Single-cell characterization and quantification of translation-competent viral reservoirs in treated and untreated HIV infection
Bibliographic record
Abstract
The phenotypic characterization of the cells in which HIV persists during antiretroviral therapy (ART) remains technically challenging. We developed a simple flow cytometry-based assay to quantify and characterize infected cells producing HIV proteins during untreated and treated HIV infection. By combining two antibodies targeting the HIV capsid in a standard intracellular staining protocol, we demonstrate that p24-producing cells can be detected with high specificity and sensitivity in the blood from people living with HIV. In untreated individuals, the frequency of productively infected cells strongly correlated with plasma viral load. Infected cells preferentially displayed a transitional memory phenotype and were enriched in Th17, peripheral Tfh and regulatory T cells subsets. These cells also preferentially expressed activation markers (CD25, HLA-DR, Ki67), immune checkpoint molecules (PD-1, LAG-3, TIGIT, Tim-3) as well as the integrins α4β7 and α4β1. In virally suppressed individuals on ART, p24-producing cells were only detected upon stimulation (median frequency of 4.3 p24+ cells/106 cells). These measures correlated with other assays assessing the size of the persistent reservoir including total and integrated HIV DNA, Tat/rev Induced Limiting Dilution Assay (TILDA) and quantitative viral outgrowth assay (QVOA). In ART-suppressed individuals, p24-producing cells preferentially displayed a transitional and effector memory phenotype, and expressed immune checkpoint molecules (PD-1, TIGIT) as well as the integrin α4β1. Remarkably, α4β1 was expressed by more than 70% of infected cells both in untreated and ART-suppressed individuals. Altogether, these results highlight a broad diversity in the phenotypes of HIV-infected cells in treated and untreated infection and suggest that strategies targeting multiple and phenotypically distinct cellular reservoirs will be needed to exert a significant impact on the size of the reservoir.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".