Protocol for screening anti-amyloidogenic compounds in cultured mammalian cells
Bibliographic record
Abstract
A functional form of amyloid aggregate can be rapidly induced by various environmental stressors, yielding subnuclear structures called amyloid bodies (A-bodies). Here, we describe a cell-based high-throughput assay to identify anti-amyloidogenic compounds capable of impairing heat-induced A-body formation in adherent cell lines. We describe steps for generating reporter cell lines, optimizing scoring algorithms, screening compound libraries, and analyzing results. This assay can be expanded to various systems to identify compounds with both anti- and pro-amyloidogenic effects. • Steps to generate a fluorescent reporter cell line • Guidance on the optimization of a cell-based high-throughput screening strategy • Instructions to screen compound libraries for anti-amyloidogenic effects Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. A functional form of amyloid aggregate can be rapidly induced by various environmental stressors, yielding subnuclear structures called amyloid bodies (A-bodies). Here, we describe a cell-based high-throughput assay to identify anti-amyloidogenic compounds capable of impairing heat-induced A-body formation in adherent cell lines. We describe steps for generating reporter cell lines, optimizing scoring algorithms, screening compound libraries, and analyzing results. This assay can be expanded to various systems to identify compounds with both anti- and pro-amyloidogenic effects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".