Secreted APP and amyloid beta quantification in SH‐SY5Y cell media using high sensitivity AlphaLISA kits
Bibliographic record
Abstract
The aggregation and accumulation of amyloid beta peptides is one of the pathological hallmarks of Alzheimer's disease (AD). Amyloid beta is produced by sequential proteolytic cleavage of Amyloid Precursor Protein (APP) by beta‐ and gamma‐secretase, which are plausible molecular targets for AD treatment. Thus, identification of modulators of these secretases could lead to new therapeutics for this prevalent and debilitating disease. SH‐SY5Y are human neuroblastoma cells that express APP and the various secretases that cleave this membrane‐bound protein into sAPPβ, sAPPα and amyloid beta peptides. We have developed new highly sensitive and specific AlphaLISA kits that allow the detection of endogenous amounts of these various APP metabolites. Specific inhibitors of alpha‐, beta‐ and gamma‐secretase were used to modulate the relative activities of these enzymes. Application of these inhibitors resulted in characteristic signature levels of product sAPPα, sAPPβ, Aβ 1–40 and Aβ x‐40 (defined as the combined amount of both Aβ 1–40 and Aβ 17–40) from SH‐SY5Y cell culture supernatants. These results show that the new AlphaLISA kits have sufficient sensitivity and specificity to study this pathway, even in wild‐type cells that do not overexpress APP or BACE. The Z′‐factor obtained for these cell‐based secretase assays was greater than 0.6, indicating reproducible and robust assays for HTS. In summary, these new AlphaLISA detection kits can accurately quantitate endogenous APP cleavage products. This will in turn result in a better understanding of the APP degradation pathway and facilitate the identification of novel therapeutic drugs for the treatment of Alzheimer's disease.
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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) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".