Single-Particle Discrimination of Retroviruses from Extracellular Vesicles by Nanoscale Flow Cytometry
Bibliographic record
Abstract
Retroviruses and small EVs overlap in size, buoyant densities, refractive indices and share many cell-derived surface markers making them virtually indistinguishable by standard biochemical methods. This poses a significant challenge when purifying retroviruses for downstream analyses or for phenotypic characterization studies of markers on individual virions given that EVs are a major contaminant of retroviral preparations. Nanoscale flow cytometry (NFC), also called flow virometry, is an adaptation of flow cytometry technology for the analysis of individual nanoparticles such as extracellular vesicles (EVs) and retroviruses. In this study we systematically optimized NFC parameters for the detection of retroviral particles in the range of 115-130 nm, including viral production, sample labeling, laser power and voltage settings. By using the retroviral envelope glycoprotein as a selection marker, and evaluating a number of fluorescent dyes and labeling methods, we demonstrate that it is possible to confidently distinguish retroviruses from small EVs by NFC. Our findings make it now possible to individually phenotype genetically modified retroviral particles that express a fluorescent envelope glycoprotein without removing EV contaminants from the sample.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".