Novel monoclonal antibodies against the C-terminal HEAT domain of Huntingtin
Bibliographic record
Abstract
Abstract BACKGROUND Reliable detection of huntingtin (HTT) is essential for understanding Huntington’s disease (HD) biology and evaluating therapeutic strategies. However, high-quality monoclonal antibodies (mAbs) against the HTT C-terminal domain remain limited. OBJECTIVE We sought to generate and validate novel monoclonal antibodies targeting the HTT C-terminal HEAT-containing domain to better detect HTT independently of potential effects of polyglutamine length that can impact some N-terminally targeted antibodies. METHODS We immunized mice with a highly purified, well-characterized recombinant protein corresponding to the HTT C-terminal domain. We generated monoclonal antibody-producing hybridoma cell lines and characterized the antibodies using parental and HTT-knockout cell lines in common immuno-applications. RESULTS Three novel, independent hybridoma lines producing anti-HTT monoclonal antibodies were derived. Using CRISPR-edited HTT knockout cell lines we identified one clone, anti-HTT [2F8], that was specific and effective across Western blot, immunofluorescence, and ELISA assays. All antibodies bound full-length HTT irrespective of HAP40 interaction or polyQ length and showed no cross-reactivity to the N-terminal HEAT domain. CONCLUSIONS These C-terminal HTT mAbs are thus valuable additional tools for studying endogenous HTT function in both normal and disease contexts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".