CotA laccase: high-throughput manipulation and analysis of recombinant enzyme libraries expressed in <i>E. coli</i> using droplet-based microfluidics
Bibliographic record
Abstract
We present a high-throughput droplet-based microfluidic analysis/screening platform for directed evolution of CotA laccase: droplet-based microfluidic modules were combined to develop an efficient system that allows cell detection and sorting based on the enzymatic activity. This platform was run on two different operating modes: the "analysis" mode allowing the analysis of the enzymatic activity in droplets at very high rates (>1000 Hz) and the "screening" mode allowing sorting of active droplets at 400 Hz. The screening mode was validated for the directed evolution of the cytoplasmic CotA laccase from B. subtilis, a potential interesting thermophilic cathodic catalyst for biofuel cells. Single E. coli cells expressing either the active CotA laccase (E. coli CotA) or an inactive frameshifted variant (E. coli ΔCotA) were compartmentalized in aqueous droplets containing expression medium. After cell growth and protein expression within the droplets, a fluorogenic substrate was "picoinjected" in each droplet. Fluorescence-activated droplet sorting was then used to sort the droplets containing the desired activity and the corresponding cells were then recultivated and identified using colorimetric assays. We demonstrated that E. coli CotA cells were enriched 191-fold from a 1 : 9 initial ratio of E. coli CotA to E. coli ΔCotA cells (or 437-fold from a 1 : 99 initial ratio) using a sorting rate of 400 droplets per s. This system allows screening of 10(6) cells in only 4 h, compared to 11 days for screening using microtitre plate-based systems. Besides this low error rate sorting mode, the system can also be used at higher throughputs in "enrichment" screening mode to make an initial purification of a library before further steps of selection. Analysis mode, without sorting, was used to rapidly quantify the activity of a CotA library constructed using error-prone PCR. This mode allows analysis of 10(6) cells in only 1.5 h.
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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 | 0.000 | 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) | 0.001 | 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 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".