Spheroplasted cells: a game changer for DNA delivery to diatoms
Bibliographic record
Abstract
ABSTRACT Diatoms produce 20% of the world’s fixed organic carbon annually, making them vital to global carbon fixation and climate change mitigation. Their potential as cell factories for biofuels, proteins, and other high value chemicals remains underutilized due to a lack of genetic engineering tools, with DNA delivery being one of the biggest challenges. Here, we present optimized electroporation and polyethylene glycol transformation methods for delivering DNA and ribonucleoprotein complexes to Phaeodactylum tricornutum , a model diatom species and emerging chassis for algal biotechnology. It was possible to recover transformants with as little as 1 ng of DNA, and to transform linear or circular episomes as large as 55.6 kb. With the optimized electroporation protocol, episomes can be assembled in the algal cell de novo through diatom in vivo assembly (DIVA), forgoing the need for time-consuming traditional cloning steps in Escherichia coli and Saccharomyces cerevisiae . It was also possible to electroporate a Cas9 ribonucleoprotein complex in P. tricornutum , providing an alternative to biolistics for DNA free genome engineering. We have demonstrated that the PEG approach can be adapted to successfully transform Thalassiosira pseudonana , demonstrating the applicability of our methods for engineering other diatom species. These tools can be used to accelerate diatom synthetic biology projects and, therefore, the development of sustainable technologies.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".