A First Tetraplex Assay for the Simultaneous Quantification of Total α-Synuclein, Tau, β-Amyloid42 and DJ-1 in Human Cerebrospinal Fluid
Bibliographic record
Abstract
The quantification of four distinct proteins (α-synuclein, β-amyloid1-42, DJ-1, and total tau) in cerebrospinal fluid (CSF) has been proposed as a laboratory-based platform for the diagnosis of Parkinson's disease (PD) and Alzheimer's disease (AD). While there is some clinical utility in measuring these markers individually, their usage in routine clinical testing remains challenging, in part due to substantial overlap of concentrations between healthy controls and diseased subjects. In contrast, measurement of different analytes in a single sample from individual patients in parallel appears to considerably improve the accuracy of AD or PD diagnosis. Here, we report the development and initial characterization of a first, electrochemiluminescence-based multiplex immunoassay for the simultaneous quantification of all four proteins ('tetraplex') in as little as 50 μl of CSF. In analytical performance experiments, we assessed its sensitivity, spike-recovery rate, parallelism and dilution linearity as well as the intra- and inter-assay variability. Using our in-house calibrators, we recorded a lower limit of detection for α-synuclein, β-amyloid42, DJ-1, and t-tau of 1.95, 1.24, 5.63, and 4.05 pg/ml, respectively. The corresponding, linear concentration range covered >3 orders of magnitude. In diluted CSF samples (up to 1:4), spike-recovery rates ranged from a low of 55% for β-amyloid42 to a high of 98% for DJ-1. Hillslopes ranged from 1.03 to 1.30, and inter-assay variability demonstrated very high reproducibility. Our newly established tetraplex assay represents a significant technical advance for fluid-based biomarker studies in neurodegenerative disorders allowing the simultaneous measurement of four pivotal makers in single CSF specimens. It provides exceptional sensitivity, accuracy and speed.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".