Optimization of staining conditions for microalgae with three lipophilic dyes to reduce precipitation and fluorescence variability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
When the fluorescence signal of a dye is being quantified, the staining protocol is an important factor in ensuring accuracy and reproducibility. Increasingly, lipophilic dyes are being used to quantify cellular lipids in microalgae. However, there is little discussion about the sensitivity of these dyes to staining conditions. To address this, microalgae were stained with either the lipophilic dyes often used for lipid quantification (Nile Red and BODIPY) or a lipophilic dye commonly used to stain neuronal cell membranes (DiO), and fluorescence was measured using flow cytometry. The concentration of the cells being stained was found not to affect the fluorescence. Conversely, the concentration of dye significantly affected the fluorescence intensity from either insufficient saturation of the cellular lipids or formation of dye precipitate. Precipitates of all three dyes were detected as events by flow cytometry and fluoresced at a similar intensity as the chlorophyll in the microalgae. Prevention of precipitate formation is, therefore, critical to ensure accurate fluorescence measurement with these dyes. It was also observed that the presence of organic solvents, such as acetone and dimethyl sulfoxide (DMSO), were not required to increase penetration of the dyes into cells and that the presence of these solvents resulted in increased cellular debris. Thus, staining conditions affected the fluorescence of all three lipophilic dyes, but Nile Red was found to have a stable fluorescence intensity that was unaffected by the broadest range of conditions and could be correlated to cellular lipid content.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it