Establishing pre-analytical requirements and maximizing peptide recovery in the analytical phase for mass spectrometric quantification of amyloid-β peptides 1–42 and 1–40 in CSF
Bibliographic record
Abstract
OBJECTIVES: Amyloid-β (Aβ) peptides in cerebrospinal fluid (CSF), including Aβ42 (residues 1-42) and Aβ40 (residues 1-40), are utilized as biomarkers in the diagnostic workup of Alzheimer's disease. Careful consideration has been given to the pre-analytical and analytical factors associated with measurement of these peptides via immunoassays; however, far less information is available for mass spectrometric methods. As such, we performed a comprehensive evaluation of pre-analytical and analytical factors specific to Aβ quantification using mass spectrometry. METHODS: Using our quantitative mass spectrometry assay for Aβ42 and Aβ40 in CSF, we investigated the potential for interference from hemolysate, bilirubin, lipids, and anti-Aβ-antibodies. We also optimized the composition of the calibrator surrogate matrix and Aβ recovery during and after solid phase extraction (SPE). RESULTS: There was no interreference observed with total protein up to 12 g/L, hemolysate up to 10% (v/v), bilirubin up to 0.5% (v/v), intralipid up to 1% (v/v), or anti-Aβ-antibodies at expected therapeutic concentrations. For hemolysate, bilirubin and lipids, visual CSF contamination thresholds were established. In the analytical phase, Aβ recovery was increased by ∼50% via SPE solvent modifications and by over 150% via modification of the SPE collection plate, which also extended analyte stability in the autosampler. CONCLUSIONS: Attention to mass spectrometric-specific pre-analytical and analytical considerations improved analytical sensitivity and reproducibility, as well as, established CSF specimen acceptance and rejection criteria for use by the clinical laboratory.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".