Implementation of an antibody characterization process: Application to the major ALS/FTD disease gene C9ORF72
Bibliographic record
Abstract
Rigorously characterized antibodies are a key resource in biomedical research, yet there are no community-accepted processes to characterize the quality of research grade antibodies. This has led to a proliferation of poorly characterized antibodies of suspect quality, which in turn has led to flaws in the literature that hamper research progress, including the study of human disease. We selected the human protein C9ORF72 as a test case to implement a more standardized antibody characterization process. Mutations in the gene C9ORF72 are the major genetic cause of amyotrophic lateral sclerosis and frontotemporal dementia but much remains unknown regarding the function of the encoded protein. We used CRISPR/Cas9-based knockout cells and knockout mice as controls to characterize 14 C9ORF72 antibodies including 12 commercially available antibodies advertised as selective for C9ORF72. We found one monoclonal antibody that is specific for the protein in immunoblot and that also recognizes C9ORF72 specifically in immunohistochemical applications on brain sections. A second C9ORF72 monoclonal antibody is effective for immunoprecipitation and immunofluorescence. Some of the antibodies that do not recognize C9ORF72 have been used in highly cited papers, raising concern over conclusions regarding previously reported C9ORF72 properties.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".