Flow Virometry Quantification of Host Proteins on the Surface of HIV-1 Pseudovirus Particles
Bibliographic record
Abstract
The HIV-1 glycoprotein spike (gp120) is typically the first viral antigen that cells encounter before initiating immune responses, and is often the sole target in vaccine designs. Thus, characterizing the presence of cellular antigens on the surfaces of HIV particles may help identify new antiviral targets or impact targeting of gp120. Despite the importance of characterizing proteins on the virion surface, current techniques available for this purpose do not support high-throughput analysis of viruses, and typically only offer a semi-quantitative assessment of virus-associated proteins. Traditional bulk techniques often assess averages of viral preparations, which may mask subtle but important differences in viral subsets. On the other hand, microscopy techniques, which provide detail on individual virions, are difficult to use in a high-throughput manner and have low levels of sensitivity for antigen detection. Flow cytometry is a technique that traditionally has been used for rapid, high-sensitivity characterization of single cells, with limited use in detecting viruses, since the small size of viral particles hinders their detection. Herein, we report the detection and surface antigen characterization of HIV-1 pseudovirus particles by light scattering and fluorescence with flow cytometry, termed flow virometry for its specific application to viruses. We quantified three cellular proteins (integrin α4β7, CD14, and CD162/PSGL-1) in the viral envelope by directly staining virion-containing cell supernatants without the requirement of additional processing steps to distinguish virus particles or specific virus purification techniques. We also show that two antigens can be simultaneously detected on the surface of individual HIV virions, probing for the tetraspanin marker, CD81, in addition to α4β7, CD14, and CD162/PSGL-1. This study demonstrates new advances in calibrated flow virometry as a tool to provide sensitive, high-throughput characterization of the viral envelope in a more efficient, quantitative manner than previously reported techniques.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 teacher head, 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".