Reliable Resolution of Full-Length Huntingtin Alleles by Quantitative Immunoblotting
Bibliographic record
Abstract
BACKGROUND: Therapeutics that lower mutant huntingtin (mHTT) have shown promise in preclinical studies and are in clinical development for the treatment of Huntington disease (HD). Multiple assays have been developed that either quantify mHTT or total HTT but may not accurately measure levels of wild type HTT (wtHTT) in biological samples. OBJECTIVE: To optimize a method that can be used to resolve, quantify and directly compare levels of full length wtHTT and mHTT in HD samples. METHODS: We provide a detailed quantitative immunoblotting protocol to reproducibly resolve full length wtHTT and mHTT in multiple HD mouse and patient samples. RESULTS: We show that this assay can be modified, depending on the sample, to resolve wtHTT and mHTT with a wide range of polyglutamine length differences (ΔQs 22-179). We also demonstrate that this method can be used to quantify allele-selective lowering of mHTT using an antisense oligonucleotide in HD patient-derived cells. CONCLUSION: This quantitative immunoblotting method can be used to reliably resolve full-length HTT alleles with ΔQs≥22 and allows for direct comparison of wtHTT and mHTT levels in HD samples.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".