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A guide to selecting high-performing antibodies for Huntingtin (UniProt ID: P42858) for use in western blot, immunoprecipitation, and immunofluorescence

2024· preprint· en· W4401549427 on OpenAlexafffund
Rebeka Fanti, Riham Ayoubi, Charles Alende, Maryam Fotouhi, Sara González Bolívar, Renu Chandrasekaran, Kathleen Southern, A.M. Edwards, Rachel Harding, Carl Laflamme

Bibliographic record

VenueF1000Research · 2024
Typepreprint
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsMcGill UniversityMontreal Neurological Institute and HospitalUniversity of Toronto
FundersMitacsGovernment of CanadaOntario GenomicsGenome Canada
KeywordsImmunoprecipitationWestern blotImmunofluorescenceUniProtAntibodyBiologyOpen peer reviewCell biologyPlant biologyImmunologyGeneticsGeneBotany

Abstract

fetched live from OpenAlex

Huntingtin encodes a 3144 amino acid protein, with a polyglutamine repeat tract at the N-terminus. Expansion of this repeat tract above a pathogenic threshold of 36 repeats is the causative mutation of Huntington's disease, a neurodegenerative disorder characterized by loss of striatal neurons. Here we have characterized twenty Huntingtin commercial antibodies for western blot, immunoprecipitation, and immunofluorescence using a standardized experimental protocol based on comparing read-outs in knockout cell lines and isogenic parental controls. These studies are part of a larger, collaborative initiative seeking to address antibody reproducibility issues by characterizing commercially available antibodies for human proteins and publishing the results openly as a resource for the scientific community. While use of antibodies and protocols vary between laboratories, we encourage readers to use this report as a guide to select the most appropriate antibodies for their specific needs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.137
Threshold uncertainty score0.458

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.011
Meta-epidemiology (narrow)0.0040.006
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0070.005
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0070.002
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.1370.239

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.

Opus teacher head0.077
GPT teacher head0.386
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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".

Quick stats

Citations4
Published2024
Admission routes2
Has abstractyes

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