Colloid chemistry pitfall for flow cytometric enumeration of viruses in water
Bibliographic record
Abstract
Flow cytomtery (FCM) has become a standard approach to enumerate viruses in water research. However, the nature of the fluorescent signal in flow cytometric analysis of water samples and the mechanism of its formation, have not been addressed for bacteriophages expected in wastewaters. Here we assess the behaviour of fluorescent DNA-staining dyes in aqueous solutions, as well as sensitivity and accuracy of FCM for enumeration of DNA-stained model bacteriophages λ, P1, and T4. We demonstrate that in aqueous systems fluorescent dyes form a self-stabilized (pseudolyophilic) emulsion of auto-fluorescing colloid particles. Sample shaking and addition of surfactants enhance auto-fluorescence due to increased dispersion and, in the presence of surfactants, stabilization of the dye emulsion. Bacteriophages with genome sizes <100 kbp (i.e. λ & P1) did not generate a distinct population signal to be detected by one of the most sensitive FCM instruments available (BD LSR Fortessa™ X-20), whereas the larger T4 bacteriophage was resolved as a distinct population of events. These results indicate that the use of fluorescent dyes for bacteriophage enumeration by flow cytometry can produce false positive signals and lead to wrong estimation of total virus counts by misreporting colloid particles as virions, depending on instrument sensitivity.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".