Flow Virometry for Characterizing the Size, Concentration, and Surface Antigens of Viruses
Bibliographic record
Abstract
Application of flow cytometry principles for the analysis of viruses has been referred to as flow virometry (FVM). FVM is a multiparametric, high-throughput, and sensitive technique that allows viral particles to be detected, quantified, and characterized based on the biophysical properties of the virus and the expression of proteins on their surface. More specifically, by calibrating the flow cytometer with reference materials, it is possible to measure the concentration of intact viral particles in a sample, the abundance of a target antigen on the surface of the virus, and the relative diameter of the virus. Here, we describe a comprehensive overview of procedures used to stain, detect, and quantify viral and host-derived proteins located on the surface of retroviruses. These outlined techniques can be applied for the rapid phenotypic characterization of retroviruses, other enveloped viruses, and generally most viruses at the single-particle level through the direct staining of viruses collected from the supernatant of infected cells, without the need for enrichment or purification. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Virus production Basic Protocol 2: Instrument setup, standardization, and quality control for fluorescence quantification Basic Protocol 3: Flow virometry analysis Basic Protocol 4: Viral surface antigen staining and fluorescence quantification Support Protocol: Determination of the optimal antibody concentration for virus staining Basic Protocol 5: Gain configuration optimization.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".