Overview of methodologies for T-cell receptor repertoire analysis.

Authors:
Elisa Rosati
Elisa Rosati
San Filippo Neri Hospital
Evaggelia Liaskou
Evaggelia Liaskou
University of Birmingham
United Kingdom
Tom H Karlsen
Tom H Karlsen
Oslo University Hospital
Norway
Andre Franke
Andre Franke
Institute of Clinical Molecular Biology
Germany

BMC Biotechnol 2017 Jul 10;17(1):61. Epub 2017 Jul 10.

Institute of Clinical Molecular Biology, Kiel University, Rosalind-Franklin-Str. 12, 24105, Kiel, Germany.

Background: The T-cell receptor (TCR), located on the surface of T cells, is responsible for the recognition of the antigen-major histocompatibility complex, leading to the initiation of an inflammatory response. Analysing the TCR repertoire may help to gain a better understanding of the immune system features and of the aetiology and progression of diseases, in particular those with unknown antigenic triggers. The extreme diversity of the TCR repertoire represents a major analytical challenge; this has led to the development of specialized methods which aim to characterize the TCR repertoire in-depth. Currently, next generation sequencing based technologies are most widely employed for the high-throughput analysis of the immune cell repertoire.

Results: Here, we report on the latest methodological advancements in the field by describing and comparing the available tools; from the choice of the starting material and library preparation method, to the sequencing technologies and data analysis. Finally, we provide a practical example and our own experience by reporting some exemplary results from a small internal benchmark study, where current approaches from the literature and the market are employed and compared.

Conclusions: Several valid methods for clonotype identification and TCR repertoire analysis exist, however, a gold standard method for the field has not yet been identified. Depending on the purpose of the scientific study, some approaches may be more suitable than others. Finally, due to possible method specific biases, scientists must be careful when comparing results obtained using different methods.

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http://dx.doi.org/10.1186/s12896-017-0379-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5504616PMC
July 2017
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