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Paired analysis of TCR[alpha] and TCR[Beta] chains at the single-cell level in mice
Characterizing the TCRα and TCR[beta] chains expressed by T cells responding to a given pathogen or underlying autoimmunity helps in the development of vaccines and immunotherapies, respectively. However, our understanding of complementary TCRα and TCR[beta] chain utilization is very limited for pat...
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Published in: | The Journal of clinical investigation 2011-01, Vol.121 (1), p.288 |
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Main Authors: | , , , , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Online Access: | Get full text |
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Summary: | Characterizing the TCRα and TCR[beta] chains expressed by T cells responding to a given pathogen or underlying autoimmunity helps in the development of vaccines and immunotherapies, respectively. However, our understanding of complementary TCRα and TCR[beta] chain utilization is very limited for pathogen- and autoantigen-induced immunity. To address this problem, we have developed a multiplex nested RT-PCR method for the simultaneous amplification of transcripts encoding the TCRα and TCR[beta] chains from single cells. This multiplex method circumvented the lack of antibodies specific for variable regions of mouse TCRα chains and the need for prior knowledge of variable region usage in the TCR[beta] chain, resulting in a comprehensive, unbiased TCR repertoire analysis with paired coexpression of TCRα and TCR[beta] chains with single-cell resolution. Using CD8+ CTLs specific for an influenza epitope recovered directly from the pneumonic lungs of mice, this technique determined that 25% of such effectors expressed a dominant, nonproductively rearranged Tcra transcript. T cells with these out-of-frame Tcra mRNAs also expressed an alternate, in-frame Tcra, whereas approximately 10% of T cells had 2 productive Tcra transcripts. The proportion of cells with biallelic transcription increased over the course of a response, a finding that has implications for immune memory and autoimmunity. This technique may have broad applications in mouse models of human disease. |
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ISSN: | 0021-9738 1558-8238 |