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Record W2973592903 · doi:10.1002/cpim.90

Characterization of Human Mucosal‐associated Invariant T (MAIT) Cells

2019· article· en· W2973592903 on OpenAlexaff
Michael N. T. Souter, Liyen Loh, Shihan Li, Bronwyn S. Meehan, Nicholas A. Gherardin, Dale I. Godfrey, Jamie Rossjohn, David P. Fairlie, Katherine Kedzierska, Daniel G. Pellicci, Zhenjun Chen, Lars Kjer‐Nielsen, Alexandra J. Corbett, James McCluskey, Sidonia B. G. Eckle

Bibliographic record

VenueCurrent Protocols in Immunology · 2019
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsInstitute of Infection and Immunity
FundersNational Health and Medical Research CouncilAustralian Research CouncilMedical Research Council
KeywordsT-cell receptorBiologyMajor histocompatibility complexFlow cytometryAntigenCytotoxic T cellCell biologyCD8MHC class IT cellMolecular biologyImmunologyImmune systemBiochemistryIn vitro

Abstract

fetched live from OpenAlex

Mucosal-associated invariant T (MAIT) cells are a subset of unconventional T cells restricted by the major histocompatibility complex (MHC) class I-like molecule MHC-related protein 1 (MR1). MAIT cells are found throughout the body, especially in human blood and liver. Unlike conventional T cells, which are stimulated by peptide antigens presented by MHC molecules, MAIT cells recognize metabolite antigens derived from an intermediate in the microbial biosynthesis of riboflavin. MAIT cells mediate protective immunity to infections by riboflavin-producing microbes via the production of cytokines and cytotoxicity. The discovery of stimulating MAIT cell antigens allowed for the development of an analytical tool, the MR1 tetramer, that binds specifically to the MAIT T cell receptor (TCR) and is becoming the gold standard for identification of MAIT cells by flow cytometry. This article describes protocols to characterize the phenotype of human MAIT cells in blood and tissues by flow cytometry using fluorescently labeled human MR1 tetramers alongside antibodies specific for MAIT cell markers. © 2019 by John Wiley & Sons, Inc. The main protocols include: Basic Protocol 1: Determining the frequency and steady-state surface phenotype of human MAIT cells Basic Protocol 2: Determining the activation phenotype of human MAIT cells in blood Basic Protocol 3: Characterizing MAIT cell TCRs using TCR-positive reporter cell lines Alternate protocols are provided for determining the absolute number, transcription factor phenotype, and TCR usage of human MAIT cells; and determining activation phenotype by staining for intracellular markers, measuring secreted cytokines, and measuring fluorescent dye dilution due to proliferation. Additional methods are provided for determining the capacity of MAIT cells to produce cytokine independently of antigen using plate-bound or bead-immobilized CD3/CD28 stimulation; and determining the MR1-Ag dependence of MAIT cell activation using MR1-blocking antibody or competitive inhibition. For TCR-positive reporter cell lines, methods are also provided for evaluating the MAIT TCR-mediated MR1-Ag response, determining the capacity of the reporter lines to produce cytokine independently of antigen, determining the MR1-Ag dependence of the reporter lines, and evaluating the MR1-Ag response of the reporter lines using IL-2 secretion. Support Protocols describe the preparation of PBMCs from human blood, the preparation of single-cell suspensions from tissue, the isolation of MAIT cells by FACS and MACS, cloning MAIT TCRα and β chain genes and MR1 genes for transduction, generating stably and transiently transfected cells lines, generating a stable MR1 knockout antigen-presenting cell line, and generating monocyte-derived dendritic cells.

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)0.000
metaresearch head score (Gemma)0.000
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: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0050.003

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 head0.023
GPT teacher head0.290
Teacher spread0.267 · 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

Citations18
Published2019
Admission routes1
Has abstractyes

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