A Three-Dimensional Flow Control Concept for Single-Cell Experiments on a Microchip. 2. Fluorescein Diacetate Metabolism and Calcium Mobilization in a Single Yeast Cell As Stimulated by Glucose and pH Changes
Bibliographic record
Abstract
Using a three-dimensional flow control concept to manipulate and retain a single yeast cell in a microchip, we were able to study the kinetics of intracellular metabolism and calcium mobilization at the single-cell level, as stimulated by glucose and pH changes. As a model study, the fluorogenic substrate fluorescein diacetate (FDA) was chosen to study how the intracellular carboxylesterase metabolize it. A single yeast cell was first cultured in the microchip. Thereafter, under a constant concentration of FDA, influx of FDA into the yeast cell occurred and FDA was hydrolyzed or metabolized. It was found that changes in both pH and glucose stimulated the FDA metabolism in a yeast cell, and the stimuli can elicit multiple responses from the cell. Since it was carried out within the microchip, the whole experiment on one single yeast cell could last for as long as 10 h. The dormant cell, budding cell, and pretreated budding cell (in low-pH buffer) of yeast resulted in different responses. Experimental data provided details on the FDA metabolism at the single-cell level and revealed strong correlations between FDA metabolism and calcium mobilization. Furthermore, efflux of the FDA metabolite fluorescein could start spontaneously if there was glucose in the medium. The experiments on a single cell were of the "human cell conservation" style because the cell responded to the reagent changes implemented by the human researcher. A mathematical model was also developed to study the influx-hydrolysis-efflux processes of the FDA metabolism using single-cell fluorescent data. These long overdue single-cell experiments are now rendered possible using the three-dimensional flow control in the microchip.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".