Abstract
B cell and T cell receptor repertoires compose the adaptive immune receptor repertoire (AIRR) of an individual. The AIRR is a unique collection of antigen-specific receptors that drives adaptive immune responses, which in turn is imprinted in each individual AIRR. This supports the concept that the AIRR could determine disease outcomes, for example in autoimmunity, infectious disease and cancer. AIRR analysis could therefore assist the diagnosis, prognosis and treatment of human diseases towards personalized medicine. High-throughput sequencing, high-dimensional statistical analysis, computational structural biology and machine learning are currently employed to study the shaping and dynamics of the AIRR as a function of time and antigenic challenges. This Primer provides an overview of concepts and state-of-the-art methods that underlie experimental and computational AIRR analysis and illustrates the diversity of relevant applications. The Primer also addresses some of the outstanding challenges in AIRR analysis, such as sampling, sequencing depth, experimental variations and computational biases, while discussing prospects of future AIRR analysis applications for understanding and predicting adaptive immune responses.
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Change history
16 February 2024
A Correction to this paper has been published: https://doi.org/10.1038/s43586-024-00299-2
References
Rappazzo, C. G. et al. Defining and studying B cell receptor and TCR interactions. J. Immunol. 211, 311–322 (2023).
Schroeder, H. W. Jr & Cavacini, L. Structure and function of immunoglobulins. J. Allergy Clin. Immunol. 125, S41–S52 (2010).
Kuhns, M. S., Davis, M. M. & Garcia, K. C. Deconstructing the form and function of the TCR/CD3 complex. Immunity 24, 133–139 (2006).
Ribot, J. C., Lopes, N. & Silva-Santos, B. γδ T cells in tissue physiology and surveillance. Nat. Rev. Immunol. 21, 221–232 (2021).
Boehme, L., Roels, J. & Taghon, T. Development of γδ T cells in the thymus — a human perspective. Semin. Immunol. 61–64, 101662 (2022).
Bosselut, R. A beginner’s guide to T cell development. Methods Mol. Biol. 2580, 3–24 (2023).
Deseke, M. & Prinz, I. Ligand recognition by the γδ TCR and discrimination between homeostasis and stress conditions. Cell. Mol. Immunol. 17, 914–924 (2020).
Willcox, B. E. & Willcox, C. R. γδ TCR ligands: the quest to solve a 500-million-year-old mystery. Nat. Immunol. 20, 121–128 (2019).
Cooper, M. D. & Alder, M. N. The evolution of adaptive immune systems. Cell 124, 815–822 (2006).
Flajnik, M. F. & Kasahara, M. Origin and evolution of the adaptive immune system: genetic events and selective pressures. Nat. Rev. Genet. 11, 47–59 (2010).
Miller, J. F. Immunological function of the thymus. Lancet 2, 748–749 (1961).
Miller, J. F. A. P. Effect of neonatal thymectomy on the immunological responsiveness of the mouse. Proc. R. Soc. Lond. 156, 415–428 (1962).
Cooper, M. D., Peterson, R. D. & Good, R. A. Delineation of the thymic and bursal lymphoid systems in the chicken. Nature 205, 143–146 (1965).
Miller, J. F., Mitchell, G. F. & Weiss, N. S. Cellular basis of the immunological defects in thymectomized mice. Nature 214, 992–997 (1967).
Tonegawa, S. Somatic generation of antibody diversity. Nature 302, 575–581 (1983).
Chien, Y. et al. A third type of murine T-cell receptor gene. Nature 312, 31–35 (1984).
Alamyar, E., Giudicelli, V., Li, S., Duroux, P. & Lefranc, M.-P. IMGT/HighV-QUEST: the IMGT® web portal for immunoglobulin (IG) or antibody and T cell receptor (TR) analysis from NGS high throughput and deep sequencing. Immunome Res. 8, 26 (2012).
Chaudhary, N. & Wesemann, D. R. Analyzing immunoglobulin repertoires. Front. Immunol. 9, 462 (2018).
Chiffelle, J. et al. T-cell repertoire analysis and metrics of diversity and clonality. Curr. Opin. Biotechnol. 65, 284–295 (2020).
Marks, C. & Deane, C. M. Antibody H3 structure prediction. Comput. Struct. Biotechnol. J. 15, 222–231 (2017).
Davis, M. M. & Bjorkman, P. J. T-cell antigen receptor genes and T-cell recognition. Nature 334, 395–402 (1988).
Xu, J. L. & Davis, M. M. Diversity in the CDR3 region of VH is sufficient for most antibody specificities. Immunity 13, 37–45 (2000).
McKean, D. et al. Generation of antibody diversity in the immune response of BALB/c mice to influenza virus hemagglutinin. Proc. Natl Acad. Sci. USA 81, 3180–3184 (1984).
Hershberg, U. & Prak, E. T. L. The analysis of clonal expansions in normal and autoimmune B cell repertoires. Philos. Trans. R. Soc. Lond. B Biol. Sci. 370, 20140239 (2015).
Muramatsu, M. et al. Class switch recombination and hypermutation require activation-induced cytidine deaminase (AID), a potential RNA editing enzyme. Cell 102, 553–563 (2000).
Stavnezer, J., Guikema, J. E. J. & Schrader, C. E. Mechanism and regulation of class switch recombination. Annu. Rev. Immunol. 26, 261–292 (2008).
Ng, J. C. F. et al. sciCSR infers B cell state transition and predicts class-switch recombination dynamics using single-cell transcriptomic data. Nat. Methods https://doi.org/10.1038/s41592-023-02060-1 (2023).
Bradley, P. & Thomas, P. G. Using T cell receptor repertoires to understand the principles of adaptive immune recognition. Annu. Rev. Immunol. 37, 547–570 (2019).
Dupic, T., Marcou, Q., Walczak, A. M. & Mora, T. Genesis of the αβ T-cell receptor. PLoS Comput. Biol. 15, e1006874 (2019).
Elhanati, Y. et al. Inferring processes underlying B-cell repertoire diversity. Philos. Trans. R. Soc. Lond. B Biol. Sci. 370, 20140243 (2015).
Mora, T. & Walczak, A. M. Quantifying lymphocyte receptor diversity. Preprint at bioRxiv https://doi.org/10.1101/046870 (2016).
Trepel, F. Number and distribution of lymphocytes in man. A critical analysis. Klin. Wochenschr. 52, 511–515 (1974).
Cosgrove, J., Hustin, L. S. P., de Boer, R. J. & Perié, L. Hematopoiesis in numbers. Trends Immunol. 42, 1100–1112 (2021).
Sender, R. et al. The total mass, number, and distribution of immune cells in the human body. Proc. Natl Acad. Sci. USA 120, e2308511120 (2023).
Greiff, V. et al. Systems analysis reveals high genetic and antigen-driven predetermination of antibody repertoires throughout B cell development. Cell Rep. 19, 1467–1478 (2017).
Klein, L., Kyewski, B., Allen, P. M. & Hogquist, K. A. Positive and negative selection of the T cell repertoire: what thymocytes see (and don’t see). Nat. Rev. Immunol. 14, 377–391 (2014).
Qi, Q. et al. Diversity and clonal selection in the human T-cell repertoire. Proc. Natl Acad. Sci. USA 111, 13139–13144 (2014).
Khosravi-Maharlooei, M. et al. Crossreactive public TCR sequences undergo positive selection in the human thymic repertoire. J. Clin. Invest. 129, 2446–2462 (2019).
Brown, A. J. et al. Augmenting adaptive immunity: progress and challenges in the quantitative engineering and analysis of adaptive immune receptor repertoires. Mol. Syst. Des. Eng. 4, 701–736 (2019).
Arnaout, R. A., Prak, E. T. L., Schwab, N., Rubelt, F. & Adaptive Immune Receptor Repertoire Community. The future of blood testing is the immunome. Front. Immunol. 12, 626793 (2021).
Zinkernagel, R. M. On differences between immunity and immunological memory. Curr. Opin. Immunol. 14, 523–536 (2002).
Galson, J. D. et al. Analysis of B cell repertoire dynamics following hepatitis B vaccination in humans, and enrichment of vaccine-specific antibody sequences. EBioMedicine 2, 2070–2079 (2015).
Setliff, I. et al. Multi-donor longitudinal antibody repertoire sequencing reveals the existence of public antibody clonotypes in HIV-1 infection. Cell Host Microbe 23, 845–854.e6 (2018).
Sui, W. et al. Composition and variation analysis of the TCR β-chain CDR3 repertoire in systemic lupus erythematosus using high-throughput sequencing. Mol. Immunol. 67, 455–464 (2015).
Madi, A. et al. T-cell receptor repertoires share a restricted set of public and abundant CDR3 sequences that are associated with self-related immunity. Genome Res. 24, 1603–1612 (2014).
Briney, B., Inderbitzin, A., Joyce, C. & Burton, D. R. Commonality despite exceptional diversity in the baseline human antibody repertoire. Nature 566, 393–397 (2019).
Lee, J. et al. Persistent antibody clonotypes dominate the serum response to influenza over multiple years and repeated vaccinations. Cell Host Microbe 25, 367–376.e5 (2019).
Bournazos, S. et al. Antibody fucosylation predicts disease severity in secondary dengue infection. Science 372, 1102–1105 (2021).
Emerson, R. O. et al. Immunosequencing identifies signatures of cytomegalovirus exposure history and HLA-mediated effects on the T cell repertoire. Nat. Genet. 49, 659–665 (2017).
Cowell, L. G. The diagnostic, prognostic, and therapeutic potential of adaptive immune receptor repertoire profiling in cancer. Cancer Res 80, 643–654 (2020).
Mason, D. M. et al. Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning. Nat. Biomed. Eng. 5, 600–612 (2021).
Rapoport, A. P. et al. NY-ESO-1-specific TCR-engineered T cells mediate sustained antigen-specific antitumor effects in myeloma. Nat. Med. 21, 914–921 (2015).
Schuster, S. J. et al. Tisagenlecleucel in adult relapsed or refractory diffuse large B-cell lymphoma. N. Engl. J. Med. 380, 45–56 (2019).
Morrissey, K. A., Stammnitz, M. R., Murchison, E. & Miller, R. D. Comparative genomics of the T cell receptor μ locus in marsupials and monotremes. Immunogenetics 75, 507–515 (2023).
Parra, Z. E., Mitchell, K., Dalloul, R. A. & Miller, R. D. A second TCRδ locus in Galliformes uses antibody-like V domains: insight into the evolution of TCRδ and TCRμ genes in tetrapods. J. Immunol. 188, 3912–3919 (2012).
Ott, J. A., Harrison, J., Flajnik, M. F. & Criscitiello, M. F. Nurse shark T-cell receptors employ somatic hypermutation preferentially to alter α/δ variable segments associated with α constant region. Eur. J. Immunol. 50, 1307–1320 (2020).
Castro, R. et al. Clonotypic IgH response against systemic viral infection in pronephros and spleen of a teleost fish. J. Immunol. 208, 2573–2582 (2022).
Castro, R. et al. Contrasted TCRβ diversity of CD8+ and CD8− T cells in rainbow trout. PLoS ONE 8, e60175 (2013).
Burnet, S. F. M. The Clonal Selection Theory of Acquired Immunity; The Abraham Flexner Lectures of Vanderbilt University (Cambridge Univ. Press, 1959).
Liu, S. et al. Spatial maps of T cell receptors and transcriptomes reveal distinct immune niches and interactions in the adaptive immune response. Immunity 55, 1940–1952.e5 (2022).
Heumos, L. et al. Best practices for single-cell analysis across modalities. Nat. Rev. Genet. 24, 550–572 (2023).
Faint, J. M. et al. Quantitative flow cytometry for the analysis of T cell receptor Vβ chain expression. J. Immunol. Methods 225, 53–60 (1999).
Pannetier, C. et al. The sizes of the CDR3 hypervariable regions of the murine T-cell receptor β chains vary as a function of the recombined germ-line segments. Proc. Natl Acad. Sci. USA 90, 4319–4323 (1993).
Pannetier, C., Even, J. & Kourilsky, P. T-cell repertoire diversity and clonal expansions in normal and clinical samples. Immunol. Today 16, 176–181 (1995).
Weinstein, J. A., Jiang, N., White, R. A. III, Fisher, D. S. & Quake, S. R. High-throughput sequencing of the zebrafish antibody repertoire. Science 324, 807–810 (2009).
Robins, H. S. et al. Comprehensive assessment of T-cell receptor β-chain diversity in αβ T cells. Blood 114, 4099–4107 (2009).
Boyd, S. D. et al. Measurement and clinical monitoring of human lymphocyte clonality by massively parallel VDJ pyrosequencing. Sci. Transl. Med. 1, 12ra23 (2009).
Rosati, E. et al. Overview of methodologies for T-cell receptor repertoire analysis. BMC Biotechnol. 17, 61 (2017).
Barennes, P. et al. Benchmarking of T cell receptor repertoire profiling methods reveals large systematic biases. Nat. Biotechnol. 39, 236–245 (2021).
Vázquez Bernat, N. et al. High-quality library preparation for NGS-based immunoglobulin germline gene inference and repertoire expression analysis. Front. Immunol. 10, 660 (2019).
Genolet, R. et al. TCR sequencing and cloning methods for repertoire analysis and isolation of tumor-reactive TCRs. Cell Rep. Methods 3, 100459 (2023).
Pai, J. A. & Satpathy, A. T. High-throughput and single-cell T cell receptor sequencing technologies. Nat. Methods 18, 881–892 (2021).
Friedensohn, S., Khan, T. A. & Reddy, S. T. Advanced methodologies in high-throughput sequencing of immune repertoires. Trends Biotechnol. 35, 203–214 (2017).
Wang, Y. et al. Multimodal single-cell and whole-genome sequencing of small, frozen clinical specimens. Nat. Genet. 55, 19–25 (2023).
Miron, M. et al. Maintenance of the human memory T cell repertoire by subset and tissue site. Genome Med. 13, 100 (2021).
Quiniou, V. et al. Human thymopoiesis produces polyspecific CD8+ α/β T cells responding to multiple viral antigens. eLife 12, e81274 (2023).
Meng, W. et al. An atlas of B-cell clonal distribution in the human body. Nat. Biotechnol. 35, 879–884 (2017).
Liu, Y.-Y. et al. Characteristics and prognostic significance of profiling the peripheral blood T-cell receptor repertoire in patients with advanced lung cancer. Int. J. Cancer 145, 1423–1431 (2019).
Rossetti, M. et al. TCR repertoire sequencing identifies synovial Treg cell clonotypes in the bloodstream during active inflammation in human arthritis. Ann. Rheum. Dis. 76, 435–441 (2017).
Bashford-Rogers, R. J. M. et al. Analysis of the B cell receptor repertoire in six immune-mediated diseases. Nature 574, 122–126 (2019).
Mastrokolias, A., den Dunnen, J. T., van Ommen, G. B., ’t Hoen, P. A. C. & van Roon-Mom, W. M. C. Increased sensitivity of next generation sequencing-based expression profiling after globin reduction in human blood RNA. BMC Genomics 13, 28 (2012).
Valpione, S. et al. Immune-awakening revealed by peripheral T cell dynamics after one cycle of immunotherapy. Nat. Cancer 1, 210–221 (2020).
Miljkovic, M. D. et al. Next-generation sequencing-based monitoring of circulating tumor DNA reveals clonotypic heterogeneity in untreated PTCL. Blood Adv. 5, 4198–4210 (2021).
Komech, E. A. et al. TCR repertoire profiling revealed antigen-driven CD8+ T cell clonal groups shared in synovial fluid of patients with spondyloarthritis. Front. Immunol. 13, 973243 (2022).
Liao, M. et al. Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19. Nat. Med. 26, 842–844 (2020).
Greenfield, A. L. et al. Longitudinally persistent cerebrospinal fluid B cells can resist treatment in multiple sclerosis. JCI Insight 4, e126599 (2019).
Meednu, N. et al. Dynamic spectrum of ectopic lymphoid B cell activation and hypermutation in the RA synovium characterized by NR4A nuclear receptor expression. Cell Rep. 39, 110766 (2022).
Gros, A. et al. PD-1 identifies the patient-specific CD8+ tumor-reactive repertoire infiltrating human tumors. J. Clin. Invest. 124, 2246–2259 (2014).
Bai, X. et al. Characteristics of tumor infiltrating lymphocyte and circulating lymphocyte repertoires in pancreatic cancer by the sequencing of T cell receptors. Sci. Rep. 5, 13664 (2015).
Langerak, A. W. Immunogenetics (Springer US, 2022).
Klein, U., Küppers, R. & Rajewsky, K. Evidence for a large compartment of IgM-expressing memory B cells in humans. Blood 89, 1288–1298 (1997).
Shi, W. et al. Transcriptional profiling of mouse B cell terminal differentiation defines a signature for antibody-secreting plasma cells. Nat. Immunol. 16, 663–673 (2015).
Bergot, A.-S. et al. TCR sequences and tissue distribution discriminate the subsets of naïve and activated/memory Treg cells in mice. Eur. J. Immunol. 45, 1524–1534 (2015).
Muraro, P. A. et al. T cell repertoire following autologous stem cell transplantation for multiple sclerosis. J. Clin. Invest. 124, 1168–1172 (2014).
Bashford-Rogers, R. J. M. et al. Network properties derived from deep sequencing of human B-cell receptor repertoires delineate B-cell populations. Genome Res. 23, 1874–1884 (2013).
Ghraichy, M. et al. Different B cell subpopulations show distinct patterns in their IgH repertoire metrics. eLife 10, e73111 (2021).
King, H. W. et al. Single-cell analysis of human B cell maturation predicts how antibody class switching shapes selection dynamics. Sci Immunol 6, eabe6291 (2021).
Han, A., Glanville, J., Hansmann, L. & Davis, M. M. Linking T-cell receptor sequence to functional phenotype at the single-cell level. Nat. Biotechnol. 32, 684–692 (2014).
Munson, D. J. et al. Identification of shared TCR sequences from T cells in human breast cancer using emulsion RT-PCR. Proc. Natl Acad. Sci. USA 113, 8272–8277 (2016).
He, B. et al. Rapid isolation and immune profiling of SARS-CoV-2 specific memory B cell in convalescent COVID-19 patients via LIBRA-seq. Signal. Transduct. Target. Ther. 6, 195 (2021).
Zhang, S.-Q. et al. High-throughput determination of the antigen specificities of T cell receptors in single cells. Nat. Biotechnol. https://doi.org/10.1038/nbt.4282 (2018).
Carlson, C. S. et al. Using synthetic templates to design an unbiased multiplex PCR assay. Nat. Commun. 4, 2680 (2013).
Bashford-Rogers, R. J. M. et al. Capturing needles in haystacks: a comparison of B-cell receptor sequencing methods. BMC Immunol. 15, 29 (2014).
Chovanec, P. et al. Unbiased quantification of immunoglobulin diversity at the DNA level with VDJ-seq. Nat. Protoc. 13, 1232–1252 (2018).
Trück, J. et al. Biological controls for standardization and interpretation of adaptive immune receptor repertoire profiling. eLife 10, e66274 (2021).
Menzel, U. et al. Comprehensive evaluation and optimization of amplicon library preparation methods for high-throughput antibody sequencing. PLoS ONE 9, e96727 (2014).
Ford, E. E. et al. FLAIRR-seq: a method for single-molecule resolution of near full-length antibody H chain repertoires. J. Immunol. 210, 1607–1619 (2023).
Mamedov, I. Z. et al. Preparing unbiased T-cell receptor and antibody cDNA libraries for the deep next generation sequencing profiling. Front. Immunol. 4, 456 (2013).
Khan, T. A. et al. Accurate and predictive antibody repertoire profiling by molecular amplification fingerprinting. Sci. Adv. 2, e1501371 (2016).
Liu, X. et al. Systematic comparative evaluation of methods for investigating the TCRβ repertoire. PLoS ONE 11, e0152464 (2016).
Schaefer, B. C. Revolutions in rapid amplification of cDNA ends: new strategies for polymerase chain reaction cloning of full-length cDNA ends. Anal. Biochem. 227, 255–273 (1995).
Lin, Y.-H. et al. Dissecting efficiency of a 5′ rapid amplification of cDNA ends (5′-RACE) approach for profiling T-cell receptor β repertoire. PLoS ONE 15, e0236366 (2020).
Ellefson, J. W. et al. Synthetic evolutionary origin of a proofreading reverse transcriptase. Science 352, 1590–1593 (2016).
Wang, C. et al. High throughput sequencing reveals a complex pattern of dynamic interrelationships among human T cell subsets. Proc. Natl Acad. Sci. USA 107, 1518–1523 (2010).
Heather, J. M. et al. Dynamic perturbations of the T-cell receptor repertoire in chronic HIV infection and following antiretroviral therapy. Front. Immunol. 6, 644 (2015).
Douek, D. C. et al. A novel approach to the analysis of specificity, clonality, and frequency of HIV-specific T cell responses reveals a potential mechanism for control of viral escape. J. Immunol. 168, 3099–3104 (2002).
Turchaninova, M. A. et al. High-quality full-length immunoglobulin profiling with unique molecular barcoding. Nat. Protoc. 11, 1599–1616 (2016).
Scheijen, B. et al. Next-generation sequencing of immunoglobulin gene rearrangements for clonality assessment: a technical feasibility study by EuroClonality-NGS. Leukemia 33, 2227–2240 (2019).
Baker, A.-M. et al. FUME-TCRseq: sensitive and accurate sequencing of the T-cell receptor from limited input of degraded RNA. Preprint at bioRxiv https://doi.org/10.1101/2023.04.24.538037 (2023).
Gupta, N. et al. Single-cell analysis and tracking of antigen-specific T cells: integrating paired chain AIRR-seq and transcriptome sequencing: a method by the AIRR community. Methods Mol. Biol. 2453, 379–421 (2022).
Fan, H. C., Fu, G. K. & Fodor, S. P. A. Combinatorial labeling of single cells for gene expression cytometry. Science 347, 1258367 (2015).
Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049–14049 (2017).
Klein, A. M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).
Mereu, E. et al. Benchmarking single-cell RNA-sequencing protocols for cell atlas projects. Nat. Biotechnol. 38, 747–755 (2020).
Yamawaki, T. M. et al. Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling. BMC Genomics 22, 66 (2021).
Nadeu, F. et al. Detection of early seeding of Richter transformation in chronic lymphocytic leukemia. Nat. Med. 28, 1662–1671 (2022).
Ostendorf, B. N. et al. Common human genetic variants of APOE impact murine COVID-19 mortality. Nature 611, 346–351 (2022).
Aird, D. et al. Analyzing and minimizing PCR amplification bias in Illumina sequencing libraries. Genome Biol. 12, R18 (2011).
Kebschull, J. M. & Zador, A. M. Sources of PCR-induced distortions in high-throughput sequencing data sets. Nucleic Acids Res. 43, e143 (2015).
Pääbo, S., Irwin, D. M. & Wilson, A. C. DNA damage promotes jumping between templates during enzymatic amplification. J. Biol. Chem. 265, 4718–4721 (1990).
Eugster, A. et al. AIRR community guide to planning and performing AIRR-seq experiments. Methods Mol. Biol. 2453, 261–278 (2022).
Koraichi, M. B., Touzel, M. P., Mazzolini, A., Mora, T. & Walczak, A. M. NoisET: noise learning and expansion detection of T-cell receptors. J. Phys. Chem. 126, 7407–7414 (2022).
Rosenfeld, A. M. et al. Computational evaluation of B-cell clone sizes in bulk populations. Front. Immunol. 9, 1472 (2018).
Friedensohn, S. et al. Synthetic standards combined with error and bias correction improve the accuracy and quantitative resolution of antibody repertoire sequencing in human naïve and memory B cells. Front. Immunol. 9, 1401 (2018).
Vollmers, C., Sit, R. V., Weinstein, J. A., Dekker, C. L. & Quake, S. R. Genetic measurement of memory B-cell recall using antibody repertoire sequencing. Proc. Natl Acad. Sci. USA 110, 13463–13468 (2013).
Shugay, M. et al. Towards error-free profiling of immune repertoires. Nat. Methods 11, 653–655 (2014).
Egorov, E. S. et al. Quantitative profiling of immune repertoires for minor lymphocyte counts using unique molecular identifiers. J. Immunol. 194, 6155–6163 (2015).
Gupta, N. et al. Bulk sequencing from mRNA with UMI for evaluation of B-cell isotype and clonal evolution: a method by the AIRR community. Methods Mol. Biol. 2453, 345–377 (2022).
Subas Satish, H. P. et al. NAb-seq: an accurate, rapid, and cost-effective method for antibody long-read sequencing in hybridoma cell lines and single B cells. mAbs 14, 2106621 (2022).
Rodriguez, O. L., Silver, C. A., Shields, K., Smith, M. L. & Watson, C. T. Targeted long-read sequencing facilitates phased diploid assembly and genotyping of the human T cell receptor α, δ, and β loci. Cell Genom. 2, 100228 (2022).
Brochu, H. N. et al. Systematic profiling of full-length Ig and TCR repertoire diversity in rhesus macaque through long read transcriptome sequencing. J. Immunol. 204, 3434–3444 (2020).
Singh, M. et al. High-throughput targeted long-read single cell sequencing reveals the clonal and transcriptional landscape of lymphocytes. Nat. Commun. 10, 3120 (2019).
Amarasinghe, S. L. et al. Opportunities and challenges in long-read sequencing data analysis. Genome Biol. 21, 30 (2020).
Schanz, M. et al. High-throughput sequencing of human immunoglobulin variable regions with subtype identification. PLoS ONE 9, e111726 (2014).
Quý, K. L. et al. Benchmarking and integrating human B-cell receptor genomic and antibody proteomic profiling.Preprint at bioRxiv https://doi.org/10.1101/2023.11.01.565093 (2023).
Chaara, W. et al. RepSeq data representativeness and robustness assessment by Shannon entropy. Front. Immunol. 9, 1038 (2018).
Shugay, M. et al. Huge overlap of individual TCR β repertoires. Front. Immunol. 4, 466 (2013).
Soto, C. et al. High frequency of shared clonotypes in human B cell receptor repertoires. Nature 566, 398–402 (2019).
Kircher, M., Sawyer, S. & Meyer, M. Double indexing overcomes inaccuracies in multiplex sequencing on the Illumina platform. Nucleic Acids Res. 40, e3 (2012).
MacConaill, L. E. et al. Unique, dual-indexed sequencing adapters with UMIs effectively eliminate index cross-talk and significantly improve sensitivity of massively parallel sequencing. BMC Genomics 19, 30 (2018).
Costello, M. et al. Characterization and remediation of sample index swaps by non-redundant dual indexing on massively parallel sequencing platforms. BMC Genomics 19, 332 (2018).
Goods, B. A. et al. Blood handling and leukocyte isolation methods impact the global transcriptome of immune cells. BMC Immunol. 19, 30 (2018).
Knecht, H. et al. Quality control and quantification in IG/TR next-generation sequencing marker identification: protocols and bioinformatic functionalities by EuroClonality-NGS. Leukemia 33, 2254–2265 (2019).
Cock, P. J. A., Fields, C. J., Goto, N., Heuer, M. L. & Rice, P. M. The sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants. Nucleic Acids Res. 38, 1767–1771 (2010).
Greiff, V., Miho, E., Menzel, U. & Reddy, S. T. Bioinformatic and statistical analysis of adaptive immune repertoires. Trends Immunol. 36, 738–749 (2015).
Pennell, M., Rodriguez, O. L., Watson, C. T. & Greiff, V. The evolutionary and functional significance of germline immunoglobulin gene variation. Trends Immunol. 44, 7–21 (2023).
deCamp, A. C. et al. Human immunoglobulin gene allelic variation impacts germline-targeting vaccine priming. Preprint at medRxiv https://doi.org/10.1101/2023.03.10.23287126 (2023).
Mikocziova, I., Greiff, V. & Sollid, L. M. Immunoglobulin germline gene variation and its impact on human disease. Genes. Immun. 22, 205–217 (2021).
Omer, A. et al. T cell receptor β germline variability is revealed by inference from repertoire data. Genome Med. 14, 2 (2022).
Ohlin, M. et al. Inferred allelic variants of immunoglobulin receptor genes: a system for their evaluation, documentation, and naming. Front. Immunol. 10, 435 (2019).
Watson, C. T., Glanville, J. & Marasco, W. A. The individual and population genetics of antibody immunity. Trends Immunol. 38, 459–470 (2017).
Mikelov, A. et al. Ultrasensitive allele inference from immune repertoire sequencing data with MiXCR. Preprint at bioRxiv https://doi.org/10.1101/2023.10.10.561703 (2023).
Corcoran, M. M. et al. Production of individualized V gene databases reveals high levels of immunoglobulin genetic diversity. Nat. Commun. 7, 13642 (2016).
Gadala-Maria, D., Yaari, G., Uduman, M. & Kleinstein, S. H. Automated analysis of high-throughput B-cell sequencing data reveals a high frequency of novel immunoglobulin V gene segment alleles. Proc. Natl Acad. Sci. USA 112, E862–E870 (2015).
Ralph, D. K. & Matsen, F. A. IV Using B cell receptor lineage structures to predict affinity. PLoS Comput. Biol. 16, e1008391 (2020).
Vázquez Bernat, N. et al. Rhesus and cynomolgus macaque immunoglobulin heavy-chain genotyping yields comprehensive databases of germline VDJ alleles. Immunity 54, 355–366.e4 (2021).
Rodriguez, O. L. et al. Genetic variation in the immunoglobulin heavy chain locus shapes the human antibody repertoire. Nat. Commun. 14, 4419 (2023).
Gidoni, M. et al. Mosaic deletion patterns of the human antibody heavy chain gene locus shown by Bayesian haplotyping. Nat. Commun. 10, 628 (2019).
Mikocziova, I. et al. Germline polymorphisms and alternative splicing of human immunoglobulin light chain genes. iScience 24, 103192 (2021).
Rubelt, F. et al. Adaptive immune receptor repertoire community recommendations for sharing immune-repertoire sequencing data. Nat. Immunol. 18, 1274–1278 (2017).
Vander Heiden, J. A. et al. AIRR community standardized representations for annotated immune repertoires. Front. Immunol. 9, 2206 (2018).
Song, L. et al. TRUST4: immune repertoire reconstruction from bulk and single-cell RNA-seq data. Nat. Methods 18, 627–630 (2021).
Rubio, T. et al. A Nextflow pipeline for T-cell receptor repertoire reconstruction and analysis from RNA sequencing data. ImmunoInformatics 6, 100012 (2022).
Bolotin, D. A. et al. Antigen receptor repertoire profiling from RNA-seq data. Nat. Biotechnol. 35, 908–911 (2017).
Glanville, J. et al. Naive antibody gene-segment frequencies are heritable and unaltered by chronic lymphocyte ablation. Proc. Natl Acad. Sci. USA 108, 20066–20071 (2011).
Weber, C. R. et al. Reference-based comparison of adaptive immune receptor repertoires. Cell Rep. Methods 2, 100269 (2022).
Ritvo, P.-G. et al. High-resolution repertoire analysis reveals a major bystander activation of TFH and TFR cells. Proc. Natl Acad. Sci. USA 115, 9604–9609 (2018).
Mhanna, V. et al. Impaired activated/memory regulatory T cell clonal expansion instigates diabetes in NOD mice. Diabetes 70, 976–985 (2021).
Olson, B. J. et al. sumrep: a summary statistic framework for immune receptor repertoire comparison and model validation. Front. Immunol. 10, 2533 (2019).
Weber, C. R. et al. immuneSIM: tunable multi-feature simulation of B- and T-cell receptor repertoires for immunoinformatics benchmarking. Bioinformatics 36, 3594–3596 (2020).
Greiff, V. et al. A bioinformatic framework for immune repertoire diversity profiling enables detection of immunological status. Genome Med. 7, 49 (2015).
Rényi, A. in Proc. Fourth Berkeley Symp. Mathematical Statistics and Probability Vol. 1: Contributions to the Theory of Statistics Vol. 4.1 (ed. Neyman, J.) 547–562 (Univ. of California Press, 1961).
Pielou, E. C. The measurement of diversity in different types of biological collections. J. Theor. Biol. 13, 131–144 (1966).
Hill, M. O. Diversity and evenness: a unifying notation and its consequences. Ecology 54, 427–432 (1973).
Dorfman, R. A formula for the Gini coefficient. Rev. Econ. Stat. 61, 146–149 (1979).
Magurran, A. E. in Ecological Diversity and Its Measurement (ed. Magurran, A. E.) 61–80 (Springer Netherlands, 1988).
Jost, L. Entropy and diversity. Oikos 113, 363–375 (2006).
Nolan, K. A. & Callahan, J. E. Beachcomber biology: The Shannon-Weiner Species Diversity Index. in Tested Studies for Laboratory Teaching. Proceedings of the 27th Workshop/Conference of the Association for Biology Laboratory Education Vol. 27 (ed. O'Donnell, M. A.) 334–338 (Association for Biology Laboratory Education, 2006).
Somerfield, P. J., Clarke, K. R. & Warwick, R. M. in Encyclopedia of Ecology (eds Jorgensen, S. V. & Fath, B.) 3252–3255 (Elsevier, 2008).
Kaplinsky, J. & Arnaout, R. Robust estimates of overall immune-repertoire diversity from high-throughput measurements on samples. Nat. Commun. 7, 11881 (2016).
Miho, E. et al. Computational strategies for dissecting the high-dimensional complexity of adaptive immune repertoires. Front. Immunol. 9, 224 (2018).
Marquez, S. et al. Adaptive immune receptor repertoire (AIRR) community guide to repertoire analysis. Methods Mol. Biol. 2453, 297–316 (2022).
Alon, U., Mokryn, O. & Hershberg, U. Using domain based latent personal analysis of B cell clone diversity patterns to identify novel relationships between the B cell clone populations in different tissues. Front. Immunol. 12, 642673 (2021).
Strauli, N. B. & Hernandez, R. D. Statistical inference of a convergent antibody repertoire response to influenza vaccine. Genome Med. 8, 60 (2016).
Vujović, M., Marcatili, P., Chain, B., Kaplinsky, J. & Andresen, T. L. Signatures of T cell immunity revealed using sequence similarity with TCRDivER algorithm. Commun. Biol. 6, 357 (2023).
Miho, E., Roškar, R., Greiff, V. & Reddy, S. T. Large-scale network analysis reveals the sequence space architecture of antibody repertoires. Nat. Commun. 10, 1321 (2019).
Arora, R. & Arnaout, R. Repertoire-scale measures of antigen binding. Proc. Natl Acad. Sci. USA 119, e2203505119 (2022).
Csardi, G. & Nepusz, T. The igraph software package for complex network research. Interj. Complex Syst. 1695, 1–9 (2006).
Hagberg, A. A., Schult, D. A. & Swart, P. J. Exploring Network Structure, Dynamics, and Function using NetworkX. in Proc. 7th Python in Science Conf. (SciPy2008) (eds Varoquaux, G., Vaught, T. & Millman, J.) 11–15 (Proceedings of the Python in Science Conference, 2008).
Bastian, M., Heymann, S. & Jacomy, M. Gephi: an open source software for exploring and manipulating. Networks. ICWSM 3, 361–362 (2009).
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).
Madi, A. et al. T cell receptor repertoires of mice and humans are clustered in similarity networks around conserved public CDR3 sequences. eLife Sci. 6, e22057 (2017).
Amoriello, R. et al. TCR repertoire diversity in multiple sclerosis: high-dimensional bioinformatics analysis of sequences from brain, cerebrospinal fluid and peripheral blood. EBioMedicine 68, 103429 (2021).
Albert, R., Jeong, H. & Barabasi, A.-L. Error and attack tolerance of complex networks. Nature 406, 378–382 (2000).
Barabási, A.-L. & Márton, P. Network Science (Cambridge Univ. Press, 2016).
Akbar, R. et al. A compact vocabulary of paratope–epitope interactions enables predictability of antibody–antigen binding. Cell Rep. 34, 108856 (2021).
Ostmeyer, J., Christley, S., Toby, I. T. & Cowell, L. G. Biophysicochemical motifs in T-cell receptor sequences distinguish repertoires from tumor-infiltrating lymphocyte and adjacent healthy tissue. Cancer Res. 79, 1671–1680 (2019).
Valkiers, S., Van Houcke, M., Laukens, K. & Meysman, P. ClusTCR: a Python interface for rapid clustering of large sets of CDR3 sequences with unknown antigen specificity. Bioinformatics 37, 4865–4867 (2021).
Mayer-Blackwell, K. et al. TCR meta-clonotypes for biomarker discovery with tcrdist3 enabled identification of public, HLA-restricted clusters of SARS-CoV-2 TCRs. eLife 10, e68605 (2021).
Zhang, H., Zhan, X. & Li, B. GIANA allows computationally-efficient TCR clustering and multi-disease repertoire classification by isometric transformation. Nat. Commun. 12, 4699 (2021).
Zhang, Z. et al. Interpreting the B-cell receptor repertoire with single-cell gene expression using Benisse. Nat. Mach. Intell. 4, 596–604 (2022).
Zhang, Z., Xiong, D., Wang, X., Liu, H. & Wang, T. Mapping the functional landscape of T cell receptor repertoires by single-T cell transcriptomics. Nat. Methods 18, 92–99 (2021).
Schattgen, S. A. et al. Integrating T cell receptor sequences and transcriptional profiles by clonotype neighbor graph analysis (CoNGA). Nat. Biotechnol. 40, 54–63 (2022).
Hoehn, K. B., Fowler, A., Lunter, G. & Pybus, O. G. The diversity and molecular evolution of B-cell receptors during infection. Mol. Biol. Evol. 33, 1147–1157 (2016).
Yermanos, A. D., Dounas, A. K., Stadler, T., Oxenius, A. & Reddy, S. T. Tracing antibody repertoire evolution by systems phylogeny. Front. Immunol. 9, 2149 (2018).
Abdollahi, N. et al. A multi-objective based clustering for inferring BCR clonal lineages from high-throughput B cell repertoire data. PLoS Comput. Biol. 18, e1010411 (2022).
Nouri, N. & Kleinstein, S. H. A spectral clustering-based method for identifying clones from high-throughput B cell repertoire sequencing data. Bioinformatics 34, i341–i349 (2018).
Yermanos, A. et al. Comparison of methods for phylogenetic B-cell lineage inference using time-resolved antibody repertoire simulations (AbSim). Bioinformatics 33, 3938–3946 (2017).
Davidsen, K. & Matsen, F. A. 4th Benchmarking tree and ancestral sequence inference for B cell receptor sequences. Front. Immunol. 9, 2451 (2018).
Zhang, C., Bzikadze, A. V., Safonova, Y. & Mirarab, S. A scalable model for simulating multi-round antibody evolution and benchmarking of clonal tree reconstruction methods. Front. Immunol. 13, 1014439 (2022).
Yaari, G. et al. Models of somatic hypermutation targeting and substitution based on synonymous mutations from high-throughput immunoglobulin sequencing data. Front. Immunol. 4, 358 (2013).
Hoehn, K. B. et al. Repertoire-wide phylogenetic models of B cell molecular evolution reveal evolutionary signatures of aging and vaccination. Proc. Natl Acad. Sci. USA 116, 22664–22672 (2019).
Hoehn, K. B., Pybus, O. G. & Kleinstein, S. H. Phylogenetic analysis of migration, differentiation, and class switching in B cells. PLoS Comput. Biol. 18, e1009885 (2022).
Jensen, C. G., Sumner, J. A., Kleinstein, S. H. & Hoehn, K. B. Inferring B cell phylogenies from paired heavy and light chain BCR sequences with Dowser. Preprint at bioRxiv https://doi.org/10.1101/2023.09.29.560187 (2023).
Ralph, D. K. & Matsen, F. A. IV Inference of B cell clonal families using heavy/light chain pairing information. PLoS Comput. Biol. 18, e1010723 (2022).
Zhou, J. Q. & Kleinstein, S. H. Cutting edge: Ig H chains are sufficient to determine most B cell clonal relationships. J. Immunol. 203, 1687–1692 (2019).
DeWitt, W. S. III, Mesin, L., Victora, G. D., Minin, V. N. & Matsen, F. A. IV Using genotype abundance to improve phylogenetic inference. Mol. Biol. Evol. 35, 1253–1265 (2018).
Abdollahi, N., Jeusset, L., de Septenville, A., Davi, F. & Bernardes, J. S. Reconstructing B cell lineage trees with minimum spanning tree and genotype abundances. BMC Bioinformatics 24, 70 (2023).
Zaragoza-Infante, L. et al. IgIDivA: immunoglobulin intraclonal diversification analysis. Brief. Bioinform. 23, bbac349 (2022).
Foglierini, M., Pappas, L., Lanzavecchia, A., Corti, D. & Perez, L. AncesTree: an interactive immunoglobulin lineage tree visualizer. PLoS Comput. Biol. 16, e1007731 (2020).
Jeusset, L. et al. ViCloD, an interactive web tool for visualizing B cell repertoires and analyzing intraclonal diversities: application to human B-cell tumors. NAR Genom. Bioinform. https://doi.org/10.1093/nargab/lqad064 (2023).
Lees, W. D. Tools for adaptive immune receptor repertoire sequencing. Curr. Opin. Syst. Biol. 24, 86–92 (2020).
Greiff, V. et al. Learning the high-dimensional immunogenomic features that predict public and private antibody repertoires. J. Immunol. 199, 2985–2997 (2017).
Venturi, V. et al. Sharing of T cell receptors in antigen-specific responses is driven by convergent recombination. Proc. Natl Acad. Sci. USA 103, 18691–18696 (2006).
Quigley, M. F. et al. Convergent recombination shapes the clonotypic landscape of the naive T-cell repertoire. Proc. Natl Acad. Sci. USA 107, 19414–19419 (2010).
Elhanati, Y., Murugan, A., Callan, C. G. Jr, Mora, T. & Walczak, A. M. Quantifying selection in immune receptor repertoires. Proc. Natl Acad. Sci. USA 111, 9875–9880 (2014).
Pogorelyy, M. V. et al. Precise tracking of vaccine-responding T cell clones reveals convergent and personalized response in identical twins. Proc. Natl Acad. Sci. USA 115, 12704–12709 (2018).
Galson, J. D. et al. Deep sequencing of B cell receptor repertoires from COVID-19 patients reveals strong convergent immune signatures. Front. Immunol. 11, 605170 (2020).
Gomez-Tourino, I., Kamra, Y., Baptista, R., Lorenc, A. & Peakman, M. T cell receptor β-chains display abnormal shortening and repertoire sharing in type 1 diabetes. Nat. Commun. 8, 1792 (2017).
Jaccard, P. The distribution of the flora in the alpine zone.1. N. Phytol. 11, 37–50 (1912).
Amoriello, R. et al. The TCR repertoire reconstitution in multiple sclerosis: comparing one-shot and continuous immunosuppressive therapies. Front. Immunol. 11, 559 (2020).
Rognes, T., Scheffer, L., Greiff, V. & Sandve, G. K. CompAIRR: ultra-fast comparison of adaptive immune receptor repertoires by exact and approximate sequence matching. Bioinformatics 38, 4230–4232 (2022).
Morisita, M. Measuring of the dispersion and analysis of distribution patterns, Memoires of the Faculty of Science, Kyushu University. Series E. Biology. Sci. Rep. 2, 215–235 (1995).
Lin, J. Divergence measures based on the Shannon entropy. IEEE Trans. Inf. Theory 37, 145–151 (1991).
Bolen, C. R., Rubelt, F., Vander Heiden, J. A. & Davis, M. M. The repertoire dissimilarity index as a method to compare lymphocyte receptor repertoires. BMC Bioinformatics 18, 155 (2017).
Raybould, M. I. J. et al. Public baseline and shared response structures support the theory of antibody repertoire functional commonality. PLoS Comput. Biol. 17, e1008781 (2021).
Arora, R., Burke, H. M. & Arnaout, R. Immunological diversity with similarity. Preprint at bioRxiv https://doi.org/10.1101/483131 (2018).
Ruffolo, J. A., Sulam, J. & Gray, J. J. Antibody structure prediction using interpretable deep learning. Patterns 3, 100406 (2022).
Abanades, B. et al. ImmuneBuilder: deep-learning models for predicting the structures of immune proteins. Commun. Biol. 6, 575 (2023).
Marks, C. & Deane, C. M. How repertoire data are changing antibody science. J. Biol. Chem. 295, 9823–9837 (2020).
Krawczyk, K. et al. Structurally mapping antibody repertoires. Front. Immunol. 9, 1698 (2018).
Kovaltsuk, A. et al. How B-cell receptor repertoire sequencing can be enriched with structural antibody data. Front. Immunol. 8, 1753 (2017).
Shcherbinin, D. S., Karnaukhov, V. K., Zvyagin, I. V., Chudakov, D. M. & Shugay, M. Large-scale template-based structural modeling of T-cell receptors with known antigen specificity reveals complementarity features. Front. Immunol. 14, 1224969 (2023).
Sela-Culang, I., Kunik, V. & Ofran, Y. The structural basis of antibody–antigen recognition. Front. Immunol. 4, 302 (2013).
Richardson, E. et al. A computational method for immune repertoire mining that identifies novel binders from different clonotypes, demonstrated by identifying anti-pertussis toxoid antibodies. mAbs 13, 1869406 (2021).
Imkeller, K. & Wardemann, H. Assessing human B cell repertoire diversity and convergence. Immunol. Rev. 284, 51–66 (2018).
Wong, W. K. et al. Ab-Ligity: identifying sequence-dissimilar antibodies that bind to the same epitope. MAbs 13, 1873478 (2021).
Dash, P. et al. Quantifiable predictive features define epitope-specific T cell receptor repertoires. Nature 547, 89–93 (2017).
Ruffolo, J. A., Guerra, C., Mahajan, S. P., Sulam, J. & Gray, J. J. Geometric potentials from deep learning improve prediction of CDR H3 loop structures. Bioinformatics 36, i268–i275 (2020).
Fernández-Quintero, M. L. et al. Challenges in antibody structure prediction. MAbs 15, 2175319 (2023).
Greiff, V., Yaari, G. & Cowell, L. G. Mining adaptive immune receptor repertoires for biological and clinical information using machine learning. Curr. Opin. Syst. Biol. 24, 109–119 (2020).
Chakravarthi Kanduri et al. simAIRR: simulation of adaptive immune repertoires with realistic receptor sequence sharing for benchmarking of immune state prediction methods. GigaScience 12, giag074 (2022).
Chernigovskaya, M. et al. Simulation of adaptive immune receptors and repertoires with complex immune information to guide the development and benchmarking of AIRR machine learning. Preprint at bioRxiv https://doi.org/10.1101/2023.10.20.562936 (2023).
Mariotti-Ferrandiz, E. et al. A TCRβ repertoire signature can predict experimental cerebral malaria. PLoS ONE 11, e0147871 (2016).
Six, A. et al. The past, present, and future of immune repertoire biology — the rise of next-generation repertoire analysis. Front. Immunol. 4, 413 (2013).
Pertseva, M., Gao, B., Neumeier, D., Yermanos, A. & Reddy, S. T. Applications of machine and deep learning in adaptive immunity. Annu. Rev. Chem. Biomol. Eng. 12, 39–62 (2021).
Hummer, A. M., Abanades, B. & Deane, C. M. Advances in computational structure-based antibody design. Curr. Opin. Struct. Biol. 74, 102379 (2022).
Hudson, D., Fernandes, R. A., Basham, M., Ogg, G. & Koohy, H. Can we predict T cell specificity with digital biology and machine learning? Nat. Rev. Immunol. 23, 511–521 (2023).
Gielis, S. et al. Detection of enriched T cell epitope specificity in full T cell receptor sequence repertoires. Front. Immunol. 10, 2820 (2019).
Luo, J. et al. Quantitative annotations of T-cell repertoire specificity. Brief. Bioinform. 24, bbad175 (2023).
Widrich, M. et al. Modern hopfield networks and attention for immune repertoire classification. Adv. Neural Inf. Process. Syst. 33, 18832–18845 (2020).
Sidhom, J.-W., Larman, H. B., Pardoll, D. M. & Baras, A. S. DeepTCR is a deep learning framework for revealing sequence concepts within T-cell repertoires. Nat. Commun. 12, 1605 (2021).
Robert, P. A. et al. Unconstrained generation of synthetic antibody–antigen structures to guide machine learning methodology for antibody specificity prediction. Nat. Comput. Sci. 2, 845–865 (2022).
Thomas, N. et al. 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 (2014).
Leem, J., Mitchell, L. S., Farmery, J. H. R., Barton, J. & Galson, J. D. Deciphering the language of antibodies using self-supervised learning. Patterns 3, 100513 (2022).
Vu, M. H. et al. Linguistically inspired roadmap for building biologically reliable protein language models. Nat. Mach. Intell. 5, 485–496 (2023).
Vu, M. H. et al. ImmunoLingo: linguistics-based formalization of the antibody language. Preprint at https://doi.org/10.48550/arXiv.2209.12635 (2022).
Ruffolo, J. A., Chu, L.-S., Mahajan, S. P. & Gray, J. J. Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies. Nat. Commun. 14, 2389 (2023).
Wang, M., Patsenker, J., Li, H., Kluger, Y. & Kleinstein, S. Language model-based B cell receptor sequence embeddings can effectively encode receptor specificity. Preprint at bioRxiv https://doi.org/10.1101/2023.06.21.545145 (2023).
Hie, B. L. et al. Efficient evolution of human antibodies from general protein language models. Nat. Biotechnol. https://doi.org/10.1038/s41587-023-01763-2 (2023).
Madani, A. et al. Large language models generate functional protein sequences across diverse families. Nat. Biotechnol. 41, 1099–1106 (2023).
Zhang, P, Bang, S., Cai, M & Lee, H. Context-aware amino acid embedding advances analysis of TCR–epitope interactions. eLife 12, RP88837 (2023).
Peng, X. et al. Characterizing the interaction conformation between T-cell receptors and epitopes with deep learning. Nat. Mach. Intell. 5, 395–407 (2023).
Drost, F. et al. Integrating T-cell receptor and transcriptome for large-scale single-cell immune profiling analysis. Preprint at bioRxiv https://doi.org/10.1101/2021.06.24.449733 (2022).
Kuhn, M. & Johnson, K. Feature Engineering and Selection: A Practical Approach for Predictive Models (CRC, 2019).
Sokolova, M. & Lapalme, G. A systematic analysis of performance measures for classification tasks. Inf. Process. Manag. 45, 427–437 (2009).
Pavlović, M. et al. The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires. Nat. Mach. Intell. 3, 936–944 (2021).
Kanduri, C. et al. Profiling the baseline performance and limits of machine learning models for adaptive immune receptor repertoire classification. Gigascience 11, giac046 (2022).
Katayama, Y. & Kobayashi, T. J. Comparative study of repertoire classification methods reveals data efficiency of k-mer feature extraction. Front. Immunol. 13, 797640 (2022).
Marcou, Q., Mora, T. & Walczak, A. M. High-throughput immune repertoire analysis with IGoR. Nat. Commun. 9, 561 (2018).
Murugan, A., Mora, T., Walczak, A. M. & Callan, C. G. Jr Statistical inference of the generation probability of T-cell receptors from sequence repertoires. Proc. Natl Acad. Sci. USA 109, 16161–16166 (2012).
Slabodkin, A. et al. Individualized VDJ recombination predisposes the available Ig sequence space. Genome Res. 31, 2209–2224 (2021).
Russell, M. L. et al. Combining genotypes and T cell receptor distributions to infer genetic loci determining V(D)J recombination probabilities. eLife 11, e73475 (2022).
Neumeier, D. et al. Phenotypic determinism and stochasticity in antibody repertoires of clonally expanded plasma cells. Proc. Natl Acad. Sci. USA 119, e2113766119 (2022).
Salou, M., Nicol, B., Garcia, A. & Laplaud, D.-A. Involvement of CD8+ T cells in multiple sclerosis. Front. Immunol. 6, 604 (2015).
Codina-Busqueta, E. et al. TCR bias of in vivo expanded T cells in pancreatic islets and spleen at the onset in human type 1 diabetes. J. Immunol. 186, 3787–3797 (2011).
Seay, H. R. et al. Tissue distribution and clonal diversity of the T and B cell repertoire in type 1 diabetes. JCI Insight 1, e88242 (2016).
Kovaltsuk, A. et al. Structural diversity of B-cell receptor repertoires along the B-cell differentiation axis in humans and mice. PLoS Comput. Biol. 16, e1007636 (2020).
Hoehn, K. B. et al. Dynamics of immunoglobulin sequence diversity in HIV-1 infected individuals. Philos. Trans. R. Soc. Lond. B Biol. Sci. 370, 2014 (2015).
Chang, C.-M. et al. Profiling of T cell repertoire in SARS-CoV-2-infected COVID-19 patients between mild disease and pneumonia. J. Clin. Immunol. 41, 1131–1145 (2021).
Priel, A., Gordin, M., Philip, H., Zilberberg, A. & Efroni, S. Network representation of T-cell repertoire — a novel tool to analyze immune response to cancer formation. Front. Immunol. 9, 2913 (2018).
Pogorelyy, M. V. et al. Detecting T cell receptors involved in immune responses from single repertoire snapshots. PLoS Biol. 17, e3000314 (2019).
Minervina, A. A. et al. Longitudinal high-throughput TCR repertoire profiling reveals the dynamics of T-cell memory formation after mild COVID-19 infection. eLife 10, e63502 (2021).
Glanville, J. et al. Identifying specificity groups in the T cell receptor repertoire. Nature 547, 94–98 (2017).
Pogorelyy, M. V. et al. Resolving SARS-CoV-2 CD4+ T cell specificity via reverse epitope discovery. Cell Rep. Med. 3, 100697 (2022).
Simnica, D. et al. Landscape of T-cell repertoires with public COVID-19-associated T-cell receptors in pre-pandemic risk cohorts. Clin. Transl. Immunol. 10, e1340 (2021).
Servaas, N. H. et al. Longitudinal analysis of T-cell receptor repertoires reveals persistence of antigen-driven CD4+ and CD8+ T-cell clusters in systemic sclerosis. J. Autoimmun. 117, 102574 (2021).
Komech, E. A. et al. CD8+ T cells with characteristic T cell receptor β motif are detected in blood and expanded in synovial fluid of ankylosing spondylitis patients. Rheumatology 57, 1097–1104 (2018).
Chiou, S.-H. et al. Global analysis of shared T cell specificities in human non-small cell lung cancer enables HLA inference and antigen discovery. Immunity 54, 586–602.e8 (2021).
Joshi, K. et al. Spatial heterogeneity of the T cell receptor repertoire reflects the mutational landscape in lung cancer. Nat. Med. 25, 1549–1559 (2019).
Hoehn, K. B. et al. Human B cell lineages associated with germinal centers following influenza vaccination are measurably evolving. eLife 10, e70873 (2021).
de Bourcy, C. F. A. et al. Phylogenetic analysis of the human antibody repertoire reveals quantitative signatures of immune senescence and aging. Proc. Natl Acad. Sci. USA 114, 1105–1110 (2017).
Stern, J. N. H. et al. B cells populating the multiple sclerosis brain mature in the draining cervical lymph nodes. Sci. Transl Med. 6, 248ra107 (2014).
Park, J.-E. et al. A cell atlas of human thymic development defines T cell repertoire formation. Science 367, eaay33224 (2020).
Trück, J. et al. Identification of antigen-specific B cell receptor sequences using public repertoire analysis. J.Immunol. 194, 252–261 (2015).
Jackson, K. J. L. et al. Human responses to influenza vaccination show seroconversion signatures and convergent antibody rearrangements. Cell Host Microbe 16, 105–114 (2014).
Dong, L., Li, P., Oenema, T., McClurkan, C. L. & Koelle, D. M. Public TCR use by herpes simplex virus-2-specific human CD8 CTLs. J. Immunol. 184, 3063–3071 (2010).
Quigley, M. et al. Transcriptional analysis of HIV-specific CD8+ T cells shows that PD-1 inhibits T cell function by upregulating BATF. Nat. Med. 16, 1147–1151 (2010).
Eugster, A. et al. High diversity in the TCR repertoire of GAD65 autoantigen-specific human CD4+ T cells. J. Immunol. 194, 2531–2538 (2015).
Musters, A. et al. In rheumatoid arthritis, synovitis at different inflammatory sites is dominated by shared but patient-specific T cell clones. J. Immunol. 201, 417–422 (2018).
Krasik, S. V. et al. Systematic evaluation of intratumoral and peripheral BCR repertoires in three cancers. Preprint at bioRxiv https://doi.org/10.1101/2023.04.16.537028 (2023).
Aran, A. et al. Analysis of tumor infiltrating CD4+ and CD8+ CDR3 sequences reveals shared features putatively associated to the anti-tumor immune response. Front. Immunol. 14, 1227766 (2023).
Dunn-Walters, D. K. The ageing human B cell repertoire: a failure of selection? Clin. Exp. Immunol. 183, 50–56 (2016).
Britanova, O. V. et al. Age-related decrease in TCR repertoire diversity measured with deep and normalized sequence profiling. J. Immunol. 192, 2689–2698 (2014).
Minervina, A. A. et al. Primary and secondary anti-viral response captured by the dynamics and phenotype of individual T cell clones. eLife 9, e53704 (2020).
Mikelov, A. et al. Memory persistence and differentiation into antibody-secreting cells accompanied by positive selection in longitudinal BCR repertoires. eLife 11, e79254 (2022).
Galson, J. D. et al. In-depth assessment of within-individual and inter-individual variation in the B cell receptor repertoire. Front. Immunol. 6, 531 (2015).
De Neuter, N. et al. Memory CD4+ T cell receptor repertoire data mining as a tool for identifying cytomegalovirus serostatus. Genes. Immun. 20, 255–260 (2019).
Liu, X. et al. T cell receptor β repertoires as novel diagnostic markers for systemic lupus erythematosus and rheumatoid arthritis. Ann. Rheum. Dis. 78, 1070–1078 (2019).
Snyder, T. M. et al. Magnitude and dynamics of the T-cell response to SARS-CoV-2 infection at both individual and population levels. Preprint at medRxiv https://doi.org/10.1101/2020.07.31.20165647 (2020).
Zaslavsky, M. E. et al. Disease diagnostics using machine learning of immune receptors. Preprint at bioRxiv https://doi.org/10.1101/2022.04.26.489314 (2023).
Akbar, R. et al. Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies. Abs 14, 2008790 (2022).
Wilman, W. et al. Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery. Brief. Bioinform. 23, bbac267(2022).
Valkiers, S. et al. Recent advances in T-cell receptor repertoire analysis: bridging the gap with multimodal single-cell RNA sequencing. ImmunoInformatics 5, 100009 (2022).
Katayama, Y., Yokota, R., Akiyama, T. & Kobayashi, T. J. Machine learning approaches to TCR repertoire analysis. Front. Immunol. 13, 858057 (2022).
Weber, A., Born, J. & Rodriguez Martínez, M. TITAN: T-cell receptor specificity prediction with bimodal attention networks. Bioinformatics 37, i237–i244 (2021).
Montemurro, A., Jessen, L. E. & Nielsen, M. NetTCR-2.1: lessons and guidance on how to develop models for TCR specificity predictions. Front. Immunol. 13, 1055151 (2022).
Springer, I., Besser, H., Tickotsky-Moskovitz, N., Dvorkin, S. & Louzoun, Y. Prediction of specific TCR-peptide binding from large dictionaries of TCR–peptide pairs. Front. Immunol. 11, 1803 (2020).
Dens, C., Bittremieux, W., Affaticati, F., Laukens, K. & Meysman, P. Interpretable deep learning to uncover the molecular binding patterns determining TCR–epitope interactions. ImmunoInformatics 11, 100027 (2023).
Bradley, P. Structure-based prediction of T cell receptor:peptide–MHC interactions. eLife 12, e82813 (2023).
Perez, M. A. S. et al. TCRpcDist: estimating TCR physico-chemical similarity to analyze repertoires and predict specificities. Preprint at bioRxiv https://doi.org/10.1101/2023.06.15.545077 (2023).
Sethna, Z., Elhanati, Y., Callan, C. G., Walczak, A. M. & Mora, T. OLGA: fast computation of generation probabilities of B- and T-cell receptor amino acid sequences and motifs. Bioinformatics 35, 2974–2981 (2019).
Pavlović, M. et al. Improving generalization of machine learning-identified biomarkers with causal modeling: an investigation into immune receptor diagnostics. Preprint at arXiv https://doi.org/10.48550/arXiv.2204.09291 (2022).
Swindells, M. B. et al. abYsis: integrated antibody sequence and structure-management, analysis, and prediction. J. Mol. Biol. 429, 356–364 (2017).
Ferdous, S. & Martin, A. C. R. AbDb: antibody structure database — a database of PDB-derived antibody structures. Database 2018, bay040 (2018).
Dunbar, J. et al. SAbDab: the structural antibody database. Nucleic Acids Res. 42, D1140–D1146 (2014).
Mahajan, S. et al. Epitope specific antibodies and T cell receptors in the immune epitope database. Front. Immunol. 9, 2688 (2018).
Shugay, M. et al. VDJdb: a curated database of T-cell receptor sequences with known antigen specificity. Nucleic Acids Res 46, D419–D427 (2018).
Dorigatti, E.et al. Predicting T cell receptor functionality against mutant epitopes. Preprine at bioRxiv https://doi.org/10.1101/2023.05.10.540189 (2023).
Taft, J. M. et al. Deep mutational learning predicts ACE2 binding and antibody escape to combinatorial mutations in the SARS-CoV-2 receptor-binding domain. Cell 185, 4008–4022.e14 (2022).
Straub, A. et al. Recruitment of epitope-specific T cell clones with a low-avidity threshold supports efficacy against mutational escape upon re-infection. Immunity 56, 1269–1284.e6 (2023).
Mayer, A. & Callan, C. G. Jr Measures of epitope binding degeneracy from T cell receptor repertoires. Proc. Natl Acad. Sci. USA 120, e2213264120 (2023).
Chronister, W. D. et al. TCRMatch: predicting T-cell receptor specificity based on sequence similarity to previously characterized receptors. Front. Immunol. 12, 640725 (2021).
De Neuter, N. et al. On the feasibility of mining CD8+ T cell receptor patterns underlying immunogenic peptide recognition. Immunogenetics 70, 159–168 (2018).
Elias, G. et al. Preexisting memory CD4 T cells in naïve individuals confer robust immunity upon hepatitis B vaccination. eLife 11, e68388 (2022).
Vujkovic, A. et al. Diagnosing viral infections through T cell receptor sequencing of activated CD8+ T cells. J. Infect. Dis. 3, jiad430 (2023).
Davidsen, K. et al. Deep generative models for T cell receptor protein sequences. eLife 8, e46935 (2019).
Akbar, R. et al. In silico proof of principle of machine learning-based antibody design at unconstrained scale. mAbs 14, 2031482 (2022).
Saka, K. et al. Antibody design using LSTM based deep generative model from phage display library for affinity maturation. Sci. Rep. 11, 5852 (2021).
Amimeur, T. et al. Designing feature-controlled humanoid antibody discovery libraries using generative adversarial networks. Preprint at bioRxiv https://doi.org/10.1101/2020.04.12.024844 (2020).
Breden, F. et al. Reproducibility and reuse of adaptive immune receptor repertoire data. Front. Immunol. 8, 1418 (2017).
Corrie, B. D. et al. iReceptor: a platform for querying and analyzing antibody/B-cell and T-cell receptor repertoire data across federated repositories. Immunol. Rev. 284, 24–41 (2018).
Christley, S. et al. VDJServer: a cloud-based analysis portal and data commons for immune repertoire sequences and rearrangements. Front. Immunol. 9, 976 (2018).
Brüggemann, M. et al. Standardized next-generation sequencing of immunoglobulin and T-cell receptor gene recombinations for MRD marker identification in acute lymphoblastic leukaemia; a EuroClonality-NGS validation study. Leukemia 33, 2241–2253 (2019).
Wilkinson, M. D. et al. The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).
Huang, Y.-N. et al. Data availability of open T-cell receptor repertoire data, a systematic assessment. Front. Syst. Biol. 2, 918792 (2022).
Bukhari, S. A. C. et al. The CAIRR pipeline for submitting standards-compliant B and T cell receptor repertoire sequencing studies to the National Center for Biotechnology Information repositories. Front. Immunol. 9, 1877 (2018).
Christley, S. et al. The ADC API: a web API for the programmatic query of the AIRR Data Commons. Front. Big Data 3, 22 (2020).
Tickotsky, N., Sagiv, T., Prilusky, J., Shifrut, E. & Friedman, N. McPAS-TCR: a manually curated catalogue of pathology-associated T cell receptor sequences. Bioinformatics 33, 2924–2929 (2017).
Raybould, M. I. J., Kovaltsuk, A., Marks, C. & Deane, C. M. CoV-AbDab: the coronavirus antibody database. Bioinformatics 37, 734–735 (2021).
Li, R. et al. A novel statistical method for decontaminating T-cell receptor sequencing data. Brief. Bioinform. 24, bbad230 (2023).
Smirnova, A. O. et al. The use of non-functional clonotypes as a natural calibrator for quantitative bias correction in adaptive immune receptor repertoire profiling. Elife 12, e69157 (2023).
Greiff, V. et al. Quantitative assessment of the robustness of next-generation sequencing of antibody variable gene repertoires from immunized mice. BMC Immunol. 15, 40 (2014).
Koraichi, M. B., Touzel, M. P., Mazzolini, A., Mora, T. & Walczak, A. M. NoisET: noise learning and expansion detection of T-cell receptors. J. Phys. Chem. A 126, 7407–7414 (2022).
Chen, L. et al. GMPR: a robust normalization method for zero-inflated count data with application to microbiome sequencing data. PeerJ 6, e4600 (2018).
Jaffe, D. B. et al. Functional antibodies exhibit light chain coherence. Nature 611, 352–357 (2022).
Holec, P. V., Berleant, J., Bathe, M. & Birnbaum, M. E. A Bayesian framework for high-throughput T cell receptor pairing. Bioinformatics 35, 1318–1325 (2019).
Howie, B. et al. High-throughput pairing of T cell receptor α and β sequences. Sci. Transl. Med. 7, 301ra131 (2015).
DeKosky, B. J. et al. In-depth determination and analysis of the human paired heavy- and light-chain antibody repertoire. Nat. Med. 21, 86–91 (2015).
Tanno, H. et al. Determinants governing T cell receptor α/β-chain pairing in repertoire formation of identical twins. Proc. Natl Acad. Sci. USA 117, 532–540 (2020).
Grigaityte, K. et al. Single-cell sequencing reveals αβ chain pairing shapes the T cell repertoire. Preprint at bioRxiv https://doi.org/10.1101/213462 (2017).
Shcherbinin, D. S., Belousov, V. A. & Shugay, M. Comprehensive analysis of structural and sequencing data reveals almost unconstrained chain pairing in TCRαβ complex. PLoS Comput. Biol. 16, e1007714 (2020).
Moris, P. et al. Current challenges for unseen-epitope TCR interaction prediction and a new perspective derived from image classification. Brief. Bioinform. 22, bbaa318 (2021).
Shemesh, O., Polak, P., Lundin, K. E. A., Sollid, L. M. & Yaari, G. Machine learning analysis of naïve B-cell receptor repertoires stratifies celiac disease patients and controls. Front. Immunol. 12, 627813 (2021).
Liu, S., Bradley, P. & Sun, W. Neural network models for sequence-based TCR and HLA association prediction. PLoS Comput. Biol 19, e1011664 (2023).
DeWitt, W. S. III et al. Human T cell receptor occurrence patterns encode immune history, genetic background, and receptor specificity. eLife 7, e38358 (2018).
Ishigaki, K. et al. HLA autoimmune risk alleles restrict the hypervariable region of T cell receptors. Nat. Genet. 54, 393–402 (2022).
Peng, K. et al. Diversity in immunogenomics: the value and the challenge. Nat. Methods 18, 588–591 (2021).
Deng, L. et al. Performance comparison of TCR–pMHC prediction tools reveals a strong data dependency. Front. Immunol. 14, 1128326 (2023).
Dens, C., Laukens, K., Bittremieux, W. & Meysman, P. The pitfalls of negative data bias for the T-cell epitope specificity challenge. Nat. Mach. Intell. 5, 1063–1065 (2023).
Meysman, P. et al. Benchmarking solutions to the T-cell receptor epitope prediction problem: IMMREP22 workshop report. ImmunoInformatics 9, 100024 (2023).
Papadopoulou, I., Nguyen, A.-P., Weber, A. & Martínez, M. R. DECODE: a computational pipeline to discover T cell receptor binding rules. Bioinformatics 38, i246–i254 (2022).
Tomsett, R., Harborne, D., Chakraborty, S., Gurram, P. & Preece, A. Sanity checks for saliency metrics. AAAI 34, 6021–6029 (2020).
Sandve, G. K. & Greiff, V. Access to ground truth at unconstrained size makes simulated data as indispensable as experimental data for bioinformatics methods development and benchmarking. Bioinformatics 38, 4994–4996 (2022).
Chen, V. et al. Best practices for interpretable machine learning in computational biology. Preprint at bioRxiv https://doi.org/10.1101/2022.10.28.513978 (2022).
Snapkov, I. et al. Progress and challenges in mass spectrometry-based analysis of antibody repertoires. Trends Biotechnol. 40, 463–481 (2021).
Ionov, S. & Lee, J. An immunoproteomic survey of the antibody landscape: insights and opportunities revealed by serological repertoire profiling. Front. Immunol. 13, 832533 (2022).
Curtis, N. C. et al. Characterization of SARS-CoV-2 convalescent patients’ serological repertoire reveals high prevalence of Iso-RBD antibodies. Preprint at bioRxiv https://doi.org/10.1101/2023.09.08.556349 (2023).
Lee, J. et al. Molecular-level analysis of the serum antibody repertoire in young adults before and after seasonal influenza vaccination. Nat. Med. 22, 1456–1464 (2016).
de Graaf, S. C., Hoek, M., Tamara, S. & Heck, A. J. R. A perspective toward mass spectrometry-based de novo sequencing of endogenous antibodies. mAbs 14, 2079449 (2022).
Yilmaz, M., Fondrie, W. E., Bittremieux, W. & Oh, S. De novo mass spectrometry peptide sequencing with a transformer model. Preprint at bioRxiv https://doi.org/10.1101/2022.02.07.479481 (2022).
Setliff, I. et al. High-throughput mapping of B cell receptor sequences to antigen specificity. Cell 179, 1636–1646.e15 (2019).
Eyer, K. et al. Single-cell deep phenotyping of IgG-secreting cells for high-resolution immune monitoring. Nat. Biotechnol. 35, 977–982 (2017).
Ma, K.-Y. et al. High-throughput and high-dimensional single-cell analysis of antigen-specific CD8+ T cells. Nat. Immunol. 22, 1590–1598 (2021).
Bentzen, A. K. et al. Large-scale detection of antigen-specific T cells using peptide–MHC-I multimers labeled with DNA barcodes. Nat. Biotechnol. 34, 1037–1045 (2016).
Minervina, A. A. et al. SARS-CoV-2 antigen exposure history shapes phenotypes and specificity of memory CD8+ T cells. Nat. Immunol. 23, 781–790 (2022).
Gérard, A. et al. High-throughput single-cell activity-based screening and sequencing of antibodies using droplet microfluidics. Nat. Biotechnol. 38, 715–721 (2020).
Malissen, M. et al. Regulation of TCR α and β gene allelic exclusion during T-cell development. Immunol. Today 13, 315–322 (1992).
Padovan, E. et al. Expression of two T cell receptor α chains: dual receptor T cells. Science 262, 422–424 (1993).
Schuldt, N. J. & Binstadt, B. A. Dual TCR T cells: identity crisis or multitaskers? J. Immunol. 202, 637–644 (2019).
Zhu, L. et al. scRNA-Seq revealed the special TCR β & α V(D)J allelic inclusion rearrangement and the high proportion dual (or more) TCR-expressing cells. Cell Death Dis. 14, 487 (2023).
Croce, G. et al. Deep learning predictions of TCR–epitope interactions reveal epitope-specific chains in dual α T cells. Preprint at bioRxiv https://doi.org/10.1101/2023.09.13.557561 (2023).
Robert, P. A., Marschall, A. L. & Meyer-Hermann, M. Induction of broadly neutralizing antibodies in germinal centre simulations. Curr. Opin. Biotechnol. 51, 137–145 (2018).
Mashiko, S. et al. Broad responses to chemical adducts shape the natural antibody repertoire in early infancy. Sci. Adv. 9, eade8872 (2023).
Harvey, E. P. et al. An in silico method to assess antibody fragment polyreactivity. Nat. Commun. 13, 7554 (2022).
Sakhnini, L., Lorenzen, N., Sormanni, P., Vendruscolo, M. & Granata, D. Development of machine learning models for prediction of antibody non-specificity. Biophys. J. 122, 463a (2023).
Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K. & Moult, J. Critical assessment of methods of protein structure prediction (CASP) — tound XIV. Proteins 89, 1607–1617 (2021).
Lensink, M. F., Velankar, S. & Wodak, S. J. Modeling protein–protein and protein–peptide complexes: CAPRI 6th edition. Proteins 85, 359–377 (2017).
Russakovsky, O. et al. ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115, 211–252 (2015).
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
Armer, C. et al. The protein engineering tournament: an open science benchmark for protein modeling and design. Preprint at arXiv arxiv-2309.09955 (2023).
Lingwood, D. et al. Structural and genetic basis for development of broadly neutralizing influenza antibodies. Nature 489, 566–570 (2012).
Jardine, J. et al. Rational HIV immunogen design to target specific germline B cell receptors. Science 340, 711–716 (2013).
Sangesland, M. et al. Allelic polymorphism controls autoreactivity and vaccine elicitation of human broadly neutralizing antibodies against influenza virus. Immunity 55, 1693–1709.e8 (2022).
Lee, J. H. et al. Vaccine genetics of IGHV1-2 VRC01-class broadly neutralizing antibody precursor naïve human B cells. NPJ Vaccines 6, 113 (2021).
Dendrou, C. A., Petersen, J., Rossjohn, J. & Fugger, L. HLA variation and disease. Nat. Rev. Immunol. 18, 325–339 (2018).
Zinkernagel, R. M. & Doherty, P. C. Restriction of in vitro T cell-mediated cytotoxicity in lymphocytic choriomeningitis within a syngeneic or semiallogeneic system. Nature 248, 701–702 (1974).
Zinkernagel, R. M. & Doherty, P. C. Immunological surveillance against altered self components by sensitised T lymphocytes in lymphocytic choriomeningitis. Nature 251, 547–548 (1974).
Ashby, K. M. & Hogquist, K. A. A guide to thymic selection of T cells. Nat. Rev. Immunol. https://doi.org/10.1038/s41577-023-00911-8 (2023).
Brown, A. et al. MHC heterozygosity reduces the T cell receptor repertoire. Cell https://doi.org/10.2139/ssrn.4555926 (2023).
Xu, J. et al. T cell receptor β repertoires in patients with COVID-19 reveal disease severity signatures. Front. Immunol. 14, 1190844 (2023).
Park, J. J. et al. Machine learning identifies T cell receptor repertoire signatures associated with COVID-19 severity. Commun. Biol. 6, 76 (2023).
Pedrioli, A. & Oxenius, A. Single B cell technologies for monoclonal antibody discovery. Trends Immunol. 42, 1143–1158 (2021).
Raybould, M. I. J. et al. Five computational developability guidelines for therapeutic antibody profiling. Proc. Natl Acad. Sci. USA 116, 4025–4030 (2019).
Shuai, R. W., Ruffolo, J. A. & Gray, J. J. IgLM: infilling language modeling for antibody sequence design. Cell Syst. 14, 979–989.e4 (2023).
Bachas, S. et al. Antibody optimization enabled by artificial intelligence predictions of binding affinity and naturalness. Preprint at bioRxiv https://doi.org/10.1101/2022.08.16.504181 (2022).
Rosenberg, S. A. & Restifo, N. P. Adoptive cell transfer as personalized immunotherapy for human cancer. Science 348, 62–68 (2015).
Sadelain, M., Rivière, I. & Riddell, S. Therapeutic T cell engineering. Nature 545, 423–431 (2017).
Shafer, P., Kelly, L. M. & Hoyos, V. Cancer therapy with TCR-engineered T cells: current strategies, challenges, and prospects. Front. Immunol. 13, 835762 (2022).
Yang, J., Chen, Y., Jing, Y., Green, M. R. & Han, L. Advancing CAR T cell therapy through the use of multidimensional omics data. Nat. Rev. Clin. Oncol. 20, 211–228 (2023).
Joshi, K., Milighetti, M. & Chain, B. M. Application of T cell receptor (TCR) repertoire analysis for the advancement of cancer immunotherapy. Curr. Opin. Immunol. 74, 1–8 (2022).
Castellanos-Rueda, R., Di Roberto, R. B., Schlatter, F. S. & Reddy, S. T. Leveraging single-cell sequencing for chimeric antigen receptor T cell therapies. Trends Biotechnol. 39, 1308–1320 (2021).
Stucchi, A., Maspes, F., Montee-Rodrigues, E. & Fousteri, G. Engineered Treg cells: the heir to the throne of immunotherapy. J. Autoimmun. https://doi.org/10.1016/j.jaut.2022.102986 (2023).
Raffin, C. et al. Development of citrullinated-vimentin-specific CAR for targeting Tregs to treat autoimmune rheumatoid arthritis. J. Immunol. 200, 176.17 (2018).
Venturi, V., Kedzierska, K., Turner, S. J., Doherty, P. C. & Davenport, M. P. Methods for comparing the diversity of samples of the T cell receptor repertoire. J. Immunol. Methods 321, 182–195 (2007).
Mayer, A., Balasubramanian, V., Mora, T. & Walczak, A. M. How a well-adapted immune system is organized. Proc. Natl Acad. Sci. USA 112, 5950–5955 (2015).
Schnaack, O. H. & Nourmohammad, A. Optimal evolutionary decision-making to store immune memory. eLife 10, e61346 (2021).
Vieira, M. C. et al. Germline-encoded specificities and the predictability of the B cell response. PLoS Pathog. 19, e1011603 (2023).
Giudicelli, V., Chaume, D. & Lefranc, M.-P. IMGT/GENE-DB: a comprehensive database for human and mouse immunoglobulin and T cell receptor genes. Nucleic Acids Res. 33 (Suppl. 1), D256–D261 (2004).
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Contributions
Introduction (E.M.-F., V.G. and V.M.); Experimentation (K.L.Q., P.B. and V.M.); Results (H.B., K.L.Q., P.R., V.G. and V.M.); Applications (E.M.-F., H.B., V.G. and V.M.); Reproducibility and data deposition (E.M.-F., H.B., V.G. and V.M.); Limitations and optimizations (H.B., V.G. and V.M.); Outlook (E.M.-F. and V.G.); Overview of the Primer (E.M.-F. and V.G.).
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V.G. declares advisory board positions in aiNET GmbH, Enpicom B.V, Specifica Inc., Adaptyv Biosystems, EVQLV, Omniscope, Diagonal Therapeutics and Absci; and is a consultant for Roche/Genentech, immunai, Proteinea and LabGenius. P.B. is now an employee of Parean Biotechnologies. E.M.-F., H.B., K.L.Q., P.R. and V.M. declare no competing interests.
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Nature Reviews Methods Primers thanks B. Chain, S. Efroni, J. Harris, K. Rodgers and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Related links
AIRR-compliant software tools: https://docs.airr-community.org/en/stable/swtools/airr_swtools_compliant.html
Guidance for AIRR software tools: https://docs.airr-community.org/en/stable/swtools/airr_swtools_standard.html
Guide for submission of AIRR-seq data to NCBI: https://docs.airr-community.org/en/stable/miairr/guide_miairr_ncbi.html
iReceptor gateway: https://gateway.ireceptor.org/login
MiAIRR: https://docs.airr-community.org/en/stable/miairr/introduction_miairr.html
Supplementary information
Glossary
- Adaptive immune receptor repertoire
-
(AIRR). The collection of adaptive immune receptors in a single individual at a single point in time.
- Adaptive immune receptors
-
(AIRs). B cell receptors, antibodies and T cell receptors.
- Class-switch recombinations
-
Processes by which proliferating B cells change their antibody production by rearranging the constant region genes in the immunoglobulin heavy chain (IgH) locus to switch from expressing one class of immunoglobulin to another. The produced isotype retains the same antigen specificity but has different effector properties.
- Clonotypes
-
Definitions range from the exact amino acid complementary determining region 3 (CDR3) to clusters of sequences to the sequence of entire variable chain regions. The debate on what constitutes a clonotype is ongoing and beyond the scope of this Primer.
- Epitope
-
The specific part of an antigen that is contacted and recognized by an adaptive immune receptor (AIR).
- Generation probability
-
Probability for observing a given recombined adaptive immune receptor sequence.
- Germline alleles
-
Variants of variable (V), diversity (D) and joining (J) genes, representing the building blocks of recombined variable regions of a B cell receptor/T cell receptor.
- Ground truth
-
An environment where any parameter (and the value thereof) that contributed to training data generation is known and controlled.
- Paratope
-
The set of amino acids in an adaptive immune receptor (AIR) that contribute to antigen/epitope binding and are in direct contact with the epitope during binding.
- Peptide–MHC complex
-
(pMHC). The major histocompatibility complex (MHC) is a highly polymorphic region of the genome that encodes MHC cell surface proteins that present antigenic peptides. T cell receptors recognize and bind peptides that are presented by the MHC. We denote a peptide when presented by the MHC as a pMHC.
- Private clonotypes
-
Adaptive immune receptor sequences that occur exclusively in the adaptive immune receptor repertoire of a single individual.
- Public clonotypes
-
Adaptive immune receptor (AIR) sequences that occur more than n times (n > 1) across a set of adaptive immune receptor repertoires (AIRRs) collected from different individuals.
- Sequencing depth
-
The number of sequencing reads for a given sample.
- Somatic hypermutations
-
(SHMs). Processes that lead to mutation(s) in the variable, (diversity) and joining (V(D)J) recombined B cell receptor (BCR) sequences, taking place predominantly in anatomical locales called germinal centres, and may be associated with the selection for improved BCR binding of a specific antigen.
- Unique dual indexes
-
Unique pairs of i5 and i7 index primers used for filtering out index-hopped or misassigned reads post sequencing.
- Unique molecular identifiers
-
(UMIs). Short sequences added to DNA/RNA fragments in some high-throughput sequencing library preparation protocols to identify the input DNA/RNA molecule, used to reduce errors and quantitative bias introduced by PCR amplification.
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Mhanna, V., Bashour, H., Lê Quý, K. et al. Adaptive immune receptor repertoire analysis. Nat Rev Methods Primers 4, 6 (2024). https://doi.org/10.1038/s43586-023-00284-1
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DOI: https://doi.org/10.1038/s43586-023-00284-1
