BMB Reports

Korean Society for Biochemistry and Molecular Biology (생화학분자생물학회)
권호 표지
DOI QR코드

DOI QR Code

Deep sequencing of B cell receptor repertoire

  • Kim, Daeun (Department of Biological Sciences, College of Natural Sciences, Ajou University) ;
  • Park, Daechan (Department of Biological Sciences, College of Natural Sciences, Ajou University)
  • Received : 2019.07.12
  • Published : 2019.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

Immune repertoire is a collection of enormously diverse adaptive immune cells within an individual. As the repertoire shapes and represents immunological conditions, identification of clones and characterization of diversity are critical for understanding how to protect ourselves against various illness such as infectious diseases and cancers. Over the past several years, fast growing technologies for high throughput sequencing have facilitated rapid advancement of repertoire research, enabling us to observe the diversity of repertoire at an unprecedented level. Here, we focus on B cell receptor (BCR) repertoire and review approaches to B cell isolation and sequencing library construction. These experiments should be carefully designed according to BCR regions to be interrogated, such as heavy chain full length, complementarity determining regions, and isotypes. We also highlight preprocessing steps to remove sequencing and PCR errors with unique molecular index and bioinformatics techniques. Due to the nature of massive sequence variation in BCR, caution is warranted when interpreting repertoire diversity from error-prone sequencing data. Furthermore, we provide a summary of statistical frameworks and bioinformatics tools for clonal evolution and diversity. Finally, we discuss limitations of current BCR-seq technologies and future perspectives on advances in repertoire sequencing.

Keywords

References

  1. Bonilla FA and Oettgen HC (2010) Adaptive immunity. J Allergy Clin Immunol 125, S33-40 https://doi.org/10.1016/j.jaci.2009.09.017
  2. Bishop GA, Haxhinasto SA, Stunz LL and Hostager BS (2003) Antigen-specific B-lymphocyte activation. Crit Rev Immunol 23, 149-197 https://doi.org/10.1615/CritRevImmunol.v23.i3.10
  3. Murphy K and Weaver C (2016) Janeway's immunobiology, 9th ed. Garland Science 410-411
  4. Weiner GJ (2015) Building better monoclonal antibodybased therapeutics. Nat Rev Cancer 15, 361-370 https://doi.org/10.1038/nrc3930
  5. Wu X, Zhou T, Zhu J et al (2011) Focused evolution of HIV-1 neutralizing antibodies revealed by structures and deep sequencing. Science 333, 1593-1602 https://doi.org/10.1126/science.1207532
  6. Doria-Rose NA, Schramm CA, Gorman J et al (2014) Developmental pathway for potent V1V2-directed HIV-neutralizing antibodies. Nature 509, 55-62 https://doi.org/10.1038/nature13036
  7. Glanville J, Zhai W, Berka J et al (2009) Precise determination of the diversity of a combinatorial antibody library gives insight into the human immunoglobulin repertoire. Proc Natl Acad Sci U S A 106, 20216-20221 https://doi.org/10.1073/pnas.0909775106
  8. Sakano H, Kurosawa Y, Weigert M and Tonegawa S (1981) Identification and nucleotide sequence of a diversity DNA segment (D) of immunoglobulin heavy-chain genes. Nature 290, 562-565 https://doi.org/10.1038/290562a0
  9. Rouet R, Jackson KJL, Langley DB and Christ D (2018) Next-generation sequencing of antibody display repertoires. Front Immunol 9, 118 https://doi.org/10.3389/fimmu.2018.00118
  10. Wardemann H and Busse CE (2017) Novel approaches to analyze immunoglobulin repertoires. Trends Immunol 38, 471-482 https://doi.org/10.1016/j.it.2017.05.003
  11. Vollmers C, Sit RV, Weinstein JA, Dekker CL and Quake SR (2013) Genetic measurement of memory B-cell recall using antibody repertoire sequencing. Proc Natl Acad Sci U S A 110, 13463-13468 https://doi.org/10.1073/pnas.1312146110
  12. Smith T, Heger A and Sudbery I (2017) UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy. Genome Res 27, 491-499 https://doi.org/10.1101/gr.209601.116
  13. Khan TA, Friedensohn S, Gorter de Vries AR, Straszewski J, Ruscheweyh HJ and Reddy ST (2016) Accurate and predictive antibody repertoire profiling by molecular amplification fingerprinting. Sci Adv 2, e1501371 https://doi.org/10.1126/sciadv.1501371
  14. Matz M, Shagin D, Bogdanova E et al (1999) Amplification of cDNA ends based on template-switching effect and step-out PCR. Nucleic Acids Res 27, 1558-1560 https://doi.org/10.1093/nar/27.6.1558
  15. Waltari E, Jia M, Jiang CS et al (2018) 5' Rapid amplification of cDNA ends and illumina MiSeq reveals B cell receptor features in healthy adults, adults with chronic HIV-1 infection, cord blood, and humanized mice. Front Immunol 9, 628 https://doi.org/10.3389/fimmu.2018.00628
  16. Calis JJ and Rosenberg BR (2014) Characterizing immune repertoires by high throughput sequencing: strategies and applications. Trends Immunol 35, 581-590 https://doi.org/10.1016/j.it.2014.09.004
  17. Chovanec P, Bolland DJ, Matheson LS et al (2018) Unbiased quantification of immunoglobulin diversity at the DNA level with VDJ-seq. Nat Protoc 13, 1232-1252 https://doi.org/10.1038/nprot.2018.021
  18. Robasky K, Lewis NE and Church GM (2014) The role of replicates for error mitigation in next-generation sequencing. Nat Rev Genet 15, 56-62 https://doi.org/10.1038/nrg3655
  19. Kircher M, Heyn P and Kelso J (2011) Addressing challenges in the production and analysis of illumina sequencing data. BMC Genomics 12, 382 https://doi.org/10.1186/1471-2164-12-382
  20. Shagin DA, Shagina IA, Zaretsky AR et al (2017) A high-throughput assay for quantitative measurement of PCR errors. Sci Rep 7, 2718 https://doi.org/10.1038/s41598-017-02727-8
  21. Yang X, Chockalingam SP and Aluru S (2013) A survey of error-correction methods for next-generation sequencing. Brief Bioinform 14, 56-66 https://doi.org/10.1093/bib/bbs015
  22. Friedensohn S, Khan TA and Reddy ST (2017) Advanced methodologies in high-throughput sequencing of immune repertoires. Trends Biotechnol 35, 203-214 https://doi.org/10.1016/j.tibtech.2016.09.010
  23. Ewing B and Green P (1998) Base-calling of automated sequencer traces using phred. II. Error probabilities. Genome Res 8, 186-194 https://doi.org/10.1101/gr.8.3.186
  24. Bolger AM, Lohse M and Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114-2120 https://doi.org/10.1093/bioinformatics/btu170
  25. Magoc T and Salzberg SL (2011) FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957-2963 https://doi.org/10.1093/bioinformatics/btr507
  26. Kivioja T, Vaharautio A, Karlsson K et al (2011) Counting absolute numbers of molecules using unique molecular identifiers. Nat Methods 9, 72-74 https://doi.org/10.1038/nmeth.1778
  27. Vander Heiden JA, Yaari G, Uduman M et al (2014) pRESTO: a toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires. Bioinformatics 30, 1930-1932 https://doi.org/10.1093/bioinformatics/btu138
  28. He L, Sok D, Azadnia P et al (2014) Toward a more accurate view of human B-cell repertoire by nextgeneration sequencing, unbiased repertoire capture and single-molecule barcoding. Sci Rep 4, 6778 https://doi.org/10.1038/srep06778
  29. Egorov ES, Merzlyak EM, Shelenkov AA et al (2015) Quantitative profiling of immune repertoires for minor lymphocyte counts using unique molecular identifiers. J Immunol 194, 6155-6163 https://doi.org/10.4049/jimmunol.1500215
  30. Edgar RC (2010) Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460-2461 https://doi.org/10.1093/bioinformatics/btq461
  31. Li W, Jaroszewski L and Godzik A (2001) Clustering of highly homologous sequences to reduce the size of large protein databases. Bioinformatics 17, 282-283 https://doi.org/10.1093/bioinformatics/17.3.282
  32. Li W and Godzik A (2006) Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658-1659 https://doi.org/10.1093/bioinformatics/btl158
  33. Lefranc MP, Giudicelli V, Duroux P et al (2015) IMGT(R), the international ImMunoGeneTics information system(R) 25 years on. Nucleic Acids Res 43, D413-422 https://doi.org/10.1093/nar/gku1056
  34. Bolotin DA, Poslavsky S, Mitrophanov I et al (2015) MiXCR: software for comprehensive adaptive immunity profiling. Nat Methods 12, 380-381 https://doi.org/10.1038/nmeth.3364
  35. Ye J, Ma N, Madden TL and Ostell JM (2013) IgBLAST: an immunoglobulin variable domain sequence analysis tool. Nucleic Acids Res 41, W34-40 https://doi.org/10.1093/nar/gkt382
  36. Giudicelli V, Chaume D and Lefranc MP (2004) IMGT/V-QUEST, an integrated software program for immunoglobulin and T cell receptor V-J and V-D-J rearrangement analysis. Nucleic Acids Res 32, W435-440 https://doi.org/10.1093/nar/gkh412
  37. Cui A, Di Niro R, Vander Heiden JA et al (2016) A Model of somatic hypermutation targeting in mice based on high-throughput Ig sequencing data. J Immunol 197, 3566-3574 https://doi.org/10.4049/jimmunol.1502263
  38. Ritvo PG, Saadawi A, Barennes P et al (2018) Highresolution repertoire analysis reveals a major bystander activation of Tfh and Tfr cells. Proc Natl Acad Sci U S A 115, 9604-9609 https://doi.org/10.1073/pnas.1808594115
  39. Thomas N, Best K, Cinelli M et al (2014) Tracking global changes induced in the CD4 T-cell receptor repertoire by immunization with a complex antigen using short stretches of CDR3 protein sequence. Bioinformatics 30, 3181-3188 https://doi.org/10.1093/bioinformatics/btu523
  40. Oksanen J, Kindt R, Legendre P et al (2007) The vegan package. Community Ecology Package 10, 631-637
  41. Hoehn KB, Lunter G and Pybus OG (2017) A phylogenetic codon substitution model for antibody lineages. Genetics 206, 417-427 https://doi.org/10.1534/genetics.116.196303
  42. Barak M, Zuckerman NS, Edelman H, Unger R and Mehr R (2008) IgTree: creating Immunoglobulin variable region gene lineage trees. J Immunol Methods 338, 67-74 https://doi.org/10.1016/j.jim.2008.06.006
  43. Gupta NT, Vander Heiden JA, Uduman M, Gadala-Maria D, Yaari G and Kleinstein SH (2015) Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data. Bioinformatics 31, 3356-3358 https://doi.org/10.1093/bioinformatics/btv359
  44. DeKosky BJ, Lungu OI, Park D et al (2016) Large-scale sequence and structural comparisons of human naive and antigen-experienced antibody repertoires. Proc Natl Acad Sci U S A 113, E2636-2645 https://doi.org/10.1073/pnas.1525510113
  45. Lee J, Boutz DR, Chromikova V et al (2016) Molecularlevel analysis of the serum antibody repertoire in young adults before and after seasonal influenza vaccination. Nat Med 22, 1456-1464 https://doi.org/10.1038/nm.4224
  46. Moorhouse MJ, van Zessen D, IJspeert H et al (2014) ImmunoGlobulin galaxy (IGGalaxy) for simple determination and quantitation of immunoglobulin heavy chain rearrangements from NGS. BMC Immunol 15, 59 https://doi.org/10.1186/s12865-014-0059-7
  47. Christley S, Scarborough W, Salinas E et al (2018) VDJServer: A cloud-Based analysis portal and data commons for immune repertoire sequences and rearrangements. Front Immunol 9, 976 https://doi.org/10.3389/fimmu.2018.00976
  48. Hurlbert SH (1971) The nonconcept of species diversity: A critique and alternative parameters. Ecology 52, 577-586 https://doi.org/10.2307/1934145
  49. Pedregosa F, Varoquaux G, Gramfort A et al (2011) Scikit-learn: Machine learning in python. J Machine Learning Res 12, 2825-2830
  50. Rajan S, Kierny MR, Mercer A et al (2018) Recombinant human B cell repertoires enable screening for rare, specific, and natively paired antibodies. Commun Biol 1, 5 https://doi.org/10.1038/s42003-017-0006-2
  51. Wu YL, Stubbington MJ, Daly M, Teichmann SA and Rada C (2017) Intrinsic transcriptional heterogeneity in B cells controls early class switching to IgE. J Exp Med 214, 183-196 https://doi.org/10.1084/jem.20161056
  52. McDaniel JR, DeKosky BJ, Tanno H, Ellington AD and Georgiou G (2016) Ultra-high-throughput sequencing of the immune receptor repertoire from millions of lymphocytes. Nat Protoc 11, 429-442 https://doi.org/10.1038/nprot.2016.024
  53. Jung ST, Reddy ST, Kang TH et al (2010) Aglycosylated IgG variants expressed in bacteria that selectively bind FcgammaRI potentiate tumor cell killing by monocytedendritic cells. Proc Natl Acad Sci U S A 107, 604-609 https://doi.org/10.1073/pnas.0908590107
  54. Piguet F, Ouldali H, Pastoriza-Gallego M, Manivet P, Pelta J and Oukhaled A (2018) Identification of single amino acid differences in uniformly charged homopolymeric peptides with aerolysin nanopore. Nat Commun 9, 966 https://doi.org/10.1038/s41467-018-03418-2
  55. Swaminathan J, Boulgakov AA, Hernandez ET et al (2018) Highly parallel single-molecule identification of proteins in zeptomole-scale mixtures. Nat Biotechnol 36, 1076-1082 https://doi.org/10.1038/nbt.4278