Bead-Extractor Assisted Ready-to-Use Reagent System (BEARS) for Immunoprecipitation Coupled to MALDI-MS
Bibliographic record
Abstract
Quantitative protein assays play an important role in the study of biological functions. Immunoassays and mass spectrometry are two main technologies for quantifying proteins in biological samples. The combination of immunoprecipitation (IP) with MALDI technology delivers high assay sensitivity and specificity, but the sample preparation procedure involves multiple washing and transfer steps. These steps can be performed either manually (requiring significant time and labor) or automatically (requiring the purchase of a complex liquid-handling workstation). This bottleneck has limited the widespread adoption of this technology. We present here the Bead-Extractor Assisted ready-to-use Reagent System (BEARS) technology for simplified, low cost protein and peptide immunoprecipitation combined with MALDI-MS detection. All of the reagents are stable during long-term storage and can be prepared in advance. In the BEARS technology, a magnetic-bead extractor is used to handle beads from 96 wells simultaneously. A BEARS-based method was developed for plasma renin activity (PRA) and was evaluated on fifty-three clinical samples. These experiments showed that the BEARS assay had an LOD and linear range comparable to the manual method and an automated iMALDI PRA assay, but was 4-times faster than the manual approach. The BEARS iMALDI results also correlated well with a conventional ELISA PRA assay, with a coefficient of determination of 0.98. The BEARS technology provides convenience and affordability, and extends the use of IP-based mass spectrometry technology to most research and clinical laboratories, including those in developing countries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.008 |
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".