Combined Analysis of Transcriptome and T-Cell Receptor Alpha and Beta (TRA /TRB ) Repertoire in Paucicellular Samples at the Single-Cell Level.
Next-generation sequencing
Repertoire
Single cell
T-cell receptor alpha
T-cell receptor beta
Transcriptome
Journal
Methods in molecular biology (Clifton, N.J.)
ISSN: 1940-6029
Titre abrégé: Methods Mol Biol
Pays: United States
ID NLM: 9214969
Informations de publication
Date de publication:
2022
2022
Historique:
entrez:
27
5
2022
pubmed:
28
5
2022
medline:
1
6
2022
Statut:
ppublish
Résumé
With the advent of next-generation sequencing (NGS) methodologies, the total repertoires of B and T cells can be disclosed in much more detail than ever before. Even though many of these strategies do provide in-depth and high-resolution information of the immunoglobulin (IG) and/or T-cell receptor (TR) repertoire, one clear disadvantage is that the IG/TR profiles cannot be connected to individual cells. Single-cell technologies do allow to study the IG/TR repertoire at the individual cell level. This is especially relevant in cell samples in which much heterogeneity of the cell population is expected. By combining the IG/TR repertoire with transcriptome data, the reactivity of the B or T cell can be associated with activation or maturation stages. An additional advantage of such single-cell technologies is that the combination of both IG and both TR chains can be studied on a per cell basis, which better reflects the antigen receptor reactivity of cells. Here we present the ICELL8 single-cell method for the parallel analysis of the TR repertoire and transcriptome, which is especially useful in samples that contain relatively few cells.
Identifiants
pubmed: 35622330
doi: 10.1007/978-1-0716-2115-8_14
pmc: PMC9761537
doi:
Substances chimiques
Immunoglobulins
0
Receptors, Antigen, T-Cell
0
Receptors, Antigen, T-Cell, alpha-beta
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
231-259Informations de copyright
© 2022. The Author(s).
Références
Clin Exp Immunol. 2013 Oct;174(1):179-91
pubmed: 23750604
Nat Rev Immunol. 2004 Feb;4(2):123-32
pubmed: 15040585
Blood. 2001 Jul 1;98(1):165-73
pubmed: 11418476
BMC Genomics. 2017 Jul 7;18(1):519
pubmed: 28687070
Front Immunol. 2020 Aug 28;11:1999
pubmed: 33013853
Bioinformatics. 2014 Mar 1;30(5):614-20
pubmed: 24142950
Genes Dev. 2017 Oct 15;31(20):2085-2098
pubmed: 29138277
Nat Genet. 2016 Jul;48(7):725-32
pubmed: 27240091
Eur J Immunol. 2005 Jun;35(6):1987-94
pubmed: 15909312
Elife. 2017 Dec 05;6:
pubmed: 29206104
Nat Biotechnol. 2019 Aug;37(8):907-915
pubmed: 31375807
Leukemia. 2003 Dec;17(12):2257-317
pubmed: 14671650
Nucleic Acids Res. 2013 Jul;41(Web Server issue):W34-40
pubmed: 23671333
Annu Rev Immunol. 1999;17:369-97
pubmed: 10358763
J Immunol. 2008 Sep 1;181(5):3665-73
pubmed: 18714042
Curr Protoc Bioinformatics. 2014 Sep 08;47:11.12.1-34
pubmed: 25199790
Cell. 2019 Jun 13;177(7):1888-1902.e21
pubmed: 31178118
Elife. 2018 Mar 20;7:
pubmed: 29555020
Int J Mol Sci. 2018 Mar 11;19(3):
pubmed: 29534489
Immunol Cell Biol. 2011 Mar;89(3):375-87
pubmed: 21301479
Nat Rev Immunol. 2018 Jan;18(1):35-45
pubmed: 28787399