Outer Limits of Flow Cytometry to Quantify Viruses in Water
Bibliographic record
Abstract
The use of flow cytometry (FCM) with environmental or clinical samples to enumerate viruses (flow virometry) has become popular with the development of sensitive fluorescent dyes that bind to nucleic acids, yet there is no quantitative evidence of the sensitivity and accuracy for flow virometry as applied to aquatic environments. Rigorous background controls are missing. Here we address the gap in our knowledge of how the background interferes with interpreting flow virometry results. To remove background interference, we discovered it was essential to separate viruses from their water matrix and resuspended them in virus free Tris-EDTA buffer. Background substances and a SYBR Green dye colloid produce “virus-like” artifacts that generate false-positive viral counts. We show that neither human enteric viruses nor bacteriophage surrogates of a small genome size (<150 kbp) can be detected using standard FCM. We concluded that the current use of flow virometry is neither sensitive nor accurate enough to quantify most natural viral populations in aquatic environments. We recommend improved procedures for unambiguously proving the FCM signal is indeed viral. However, flow virometry is still limited by the inability of instruments to detect most natural viruses, yet other methods with the example of a sensitive prototype flow cytometer developed for nanomaterials (with a throughput of 10000 viruses per minute) could have the potential for online monitoring of viral abundance in both natural and engineered environments.
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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 | 0.011 | 0.020 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".