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The identification of high-performing antibodies for RNA-binding protein FUS for use in Western Blot, immunoprecipitation, and immunofluorescence

2023· preprint· en· W4382136349 on OpenAlexafffund
Walaa Alshalfie, Maryam Fotouhi, Riham Ayoubi, Zhipeng You, Kathleen Southern, Peter S. McPherson, Carl Laflamme

Bibliographic record

VenueF1000Research · 2023
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsStructural Genomics ConsortiumMcGill UniversityMontreal Neurological Institute and Hospital
FundersCanadian Institutes of Health ResearchGenentechMitacsMotor Neurone Disease AssociationGovernment of CanadaOntario Genomics InstituteEuropean Federation of Pharmaceutical Industries and AssociationsMerck KGaABayerALS Society of CanadaOntario GenomicsGenome CanadaBristol-Myers SquibbPfizerALS Association
KeywordsWestern blotImmunoprecipitationImmunofluorescenceBiologyAntibodyNuclear localization sequenceRNACytoplasmBlotMolecular biologyCell biologyVirologyImmunologyGeneticsGene

Abstract

fetched live from OpenAlex

<ns4:p>RNA-binding protein Fused-in Sarcoma (FUS) plays an essential role in various cellular processes. Mutations in the C-terminal domain region, where the nuclear localization signal (NLS) is located, causes the redistribution of FUS from the nucleus to the cytoplasm. In neurons, neurotoxic aggregates are formed as a result, contributing to neurogenerative diseases. Well-characterized anti-FUS antibodies would enable the reproducibility of FUS research, thereby benefiting the scientific community.</ns4:p> <ns4:p> <ns4:bold> </ns4:bold> In this study, we characterized ten FUS 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. </ns4:p> <ns4:p>We identified many high-performing antibodies and encourage readers to use this report as a guide to select the most appropriate antibody for their specific needs.</ns4:p>

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.582

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.049
GPT teacher head0.353
Teacher spread0.304 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations8
Published2023
Admission routes2
Has abstractyes

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