A Bayesian framework for high-throughput T cell receptor pairing.
Journal
Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944
Informations de publication
Date de publication:
15 04 2019
15 04 2019
Historique:
received:
15
03
2018
revised:
29
08
2018
accepted:
11
09
2018
pubmed:
15
9
2018
medline:
19
2
2020
entrez:
15
9
2018
Statut:
ppublish
Résumé
The study of T cell receptor (TCR) repertoires has generated new insights into immune system recognition. However, the ability to robustly characterize these populations has been limited by technical barriers and an inability to reliably infer heterodimeric chain pairings for TCRs. Here, we describe a novel analytical approach to an emerging immune repertoire sequencing method, improving the resolving power of this low-cost technology. This method relies upon the distribution of a T cell population across a 96-well plate, followed by barcoding and sequencing of the relevant transcripts from each T cell. Multicell Analytical Deconvolution for High Yield Paired-chain Evaluation (MAD-HYPE) uses Bayesian inference to more accurately extract TCR information, improving our ability to study and characterize T cell populations for immunology and immunotherapy applications. The MAD-HYPE algorithm is released as an open-source project under the Apache License and is available from https://github.com/birnbaumlab/MAD-HYPE. Supplementary data are available at Bioinformatics online.
Identifiants
pubmed: 30215679
pii: 5095649
doi: 10.1093/bioinformatics/bty801
pmc: PMC6477980
doi:
Substances chimiques
Receptors, Antigen, T-Cell
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
1318-1325Subventions
Organisme : NCI NIH HHS
ID : P30 CA014051
Pays : United States
Organisme : NIGMS NIH HHS
ID : T32 GM087237
Pays : United States
Informations de copyright
© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Références
Science. 1993 Oct 15;262(5132):422-4
pubmed: 8211163
Nat Methods. 2016 Apr;13(4):329-332
pubmed: 26950746
Nat Rev Immunol. 2012 Mar 22;12(4):269-81
pubmed: 22437939
Nature. 2017 Jul 6;547(7661):94-98
pubmed: 28636589
PLoS Comput Biol. 2017 Jan 19;13(1):e1005313
pubmed: 28103239
Genes Immun. 2016 Apr;17(3):153-64
pubmed: 26963138
Cancer Immunol Immunother. 2013 Sep;62(9):1453-61
pubmed: 23771160
Nat Genet. 2017 May;49(5):659-665
pubmed: 28369038
J Clin Invest. 2011 Jan;121(1):288-95
pubmed: 21135507
Cell. 2015 May 21;161(5):1202-1214
pubmed: 26000488
Immunol Cell Biol. 2016 Jul;94(6):604-11
pubmed: 26860370
PLoS One. 2014 Oct 02;9(9):e108658
pubmed: 25275470
J Immunol Methods. 2010 Feb 28;353(1-2):124-37
pubmed: 19931272
Cell. 2017 Jun 15;169(7):1342-1356.e16
pubmed: 28622514
Nature. 2017 Jul 6;547(7661):89-93
pubmed: 28636592
Sci Transl Med. 2015 Aug 19;7(301):301ra131
pubmed: 26290413