SISCAPA Peptide Enrichment on Magnetic Beads Using an In-line Bead Trap Device
Bibliographic record
Abstract
A SISCAPA (stable isotope standards and capture by anti-peptide antibodies) method for specific antibody-based capture of individual tryptic peptides from a digest of whole human plasma was developed using a simplified magnetic bead protocol and a novel rotary magnetic bead trap device. Following off-line equilibrium binding of peptides by antibodies and subsequent capture of the antibodies on magnetic beads, the bead trap permitted washing of the beads and elution of bound peptides inside a 150-microm-inner diameter capillary that forms part of a nanoflow LC-MS/MS system. The bead trap sweeps beads against the direction of liquid flow using a continuous succession of moving high magnetic field-gradient trap regions while mixing the beads with the flowing liquid. This approach prevents loss of low abundance captured peptides and allows automated processing of a series of SISCAPA reactions. Selected tryptic peptides of alpha(1)-antichymotrypsin and lipopolysaccharide-binding protein were enriched relative to a high abundance serum albumin peptide by 1,800 and 18,000-fold, respectively, as measured by multiple reaction monitoring. A large majority of the peptides that are bound nonspecifically in SISCAPA reactions were shown to bind to components other than the antibody (e.g. the magnetic beads), suggesting that substantial improvement in enrichment could be achieved by development of improved inert bead surfaces.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".