Substrate Engineering Enabling Fluorescence Droplet Entrapment for IVC-FACS-Based Ultrahigh-Throughput Screening
Bibliographic record
Abstract
In vitro compartmentalization-based fluorescence-activated cell sorting (IVC-FACS) is a powerful screening tool for directed evolution of enzymes. However, the efficiency of IVC-FACS is limited by the tendency of the fluorescent reporter to diffuse out of the droplets, which decouples the genotype and phenotype of the target gene. Herein we present a new strategy called fluorescence droplet entrapment (FDE) to solve this problem. The substrate is designed with a polarity that enables it to pass through the oil phase, react with the enzyme and generate an oil-impermeable and fluorescent product that remains entrapped inside the droplet. Several FDE substrates were designed, using two distinct substrate engineering strategies, for the detection of phosphotriesterases, carboxylesterases, and glycosidases activities. Model screening assays in which rare phosphotriesterase-active cells were enriched from large excesses of inactive cells showed that the enrichment efficiency achievable using an FDE substrate was as high as 900-fold: the highest yet reported in such an IVC-FACS system. Thus, FDE provides a means to tightly control the onset of the enzymatic reaction, minimize droplet cross-talk, and lower the background fluorescence. It therefore may serve as a useful strategy for the IVC-FACS screening of enzymes, antibodies, and other proteins.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".