Sampling efficiency of a single‐cell capillary electrophoresis system
Bibliographic record
Abstract
Capillary electrophoresis (CE) combined with a laser-induced fluorescence (LIF) detection scheme is a powerful approach for single-cell analysis. For measurements requiring a high temporal resolution, CE-LIF is often combined with cell lysis systems based on pulsed lasers. Although extremely rapid, laser lysis has raised some concerns about the efficiency at which the cell contents are sampled. We have assembled a single-cell CE-LIF mounted on the stage of a microscope. This system was coupled with a nanosecond pulsed laser for cell lysis. We have analyzed green fluorescent protein (GFP) expressed in single mammalian cells and developed a novel approach to estimate the cell sampling efficiency (SE) based on the use of fluorescent calibration microspheres and flow cytometry. A significant advantage of this method is that it does not require any knowledge or assumption regarding the cell volume. We have evaluated the SE for different laser pulse energies (from 2 to 9 microJ) and two different pulse focal positions in the xy plane (0-10 microm from the center of the cell). We found the maximum SE at the lowest energy (2 microJ), with the pulse focused directly on the cell. We have demonstrated the utility of a novel method to measure the SE of a single-cell CE system. The measurements presented in this study indicate that rapid cell lysis with nanosecond lasers requires careful optimization of pulse parameters for maximum sampling of the cell contents.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".