Microfluidic high-throughput selection of microalgal strains with superior photosynthetic productivity using competitive phototaxis
Bibliographic record
Abstract
Microalgae possess great potential as a source of sustainable energy, but the intrinsic inefficiency of photosynthesis is a major challenge to realize this potential. Photosynthetic organisms evolved phototaxis to find optimal light condition for photosynthesis. Here we report a microfluidic screening using competitive phototaxis of the model alga, Chlamydomonas reinhardtii, for rapid isolation of strains with improved photosynthetic efficiencies. We demonstrated strong relationship between phototaxis and photosynthetic efficiency by quantitative analysis of phototactic response at the single-cell level using a microfluidic system. Based on this positive relationship, we enriched the strains with improved photosynthetic efficiency by isolating cells showing fast phototactic responses from a mixture of 10,000 mutants, thereby greatly improving selection efficiency over 8 fold. Among 147 strains isolated after screening, 94.6% showed improved photoautotrophic growth over the parental strain. Two mutants showed much improved performances with up to 1.9- and 8.1-fold increases in photoautotrophic cell growth and lipid production, respectively, a substantial improvement over previous approaches. We identified candidate genes that might be responsible for fast phototactic response and improved photosynthesis, which can be useful target for further strain engineering. Our approach provides a powerful screening tool for rapid improvement of microalgal strains to enhance photosynthetic productivity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".