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A guide to selecting high-performing antibodies for RNA-binding protein TIA1 for use in Western Blot, immunoprecipitation and immunofluorescence

2024· preprint· en· W4394576575 on OpenAlexafffund
Maryam Fotouhi, Donovan Worrall, Riham Ayoubi, Kathleen Southern, Peter S. McPherson, Carl Laflamme

Bibliographic record

VenueF1000Research · 2024
Typepreprint
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersJanssen BiotechCanadian Institutes of Health ResearchGenentechMitacsMotor Neurone Disease AssociationOntario Genomics InstituteGovernment of CanadaEuropean Federation of Pharmaceutical Industries and AssociationsMerck KGaAOntario GenomicsGenome CanadaTakeda Pharmaceuticals InternationalALS Society of CanadaBayerBristol-Myers SquibbBoehringer IngelheimPfizerALS Association
KeywordsStress granuleWestern blotImmunoprecipitationRNA-binding proteinImmunofluorescenceAntibodyBiologyMolecular biologyRNAVirologyMessenger RNAImmunologyGeneGeneticsTranslation (biology)

Abstract

fetched live from OpenAlex

A member of the RNA-binding protein family, T-cell intracellular antigen-1 (TIA1) regulates mRNA translation and splicing as well as cellular stress by promoting stress granule formation. Variants of the TIA1 gene have implications in neurogenerative disorders including amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Reproducible research on TIA1 would be enhanced with the availability of high-quality anti-TIA1 antibodies. In this study, we characterized twelve TIA1 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. 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.

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)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.099
Threshold uncertainty score0.333

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0060.007
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0070.004
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0060.002
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0990.170

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 head0.068
GPT teacher head0.398
Teacher spread0.330 · 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 designBench or experimental
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

Citations2
Published2024
Admission routes2
Has abstractyes

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