Novel One-step Immunoassays to Quantify α-Synuclein
Bibliographic record
Abstract
Familial Parkinson disease (PD) can result from α-synuclein gene multiplication, implicating the reduction of neuronal α-synuclein as a therapeutic target. Moreover, α-synuclein content in human cerebrospinal fluid (CSF) represents a PD biomarker candidate. However, capture-based assays for α-synuclein quantification in CSF (such as by ELISA) have shown discrepancies and have limited suitability for high-throughput screening. Here, we describe two sensitive, in-solution, time-resolved Förster's resonance energy transfer (TR-FRET)-based immunoassays for total and oligomeric α-synuclein quantification. CSF analysis showed strong concordance for total α-synuclein content between two TR-FRET assays and, in agreement with a previously characterized 36 h protocol-based ELISA, demonstrated lower α-synuclein levels in PD donors. Critically, the assay suitability for high-throughput screening of siRNA constructs and small molecules aimed at reducing endogenous α-synuclein levels was established and validated. In a small-scale proof of concept compound screen using 384 well plates, signals ranged from <30 to >120% of the mean of vehicle-treated cells for molecules known to lower and increase cellular α-synuclein, respectively. Furthermore, a reverse genetic screen of a kinase-directed siRNA library identified seven genes that modulated α-synuclein protein levels (five whose knockdown increased and two that decreased cellular α-synuclein protein). This provides critical new biological insight into cellular pathways regulating α-synuclein steady-state expression that may help guide further drug discovery efforts. Moreover, we describe an inherent limitation in current α-synuclein oligomer detection methodology, a finding that will direct improvement of future assay design. Our one-step TR-FRET-based platform for α-synuclein quantification provides a novel platform with superior performance parameters for the rapid screening of large biomarker cohorts and of compound and genetic libraries, both of which are essential to the development of PD therapies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".