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Record W4293416770 · doi:10.1016/j.xpro.2022.101643

Mass cytometry immunostaining protocol for multiplexing clinical samples

2022· article· en· W4293416770 on OpenAlexafffund
Ramy Gadalla, Giselle M. Boukhaled, David G. Brooks, Ben X. Wang

Bibliographic record

VenueSTAR Protocols · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSingle-cell and spatial transcriptomics
Canadian institutionsUniversity of TorontoUniversity Health NetworkPrincess Margaret Cancer Centre
FundersCanadian Cancer Society Research InstituteCanadian Institutes of Health ResearchJ.P. Bickell FoundationNational Institutes of HealthTerry Fox Research InstituteCanadian Cancer Society
KeywordsImmunostainingMass cytometryMultiplexStainingFlow cytometryBiologyMolecular biologyAntibodyPrimary and secondary antibodiesImmunohistochemistryImmunologyBiochemistryBioinformaticsGeneGeneticsPhenotype

Abstract

fetched live from OpenAlex

This is a cytometry by time-of-flight (CyTOF) staining protocol for hematopoietic-derived cells, that leverages live-cell barcoding using receptor-type tyrosine-protein phosphatase C (CD45) antibodies conjugated to metal isotopes in combination with DNA-based palladium barcoding to multiplex up to 40 samples. In this protocol, DNA-based barcoding is performed before surface and intracellular immunostaining, which reduces the batch effects that result from day-to-day variations in staining and instrument sensitivity. This protocol also reduces antibody consumption and eliminates the need for repeated instrument adjustment.

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)0.002
metaresearch head score (Gemma)0.002
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: Protocol · Consensus signal: none
Teacher disagreement score0.060
Threshold uncertainty score0.200

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0600.036

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 head0.134
GPT teacher head0.422
Teacher spread0.287 · 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
GenreProtocol

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

Citations7
Published2022
Admission routes2
Has abstractyes

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