A Suite of Stains: Characterization of four fluorophores as complementary tools for visualizing neutral lipids in an extremophilic green alga
Bibliographic record
Abstract
Abstract Understanding lipid metabolism in algae is critical to advancing our knowledge on fundamental algal physiology and for harnessing these organisms as platforms for the sustainable production of high-energy lipids. BODIPY is the most prevalently used fluorescent dye for the visualization of lipid droplets (LDs) in algae; however, its limitations warrant exploration of alternatives. Here we evaluate and compare four lipophilic fluorophores (BODIPY, DAF, Cou, DPAS) for their effectiveness in staining LDs in the extremophilic green alga Chlamydomonas priscui . We assess each dye’s photophysical properties, synthetic accessibility, LD specificity, cellular toxicity, and suitability for microscopy and flow cytometry. All four dyes successfully stain LDs, but their performance diverges under different experimental conditions. BODIPY permits long-term incubation allowing quantification in time-course studies but exhibits poor LD specificity and high susceptibility to photobleaching. DAF enables polarity-sensitive staining but is highly toxic on prolonged exposure or during cellular stress. Cou and DPAS yield strong LD-specific signals with low cytotoxicity, making them ideal for studies involving environmental stress. However, DPAS requires room-temperature incubation, pointing toward greater potential utility for non-extremophilic algae. These results expand the toolbox for lipid biotechnology research in extremophiles and underscore the importance of tailoring dye selection and experimental conditions to algal physiology.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".