Quantification of SNAP-25 with mass spectrometry and Simoa: a method comparison in Alzheimer’s disease
Bibliographic record
Abstract
Abstract Background Synaptic dysfunction and degeneration are central to Alzheimer’s disease (AD) and have been found to correlate strongly with cognitive decline. Thus, studying cerebrospinal fluid (CSF) biomarkers reflecting synaptic degeneration, such as the presynaptic protein synaptosomal-associated protein 25 (SNAP-25), is of importance to better understand the AD pathophysiology. Methods We compared a newly developed Single molecule array (Simoa) immunoassay for SNAP-25 with an in-house immunoprecipitation mass spectrometry (IP-MS) method in a well-characterized clinical cohort (n = 70) consisting of cognitively unimpaired (CU) and cognitively impaired (CI) individuals with and without Aβ pathology (Aβ+ and Aβ−). Results A strong correlation (Spearman’s rank correlation coefficient (rs) > 0.88; p < 0.0001) was found between the Simoa and IP-MS methods, and no statistically significant difference was found for their clinical performance to identify AD pathophysiology in the form of Aβ pathology. Increased CSF SNAP-25 levels in CI Aβ+ compared with CU Aβ− (Simoa, p ≤ 0.01; IP-MS, p ≤ 0.05) and CI Aβ− (Simoa, p ≤ 0.01; IP-MS, p ≤ 0.05) were observed. In independent blood samples (n = 32), the Simoa SNAP-25 assay was found to lack analytical sensitivity for quantification of SNAP-25 in plasma. Conclusions These results indicate that the Simoa SNAP-25 method can be used interchangeably with the IP-MS method for the quantification of SNAP-25 in CSF. Additionally, these results confirm that CSF SNAP-25 is increased in relation to amyloid pathology in the AD continuum.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".