Ultrasensitive Protein Aggregate Quantification Assays for Neurodegenerative Diseases on the Simoa Platform
Bibliographic record
Abstract
Nanoscale aggregates play a key role in the pathogenesis of neurodegenerative disorders such as Alzheimer's and Parkinson's disease. However, quantifying these aggregates in complex biological samples, such as biofluids and postmortem brain tissue, has been challenging due to their low concentration and small size, necessitating the development of methods with high sensitivity and specificity. Here, we have developed ultrasensitive assays utilizing the Quanterix Simoa platform to detect α-synuclein, β-amyloid and tau aggregates, including those with common posttranslational modifications such as truncation of α-synuclein and AT8 phosphorylation of tau aggregates. All assays had a detection limit in the low pM range. As a part of this work, we developed silica-nanoparticle calibrators, allowing for the quantification of all aggregates. These assays were validated for aggregate and target specificity through denaturation and cross-reactivity experiments. We then applied these assays to brain homogenate samples from Alzheimer's disease and control samples, demonstrating their applicability to postmortem tissue. Lastly, we explored the potential of these assays for blood-based diagnostics by detecting aggregates in serum samples from early Alzheimer's disease patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".