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The identification of high-performing antibodies for Charged multivesicular body protein 2b for use in Western Blot, immunoprecipitation and immunofluorescence

2023· preprint· en· W4385237942 on OpenAlexafffund
Walaa Alshafie, Maryam Fotouhi, Riham Ayoubi, Irina Shlaifer, Kathleen Southern, Peter S. McPherson, Carl Laflamme

Bibliographic record

VenueF1000Research · 2023
Typepreprint
Languageen
FieldImmunology and Microbiology
TopicGalectins and Cancer Biology
Canadian institutionsStructural Genomics ConsortiumMcGill UniversityMontreal Neurological Institute and Hospital
FundersGenentechCanadian Institutes of Health ResearchInnovative Medicines InitiativeMitacsMotor Neurone Disease AssociationGovernment of CanadaOntario Genomics InstituteEuropean Federation of Pharmaceutical Industries and AssociationsMerck KGaABayerALS Society of CanadaOntario GenomicsGenome CanadaBristol-Myers SquibbPfizerALS Association
KeywordsImmunoprecipitationWestern blotImmunofluorescenceIdentification (biology)AntibodyOpen peer reviewPlant biologyBiologyCell biologyVirologyImmunologyBiochemistryBotany

Abstract

fetched live from OpenAlex

<ns4:p> Charged multivesicular body protein 2B is a subunit of the endosomal sorting complex required for transport III (ESRCT-III), a complex implicated in the lysosomal degradation pathway and formation of multivesicular bodies. Mutations to the <ns4:italic>CHMP2B</ns4:italic> gene can result in abnormal protein aggregates in neurons and is therefore predicted to be associated in neurodegenerative diseases, including across the ALS-FTD spectrum. Through our standardized experimental protocol which compares read-outs in knockout cell lines and isogenic parental controls, this study aims to enhance the reproducibility of research on this target by characterizing eight commercial antibodies against charged multivesicular body protein 2b using Western Blot, immunoprecipitation, and immunofluorescence. 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)0.002
metaresearch head score (Gemma)0.001
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 score0.373
Threshold uncertainty score0.702

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 science0.0000.000
Research integrity0.0000.001
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 head0.052
GPT teacher head0.341
Teacher spread0.288 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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