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The identification of high-performing antibodies for transmembrane protein 106B (TMEM106B) for use in Western blot, immunoprecipitation, and immunofluorescence

2023· preprint· en· W4328095950 on OpenAlexafffund
Riham Ayoubi, Maryam Fotouhi, Kathleen Southern, Ritika Bhajiawala, Rebeka Fanti, Panagiotis Prinos, Peter S. McPherson, Carl Laflamme

Bibliographic record

VenueF1000Research · 2023
Typepreprint
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsUniversity of TorontoMcGill UniversityStructural Genomics ConsortiumMontreal 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 blotImmunofluorescenceAntibodyIdentification (biology)Transmembrane proteinBiologyImmunologyBiochemistryBotany

Abstract

fetched live from OpenAlex

Transmembrane protein 106B (TMEM106B), a protein that is localized to the lysosome, is genetically linked to many neurodegenerative diseases and forms fibrils in diseased brains. The reproducibility of TMEM106B research would be enhanced if the community had access to well-characterized anti-TMEM106B antibodies. In this study, we characterized six commercially available TMEM106B antibodies for their performance in 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 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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.550
Threshold uncertainty score0.916

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.085
GPT teacher head0.380
Teacher spread0.294 · 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

Citations12
Published2023
Admission routes2
Has abstractyes

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