Micro-plaque assays: A high-throughput method to detect, isolate, and characterize bacteriophages
Bibliographic record
Abstract
Abstract The gold standard for the isolation and characterization of bacteriophages (phages), the plaque assay, has remained almost unchanged for over 100 years. The need for improvements to its scalability has been driven home by successes with personalized phage therapy requiring large phage libraries and rapid sensitivity testing. Using a robotic pinning platform, we miniaturized plaque assays from bacterial lawns to micro-colonies from 100 nl of inoculant, increasing throughput by >1000 fold without compromising sensitivity. A comparable manual workflow with one quarter the throughput maintained the same sensitivity. These micro-plaque assays can replace plaque assays as a new gold standard in phage biology. As proof of principle, we used our technique to isolate and de-replicate 21 unique Pseudomonas aeruginosa phages from a single environmental sample. We then demonstrated – using the same assay - that of 17 multi-drug resistant clinical P. aeruginosa strains, 15 were susceptible to infection by one or more of the 21 phages tested. Our method allows rapid isolation and de-replication of phages, as well as enabling screening of large phage libraries against bacterial isolates of interest.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".