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Quantitative Characterization of the T Cell Receptor Repertoire of Naive and Memory subsets Using an Integrated experimental and Computational Pipeline Which Is Robust, economical, and Versatile

Oakes, T; Heather, JM; Best, K; Byng-Maddick, R; Husovsky, C; Ismail, M; Joshi, K; ... Chain, B; + view all (2017) Quantitative Characterization of the T Cell Receptor Repertoire of Naive and Memory subsets Using an Integrated experimental and Computational Pipeline Which Is Robust, economical, and Versatile. Frontiers in Immunology , 8 , Article 1267. 10.3389/fimmu.2017.01267. Green open access

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Abstract

The T cell receptor (TCR) repertoire can provide a personalized biomarker for infectious and non-infectious diseases. We describe a protocol for amplifying, sequencing, and analyzing TCRs which is robust, sensitive, and versatile. The key experimental step is ligation of a single-stranded oligonucleotide to the 3′ end of the TCR cDNA. This allows amplification of all possible rearrangements using a single set of primers per locus. It also introduces a unique molecular identifier to label each starting cDNA molecule. This molecular identifier is used to correct for sequence errors and for effects of differential PCR amplification efficiency, thus producing more accurate measures of the true TCR frequency within the sample. This integrated experimental and computational pipeline is applied to the analysis of human memory and naive subpopulations, and results in consistent measures of diversity and inequality. After error correction, the distribution of TCR sequence abundance in all subpopulations followed a power law over a wide range of values. The power law exponent differed between naïve and memory populations, but was consistent between individuals. The integrated experimental and analysis pipeline we describe is appropriate to studies of T cell responses in a broad range of physiological and pathological contexts.

Type: Article
Title: Quantitative Characterization of the T Cell Receptor Repertoire of Naive and Memory subsets Using an Integrated experimental and Computational Pipeline Which Is Robust, economical, and Versatile
Open access status: An open access version is available from UCL Discovery
DOI: 10.3389/fimmu.2017.01267
Publisher version: https://doi.org/10.3389/fimmu.2017.01267
Language: English
Additional information: © 2017 Oakes, Heather, Best, Byng-Maddick, Husovsky, Ismail, Joshi, Maxwell, Noursadeghi, Riddell, Ruehl, Turner, Uddin and Chain. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Keywords: Science & Technology, Life Sciences & Biomedicine, Immunology, T cell Receptor, Repertoire Analysis, Naive T cells, Memory T cells, Unique Molecular Identifier, Power-Law Distributions, Immune Repertoires, Antigen Receptor, TCR, Alpha, Amplification, Selection, Ligation, Complex, PCR
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute > Research Department of Oncology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Infection and Immunity
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: http://discovery.ucl.ac.uk/id/eprint/10026098
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