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Related Concept Videos

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T cells are integral to our adaptive immune system, recognizing and effectively responding to foreign antigens. T cell activation and clonal selection are pivotal in orchestrating this immune response. This article elucidates these mechanisms, detailing the roles of cluster of differentiation (CD) markers, major histocompatibility complex (MHC) molecules, costimulatory signals, and the process of clonal selection.
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Receptor tyrosine kinases or RTKs are membrane-bound receptors that phosphorylate specific tyrosine on protein substrates. RTKs regulate cellular growth, differentiation, survival, and migration. They contain an extracellular ligand binding domain, a transmembrane domain, and a cytosolic tail with intrinsic kinase activity. Several extracellular signaling molecules activate RTKs in one or more ways and relay the signal downstream. Ligands such as platelet-derived growth factor (PDGF) or...
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Antigen receptors are essential components of the immune system crucial in defending the body against foreign invaders. These receptors are present on the surface of B and T cells, enabling them to recognize antigens and mount an appropriate immune response.
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Related Experiment Video

Updated: Nov 5, 2025

A TIRF Microscopy Technique for Real-time, Simultaneous Imaging of the TCR and its Associated Signaling Proteins
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Rapid T-cell receptor interaction grouping with ting.

Felix Mölder1,2, Ulrik Stervbo3, Lucie Loyal4,5,6

  • 1Genome Informatics, Institute of Human Genetics, University of Duisburg-Essen, 45147 Essen, Germany.

Bioinformatics (Oxford, England)
|May 13, 2021
PubMed
Summary

A new algorithm, ting, rapidly clusters T-cell receptor repertoire (TCRR) sequences, overcoming limitations of previous tools like GLIPH. This advancement significantly speeds up analysis of large TCRR datasets for antigen specificity studies.

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Area of Science:

  • Immunoinformatics
  • Computational Biology
  • T-cell Receptor Repertoire Analysis

Background:

  • Clustering T-cell receptor repertoire (TCRR) sequences by antigen specificity is a significant computational challenge.
  • Existing tools like GLIPH are too slow for large-scale repertoire analysis and have methodological limitations.
  • These limitations include non-determinism and potential inaccuracies in identifying antigen-specific sequences.

Purpose of the Study:

  • To develop a novel, efficient algorithm for clustering TCRR sequences based on antigen specificity.
  • To provide a scalable and accurate computational tool for analyzing large TCRR datasets.
  • To address the speed and accuracy limitations of previous TCRR clustering methods.

Main Methods:

  • An efficient algorithm for TCRR sequence clustering was developed and implemented in Python as the 'ting' tool.
  • The 'ting' algorithm was benchmarked against GLIPH and GLIPH2 using 36 real-world datasets.
  • Performance was evaluated based on clustering speed and accuracy in identifying antigen-specific motifs and clusters.

Main Results:

  • The 'ting' algorithm achieved a clustering speed of approximately one minute for datasets that took GLIPH multiple weeks.
  • Benchmarking demonstrated 'ting's efficiency in handling large repertoires with up to 62,000 unique CDR3β sequences.
  • In naïve repertoires, 'ting' effectively identified fewer motifs and produced smaller clusters, indicating higher specificity compared to other methods.

Conclusions:

  • The 'ting' algorithm offers a significant improvement in speed and scalability for TCRR sequence clustering.
  • This tool addresses the practical limitations of previous methods, enabling large-scale immunoinformatics studies.
  • The method demonstrates enhanced accuracy in distinguishing antigen-specific T-cell responses.