Combined single-cell transcriptional, translational, and genomic profiling reveals HIV-1 reservoir diversity
Bibliographic record
Abstract
Although understanding the diversity of HIV-1 reservoirs is key to achieving a cure, their study at the single-cell level in primary samples remains challenging. We combine flow cytometric multiplexed fluorescent in situ RNA hybridization for different viral genes with HIV-1 p24 protein detection, cell phenotyping, and downstream near-full-length single-cell vDNA sequencing. Stimulation-induced viral RNA-positive (vRNA + ) cells from viremic and antiretroviral-therapy (ART)-suppressed individuals differ in their ability to produce p24. In participants on ART, latency-reversing agents (LRAs) induce a wide variety of viral gene transcription and translation patterns with LRA class-specific differences in reactivation potency. Reactivated proviruses, including in p24 + cells, are mostly defective. Although LRAs efficiently induce transcription in all memory cell subsets, we observe induction of translation mostly in effector memory cells, rather than in the long-lived central memory pool. We identify HIV-1 clones with diverse transcriptional and translational patterns between individual cells, and this finding suggests that cell-intrinsic factors influence reservoir persistence and heterogeneity.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".