A high-content imaging workflow to screen for molecules that reduce cellular uptake of α-synuclein preformed fibrils
Bibliographic record
Abstract
Abstract A classical pathological hallmark of many neurodegenerative diseases is the formation of protein-rich aggregates and inclusions. In Parkinson’s disease (PD), α-synuclein (α-syn) constitutes a major protein component of pathological inclusions, termed Lewy bodies. These α-syn aggregates are hypothesized to spread throughout the nervous system by cell-to-cell transmission acting as templates to amplify aggregate formation. In vitro generated α-syn aggregates, commonly called preformed fibrils (PFFs), have been used to investigate a number of aspects related to α-syn mediated pathology across different model systems. Here we describe a semi-automated assay to screen for small molecules that interfere with the cellular uptake and accumulation of PFFs. The assay uses dopaminergic progenitor cells (DPCs), derived from human induced pluripotent stem cells (hiPSCs). In an initial screen, we tested 1520 small molecules and identified several molecules that strongly reduce intracellular PFF load in DPCs. From these hits, candidate compounds were validated in dopaminergic neurons (DNs) to demonstrate the utility of the assay. This assay provides a robust, scalable and adaptable tool to screen for molecules that affect PFF uptake in hiPSC-derived cell models. Within the scope of this screen, it led to the identification of a set of compounds with diverse annotated targets that effectively reduce the uptake of synuclein aggregates in DPCs and DNs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".