Analyzing T cell repertoire diversity by high-throughput sequencing

Diversity on a large scale is one of the most striking and powerful features utilized by the mammalian immune system to fight off a vast universe of pathogens. The T-cell driven immune response is characterized by a multitude of distinct receptors capable of antigen recognition with high specificity. Using high-throughput sequencing we are able to investigate the T cell receptor (TCR) repertoire as the collection of its individual receptors, aiming to profile the global properties of the complementarily determining region 3 (CDR3) of the TCR in human immunity. However, analysis of the data is highly sensitive to single nucleotide polymoprhisms, read length and error rate, accuracy in mapping to a genomic reference, and our ability to translate the sequence, in silico, in the appropriate reading frame. We have developed a computational pipeline that performs error correction on overlapping paired-end long (250 nt) reads, and maps the reads unambiguously to V and J cassettes corresponding to TCR-α and -β chains. Our methods were applied to functional T cell receptors from healthy blood tissue and to several patients with low grade glioma (LGG) and glioblastoma multiforme (GBM). We used Shannon entropy to measure levels of diversity in the productive T cell repertoire and observed that greater than 50% of the TCR diversity can be explained by V, J cassette usage.

[1]  M. Lefranc IMGT, the International ImMunoGeneTics Information System. , 2011, Cold Spring Harbor protocols.

[2]  Thierry Mora,et al.  Statistical inference of the generation probability of T-cell receptors from sequence repertoires , 2012, Proceedings of the National Academy of Sciences.

[3]  R. Holt,et al.  Profiling the T-cell receptor beta-chain repertoire by massively parallel sequencing. , 2009, Genome research.

[4]  Susan M. Chang,et al.  Individual Patient-Specific Immunity against High-Grade Glioma after Vaccination with Autologous Tumor Derived Peptides Bound to the 96 KD Chaperone Protein , 2012, Clinical Cancer Research.

[5]  Allen Waziri,et al.  Dynamics of central and peripheral immunomodulation in a murine glioma model , 2009, BMC Immunology.

[6]  Rustom Antia,et al.  Peripheral selection rather than thymic involution explains sudden contraction in naive CD4 T-cell diversity with age , 2012, Proceedings of the National Academy of Sciences.

[7]  Richard Durbin,et al.  Fast and accurate long-read alignment with Burrows–Wheeler transform , 2010, Bioinform..

[8]  C. Carlson,et al.  Overlap and Effective Size of the Human CD8+ T Cell Receptor Repertoire , 2010, Science Translational Medicine.

[9]  David E. Anderson,et al.  Preferential In Situ CD4+CD56+ T Cell Activation and Expansion within Human Glioblastoma , 2008, The Journal of Immunology.

[10]  James R. Knight,et al.  Genome sequencing in microfabricated high-density picolitre reactors , 2005, Nature.

[11]  A. Friedman,et al.  Increased regulatory T-cell fraction amidst a diminished CD4 compartment explains cellular immune defects in patients with malignant glioma. , 2006, Cancer research.

[12]  C. Morris,et al.  Mononuclear cell infiltration in central portions of human astrocytomas. , 1988, Journal of neurosurgery.

[13]  Abigail Wacher,et al.  Comprehensive assessment of T-cell receptor beta-chain diversity in alphabeta T cells. , 2009, Blood.

[14]  Baback Gharizadeh,et al.  High throughput sequencing reveals a complex pattern of dynamic interrelationships among human T cell subsets , 2010, Proceedings of the National Academy of Sciences.

[15]  Mushtaq Ahmed,et al.  Age-associated decline in T cell repertoire diversity leads to holes in the repertoire and impaired immunity to influenza virus , 2008, The Journal of experimental medicine.

[16]  I. Messaoudi,et al.  The many important facets of T-cell repertoire diversity , 2004, Nature Reviews Immunology.

[17]  M. Metzker Sequencing technologies — the next generation , 2010, Nature Reviews Genetics.