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A guide to selecting high-performing antibodies for Synaptotagmin-1 (Uniprot ID P21579) for use in western blot, immunoprecipitation, immunofluorescence and flow cytometry

2024· preprint· en· W4400818170 on OpenAlexafffund
Michael Biddle, Charles Alende, Maryam Fotouhi, Carolyn Jones, Riham Ayoubi, Kathleen Southern, Carl Laflamme, Harvinder Virk

Bibliographic record

VenueF1000Research · 2024
Typepreprint
Languageen
FieldMedicine
TopicPeptidase Inhibition and Analysis
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersNational Institute on AgingGenome CanadaNational Institute for Health and Care ResearchOntario GenomicsMitacsGovernment of Canada
KeywordsUniProtImmunoprecipitationSynaptotagmin 1Western blotFlow cytometryImmunofluorescenceOpen peer reviewAntibodyBiologyComputational biologyPlant biologyMolecular biologyImmunologyBiochemistryBotanyGene

Abstract

fetched live from OpenAlex

<ns4:p> Synaptotagmin-1 is a synaptic vesicle transmembrane protein that senses calcium influx via its tandem C2-domains, triggering synchronous neurotransmitter release. Disruption to <ns4:italic>SYT1</ns4:italic> is associated with neurodevelopmental disorders, highlighting the importance of identifying high-quality research reagents to enhance understanding of Synaptotagmin-1 in health and disease. Here we have characterized thirteen Synaptotagmin-1 commercial antibodies for western blot, immunoprecipitation, immunofluorescence and flow cytometry using a standardized experimental protocol based on comparing read-outs in knockout cell lines and isogenic parental controls. These studies are part of a larger, collaborative initiative seeking to address antibody reproducibility issues by characterizing commercially available antibodies for human proteins and publishing the results openly as a resource for the scientific community. While use of antibodies and protocols vary between laboratories, we encourage readers to use this report as a guide to select the most appropriate antibodies 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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.423
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.046
GPT teacher head0.390
Teacher spread0.344 · 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.

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

Citations3
Published2024
Admission routes2
Has abstractyes

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