Femtomolar Concentration Detection Limit and Zeptomole Mass Detection Limit for Protein Separation by Capillary Isoelectric Focusing and Laser-Induced Fluorescence Detection
Bibliographic record
Abstract
Fluorescence tends to produce the lowest detection limits for most forms of capillary electrophoresis. Two issues have discouraged its use in capillary isoelectric focusing. The first issue is fluorescent labeling of proteins. Most labeling reagents react with lysine residues and convert the cationic residue to a neutral or anionic product. At best, these reagents perturb the isoelectric point of the protein. At worse, they convert each protein into hundreds of different fluorescent products that confound analysis. The second issue is the large background signal generated by impurities within commercial ampholytes. This background signal is particularly strong when excited in the blue portion of the spectrum, which is required by many common fluorescent labeling reagents. This paper addresses these issues. For labeling, we employ Chromeo P540, which is a fluorogenic reagent that converts cationic lysine residues to cationic fluorescent products. The reaction products are excited in the green, which reduces the background signal generated by impurities present within the ampholytes. To further reduce the background signal, we photobleach ampholytes with high-power photodiodes. Photobleaching reduced the noise in the ampholyte blank by an order of magnitude. Isoelectric focusing performed with photobleached pH 3-10 ampholytes produced concentration detection limits of 270 +/- 25 fM and mass detection limits of 150 +/- 15 zmol for Chromeo P540 labeled beta-lactoglobulin. Concentration detection limits were 520 +/- 40 fM and mass detection limits were 310 +/- 30 zmol with pH 4-8 ampholytes. A homogenate was prepared from a Barrett's esophagus cell line and separated by capillary isoelectric focusing, reproducibly generating dozens of peaks. The sample taken for the separation was equal to the labeled protein homogenate from three cells.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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".