substantial challenge of the IPDA and related 2-target approaches is the reported assay failure rate of approximately 12-30% in HIV subtype B, largely due to intra- and inter-individual HIV sequence heterogeneity leading to primer/probe mismatches6,13,17,20–24. Strategies to overcome these failures have included the use of secondary backup primer/probe sets or individualized primer/probe designs based on additional proviral sequencing17,20,23. While individualized primer/probes can rescue detection in some cases, they introduce significant challenges related to scalability, cost, labor, and the need for additional sample material, limiting their broader applicability in clinical research settings. Q4PCR, a 4-target qPCR method combined with near full-length sequencing, addresses some of these limitations by increasing the number of genomic targets used for classification15,6. It improves specificity and allows confirmation of sequence integrity and clonality on a molecular level. However, scalability is constrained by the assay’s labor-intensive workflow, which involves limiting-dilution long-distance PCR, 4-target qPCR, and near full-length sequencing15,6. In particular, the long-distance PCR (approximately 9 kb) is inherently inefficient, resulting in reduced sensitivity for proviral detection and ultimately contributing to an underestimation of the intact reservoir7,25,26.

With the aim of developing a new standard for high-throughput, cost-effective, sensitive, and precise reservoir quantification, we developed a novel droplet digital PCR assay called Q4ddPCR that draws on the strengths of both IPDA and Q4PCR. By incorporating four conserved targets in the HIV genome, Q4ddPCR is designed to enhance both sensitivity and specificity. We also leverage the ability to consider multiple different combinations of target detection to mitigate the impact of detection failures resulting from inter-individual sequence heterogeneity. We first validated the assay using longitudinal samples from 13 virally suppressed people with HIV (PWH) for whom near full-length proviral sequences had been previously obtained using Q4PCR6,15,27,28. This allowed for direct benchmarking and a standardized analysis framework for reporting intact proviral genomes. To further evaluate clinical utility, we applied Q4ddPCR to three distinct cohorts. In the KOHIVI cohort, we performed a direct, side-by-side comparison of Q4ddPCR and the IPDA on matched samples to assess differences in quantification accuracy and assay robustness. We also examined the relationship between molecular and functional reservoir measurements in a subset of 16 ART-suppressed PWH, comparing Q4ddPCR and IPDA with the quantitative viral outgrowth assay (QVOA). Finally, we implemented Q4ddPCR in a longitudinal study of 42 PWH enrolled in ACTG trials who later entered a prospective cohort study (ACTGA5321) to characterize intact reservoir dynamics over the first 4.5 years of ART29. This included assessing assay performance over time and testing the hypothesis - supported by prior mathematical modeling - that multi-target approaches provide a more accurate estimate of intact reservoir decay compared to 2-target assays.

Results
Design and optimization of the Q4ddPCR assay
We developed a 4-target droplet digital PCR assay (Q4ddPCR) for high-throughput quantification of the intact HIV reservoir, incorporating primer/probe sets targeting conserved regions in env, Ψ, gag, and pol. Two assay versions were evaluated: one with primer/probes entirely based on Q4PCR primer/probe sequences (Q4-based Q4ddPCR), and another combining IPDA-derived primer/probes for Ψ and env with Q4PCR-derived primer/probes for gag and pol (IPDA-based Q4ddPCR, Fig. 1a, Supplementary Table 1). The two versions differ in the sequence of the Ψ-targeting primer/probes and fluorophore configuration for Ψ and gag probes (Supplementary Fig. 1)5,15. We included a separate reaction to quantify cell equivalents and to correct for shearing using the RPP30-reaction that is used for IPDA5. We optimized primer/probe concentrations and cycling conditions using DNA from J-Lat 6.3 cells harboring a single integrated intact provirus per cell (Fig. 1b, c, Supplementary Fig. 1c). We systematically evaluated annealing temperatures and number of PCR cycles, identifying 55°C and 60 cycles as the conditions that yielded the cleanest cluster separation with minimal intermediate fluorescence values.

We next assessed linearity and dynamic range following ddPCR guidelines30. Three independent 10-step dilution series were prepared using DNA from spiked-in J-Lat 6.3 cells into PBMCs from people without HIV (PWoH), spanning 0.625 to 3000 copies, and run in eight replicates per dilution. Both Q4-based and IPDA-based Q4ddPCR showed good linearity, with R² > 0.99 and a median slope of 1.0 (range: 0.88–1.18) across all intact-defining readouts and single-target measurements (Supplementary Fig. 2). Coefficients of variation (CV%) were comparable between dilution curves and increased at lower input copy numbers as expected (Supplementary Table 2). To assess assay variability in samples from PWH with low reservoir sizes, we repeated the full workflow from CD4⁺ T-cell isolation to DNA extraction and Q4ddPCR 7 to 12 times in four participants with geometric mean reservoir sizes of 5–43 intact proviruses per 10⁶ CD4⁺ T cells. Q4-based and IPDA-based Q4ddPCR were highly concordant (ρ = 0.75; 95% CI, 0.07–0.95), with 84% of replicate measurements for individual targets or intact-defining readouts falling within a 2-fold range of the mean (Supplementary Fig. 3a).

The limit of detection (LoD), defined as the lowest dilution at which ≥95% of replicates were positive, was 10 copies for Q4-based Q4ddPCR and 5 copies for IPDA-based Q4ddPCR (Supplementary Fig. 3b). In 94 (Q4-based) and 157 (IPDA-based) negative-control reactions using PBMC DNA from PWoH, no false triple- or quadruple-positive droplets were detected. Only a single IPDA-based Q4ddPCR reaction contained one false env-gag-positive droplet. Accordingly, the limit of blank (LOB) for intact readouts (4-color, env-gag-Ψ, env-Ψ-pol and env-Ψ) was 0 copies for both assay versions, except for env-gag in the IPDA-based assay (LOB = 0.008 copies). For single-target readouts, LOB values were as follows: Q4-based Q4ddPCR: env 0.01, Ψ 0.49, gag 0.13, pol 0.27 copies; IPDA-based Q4ddPCR: env 0.32, Ψ 0.44, gag 0.06, pol 0.77 copies.

Next, we evaluated the inclusion of a fifth probe targeting the 5′ LTR region to quantify total HIV DNA17,31,32. However, substantial fluorescence spillover was observed in both standard material and participant samples (Supplementary Fig. 4a, b). The magnitude of this spillover overlapped with the expected signal range of true pol-positive droplets, making computational compensation impractical, as it would artificially reduce pol detection. Finally, we compared total HIV DNA quantification by Q4ddPCR, calculated by summing single-target positive proviruses with correction for overlap, with total HIV DNA measured by a single-probe 5′ LTR ddPCR assay in 28 PWH (Supplementary Table 3). Q4ddPCR total HIV DNA strongly correlated and was highly concordant with 5′LTR measurements (Q4-based: Spearman r = 0.87, p < 0.0001, ρc = 0.83, 95% CI 0.67−0.92; IPDA-based: r = 0.86, p < 0.0001, ρc = 0.80, 95% CI 0.61−0.90) (Supplementary Fig. 4c). Thus, the 4-probe Q4ddPCR assay achieves robust total HIV DNA quantification and avoids fluorescence spillover-related artifacts that can impair assay specificity.

Performance evaluation of Q4ddPCR using previously characterized HIV reservoir samples
To rigorously validate the Q4ddPCR assay, we leveraged longitudinal samples from a cohort of 13 virally suppressed individuals that had previously been characterized using Q4PCR (Fig. 2a, Supplementary Table 4)6,15. These participants had been enrolled in studies evaluating broadly neutralizing antibodies (bNAbs), and while all were receiving ART at the initial sampling time point, ART was interrupted at the second time point as part of an analytical treatment interruption (ATI) protocol. Despite ART interruption, plasma viral loads remained below the limit of detection at both time points27,28. In our earlier Q4PCR analysis, all samples positive for at least two targets underwent sequencing, yielding a total of 3,650 proviral sequences including 558 intact proviral genomes6,15. Utilizing these deeply characterized samples enabled precise benchmarking of Q4ddPCR-derived results against high-resolution, sequence-confirmed reference data.

To assess assay performance, we compared different Q4ddPCR readouts - categorized by proviruses positive for 1-, 2-, 3-, or all 4-targets - to the frequency of intact proviruses per 10⁶ CD4⁺ T cells previously measured by Q4PCR (Fig. 2a, b; Supplementary Fig. 5a). Using the 4-target readout, proviral frequencies measured by Q4ddPCR showed strong correlation with Q4PCR-derived intact proviral frequencies (Spearman’s r = 0.72, p < 0.0001 for Q4-based Q4ddPCR; Spearman’s r = 0.62, p = 0.001 for IPDA-based Q4ddPCR) albeit with slightly higher median values (2-fold for Q4-based; 3-fold for IPDA-based Q4ddPCR, Fig. 2b, c, Supplementary Fig. 5a, c). In contrast, lower-order readouts (1-, 2-, or 3-target combinations) systematically overestimated reservoir sizes relative to Q4PCR. Specifically, median proviral counts were 40- and 43-fold higher using 1-target readout, 15- and 18-fold higher using 2-targets, and 3- and 5-fold higher using 3-target combinations for Q4- and IPDA-based Q4ddPCR assays, respectively (Fig. 2b, Supplementary Fig. 5a). Notably, among the 2-target readouts, the env-Ψ combination measured using IPDA-based Q4ddPCR showed the strongest correlation with Q4PCR-derived intact proviral frequencies (Supplementary Fig. 5c).

A known limitation of PCR-based methods for quantifying intact proviruses, including IPDA, is amplification failures caused by HIV sequence variability, giving rise to mismatches in primer/probe binding regions20. To address this, we tested both Q4- and IPDA-based primer/probe sets on the same samples. Using both primer/probe sets successfully restored Ψ signal in 3 of 4 participants with amplification failures: Two IPDA-Ψ failures were restored using the Q4-Ψ, one Q4-Ψ failure was restored using IPDA-Ψ (Fig. 2d, Supplementary Fig 4d). Importantly, since Q4PCR includes sequencing of proviruses amplified with the same target combinations as Q4ddPCR, we were able to track individual sequence-confirmed clones over time. For example, in participant 9242, the dominant intact clones were env-Ψ-positive. However, sequence-confirmed intact env-Ψ-negative clones which declined over time had also been previously identified by Q4PCR. These clones were captured by alternative Q4ddPCR readouts such as env-gag (Fig. 2d, Supplementary Fig. 5b, d) and would have been overlooked by standard IPDA, underscoring how the flexibility to use multiple primer/probe combinations in Q4ddPCR can effectively overcome assay limitations imposed by HIV sequence variability.

Collectively, these results demonstrate that Q4ddPCR closely approximates intact proviral frequencies measured by Q4PCR. By leveraging multiple conserved targets and alternative primer/probe sets without requiring a sequencing step, Q4ddPCR combines high-throughput capability with the flexibility needed to overcome sequence heterogeneity within the reservoir. Furthermore, in some cases, Q4ddPCR enables tracking of individual intact clones that would otherwise be missed by exclusively looking at env-Ψ-positive proviruses.

Standardized decision tree for reporting intact proviruses by Q4ddPCR
To assess how accurately different Q4ddPCR readouts detect intact proviral DNA, we compared Q4ddPCR results to matched proviral sequences from all 13 participants obtained by Q4PCR. These sequences were derived from amplicons that have been tested positive in the Q4PCR qPCR step using the same primer/probe combination as Q4ddPCR6,15. This allowed us to benchmark Q4ddPCR detection against a sequence-confirmed reference framework. For each Q4ddPCR probe combination, we calculated sensitivity as the proportion of sequence-confirmed intact proviruses detected and specificity as the proportion of defective sequences correctly excluded. This approach reflects both the likelihood of detecting truly intact proviruses and the accuracy with which intactness is assigned by each target combination.

While detection frequencies expectedly decline with additional targets, the specificity of accurately identified intact proviral genomes increases from 42% for 1-target detection to 95% for 4-target (4-colors) readout (Fig. 3a, Supplementary Table 5).

However, we observed substantial inter-individual variability in the diagnostic performance of different target combinations (Fig. 3b, Supplementary Fig. 6). In participant 5111, 4-color positive proviruses could not be detected, and env-gag-Ψ and env-Ψ positives showed equal proviral counts with identical sensitivity and specificity. In participants with a more clonal reservoir (e.g., 5104), the 4-target readout maintained high sensitivity while offering substantially higher specificity compared to 2-target readouts (57% vs. 38%). In participants 9244 and 9255 where sequence mismatches in the Q4-based Ψ primers and probes result in amplification failures; alternative readouts (env-gag or env-gag-pol) resulted in sensitive and specific estimates for intact proviral frequencies (Fig. 3b, Supplementary Fig. 6).

Overall, these findings demonstrate that no single target combination performs optimally across all individuals, underscoring the need for a structured analysis framework to fully leverage the flexibility of our 4-target assay.

To address this, we developed a decision tree for classifying intact proviruses based on the specificity-focused performance of different Q4ddPCR readouts (Fig. 3c). Because accurate reservoir decay estimation depends on high specificity7, we prioritized reporting 4-target positive proviruses when detected, followed by env-Ψ-including 3-target combinations. The env-target showed the highest sensitivity and negative predictive value in the Q4PCR-derived sequence data (Supplementary Table 5). Consequently, target combinations lacking env were excluded from the definition of intactness.

In our dataset, we identified samples (participants 9244, 9255) with sequence-confirmed intact proviruses lacking env-Ψ signal due to mismatches in the Ψ-binding site (Fig. 3b, Supplementary Fig. 6). This was resolved in some cases using the alternative Ψ primer/probe set; however, in participant 9244, both Ψ sets failed despite the presence of intact sequences confirmed by Q4PCR. To account for such cases without resorting to custom primer design, we included env-gag and env-gag-pol combinations in the decision tree. Although these readouts may slightly overestimate reservoir size due to imperfect shearing correction, the impact is limited and outweighed by the improved sensitivity of including these combinations. We excluded env-pol combinations due to their lower positive and negative predictive value (Supplementary Table 5). Applying the decision tree enabled intact provirus quantification in all 13 participants, highlighting the enhanced robustness gained by assay modularity.

In summary, these results demonstrate that multi-target flexibility in Q4ddPCR allows for standardized and sequence-informed readout strategies that preserve high sensitivity while improving specificity across genetically heterogeneous HIV reservoirs.

Q4ddPCR preserves IPDA information yet aligns more closely with inducible virus
To assess the performance of Q4ddPCR on clinical cohort samples, we applied Q4ddPCR to peripheral blood CD4⁺ T cells from 27 ART-treated individuals with HIV-1 subtype B enrolled in the Berlin KOHIVI cohort (Table 1). All participants had undetectable plasma viral loads at sampling. We performed side-by-side comparisons among Q4-based Q4ddPCR, IPDA-based Q4ddPCR, and the original IPDA. For two individuals, limited sample availability precluded the IPDA-based Q4ddPCR; therefore, only IPDA and Q4-based Q4ddPCR were applied.

Across all samples, intact proviruses were quantifiable in 26 participants using Q4ddPCR, compared to 23 participants by IPDA. Only one participant had intact proviruses detected by IPDA but not by Q4ddPCR. Notably, all cases of IPDA failure were successfully resolved through alternative readouts or primer/probe sets available within the Q4ddPCR assay, underscoring its robustness and flexibility in accommodating sequence variability and resulting primer/probe mismatches (Fig. 4a, c).

Total HIV DNA levels measured by Q4ddPCR and IPDA were highly correlated and concordant (Spearman r = 0.98, p < 0.0001, ρc = 0.92 for Q4-based and ρc = 0.96 for IPDA-based Q4ddPCR, Fig. 4a, b). The env-Ψ readout of Q4ddPCR, which parallels the IPDA readout, also correlated strongly with IPDA measurements, especially when using the IPDA-based Q4ddPCR design. This particular correlation was driven partly by matching signal dropout for the Ψ-target in 3 of 27 participants, as both assays failed to detect these proviruses. These Ψ-target failures in Q4ddPCR were rescued by switching to the second Ψ probe (Fig. 4a).

In contrast, intact provirus measurements differed considerably between Q4ddPCR and IPDA, with ≥2-fold differences observed in 78% of samples using Q4-based Q4ddPCR and in 72% using IPDA-based Q4ddPCR (Fig. 4c). The geometric mean ratio of intact provirus frequencies measured by Q4ddPCR vs. IPDA was 0.40 (range 0.15-2.0) for the Q4-based assay and 0.38 (range 0.06-1.5) for the IPDA-based assay. These results are consistent with prior evidence suggesting that IPDA overestimates intact proviruses relative to multi-probe or sequence-based methods. Overall, our Q4ddPCR preserves the information provided by IPDA in terms of high concordance for total HIV DNA or env-Ψ quantification but offers greater flexibility and improved accuracy in assessing the frequency of intact proviruses, particularly in the context of primer and/or probe mismatches.

To further evaluate the biological relevance of Q4ddPCR readouts, we examined their relationship with inducible, replication-competent virus measured by quantitative viral outgrowth assay (QVOA) in a subset of 16 ART-treated participants enrolled in different observational studies (Supplementary Table 3). Viral outgrowth was quantified by p24 enzyme-linked immunosorbent assay (ELISA) 14 days after stimulation.

Intact proviral frequencies measured by Q4ddPCR showed strong correlations with QVOA (Q4-based Q4ddPCR: Spearman r = 0.74, p = 0.01; IPDA-based Q4ddPCR: r = 0.52, p = 0.02), whereas IPDA intact counts did not correlate significantly with QVOA (r = 0.34, p = 0.19; Fig. 4d).

Q4ddPCR intact proviruses were determined using the decision tree (Fig. 3c): 11 of 16 samples yielded 4-target-positive proviruses, while the remaining samples were classified based on 3-target positives or, in a single case, 2-target positive proviruses. As expected, QVOA frequencies were lower than Q4ddPCR-derived intact provirus estimates, but the fold differences between QVOA and Q4ddPCR were smaller and more consistent than those observed for IPDA (Fig. 4e).

Together, these results indicate that Q4ddPCR closely reflects the inducible reservoir, supporting its enhanced specificity for identifying replication-competent proviruses through multi-target PCR.

Q4ddPCR enables more sensitive and reliable detection of intact HIV proviruses than IPDA across longitudinal samples
To evaluate Q4ddPCR performance in assessing HIV reservoir dynamics, we applied the assay to 99 samples from 42 participants enrolled in the ACTGA5321 cohort. All participants had chronic HIV-1 subtype B infection, initiated ART in ACTG trials for treatment-naive persons, and achieved sustained virologic suppression (HIV-1 RNA < 50 copies/mL) by week 48 of ART, with no reported treatment interruptions (Fig. 5a, Table 2)29. Due to limited DNA availability per sample and to facilitate comparisons with existing IPDA data from later time points in this cohort, we primarily used IPDA-based Q4ddPCR, supplemented by Q4-based Q4ddPCR in cases of amplification failure. Notably, env signal dropout was resolved in several cases by using a published backup primer/probe set20.

Overall, Q4ddPCR successfully quantified intact proviruses in 94% (93/99) of samples, compared to only 77% (76/99) for IPDA. Signal dropout for at least one target occurred less frequently with Q4ddPCR (11%) compared to IPDA (15%). Importantly, Q4ddPCR identified 4-target positive proviruses in 64% of samples, and at least 3-target positive intact proviruses in 81% (Fig. 5b). Furthermore, 74% (17/23) of IPDA failures were rescued using alternative primer/probe sets (13/17) or readout combinations available within Q4ddPCR (env-gag-pol (1/17) or env-gag readout (3/17)), across a wide dynamic range (7-722 intact proviruses per 10⁶ CD4⁺ T cells) (Fig. 5d). These findings highlight that the increased sensitivity and robustness of Q4ddPCR are beneficial across a broad spectrum of reservoir sizes, not limited to samples with small reservoir sizes.

Q4ddPCR reveals a lower proportion and steeper decline of intact proviruses compared to IPDA
As anticipated, the proportion of proviruses detected varied based on the number and combination of Q4ddPCR-targets (Fig. 5c). Frequencies of intact proviruses differed substantially between Q4ddPCR and IPDA, with a geometric mean ratio Q4ddPCR/IPDA of 0.61 (range 0.03− 1). Across all samples, Q4ddPCR reported significantly lower intact reservoir sizes than IPDA, underscoring its higher specificity (two-sided Wilcoxon signed-rank test, p < 0.0001, Fig. 5d).

Longitudinal analyses revealed a decline in intact proviral frequencies for most participants. Specifically, in 24 of 42 participants (57%), reservoir size decreased by at least 15% over a median follow-up of 2.3 years, although some participants exhibited increases or fluctuations. Decay rate modeling using a log-linear mixed-effects model (accounting for random intercept and slope effects) showed consistent assay-dependent differences, although these did not reach statistical significance (Chi² test, p = 0.33). Q4ddPCR estimated a 43% annual decline (half-life 1.2 years, SE 0.3), compared to a 37% annual decline for IPDA (half-life 1.5 years, SE 0.4). The discrepancy between intact proviral frequencies measured by Q4ddPCR or IPDA widened progressively over time, becoming most pronounced at the latest time points (Fig. 5e). When excluding those samples where IPDA failed while Q4ddPCR rescued reservoir quantification, we obtained similar results, with an annual decline of 48% (half-life 1.1 years, SE 0.2) for Q4ddPCR and 39% (half-life 1.4 years, SE 0.3) for IPDA (Chi² test, p = 0.11).

Finally, Q4ddPCR revealed a consistently lower proportion of intact proviruses among total HIV DNA compared to IPDA with the highest relative difference observed at the latest time point. Specifically, the intact fraction measured by Q4ddPCR was 1.6-, 1.7- and 1.8-fold lower than by IPDA at the first, second, and third time points, respectively (Fig. 5f). Although the difference in estimated annual decay rates between assays did not reach statistical significance, this progressively widening difference experimentally validates mathematical modeling7 and suggests that Q4ddPCR more accurately captures intact reservoir decay dynamics by more effectively excluding defective proviruses misclassified by IPDA-especially in scenarios where truly intact proviruses are preferentially being eliminated. These differences were not solely attributable to samples in which IPDA failed; rather, they reflect Q4ddPCR’s enhanced ability to detect true reservoir changes. Thus, Q4ddPCR provides a more precise tool for longitudinally assessing intact HIV reservoir dynamics, potentially offering critical advantages for evaluating HIV cure interventions.

Methods
Participants and ethics statement
We studied 110 participants with HIV-1 subtype B infection enrolled in either published trials (https://www.clinicaltrials.gov; NCT03526848 and NCT02825797; EudraCT: 2016-002803-25)27,28 or in observational studies at one of the following sites: Charité Universitätsmedizin Berlin, Germany (KOHIVI study), Maple Leaf Medical Clinic, Toronto, Canada, or the New York-Presbyterian Hospital at Weill Cornell Medicine, United States. Apheresis from people without HIV was collected from the Gulf Coast Regional Blood Center, United States. Ethical approval to conduct this study was obtained from University of Toronto (reference number: 12-378) and the Weill Cornell Medicine Institutional Review Boards (reference number: 21-03023420), as well as the Ethics Committee of Charité Universitätsmedizin Berlin (reference number EA2/077/23). Participants enrolled in studies of the AIDS Clinical Trials Group (ACTG) initiated ART in ACTG trials and were subsequently followed in the A5321 cohort29. A5321 participants were recruited from 17 clinical research sites in the United States. Each clinical research site had the ACTG-approved A5321 protocol and consent form, and its relevant parental protocols and consent forms, approved by their local institutional review boards. PBMC samples from the A5321 cohort were obtained from the ACTG Specimen Repository through new work concept sheet NWCS 578. All participants provided written informed consent before participation, and the studies were conducted in accordance with Good Clinical Practice.

Proviral sequence data
Proviral sequence data was obtained from Q4PCR as published6,15. For accuracy calculation, sensitivity was defined as the proportion of intact sequences identified by a given Q4PCR-target combination relative to the total number of sequence-confirmed intact proviruses. Specificity was calculated as the proportion of defective sequences not detected by a given readout, relative to the total number of defective sequences (Supplementary Table 5).

Q4ddPCR and 5’LTR ddPCR
CD4+ T cells were isolated from 5 to 50 × 10⁶ cryopreserved PBMC samples using the CD4+ T Cell Isolation Kit (Miltenyi Biotec) or EasySep™ Human CD4+ T Cell Isolation Kit (Stemcell Technologies) according to the manufacturer’s instructions. Genomic DNA was then extracted using the DNeasy Blood and Tissue Kits for DNA Isolation (Qiagen) and eluted in 60 µL H2O per 5 × 10⁶ CD4+ T cells lysed and immediately stored at −20°C for up to 12 weeks. DNA quality and concentration was assessed using NanoDrop 1000 spectrophotometer or Qubit 4 Fluorometer with the dsDNA Quantification HS Kit (Thermo Fisher Scientific). DNA samples with concentrations above 100 ng/µL were diluted with H2O.

Upto 750 ng of DNA per well was combined with 9.5 µL supermix for probes (no dUTP) (Bio-Rad) and the 2.5 µL of Q4ddPCR primer/probe mixes. These mixes included four fluorescently labeled internal hydrolysis probes, along with an unlabeled hypermutant-env probe. The primer and probe sequences were based on published sequences, with modifications to fluorophore labeling5,15,20,31,32. Fluorophore labeling, concentration of primer and probes, as well as cycling conditions have been optimized and validated on genomic DNA from J-Lat 6.3 cells (NIH HIV Reagent Program)40 to obtain the optimal separation between different clusters and reduce ‘rain’ in the ddPCR reaction.

The sequences and concentrations for each target are as follows:
env: Primer (0.225 µM): forward AGTGGTGCAGAGAGAAAAAAGAGC, reverse GTCTGGCCTGTACCGTCAGC, probe (0.0625 µM): /5-VIC-CCTTGGGTTCTTGGGA-MGBNFQ, unlabeled hypermutant probe for discrimination of hypermutations within the target: CCTTAGGTTCTTAGGAGC- MGBNFQ; backup-primer (0.9 µM): forward ACTATGGGCGCAGCGTC, reverse CCCCAGACTGTGAGTTGCA, backup-probe (0.25 µM): VIC-CTGGCCTGTACCGTCAG-MGBNFQ, Packaging Signal (Ψ): Q4-based primer (0.9 µM): forward TCT CTC GAC GCA GGA CTC, reverse TCT AGC CTC CGC TAG TCA AA, Q4-based probe (0.25 µM) /5Cy5/TT TGG CGT A/TAO/C TCA CCA GTC GCC /3IAbRQSp/, IPDA-based primer (0.9 µM): forward CAG GAC TCG GCT TGC TGA AG, reverse GCA CCC ATC TCT CTC CTT CTA GC, IPDA-based probe (0.25 µM): /56-FAM/TTTTGGCGTACTCACCAGT-MGBNFQ; gag: Primer (0.9 µM): forward ATG TTT TCA GCA TTA TCA GAA GGA, reverse TGC TTG ATG TCC CCC CCA CT, Q4-based probe (0.25 µM): /5‘6-FAM/CC ACC CCA C/ZEN/A AGA TTT AAA CAC CAT GCT AA/3IABkFQ/, IPDA-based probe (0.25 µM): /5Cy5/CCACCCCAC/TAO/AAGATTTAAACACCATGCTAA/3IAbRQSp/; pol: Primer (0.9 µM): forward GCA CTT TAA ATT TTC CCA TTA GTC CTA, reverse CAA ATT TCT ACT AAT GCT TTT ATT TTT TC, probe (0.25 µM): /5ATTO590N/AAGCCAGGAATGGATGGCC/3IAbRQSp/. For the 5’LTR ddPCR 11.5 µL of the supermix for probes (no dUTP) (Bio-Rad) was combined with the RU5 primer/probe mix: Primer (0.9 µM): forward TTA AGC CTC AAT AAA GCT TGCC, reverse GTT CGG GCG CCA CTG CTA GA, probe (0.25 µM): /56-ROXN/CC AGA GTC ACA CAA CAG ACG GGC ACA /3IAbRQSp/.

Primers and probes were purchased from Integrated DNA Technologies (IDT), except for the env-, unlabeled, and IPDA-based Ψ-probes, as well as probes needed for the RPP30 assay, which contained a minor groove binder (Thermo Fisher Scientific). Reactions were performed in a total volume of 20 µL per well, in up to 20 replicate wells. Droplets were generated with the manual or automated QX200 droplet generator (Bio-Rad). Thermocycling was performed with a 2°C ramp rate, with an initial denaturation at 95°C for 10 min, followed by 60 cycles (30 s at 94°C and 1 min at 55°C per cycle) and a final step 10 min at 98°C before incubation at 4°C. Droplets were read on the QX600 Droplet Reader (Bio-Rad). IPDA and parallel RPP30 assays for shearing correction and calculation of cell equivalents in IPDA and Q4ddPCR as previously described in four replicate wells per sample5.

Wells containing fewer than 7,500 droplets or samples with less than 40,000 cell equivalents were excluded. We recommend aiming for 200,000 cell equivalents and considering differences in analyzed cell equivalents when evaluating longitudinal samples. Positive controls with J-Lat 6.3 DNA (Fig. 1b, c, Supplementary Fig. 1c) and negative controls using (Supplementary Fig. 1b, d) genomic DNA from people without HIV, as well as no-template controls, were performed in duplicates. Gates were manually set for each sample using QX Manager Software, Standard Edition v2.0. Merged replicates were gated in 2D scatter plots for each 2-target combination. Replicate wells were merged and gated in two-dimensional scatter plots for all possible 2-target combinations. In cases where merged replicates showed unclear cluster separation, replicates were inspected and gated individually. ‘Cluster data’ and ‘data table’ were exported and further processed using in-house R-codes (codes and usage documentation available on Buchauer lab and Gaebler lab GitHub page). These scripts automate the calculation of 1-, 2-, 3-, and 4-target-positive proviruses. Total HIV DNA is computed within the ‘Multiplex PCR Analyser’ package as the sum of single-target signals corrected for overlapping shear-corrected target combinations. A sample-wise decision tree is applied across all available time points using the ‘Processing_Q4ddPCR.R’ workflow to minimize dropouts and ensure consistent classification.

Quantitative viral outgrowth assays
For the quantitative viral outgrowth assay (QVOA) CD4+ T cells were isolated from 100 to 300 × 10⁶ PBMCs per participant using the EasySep™ Human CD4+ T Cell Isolation Kit (Stemcell Technologies) and cultured in RPMI media (10% FBS, 1% Hepes, 2 mM L-Glutamine, 1% penicillin/streptomycin) with 50 U/mL IL-2 and 10 ng/mL IL-15. CD4+ T cells were seeded in each 12 replicates of dilutions ranging from 0.05 × 10⁶ to 1 × 10⁶ CD4+ T cells per well in 24-well plates and stimulated with 1 µg/mL phytohemagglutinin (PHA) and 1 × 10⁶ irradiated feeder cells from PWoH. Total number of CD4+ T cells used for QVOA was determined by Q4ddPCR results (total number of CD4+ T cells used: 22.8 × 10⁶ in 14 participants, 11.4 × 10⁶ in 2 participants). After 24 hours media was changed to remove PHA, and 0.2 × 10⁶ MOLT-4/CCR5 cells (NIH HIV Reagent Program)41 were added to each well. Cultures were maintained for 2 weeks, changing media every three days. p24 enzyme-linked immunosorbent assays (ELISA) were run on supernatants collected at day 14 to identify positive wells. Absorbance values above the absorbance of the lowest p24 standard (3.25 pg/µL) and the average absorbance plus 3 standard deviations of negative controls were designated as p24-positive. The frequency of inducible cells was calculated through the IUPM algorithm developed by the Siliciano laboratory (http://silicianolab.johnshopkins.edu)42. We detected viral outgrowth in all samples tested.

Statistical analyses and modeling of reservoir decay
To analyze reservoir decay dynamics, we used a log-linear mixed-effects model with fixed effects for time on ART, assay type (Q4ddPCR or IPDA), and their interaction, as well as participant-specific random effects for both intercept and slope. IPDA results with signal failures were excluded from analysis. Reservoir size values of 0 were set to 1. Model fitting was assessed by inspecting residual plots, Q-Q plots, and histogram distributions. Model convergence was verified via the optimization status code.

Statistical analyses were performed using GraphPad Prism Version 10.5.0 and R 4.5.0 for MacOSX. For the calculation of the concordance correlation coefficient (ρc) reservoir values were log10-transformed, and values of 0 were set to 1.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Scheck and co-authors developed Q4ddPCR assay, an droplet digital PCR-based assay to measure intact (and defective) HIV proviral DNA. Taking advantage of the recent advances in digital PCR technology that allow to detect more than two colors at the same time, the assay simultaneously targets four conserved regions in the HIV genome. The primers and probes that are used in this assay are not new, but have been developed and published previously by the authors (Q4PCR assay) or others (IPDA assay). The principle of Q4ddPCR is the same as that of IPDA, the only difference is that four regions in the HIV genome are targeted instead of two. In this regard, q4ddPCR can be considered an improved version of IPDA. Not surprisingly, Q4ddPCR appears to be a more specific assay to measure the intact reservoir than IPDA, as the addition of two targets provides a better specificity for intact proviruses. Also not surprisingly, however, the sensitivity of the assay is decreased compared to two-target assays such as IPDA, as more targets mean more possibilities for false-negative results due to primer/probe-template mismatches. In particular, Q4ddPCR with four targets could detect only ~40% of sequence-confirmed intact proviruses, leading to the serious underestimation of the intact reservoir. The authors try to mitigate the sensitivity issue by using alternative readouts (two-target or three-target readouts, see decision tree at fig. 3c). However, the whole point of this assay was to add more targets in order to increase precision and specificity for intact proviruses. It is surely possible to use 2 or 3 targets instead of 4, which will increase sensitivity - but at the cost of specificity and precision. Overall, the manuscript is well written and the data are clearly presented. However, this reviewer feels that the conceptual advance provided by this study is rather limited, for the reasons specified above and also because a very similar \"multi-color\" assay has been already described in a publication by Delporte et al. (Integrative Assessment of Total and Intact HIV-1 Reservoir by a 5-Region Multiplexed Rainbow DNA Digital PCR Assay, Clin Chem 71, 203-214, 2025). That publication reports a 5-region digital PCR, which is even one region more than the current manuscript so in theory should provide an even better specificity for intact proviruses. The only difference is that the current assay is based on QX600 droplet digital PCR system (Bio-Rad) and the \"Rainbow assay\" from Delporte et al - on QIAcuity Digital PCR System (Qiagen). But the principle is the same. Even the \"decision tree\" (fig. 3c in this manuscript) is not new as a very similar decision tree is present in the Delporte paper (fig. 6). Minor point: it would be useful to provide an exact p value of the comparison between Q4PCR and 4-target Q4ddPCR in figure 2b. It only says \"ns\" (not significant) which means it is 0.05, but a p value of 0.5 is surely different from a p value of 0.06. From looking at the figure, it is clear though, that Q4ddPCR still overestimates the number of intact proviruses measured by Q4PCR.", "reviewer_2": "Scheck etal report the modification of the sequencing based Q4PCR to a ddPCR based assay for high throughput quantification of intact proviruses. The assay is validated on a previously sequenced cohort and is then used to describe intact proviruses in a number of cohorts including a longitudinal cohort where they show a faster decline in intact proviruses compared to standard IPDA. There appears to be good separation between the different populations which is good and the assay seems robust. Overall they show that Q4ddPCR reports less intact proviruses compared to standard IPDA which they attribute to IPDA overcalling intact proviruses. There is no doubt that correct enumeration of intact proviruses is of critical need to the field however, a number of other assays have been published using more than the standard two targets such as the 5T-IPDA (PMID: 33948574) and the Rainbow assay (PMID: 39749517), although it appears as though the dropout rate is much reduced here. Therefore, its an important technical advance. It doesn’t solve one of the major issues holding back the field which is applicability to diverse subtypes. Specific comments below 1. What proportion of samples can be enumerated using the 4 target system and how many must revert to 3 or 2 targets. This is an important piece of information that would allow for the reader to understand how significant an improvement this assay is. Perhaps this can be reported in the decision tree in figure 3 (for each cohort or combined)? 2. Not fully clear to this reviewer, are the authors advocating for Q4ddPCR or for the IDPA-Q4ddPCR? The longitudinal analysis appears done with IDPA-Q4ddPCR. Perhaps this can be made clear in the abstract. 3. The differences in intact provirus decline observed in ACTG cohort, is there statistical justification to demonstrate these are real differences? They should be reported as this is claimed in the abstract 4. Assume difference between Fig 2 and extended Fig 2 is one is Q4ddPCR and one is the IPDA-based Q4ddPCR. The labeling is exactly the same on both though, could this be made clearer? 5. Fig 2b: many data points missing for ‘4 targets’, is it because no counts observed? Can this be reflected in the graph? 6. Line 180: If interpreted correctly, it would be worth stating that psi was restored with IPDA-version in 2 of 4 donors and 1 of 4 restored using Q4-version 7. Line 185: This seems an important point but is this data captured in fig 2d and ext fig 2c - the circles for env-Ψ are bigger than env-gag and env-gag-pol. Can this be demonstrated in a different way? 8. Line 300: One assumed because this is IPDA-Q4ddPCR this is due to Psi failure and use of env-gag droplets to quantify intact? Can authors confirm this was how samples were rescued? 9. Line 407: Is this statement correct? Was there not a significantly higher detection of intact proviruses by IPDA (figure 4c)? 10. Line 526: Is it possible to provide further information about how droplets were classified as 2, 3 or 4 target positive? Was this performed using the biorad software or required in house R code? This information may be helpful for other labs that may be interested in establishing the q4ddPCR and making decisions about what machines to purchase (maybe even in extended data).", "reviewer_3": "Scheck et al. present a multiplexed digital PCR version that is based on their previously published Q4PCR workflow backed up with a solid evaluation against full-length sequences. I would advise the authors to consider the following. 1/ Consult the dMIQE2020 update guidelines and checklist for dPCR experiment reporting to inspect whether or not additional information could be provided. 2/ Assay performance characteristics and metrics could be extended to determine LOB, LOD, LOQ, resolution between clusters. Are there dilution series performed on J-Lats? If not, would advise to include this. If sufficient NTCs were already run a LOB calculation could also be included. At the end the authors evaluate specificity and sensitivity but this could be confusing as these also can refer to the more technical aspects of an assay. 3/ There is competition between the ENV-GAG and the GAG-POL, were any optimization approaches tried to further orthogonalize the position of the clusters? 4/ In terms of visualization, it would be helpful to color the true 4 color positive partitions in the 2D or 3D plots, so a visual inspection can be done of where these partitions lay. This can help in thresholding these out as the abovementioned competition could result in partitions that are close to the single positive cluster. 5/ Add comment on potential subtype performance of the Q4ddPCR (subtype B oriented) or highlight flexible redesign opportunities of these type of assays to accommodate other subtypes. 6/ On the Decision tree. How does it compare to other decision trees that are published? Would add section on this. 7/ Would the authors have data to support or hint towards the merit of adding more regions for intactness readouts and if there would be a limit in added value of performing for instance a 10 plex reaction. In other words, where is the limit? 8/ The authors mention 40K cells as minimal input. Could the authors elaborate on this threshold number or other QC for input? Also, if a 0 results is present, could they comment on the absence of signal not meaning absence of intact virus. 9/ The authors might consider to rename to Q4dPCR in stead of Q4ddPCR, referring to all digital PCR systems rather then only droplet-based systems. 10/ The evaluation of Q4ddPCR with Q4PCR generated viral sequence data could skew/bias the absolute % of sensitivity and specificity as the same primer sets are used. A cautionary note could be added while interpreting these values."}, "author_response": "Response to Reviewers’ comments\nWe would like to thank the reviewers for the time and effort they have dedicated to evaluating our manuscript\nand for the valuable feedback. The reviewers’ suggestions have improved the technical depth of our analyses\nand enhanced the overall quality of the work, including the addition of new comparative data with functional\nreservoir analyses. Replies to the reviewers’ comments (in green) are outlined below (in black), and all changes\ncan be found in the revised manuscript version with tracked changes.\nReviewer #1 (Remarks to the Author):\nWe thank the reviewer for pointing out that:\n“Overall, the manuscript is well written and the data are clearly presented.”\nThe reviewer raises the following points:\n“Scheck and co-authors developed Q4ddPCR assay, a droplet digital PCR-based assay to measure intact (and\ndefective) HIV proviral DNA. Taking advantage of the recent advances in digital PCR technology that allow to\ndetect more than two colors at the same time, the assay simultaneously targets four conserved regions in the\nHIV genome. The primers and probes that are used in this assay are not new, but have been developed and\npublished previously by the authors (Q4PCR assay) or others (IPDA assay). The principle of Q4ddPCR is the\nsame as that of IPDA, the only difference is that four regions in the HIV genome are targeted instead of two. In\nthis regard, q4ddPCR can be considered an improved version of IPDA. Not surprisingly, Q4ddPCR appears to\nbe a more specific assay to measure the intact reservoir than IPDA, as the addition of two targets provides a\nbetter specificity for intact proviruses. Also not surprisingly, however, the sensitivity of the assay is decreased\ncompared to two-target assays such as IPDA, as more targets mean more possibilities for false-negative results\ndue to primer/probe-template mismatches. In particular, Q4ddPCR with four targets could detect only ~40% of\nsequence-confirmed intact proviruses, leading to the serious underestimation of the intact reservoir. The authors\ntry to mitigate the sensitivity issue by using alternative readouts (two-target or three-target readouts, see\ndecision tree at fig. 3c). However, the whole point of this assay was to add more targets in order to increase\nprecision and specificity for intact proviruses. It is surely possible to use 2 or 3 targets instead of 4, which will\nincrease sensitivity - but at the cost of specificity and precision.\nWe thank the reviewer for bringing this up and agree that the primary aim of Q4ddPCR is to maximize precision\nand specificity through the addition of more targets. In fact, 65% of all tested samples across 4 cohorts and 110\nindividuals yielded detectable 4-color positive proviruses, which provide the highest predictive value for\nintactness and the greatest potential to capture dynamic changes in the intact reservoir.\nThe relatively low fraction of sequence-confirmed proviruses showing Q4PCR 4-color positivity (45.5%,\nExtended Data Table 5) in our sequence-validation cohort reflects the intentional selection of challenging\nparticipants. This cohort was shown to have an unusually high signal failure rate in a thorough comparative\nvalidation study across different patient cohorts (Gaebler, Falcinelli et al. - JVI 2021, DOI: 10.1128/JVI.01986-20)\nand included individuals with known primer/probe mismatches (5106, 5114, 5115, 9242, 9243, 9244, 9255),\nknown amplification failures (5114, 5115, 9243, 9244, 9255), very small reservoir sizes (5106, 5115) and\nunusually steep reservoir declines (5104). This cohort was chosen to rigorously validate Q4ddPCR with a high\nbenchmark. Our goal was to assess whether a high-throughput platform such as Q4ddPCR could (1) capture\nthis level of biological complexity and (2) provide practicable solutions such as validated ready-made backup\nprimer/probes or alternative readouts to mitigate failures without compromising data interpretability. To this end,\nwe used this cohort to implement systematic readouts guided by the 3,650 proviral sequences (derived from\nPCR signals using the same primer/probes as Q4ddPCR) and to develop the hierarchical decision framework\nthat maximizes specificity across participants while preserving sensitivity.\nThe resulting 4-color readout in 65% of all tested samples as well as the very low drop-out rate of 5% across 179\nsamples demonstrates the success of this strategy and underscores the robustness of the Q4ddPCR framework.\nIn addition, the strong correlation of decision-tree based Q4ddPCR results with functional viral outgrowth\nreservoir estimates underscores the precision and specificity of our approach.\n[Redacted]\nSimilar issues with high drop-out rates have been observed in other trials for example, AELIX-003 which\nreported no statistically significant reservoir reductions but had high assay failure rates (20%: 10% in\nintervention and 40% in placebo group).\n[Redacted]\nThe reviewer raises additional points:\n“Overall, the manuscript is well written and the data are clearly presented. However, this reviewer feels that the\nconceptual advance provided by this study is rather limited, for the reasons specified above and also because a\nvery similar \"multi-color\" assay has been already described in a publication by Delporte et al. (Integrative\nAssessment of Total and Intact HIV-1 Reservoir by a 5-Region Multiplexed Rainbow DNA Digital PCR Assay,\nClin Chem 71, 203-214, 2025). That publication reports a 5-region digital PCR, which is even one region more\nthan the current manuscript so in theory should provide an even better specificity for intact proviruses. The only\ndifference is that the current assay is based on QX600 droplet digital PCR system (Bio-Rad) and the \"Rainbow\nassay\" from Delporte et al - on QIAcuity Digital PCR System (Qiagen). But the principle is the same. Even the\n\"decision tree\" (fig. 3c in this manuscript) is not new as a very similar decision tree is present in the Delporte\npaper (fig. 6).”\nWe thank the reviewer and agree that both Q4ddPCR and the Rainbow Assay rely on the concept of prioritizing\nhigh-specificity multi-color readouts as the strongest evidence of intactness. However, the use of the Bio-Rad\nddPCR platform is a critical feature of Q4ddPCR since the current gold-standard IPDA assay uses the same\nplatform. Q4ddPCR was intentionally designed to ensure comparability within the field as well as backwards\ncompatibility with IPDA results.\nIn addition, our manuscript offers a deeper sequence-informed design and validation of the assay, together with\nevidence of successful implementation across multiple independent cohorts. As a result, Q4ddPCR differs from\nthe Rainbow assay in several key respects:\nFirst, Q4ddPCR incorporates ready-to-use backup primer/probe sets and alternative readouts that directly\naddress IPDA amplification failures, reducing the overall failure rate from 12-30% to only 5%. In contrast, all\nalternative Rainbow-readouts still depend on env-Ψ-positivity and therefore do not mitigate these failures.\nMoreover, the alternative readouts of Q4ddPCR allow sensitive tracking of very small reservoir sizes when no 4-\ntarget positive proviruses are detected. By comparison, the Rainbow assay first tests for amplification failures at\nthe single-channel level and only falls back to 4- to 2-color env-Ψ-positive readouts when failures are detected.\nIn practical terms, this strategy leads to (1) underestimation of intact reservoirs in participants who do not have\nsignal failures, but whose reservoir size falls below the limit of detection of a five-target assay and (2) high\nfrequencies of samples with no reportable intact proviral values. Indeed, across the Rainbow cohort using their\ndecision tree including the individualized primer/probes, intact reservoirs were quantifiable in only 59% of\nparticipants.\nSecond, the pre-validated alternative primer/probes and readouts in Q4ddPCR make it markedly more labor-\nefficient, cost-effective and suitable for large clinical trials than the Rainbow assay, which suggests designing\nindividualized primer/probe sets. Our approach was driven by our experience in the eCLEAR trial (Gunst et al. –\nNature Medicine 2023, DOI: 10.1038/s41591-022-02023-7), where we designed participant specific\nprimer/probes for participants with IPDA failures which exceeded the capacities for most clinical trials. Avoiding\nindividualized primer probe sets and decreasing the dropout rate in clinical trial is particularly important as it can\ncompromise statistical power.\nTherefore, we strongly believe that Q4ddPCR provides a flexible, scalable and specific tool that has major\nadvantages over Rainbow assay and can be readily implemented in the field. In fact, Q4ddPCR is already being\nused in several clinical trials such as the ACTG A5386 trial (NCT04340596) as well as the landmark RIO study\n(NCT04319367).\nWe thank the reviewer for suggesting the following point:\n“Minor point: it would be useful to provide an exact p value of the comparison between Q4PCR and 4-target\nQ4ddPCR in figure 2b. It only says \"ns\" (not significant) which means it is 0.05, but a p value of 0.5 is surely\ndifferent from a p value of 0.06. From looking at the figure, it is clear though, that Q4ddPCR still overestimates\nthe number of intact proviruses measured by Q4PCR.”\nWe thank the reviewer for this remark. The figure has been updated accordingly. The 4-target Q4ddPCR readout\nshows slightly higher intact proviral frequencies than Q4PCR, although the difference is not statistically\nsignificant. This pattern aligns with known limitations of Q4PCR, where the long-distance PCR step can\nunderestimate full-length intact genomes by preferentially amplifying shorter defective proviruses. While some\ndegree of overestimation by Q4ddPCR cannot be ruled out, underestimation by Q4PCR likely contributes to the\nobserved difference between the assays.\nReviewer #2 (Remarks to the Author):\nWe thank the reviewer for pointing out that:\n“Scheck et al report the modification of the sequencing based Q4PCR to a ddPCR based assay for high\nthroughput quantification of intact proviruses. The assay is validated on a previously sequenced cohort and is\nthen used to describe intact proviruses in a number of cohorts including a longitudinal cohort where they show a\nfaster decline in intact proviruses compared to standard IPDA. There appears to be good separation between\nthe different populations which is good and the assay seems robust.”\nAnd that:\n“Overall they show that Q4ddPCR reports less intact proviruses compared to standard IPDA which they attribute\nto IPDA overcalling intact proviruses. There is no doubt that correct enumeration of intact proviruses is of critical\nneed to the field however, a number of other assays have been published using more than the standard two\ntargets such as the 5T-IPDA (PMID: 33948574) and the Rainbow assay (PMID: 39749517), although it appears\nas though the dropout rate is much reduced here. Therefore, its an important technical advance.”\nWe thank the reviewer for recognizing the thorough sequence-based validation of Q4ddPCR and for noting that\nQ4ddPCR represents a meaningful technical advance.\nIn addition to a reduced assay dropout rate, we would like to highlight important differences between Q4ddPCR\nand other multi-target assays such as the Rainbow and 5T-IPDA assay:\nQ4ddPCR Rainbow 5T-IPDA\n5 target assay:\n2 separate sets of Combination of 2 triplex\nprimer/probes with 4 5 target assay: env, Ψ, gag, ddPCRs with each two\ntargeting regions (Q4- and pol and RU5 for total HIV unique targets (3’ end pol\nIPDA-based Q4ddPCR): DNA, one fluophore per and tat, and LTR/gag and\nenv, Ψ gag, and pol, one target, computational 5’end pol) and one common\nAssay design fluophore per target, compensation for signal target for inter-assay control\nprimer/probes derived spillover, env- and Ψ- (env-target), shared\nentirely from Q4PCR or env- primer/probes derived from fluophores with different\nand Ψ-primer/probes IPDA, gag and pol derived concentrations (FAM low\nderived from IPDA, gag and from Q4PCR and high, and HEX low and\npol derived from Q4PCR high), newly designed\nprimer/probes\nStandardized decision tree,\nprioritizes specificity,\nStandardized decision tree,\nalternative readouts only\nprioritizes specificity,\nin cases of amplification\nReporting for includes ready-to-use 5 targets must be positive\nfailure, only env-Ψ-\nintactness backup primer/probes and for intactness\npositive readouts,\nalternative readouts\nproposed backup strategy:\ndesign of custom\nprimer/probes\nDrop-out rate 5% in 179 samples 41% in 83 samples 20% in 157 samples\nStandard material, PBMCs\nStandard material, HIV-\nStandard material and infected in vitro with 6 HIV-1\nPULSE sequencing and\nQ4PCR-target isolates, in silico analysis\nsubsequent in silico\nValidation combination-matched against clade B sequences\nanalysis with defined criteria\nsequences (3,650 with defined criteria for\nfor mismatches (855\nsequences, 558 intact) mismatches (1071\nsequences, 67 intact)\nsequences, 67 intact)\nIn silico criteria: 5\nIn silico criteria: 5\nCriteria for Target-combination mismatches in\nmismatches from the primer\ndPCR assay matched qPCR signal in primers/probe set and a\nsequences and 0\nperformance Q4PCR maximum of 3 mismatches\nmismatches from the probe\nper primer/probe\nCorrelation with QVOA\nAdditional (Q4-based assay Correlation with QVOA\nspecificity Spearman’s r = 0.74, p = No data reported (Spearman’s r = 0.48, p =\nassessment 0.001 and IPDA-based 0.02)\nassay r = 0.57, p = 0.02\nGating with ddpcRquant\nGating in QX-Manager, with manual adaptation,\nautomated processing with analysis with R Shiny web Automated gating, analysis\nAnalysis R, Output format: application either with Excel tables or\ncopies/106 CD4+ cells (https://digpcr.ugent.be/soft R-script\nshear corrected ware.html), Output format\ncopies/µL\nNormalization Quantification per T cells (T\nand shear RPP30 RPP30 cell receptor D gene) and\ncorrection RPP30\nPlatform QX600 (Bio-Rad) QIAcuity (Qiagen) QX200 (Bio-Rad)\nA key distinction between Q4ddPCR and other recently published multi-target assays such as the 5T-IPDA and\nRainbow assay lies in the strategy used to evaluate assay performance. Both the 5T-IPDA and Rainbow assay\nvalidated assay specificity primarily through in silico matching of primers and probes to participant-derived\nproviral sequences. This approach is informative, yet it is also limited, because in silico predictions cannot\nalways reliably determine whether a given pattern of mismatches will extinguish or merely attenuate PCR signal.\nAs a result, in silico validation risks overestimating specificity by assuming that sequences predicted to fail\namplification indeed fail in wet-lab conditions.\nTo avoid this limitation, we used a different validation strategy: we selected samples from 13 PWH for whom we\nhad experimentally derived proviral sequences directly linked to Q4PCR signals generated with the same\nprimer/probe sets used in Q4ddPCR. This allowed us to evaluate specificity using observed rather than\npredicted amplification behavior and provided a rigorous benchmark for developing the decision tree. The trade-\noff of this approach is that long-distance PCR used in Q4PCR under-amplifies full-length intact proviruses,\nlikely causing a slight underestimation of Q4ddPCR specificity in our dataset. Besides validating Q4ddPCR,\nthis also provides valuable insights into reservoir composition of different individuals and shows that these insights can be\nachieved using high throughput methods as Q4ddPCR.\nNotably, our evaluation included a much higher number of sequences, especially we used substantially more\nintact sequences (558) compared to 29 in the Rainbow assay or 67 for the 5T-IPDA, which allowed higher\nconfidence in our specificity estimates.\nFinally, Q4ddPCR was intentionally built and validated to be backward-compatible with IPDA, enabling\nintegration into longitudinal cohorts where IPDA data already exist and ensuring continuity across ongoing\nstudies. This differentiates Q4ddPCR from the Rainbow assay and 5T-IPDA and supports its practical adoption\nin the field.\nThe reviewer raises additional points:\n“It doesn’t solve one of the major issues holding back the field which is applicability to diverse subtypes.”\nWe agree that applicability across diverse HIV subtypes remains a major challenge for all current molecular\nreservoir assays, including our own. Current implementation performs best for subtype B. However, because\nQ4ddPCR is modular, primer/probe sets can be exchanged by subtype-specific alternatives and our preliminary\nwork with subtype C env- and Ψ-primer/probes demonstrates feasibility. Nevertheless, full validation across\ndiverse subtypes will require future work.\n“1. What proportion of samples can be enumerated using the 4 target system and how many must revert\nto 3 or 2 targets. This is an important piece of information that would allow for the reader to understand\nhow significant an improvement this assay is. Perhaps this can be reported in the decision tree in figure\n3 (for each cohort or combined)?”\nThank you for pointing this out. We agree that this is important information and have added text to the discussion\nof our manuscript. In our dataset of 179 samples, in 65% proviruses were detectable with 4 targets, while 24%\nused 3-target-positive proviruses as surrogate for intact proviruses (21% env-gag-Ψ, 2% env-Ψ-pol, 1% env-\ngag-pol). An additional 6% had to rely on 2-target-positive proviruses as readout for intactness (3% env-Ψ, 3%\nenv-gag). In 5% of samples no intact proviruses were detected. Thus, a total of 86% of samples had a\nquantifiable reservoir using the two highest hierarchies of the decision tree.\n“2. Not fully clear to this reviewer, are the authors advocating for Q4ddPCR or for the IDPA-Q4ddPCR?\nThe longitudinal analysis appears done with IDPA-Q4ddPCR. Perhaps this can be made clear in the\nabstract.”\nWe thank the reviewer for this comment. To clarify both the Q4-based Q4ddPCR and the IPDA-based Q4ddPCR\nare implementations of the same 4-target Q4ddPCR framework. We validated the assay primarily using the Q4-\nbased Q4ddPCR, as it employs exactly the same primer/probe sets as Q4PCR and therefore allows a direct,\nsequence-matched assessment of sensitivity and specificity.\nWe introduced the IPDA-based Q4ddPCR for two reasons: (1) to provide a practical and easy solution for Ψ-\ntarget failures observed in a subset of samples, and (2) to ensure backward compatibility with existing IPDA\ndatasets. Because the A5321 cohort analysis was part of a longitudinal study for which IPDA measurements had\nbeen generated at different time points in the study, we used the IPDA-based Q4ddPCR for that longitudinal\nassessment, complementing it with Q4-based Q4ddPCR when amplification failures occurred. Overall, we\nobserved similar performance and high correlation between our two Q4ddPCR assays as well as with viral\noutgrowth. For clarification, we have revised the abstract to explicitly introduce the two primer/probe sets.\n“3. The differences in intact provirus decline observed in ACTG cohort, is there statistical justification to\ndemonstrate these are real differences? They should be reported as this is claimed in the abstract”\nWe thank the reviewer for raising this important point. We performed a log-linear mixed-effects analysis to test\nwhether the decline in intact proviruses observed with Q4ddPCR differed from that measured by IPDA. The\ndifference in slopes between Q4ddPCR and IPDA was not statistically significant (Chi² test, p = 0.33). When\nexcluding samples with IPDA amplification failures, the difference approached but did not reach statistical\nsignificance (p = 0.11). We have added this information and the p-values to the manuscript. Although the\ndifference is not statistically significant, we believe the observed divergence is still meaningful to report for two\nreasons. First, it is consistent with prior mathematical modeling and with intact reservoir decline patterns\nobserved using Q4PCR and second the separation between assays increases over time for both the absolute\nnumber of intact proviruses and the intact proviral fraction, suggesting a biological rather than stochastic\ndifference.\n“4. Assume difference between Fig 2 and extended Fig 2 is one is Q4ddPCR and one is the IPDA-based\nQ4ddPCR. The labeling is exactly the same on both though, could this be made clearer?”\nThank you, we added clearer labels to the figures.\n“5. Fig 2b: many data points missing for ‘4 targets’, is it because no counts observed? Can this be\nreflected in the graph?”\nThe absence of data points in the “4-target” category is indeed due to samples in which no 4-color-positive\nproviruses were detected. This is due to our validation cohort which was selected to represent a high benchmark\nspecifically including participants with known primer/probe mismatches and amplification-failures. This choice\ninherently increases the proportion of samples lacking 4-target (and to a lesser extent 3-target) positive counts.\nTo make this clearer, we have now explicitly added the missing data points to Fig. 2b and Extended Data Fig. 5a,\nallowing readers to directly visualize the samples in which no 4- or 3-target positives were observed.\n“6. Line 180: If interpreted correctly, it would be worth stating that psi was restored with IPDA-version in\n2 of 4 donors and 1 of 4 restored using Q4-version “\nThank you, we clarified this in the manuscript.\n“7. Line 185: This seems an important point but is this data captured in fig 2d and ext fig 2c - the circles\nfor env-Ψ are bigger than env-gag and env-gag-pol. Can this be demonstrated in a different way?”\nWe thank the reviewer for pointing out the importance of this observation. We clarified in the revised manuscript\nthat in participant 9242 the dominant intact clone is env-Ψ-positive, but that a smaller population of sequence-\nconfirmed intact env-gag- (and env-gag-pol-)positive clones is also present. These smaller intact clones decline\nover time and would be missed if only env-Ψ/IPDA-type readouts were used. This illustrates that Q4ddPCR may\noffer semi-qualitative insights into reservoir composition and clonal dynamics in a high throughput setting.\nTo address the reviewer’s suggestion for clearer visualization, we added a new panel to Extended Data Fig. 5.\nIn this panel, we aggregated env-Ψ-positive and env-gag-positive signals separately. For direct comparison and\nvisualization, we normalized Q4PCR sequence counts and Q4ddPCR proviral counts to the env-Ψ-value at the\nfirst time point. This allows direct comparison of the longitudinal dynamics captured by Q4PCR and Q4ddPCR\nand illustrates how Q4ddPCR detects changes in minor clones that would otherwise be overlooked.\n“8. Line 300: One assumed because this is IPDA-Q4ddPCR this is due to Psi failure and use of env-gag\ndroplets to quantify intact? Can authors confirm this was how samples were rescued?”\nYes, thank you for pointing this out. We added a statement on how they were rescued in the results text.\n“9. Line 407: Is this statement correct? Was there not a significantly higher detection of intact proviruses\nby IPDA (figure 4c)?”\nThe comparison shown in Figure 4c compares intact proviral estimates obtained using the full Q4ddPCR\ndecision tree with the conventional IPDA intact proviruses obtained in a separate reaction. Because the decision\ntree prioritizes 4- and some 3-target-positive classifications over the env-Ψ-readout, Q4ddPCR yields lower\nintact counts than IPDA in this comparison.\nHowever, the original statement in the manuscript “In the KOHIVI cohort, Q4ddPCR env-Ψ values closely\nmatched IPDA estimates, showing that Q4ddPCR preserves IPDA information for studies seeking backward\ncompatibility.” refers to the middle panels of Figure 4a, where we directly compared IPDA intact proviral counts\nwith the env-Ψ-only readout extracted from Q4ddPCR (i.e., intentionally ignoring gag and pol targets to mimic\nIPDA classification). In this comparison, Q4ddPCR and IPDA show high correlation and concordance\n(Spearman’s r = 0.77 and 0.81, p < 0.0001; ρ = 0.76 [95% CI 0.54 - 0.89] and 0.75 [95% CI 0.50 - 0.88]).\nc\nThis supports our conclusion that Q4ddPCR preserves IPDA-equivalent information, enabling backward\ncompatibility for studies or cohorts with previously acquired IPDA time points while providing a more specific\nclassification when the full decision tree is used.\n“10. Line 526: Is it possible to provide further information about how droplets were classified as 2, 3 or 4\ntarget positive? Was this performed using the biorad software or required in house R code? This\ninformation may be helpful for other labs that may be interested in establishing the q4ddPCR and\nmaking decisions about what machines to purchase (maybe even in extended data).”\nWe thank the reviewer for this helpful suggestion. Additional methodological details have been added to the\nrevised manuscript. In brief: Initial droplet gating is performed in Bio-Rad’s QX Manager Software, using all six\npossible 2-dimensional fluorophore combinations. The gated data are exported and processed using an R\npackage available on the Buchauer Lab GitHub (https://doi.org/10.5281/zenodo.15791354). To simplify use, the\nGabler Lab GitHub page (https://doi.org/10.5281/zenodo.16414846) provides a guide, a script (RunFile.R) for\nrunning the package and an additional script (Processing_Q4ddPCR.R) that automates downstream processing.\nThe ‘RunFile.R’ computes all possible 1-, 2-, 3-, and 4-target combinations, provides their concentrations\n(copies/μL) and normalized values (per million CD4+ T cells). It automatically applies shear correction and\ncalculates total HIV DNA. The ‘Processing_Q4ddPCR.R’ script implements the Q4ddPCR decision tree to\ndetermine intact classification, a quality control, and if desired IPDA-equivalent values.\nReviewer #3 (Remarks to the Author):\nWe thank the reviewer for pointing out that:\n“Scheck et al. present a multiplexed digital PCR version that is based on their previously published Q4PCR\nworkflow backed up with a solid evaluation against full-length sequences.“\nThe reviewer brings up the following considerations:\n“1/ Consult the dMIQE2020 update guidelines and checklist for dPCR experiment reporting to inspect\nwhether or not additional information could be provided.”\nWe thank the reviewer for this helpful suggestion, which guided us to expand the methodological description and\nto align more closely with the dMIQE2020 guidelines. Specifically, we added detailed information on nucleic acid\nextraction, quantification and storage, amplicon lengths and HXB2 genomic coordinates for Q4ddPCR\nprimer/probe sets even when previously published, additional details on assay optimization using standard\nmaterial (J-Lat 6.3 DNA), descriptions of analytical sensitivity, analytical specificity, technical repeatability and\nreproducibility and representative examples of positive and negative two-dimensional droplet plots.\n“2/ Assay performance characteristics and metrics could be extended to determine LOB, LOD, LOQ,\nresolution between clusters. Are there dilution series performed on J-Lats? If not, would advise to\ninclude this. If sufficient NTCs were already run a LOB calculation could also be included. At the end the\nauthors evaluate specificity and sensitivity but this could be confusing as these also can refer to the\nmore technical aspects of an assay.”\nWe appreciate the reviewer's recommendation to expand the technical performance metrics. We added new\nExtended Data Figures and Tables summarizing the assessment of linearity and dynamic range, repeatability\nand reproducibility as well as analytical sensitivity and specificity.\nWe performed dilution series using J-Lat 6.3 DNA, but because HIV proviral templates in clinical samples\nfrequently contain sequence mismatches that reduce signal amplitude and cluster separation, we complemented\nthese experiments with full-process (from thawing PBMCs, isolating CD4+ T cells, extracting DNA to performing\nthe essay itself) reproducibility measurements in four participants with lower reservoir sizes. These experiments\nconfirmed internal consistency and close agreement between the Q4-based and IPDA-based Q4ddPCR\nconfigurations.\nWe also clarified throughout the manuscript when we refer to analytical sensitivity/specificity (e.g., LOB, LOD)\nversus the biological sensitivity/specificity describing the predictive capacity of Q4ddPCR to classify intact\nproviruses in clinical samples despite HIV sequence heterogeneity.\n“3/ There is competition between the ENV-GAG and the GAG-POL, were any optimization approaches\ntried to further orthogonalize the position of the clusters?”\nWe thank the reviewer for this comment. We agree that maximizing cluster separation is critical for robust gating\nof multi-probe combinations, particularly in the context of the HIV sequence heterogeneity where in some\nsamples primer/probe mismatches may result in reduced amplitude rather than complete extinction of signal.\nTo address this, we performed extensive optimization during assay development.\nSpecifically, we evaluated:\n- Probe concentration titrations for all four targets in both Q4ddPCR variants\n- Alternative fluorophore combinations to minimize spectral bleed-through and maximize cluster\nseparation\n- Probe modifications (locked nucleic acids or minor groove binders (MGB)) which improved\ncluster resolution only for the MGB-modified IPDA-Ψ and env-probes (including the unlabeled\nhypermutant probe). In contrast, modifying the gag or pol probes did not substantially improve\northogonality in the gag-pol and env-gag clusters.\n- Thermal cycling parameters, including annealing temperatures and number of cycles\nAs part of our design goal to maintain backwards compatibility with IPDA, we did not alter IPDA primers/probes\nnor their fluorophores.\nWe primarily optimized the assay on standardized material (J-Lat 6.3 DNA) but also on clinical samples, since\ncluster behavior can differ substantially in the presence of sequence heterogeneity. The final probe\nconcentrations, fluorophore assignments and cycling conditions represent the configuration that maximized\nresolution across both standard material and clinical samples.\n“4/ In terms of visualization, it would be helpful to color the true 4 color positive partitions in the 2D or 3D\nplots, so a visual inspection can be done of where these partitions lay. This can help in thresholding\nthese out as the abovementioned competition could result in partitions that are close to the single\npositive cluster.”\nWe agree that visualizing the 4-color-positive droplets would be extremely valuable for assessing their position\nrelative to negative clusters. We have consulted with Bio-Rad about this, as we were also interested in\ngenerating such visualizations. Unfortunately, the current version of QX Manager does not allow 4-channel\nvisualization of droplets within the same plot. To address this limitation, our gating strategy uses all six possible\ntwo-channel combinations, which allows us to infer the location of 4-target-positive droplets. In experiments with\nJ-Lat 6.3 DNA, we observed that most 4-target-positive droplets clearly separate from the negative clusters.\nHowever, we also acknowledge that a subset can lie close to the negative population, particularly in the pol-\nchannel (Fig. 1b, Extended Data Fig. 1c).\n“5/ Add comment on potential subtype performance of the Q4ddPCR (subtype B oriented) or highlight\nflexible redesign opportunities of these type of assays to accommodate other subtypes.”\nWe thank the reviewer for raising this important point that has been raised by Reviewer 2 as well. Q4ddPCR,\nlike IPDA, is currently optimized for subtype B, and its intactness calling relies on the same env probe used in IPDA.\nHowever, because Q4ddPCR incorporates built-in backup options (alternative probes and readouts), it is\ninherently more robust to subtype-related sequence variation than a strictly two-target assay. An additional\nadvantage is the modular design of Q4ddPCR. Primer/probe sets can be readily exchanged and subtype-\nspecific variants such as the published subtype B and C IPDA primer/probes can be directly integrated into the\nQ4ddPCR. This provides a practical path for adapting the method to non-B subtypes. However, validation on\nnon-B samples will be required before drawing definitive conclusions about subtype performance.\n“6/ On the Decision tree. How does it compare to other decision trees that are published? Would add\nsection on this.”\nWe thank the reviewer for raising this point. We compared our decision tree to other published frameworks as\npart of our response to Reviewer 1 and 2.\n“7/ Would the authors have data to support or hint towards the merit of adding more regions for\nintactness readouts and if there would be a limit in added value of performing for instance a 10 plex\nreaction. In other words, where is the limit?”\nWe thank the reviewer for this question. To explore the theoretical benefit of adding additional targets, we used\nour sequence-resolved dataset from 13 PWH for whom Q4PCR data were available. We constructed a\nmathematical model that estimated the expected sensitivity and specificity of hypothetical multi-probe\ncombinations by using the experimentally observed performance of each single probe (see figure below). As\nanticipated, increasing the number of targets improved specificity for detecting intact proviruses (a, d). However,\nthis gain exhibited saturation between four and five probes, while sensitivity (i.e., the probability that all probes\nwould test positive in this case) declined progressively as more probes were added (b, e). The combination of\nsensitivity and specificity reaches an optimal point with 4 targets (c).\nWe evaluated this model under two scenarios: (1) assuming no primer/probe mismatches (a-c) and (2)\nincorporating the mismatches observed in our dataset (d, e). In the second, more realistic scenario, sensitivity\ndropped sharply with each additional probe. This finding further strengthens the need for backup strategies such\nas alternative probes/readouts rather than simply increasing the number of primary probes. But it has also to be\nnoted that this model is simplified as it does not take into account the different specificity that specific probe\ncombinations might have (e.g. the higher specificity of env-Ψ-positive combinations over Ψ-gag-positive\ncombinations).\nFrom a practical perspective, we also found technical barriers to implementing 5- or 6-plex ddPCR, including\nfluorophore bleed-through, particularly problematic when partial probe mismatches cause reduced (rather than\nabsent) probe amplitudes. Finally, we did not include a 5′LTR probe because our Q4ddPCR readouts were\nalready highly concordant with total HIV DNA quantified by a 5′LTR assay (Extended Data Fig. 4).\nTaken together, both modeling and experimental constraints suggest diminishing benefits beyond four well-\nperforming targets, with increasing risk of sensitivity loss, technical artefact and mismatch-driven dropout when\nmoving toward very high-plex assays.\n“8/ The authors mention 40K cells as minimal input. Could the authors elaborate on this threshold\nnumber or other QC for input? Also, if a 0 results is present, could they comment on the absence of\nsignal not meaning absence of intact virus.”\nWe thank the reviewer for raising this important point. The threshold of 40,000 cell equivalents emerged from\npractical constraints associated with the ACTG A5321cohort, in which sample material was very limited and with\nthe aim of designing an assay that is suitable for other cohorts were sample size is limited e.g. pediatric cohorts\nor samples derived from mouse models. Because intact frequencies are normalized per million cells, lower DNA\ninputs inherently increase the risk of overestimation, particularly when 4-color droplets are rare. To evaluate the\nmagnitude of this effect, we computationally simulated how identical droplet counts would translate into intact\nfrequencies when assuming 20,000, 30,000, 40,000, 80,000, or 200,000 cell equivalents (the latter being our\nusual input).\nAs expected, estimates became increasingly inflated as input dropped below 40,000 cell equivalents, particularly\nin individuals with low reservoir sizes (10 - 30 intact/10⁶ cells, see below, each dot represents one replicative\nwell). Nevertheless, the deviation at 40,000 remained within an interpretable range for most samples. Based on\nthese analyses, we recommend aiming for 150,000 - 200,000 cell equivalents whenever possible and applying\nadditional caution when interpreting longitudinal trends if input varies substantially between time points.\nMonitoring cell equivalents via RPP30 thus serves as an important quality control criterion.\nRegarding 0 values, we agree with the reviewer that the absence of detectable 4-color positive proviruses could\ntheoretically mean that there are no intact proviruses present. This issue is intrinsic to all reservoir assays. We\nconsidered this carefully when comparing QVOA and Q4ddPCR. In all 16 PWH we had successful viral\noutgrowth. In this sample set Q4-based Q4ddPCR detected intact proviruses in 11 cases through 4-color\npositives and the remainder through valid 3-color positives. Similarly, IPDA-based Q4ddPCR detected intact\nproviruses using 4-colors in 15, 3-colors in 4 and 2-colors in 1 case. These findings support the robustness of\nour decision tree as Q4ddPCR correlates with viral outgrowth when it is applied and supports the hypothesis that\nif 4- or even 3-color positives are absent it does not necessarily mean that there are no intact proviruses.\nTherefore, in cases where zero results are obtained, we recommend (1) using alternative primer/probe sets to\nmitigate primer/probe mismatch associated dropout, (2) increasing cell equivalents where feasible and (3)\ncombining different reservoir measurements before concluding true absence of intact virus\n“9/ The authors might consider to rename to Q4dPCR in stead of Q4ddPCR, referring to all digital PCR\nsystems rather then only droplet-based systems.”\nWe thank the reviewer for this suggestion. At this stage, we prefer to retain the name Q4ddPCR as the assay\nwas developed, optimized and validated specifically on droplet-based digital PCR platforms which is well\nestablished and known in the field. But we certainly recognize that the underlying conceptual framework could\nbe adapted in the future to other digital PCR systems.\n“10/ The evaluation of Q4ddPCR with Q4PCR generated viral sequence data could skew/bias the\nabsolute % of sensitivity and specificity as the same primer sets are used. A cautionary note could be\nadded while interpreting these values.”\nWe thank the reviewer for highlighting this important point. Indeed, because Q4PCR relies on long-distance\nPCR, it is biased toward recovering defective proviruses and tends to under-represent full-length genomes. This\nbias can carry over into the performance evaluation of Q4ddPCR, since the sensitivity and specificity estimates\nare based on sequences that were themselves obtained using Q4PCR. As a result, our calculated specificity is\nlikely conservative: the reference dataset tends to be enriched for defective proviruses, increasing the probability\nof underestimating the accuracy of Q4ddPCR readouts. We have added a cautionary note in the discussion."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors provided a revised version of the manuscript and an extensive response to reviewers comments. While I appreciate their efforts, I still think their work provides only an incremental technical advance over the state of the art (e.g. the Rainbow assay) for measuring the HIV reservoir, rather than any conceptual advance. While this assay will probably be useful for HIV reservoir researchers, this is essentially a technical report describing a variation of already published assays with the same underlying principle.", "reviewer_2": "The authors have addressed my concerns and suggestions appropriately. The updated figures are improved for clarity. I have no further concerns. Michael Roche.", "reviewer_3": "No further comments. I thank the team and authors for adding clarifications and desired adaptations to the manuscript."}, "author_response": ""}]

Decision letter