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Record W2087946487 · doi:10.3791/4269

Examination of Thymic Positive and Negative Selection by Flow Cytometry

2012· article· en· W2087946487 on OpenAlexafffund
Qian Hu, Stephanie A. Nicol, Alexander Y.W. Suen, Troy A. Baldwin

Bibliographic record

VenueJournal of Visualized Experiments · 2012
Typearticle
Languageen
FieldMedicine
TopicAdrenal Hormones and Disorders
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Heritage Foundation for Medical ResearchCanadian Institutes of Health ResearchAlberta Innovates - Health Solutions
KeywordsFlow cytometrySelection (genetic algorithm)BiologyNegative selectionMedicineImmunologyComputer scienceGeneticsArtificial intelligence

Abstract

fetched live from OpenAlex

A healthy immune system requires that T cells respond to foreign antigens while remaining tolerant to self-antigens. Random rearrangement of the T cell receptor (TCR) α and β loci generates a T cell repertoire with vast diversity in antigen specificity, both to self and foreign. Selection of the repertoire during development in the thymus is critical for generating safe and useful T cells. Defects in thymic selection contribute to the development of autoimmune and immunodeficiency disorders(1-4). T cell progenitors enter the thymus as double negative (DN) thymocytes that do not express CD4 or CD8 co-receptors. Expression of the αβTCR and both co-receptors occurs at the double positive (DP) stage. Interaction of the αβTCR with self-peptide-MHC (pMHC) presented by thymic cells determines the fate of the DP thymocyte. High affinity interactions lead to negative selection and elimination of self-reactive thymocytes. Low affinity interactions result in positive selection and development of CD4 or CD8 single positive (SP) T cells capable of recognizing foreign antigens presented by self-MHC(5). Positive selection can be studied in mice with a polyclonal (wildtype) TCR repertoire by observing the generation of mature T cells. However, they are not ideal for the study of negative selection, which involves deletion of small antigen-specific populations. Many model systems have been used to study negative selection but vary in their ability to recapitulate physiological events(6). For example, in vitro stimulation of thymocytes lacks the thymic environment that is intimately involved in selection, while administration of exogenous antigen can lead to non-specific deletion of thymocytes(7-9). Currently, the best tools for studying in vivo negative selection are mice that express a transgenic TCR specific for endogenous self-antigen. However, many classical TCR transgenic models are characterized by premature expression of the transgenic TCRα chain at the DN stage, resulting in premature negative selection. Our lab has developed the HY(cd4) model, in which the transgenic HY TCRα is conditionally expressed at the DP stage, allowing negative selection to occur during the DP to SP transition as occurs in wildtype mice(10). Here, we describe a flow cytometry-based protocol to examine thymic positive and negative selection in the HY(cd4) mouse model. While negative selection in HY(cd4) mice is highly physiological, these methods can also be applied to other TCR transgenic models. We will also present general strategies for analyzing positive selection in a polyclonal repertoire applicable to any genetically manipulated mice.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.018
GPT teacher head0.400
Teacher spread0.382 · 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 designBench or experimental
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

Citations22
Published2012
Admission routes2
Has abstractyes

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