An automated workflow for quantifying the formation of synuclein aggregates in human dopaminergic neurons
Bibliographic record
Abstract
Abstract Parkinson’s disease (PD) is a neurodegenerative disorder characterized by alpha-synuclein (α-syn) aggregates termed Lewy bodies. To model PD pathology in vitro , preformed fibrils of α-syn (PFFs), which can be taken up by cells, provide a seed that drives misfolding and aggregation of endogenous α-syn, with new aggregates amplifying this process. External application of PFFs to dopaminergic neurons (DNs) increases aggregate formation, marked by α-syn phosphorylation at serine 129 (pS129-syn), a pathological PD marker. Building on this, we developed an automated synuclein seeding assay to quantify new α-syn aggregates in iPSC-derived DNs. Using pS129-syn as a readout, we show that PFFs elicit a time- and dose-dependent increase in pS129-syn aggregates. Our high-throughput assay further revealed that aggregate formation depends on endogenous α-syn levels. Treatment with PFFs produced a greater increase in pS129-syn aggregates in iPSC DNs derived from a PD patient with a triplication in the SNCA gene, which encodes the α-syn protein and which elevates total α-syn levels, relative to DNs from an isogenic iPSC line from the same individual, in which the SNCA gene mutation had been corrected by CRISPR/Cas9. In contrast, no pS129-syn signal was detected in neurons in which all copies of the SNCA gene had been knocked out (KO). This high-content imaging assay for synuclein seeding offers a platform for assessing compounds and therapeutics that may impede α-syn aggregate formation.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".