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The identification of high-performing antibodies for Sequestosome-1 for use in Western blot, immunoprecipitation and immunofluorescence

2024· preprint· en· W4400538586 on OpenAlexafffund
Riham Ayoubi, Walaa Alshafie, Irina Shlaifer, Kathleen Southern, Peter S. McPherson, Carl Laflamme

Bibliographic record

VenueF1000Research · 2024
Typepreprint
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
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
KeywordsSequestosome 1Western blotImmunofluorescenceAntibodyBiologyImmunoprecipitationNeurodegenerationAmyotrophic lateral sclerosisCell biologyImmunologyMedicinePathologyBiochemistryGeneDiseaseImmunoglobulin light chain

Abstract

fetched live from OpenAlex

, functions as a bridge between ubiquitinated proteins and the proteasome or autophagosome, thereby regulating protein degradation pathways. Loss of Sequestosome-1 is hypothesized to enhance neurodegeneration progression in several diseases, including amyotrophic lateral sclerosis (ALS) and frontotemporal disorders (FTD). Sequestosome-1 reproducible research would be facilitated with the availability of well-characterized anti-Sequestosome-1 antibodies. In this study, we characterized seventeen Sequestosome-1 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)0.003
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.006

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 head0.053
GPT teacher head0.387
Teacher spread0.334 · 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

Citations5
Published2024
Admission routes2
Has abstractyes

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