Development of a Novel Electrochemiluminescence ELISA for Quantification of α-Synuclein Phosphorylated at Ser<sup>129</sup> in Biological Samples
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Synucleinopathies are a group of neurodegenerative diseases including Parkinson’s disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA). These diseases are characterized by the aggregation and deposition of α-synuclein (α-syn) in Lewy bodies (LBs) in PD and DLB or as glial cytoplasmic inclusions in MSA. In healthy brains, only ∼4% of α-syn is phosphorylated at Ser 129 (pS 129 -α-syn), whereas >90% pS 129 -α-syn may be found in LBs, suggesting that pS 129 -α-syn could be a useful biomarker for synucleinopathies. However, a widely available, robust, sensitive, and reproducible method for measuring pS 129 -α-syn in biological fluids is currently missing. We used Meso Scale Discovery (MSD)’s electrochemiluminescence platform to create a new assay for sensitive detection of pS 129 -α-syn. We evaluated several combinations of capture and detection antibodies and used semisynthetic pS 129 -α-syn as a standard for the assay at a concentration range from 0.5 to 6.6 × 10 4 pg/mL. Using the antibody EP1536Y for capture and an anti-human α-syn antibody (MSD) for detection was the best combination in terms of assay sensitivity, specificity, and reproducibility. We tested the utility of the assay for the detection and quantification of pS 129 -α-syn in human cerebrospinal fluid, serum, plasma, saliva, and CNS-originating small extracellular vesicles, as well as in mouse brain lysates. Our data suggest that the assay can become a widely used method for detecting pS 129 -α-syn in biomedical studies including when only a limited volume of sample is available and high sensitivity is required, offering new opportunities for diagnostic biomarkers, monitoring disease progression, and quantifying outcome measures in clinical trials.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".