Multiple Sampling in Single-Cell Enzyme Assays Using CE-Laser-Induced Fluorescence to Monitor Reaction Progress
Bibliographic record
Abstract
A novel method for assaying enzymes from a single cell or small cell populations is described. The key advantage of this method is the ability to repeatedly sample a single cell enzyme reaction. Whereas multiple sampling has been achieved for larger cell types with a diameter of 1 mm, we report a technique by which single cell enzyme assays of small cells (15 microm in diameter) can be repeatedly carried out. Individual cells were isolated using an in-house-built micromanipulator and placed in nanoliter-scale reaction vessels. The cells were lysed with solution containing substrate, and enzyme activity was assayed by removing 5-nL aliquots with a recently developed nanopipettor. The reaction aliquot was then analyzed using capillary electrophoresis with laser-induced fluorescence detection to quantitate enzyme activity. Sf9 cells were assayed at the single cell level and found to be highly heterogeneous with respect to alpha-glucosidase II activity. Since only 5 nL of the single cell reaction was removed, multiple sampling was possible, allowing triplicate analysis of enzyme activity for each individual cell. Multiple sampling also permitted a single cell reaction to be monitored over time. The sensitivity of this method was demonstrated in the analysis of a low-abundance enzyme, alpha1,3-N-acetylgalactosaminyltransferase, from single HT29 cells. Detecting the product of this enzyme reaction required minimizing the dilution of cellular contents. To demonstrate the potential applications of this methodology in small scale biochemical analyses, single Arabidopsis knf embryos lacking the alpha-glucosidase I encoding KNOPF gene were assayed. Mutant embryos demonstrated insignificant conversion of a triglucose substrate, as compared to wild type, confirming the deletion of alpha-glucosidase I. Embryos were simultaneously assayed for a second enzyme, beta-galactosidase, illustrating that the mutants were viable except for their lack of alpha-glucosidase I activity.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".