Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Single-cell immune repertoire analysis

Abstract

Single-cell T cell and B cell antigen receptor-sequencing data analysis can potentially perform in-depth assessments of adaptive immune cells that inform on understanding immune cell development to tracking clonal expansion in disease and therapy. However, it has been extremely challenging to analyze and interpret T cells and B cells and their adaptive immune receptor repertoires at the single-cell level due to not only the complexity of the data but also the underlying biology. In this Review, we delve into the computational breakthroughs that have transformed the analysis of single-cell T cell and B cell antigen receptor-sequencing data.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Typical workflow of scTCR/BCR-seq.
Fig. 2: scTCR/BCR-seq data processing considerations.
Fig. 3: Multimodal integration of TCR with other single-cell data modes.
Fig. 4: scTCR/BCR-seq analysis in COVID-19, T cell development and tumor immunology.

Similar content being viewed by others

References

  1. Gellert, M. V(D)J recombination: RAG proteins, repair factors, and regulation. Annu. Rev. Biochem. 71, 101–132 (2002).

    Article  CAS  PubMed  Google Scholar 

  2. Zhang, Y. et al. Single-cell analyses reveal key immune cell subsets associated with response to PD-L1 blockade in triple-negative breast cancer. Cancer Cell 39, 1578–1593 (2021).

    Article  CAS  PubMed  Google Scholar 

  3. Poran, A. et al. Combined TCR repertoire profiles and blood cell phenotypes predict melanoma patient response to personalized neoantigen therapy plus anti-PD-1. Cell Rep. Med. 1, 100141 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Pilkinton, M. A. et al. In chronic infection, HIV Gag-specific CD4+ T cell receptor diversity is higher than CD8+ T cell receptor diversity and is associated with less HIV quasispecies diversity. J. Virol. 95, e02380–20 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Kotagiri, P. et al. B cell receptor repertoire kinetics after SARS-CoV-2 infection and vaccination. Cell Rep. 38, 110393 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Pai, J. A. & Satpathy, A. T. High-throughput and single-cell T cell receptor sequencing technologies. Nat. Methods 18, 881–892 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Joglekar, A. V. & Li, G. T cell antigen discovery. Nat. Methods 18, 873–880 (2021).

    Article  CAS  PubMed  Google Scholar 

  8. Lefranc, M. P. et al. IMGT, the international ImMunoGeneTics database. Nucleic Acids Res. 27, 209–212 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Tang, F. et al. mRNA-seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).

    Article  CAS  PubMed  Google Scholar 

  10. Stubbington, M. J. T. et al. T cell fate and clonality inference from single-cell transcriptomes. Nat. Methods 13, 329–332 (2016). This study described TraCeR, the tool that reconstructed TCRs from scRNA-seq data.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Ye, J., Ma, N., Madden, T. L. & Ostell, J. M. IgBLAST: an immunoglobulin variable domain sequence analysis tool. Nucleic Acids Res. 41, W34–W40 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Afik, S. et al. Targeted reconstruction of T cell receptor sequence from single cell RNA-seq links CDR3 length to T cell differentiation state. Nucleic Acids Res. 45, e148 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Song, L. et al. TRUST4: immune repertoire reconstruction from bulk and single-cell RNA-seq data. Nat. Methods 18, 627–630 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Bolotin, D. A. et al. MiXCR: software for comprehensive adaptive immunity profiling. Nat. Methods 12, 380–381 (2015).

    Article  CAS  PubMed  Google Scholar 

  15. Canzar, S., Neu, K. E., Tang, Q., Wilson, P. C. & Khan, A. A. BASIC: BCR assembly from single cells. Bioinformatics 33, 425–427 (2017).

    Article  CAS  PubMed  Google Scholar 

  16. Lindeman, I. et al. BraCeR: B-cell-receptor reconstruction and clonality inference from single-cell RNA-seq. Nat. Methods 15, 563–565 (2018).

    Article  CAS  PubMed  Google Scholar 

  17. Rizzetto, S. et al. B-cell receptor reconstruction from single-cell RNA-seq with VDJPuzzle. Bioinformatics 34, 2846–2847 (2018).

    Article  CAS  PubMed  Google Scholar 

  18. Andreani, T. et al. Benchmarking computational methods for B-cell receptor reconstruction from single-cell RNA-seq data. NAR Genom. Bioinform. 4, lqac049 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Gupta, N. T. et al. Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data. Bioinformatics 31, 3356–3358 (2015). This study introduced Changeo, one of the most widely used immune repertoire sequencing data analysis software as part of the Immcantation suite.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Shugay, M. et al. VDJtools: unifying post-analysis of T cell receptor repertoires. PLoS Comput. Biol. 11, e1004503 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Rubelt, F. et al. Adaptive Immune Receptor Repertoire Community recommendations for sharing immune-repertoire sequencing data. Nat. Immunol. 18, 1274–1278 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Sturm, G. et al. Scirpy: a Scanpy extension for analyzing single-cell T-cell receptor-sequencing data. Bioinformatics 36, 4817–4818 (2020). This study introduced Scirpy, the first and the most widely used Python package that specifically dealt with scTCR-seq data, as an extension of the Scanpy scRNA-seq package.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Stephenson, E. et al. Single-cell multi-omics analysis of the immune response in COVID-19. Nat. Med. 27, 904–916 (2021). This study introduced the first iteration of Dandelion as a scBCR-seq analysis tool written in Python and introduced network-based diversity analysis for scBCR-seq data.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Suo, C. et al. Dandelion uses the single-cell adaptive immune receptor repertoire to explore lymphocyte developmental origins. Nat. Biotechnol. 42, 40–51 (2023). This study introduced an updated version of Dandelion and also introduced new concepts for analyzing scTCR/BCR-seq data, including trajectory analysis of pseudobulked cell neighborhoods using TCR usage frequencies.

  25. Borcherding, N., Bormann, N. L. & Kraus, G. scRepertoire: an R-based toolkit for single-cell immune receptor analysis. F1000Res. 9, 47 (2020). This work describes scRepertoire, one of the most widely used scTCR/BCR-seq analysis software in R that integrates with Seurat and SingleCellExperiment formats.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Kepler, T. B. et al. Immunoglobulin gene insertions and deletions in the affinity maturation of HIV-1 broadly reactive neutralizing antibodies. Cell Host Microbe 16, 304–313 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Yaari, G. & Kleinstein, S. H. Practical guidelines for B-cell receptor repertoire sequencing analysis. Genome Med. 7, 121 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Chen, H. et al. BCR selection and affinity maturation in Peyer’s patch germinal centres. Nature 582, 421–425 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Yaari, G., Uduman, M. & Kleinstein, S. H. Quantifying selection in high-throughput immunoglobulin sequencing data sets. Nucleic Acids Res. 40, e134 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Glanville, J. et al. Identifying specificity groups in the T cell receptor repertoire. Nature 547, 94–98 (2017). This study uses an amino acid-based motif approach to quantify repertoire dynamics and to identify patterns in epitope specificity in the context of Mycobacterium tuberculosis.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Huang, H., Wang, C., Rubelt, F., Scriba, T. J. & Davis, M. M. Analyzing the Mycobacterium tuberculosis immune response by T-cell receptor clustering with GLIPH2 and genome-wide antigen screening. Nat. Biotechnol. 38, 1194–1202 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. 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).

    Article  CAS  PubMed  Google Scholar 

  33. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Dash, P. et al. Quantifiable predictive features define epitope-specific T cell receptor repertoires. Nature 547, 89–93 (2017). This study introduced the use of the edit distance of the CDR loop in grouping viral antigen-specific sequences.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Zhang, H. et al. Investigation of antigen-specific T-cell receptor clusters in human cancers. Clin. Cancer Res. 26, 1359–1371 (2020).

    Article  CAS  PubMed  Google Scholar 

  37. Pogorelyy, M. V. et al. Detecting T cell receptors involved in immune responses from single repertoire snapshots. PLoS Biol. 17, e3000314 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Marcou, Q., Mora, T. & Walczak, A. M. High-throughput immune repertoire analysis with IGoR. Nat. Commun. 9, 561 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Klinger, M. et al. Multiplex identification of antigen-specific T cell receptors using a combination of immune assays and immune receptor sequencing. PLoS ONE 10, e0141561 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Su, Y. et al. Multiple early factors anticipate post-acute COVID-19 sequelae. Cell 185, 881–895 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Lu, T. et al. Deep learning-based prediction of the T cell receptor–antigen binding specificity. Nat. Mach. Intell. 3, 864–875 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  42. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Nouri, N. & Kleinstein, S. H. Somatic hypermutation analysis for improved identification of B cell clonal families from next-generation sequencing data. PLoS Comput. Biol. 16, e1007977 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Hoehn, K. B. & Kleinstein, S. H. B cell phylogenetics in the single cell era. Trends Immunol. 45, 62–74 (2024).

    Article  CAS  PubMed  Google Scholar 

  47. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Lun, A., Risso, D. & Korthauer, K. SingleCellExperiment: S4 classes for single cell data. R package version 1 (2018),

  49. Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Bredikhin, D., Kats, I. & Stegle, O. MUON: multimodal omics analysis framework. Genome Biol. 23, 42 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Virshup, I. et al. The scverse project provides a computational ecosystem for single-cell omics data analysis. Nat. Biotechnol. 41, 604–606 (2023).

    Article  CAS  PubMed  Google Scholar 

  52. Henikoff, S. & Henikoff, J. G. Amino acid substitution matrices from protein blocks. Proc. Natl Acad. Sci. USA 89, 10915–10919 (1992).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Yermanos, A. et al. Platypus: an open-access software for integrating lymphocyte single-cell immune repertoires with transcriptomes. NAR Genom. Bioinform. 3, lqab023 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Samokhina, M. et al. immunomind/immunarch: Immunarch 0.9.0. Zenodo. https://doi.org/10.5281/zenodo.7446955 (2022).

  55. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Bashford-Rogers, R. J. M. et al. Analysis of the B cell receptor repertoire in six immune-mediated diseases. Nature 574, 122–126 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Fitzpatrick, Z. et al. Gut-educated IgA plasma cells defend the meningeal venous sinuses. Nature 587, 472–476 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. 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).

    Article  PubMed  Google Scholar 

  59. Alamyar, E., Giudicelli, V., Duroux, P. & Lefranc, M. -P. IMGT/HighV-QUEST: a high-throughput system and Web portal for the analysis of rearranged nucleotide sequences of antigen receptors—high-throughput version of IMGT/V-QUEST. in Journées Ouvertes de Biologie, Informatique et Mathématiques 60 (2010).

  60. Lorenz, M., Jung, S. & Radbruch, A. Switch transcripts in immunoglobulin class switching. Science 267, 1825–1828 (1995).

    Article  CAS  PubMed  Google Scholar 

  61. Lange, M. et al. CellRank for directed single-cell fate mapping. Nat. Methods 19, 159–170 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Jaffe, D. B. et al. enclone: precision clonotyping and analysis of immune receptors. Preprint at bioRxiv https://doi.org/10.1101/2022.04.21.489084 (2022).

  63. Jaffe, D. B. et al. Functional antibodies exhibit light chain coherence. Nature 611, 352–357 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Rodriguez, O. L. et al. Genetic variation in the immunoglobulin heavy chain locus shapes the human antibody repertoire. Nat. Commun. 14, 4419 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. 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). This study demonstrates how neighborhood graphs from single-cell and TCR data can be integrated to achieve integrated analysis.

    Article  CAS  PubMed  Google Scholar 

  67. Zhang, Z. et al. Interpreting the B-cell receptor repertoire with single-cell gene expression using Benisse. Nat. Mach. Intell. 4, 596–604 (2022).

    Article  Google Scholar 

  68. Atchley, W. R., Zhao, J., Fernandes, A. D. & Drüke, T. Solving the protein sequence metric problem. Proc. Natl Acad. Sci. USA 102, 6395–6400 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Zhang, B. et al. Multimodal single-cell datasets characterize antigen-specific CD8+ T cells across SARS-CoV-2 vaccination and infection. Nat. Immunol. 24, 1725–1734 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Ren, X. et al. COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas. Cell 184, 1895–1913 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Liu, C. et al. Time-resolved systems immunology reveals a late juncture linked to fatal COVID-19. Cell 184, 1836–1857 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Takahashi, T. et al. Sex differences in immune responses that underlie COVID-19 disease outcomes. Nature 588, 315–320 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Su, Y. et al. Multi-omics resolves a sharp disease-state shift between mild and moderate COVID-19. Cell 183, 1479–1495 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Davis, H. E., McCorkell, L., Vogel, J. M. & Topol, E. J. Long COVID: major findings, mechanisms and recommendations. Nat. Rev. Microbiol. 21, 133–146 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Cheon, I. S. et al. Immune signatures underlying post-acute COVID-19 lung sequelae. Sci. Immunol. 6, eabk1741 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Brodin, P. et al. Studying severe long COVID to understand post-infectious disorders beyond COVID-19. Nat. Med. 28, 879–882 (2022).

    Article  CAS  PubMed  Google Scholar 

  78. Domínguez Conde, C. et al. Cross-tissue immune cell analysis reveals tissue-specific features in humans. Science 376, eabl5197 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  79. Park, J.-E. et al. A cell atlas of human thymic development defines T cell repertoire formation. Science 367, eaay3224 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Suo, C. et al. Mapping the developing human immune system across organs. Science 376, eabo0510 (2022).

    Article  CAS  PubMed  Google Scholar 

  81. Lindeboom, R. G. H., Regev, A. & Teichmann, S. A. Towards a human cell atlas: taking notes from the past. Trends Genet. 37, 625–630 (2021).

    Article  CAS  PubMed  Google Scholar 

  82. Skok, J. A. et al. Reversible contraction by looping of the Tcra and Tcrb loci in rearranging thymocytes. Nat. Immunol. 8, 378–387 (2007).

    Article  CAS  PubMed  Google Scholar 

  83. Cordes, M. et al. Single-cell immune profiling reveals thymus-seeding populations, T cell commitment, and multilineage development in the human thymus. Sci. Immunol. 7, eade0182 (2022).

    Article  CAS  PubMed  Google Scholar 

  84. Kitaura, K. et al. Different somatic hypermutation levels among antibody subclasses disclosed by a new next-generation sequencing-based antibody repertoire analysis. Front. Immunol. 8, 389 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  85. Baumgarth, N. The double life of a B-1 cell: self-reactivity selects for protective effector functions. Nat. Rev. Immunol. 11, 34–46 (2011).

    Article  CAS  PubMed  Google Scholar 

  86. Griffin, D. O., Holodick, N. E. & Rothstein, T. L. Human B1 cells in umbilical cord and adult peripheral blood express the novel phenotype CD20+CD27+CD43+CD70. J. Exp. Med. 208, 67–80 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Holodick, N. E., Tumang, J. R. & Rothstein, T. L. Immunoglobulin secretion by B1 cells: differential intensity and IRF4-dependence of spontaneous IgM secretion by peritoneal and splenic B1 cells. Eur. J. Immunol. 40, 3007–3016 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Dann, E., Henderson, N. C., Teichmann, S. A., Morgan, M. D. & Marioni, J. C. Differential abundance testing on single-cell data using k-nearest neighbor graphs. Nat. Biotechnol. 40, 245–253 (2022).

    Article  CAS  PubMed  Google Scholar 

  89. Setty, M. et al. Characterization of cell fate probabilities in single-cell data with Palantir. Nat. Biotechnol. 37, 451–460 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Karimi, M. M. et al. The order and logic of CD4 versus CD8 lineage choice and differentiation in mouse thymus. Nat. Commun. 12, 99 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Qian, L. et al. Suppression of ILC2 differentiation from committed T cell precursors by E protein transcription factors. J. Exp. Med. 216, 884–899 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Rojas, L. A. et al. Personalized RNA neoantigen vaccines stimulate T cells in pancreatic cancer. Nature 618, 144–150 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Hudson, W. H. & Sudmeier, L. J. Localization of T cell clonotypes using the Visium spatial transcriptomics platform. STAR Protoc. 3, 101391 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. 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 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Benotmane, J. K. et al. High-sensitive spatially resolved T cell receptor sequencing with SPTCR-seq. Nat. Commun. 14, 7432 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  96. Engblom, C. et al. Spatial transcriptomics of B cell and T cell receptors reveals lymphocyte clonal dynamics. Science 382, eadf8486 (2023).

    Article  CAS  PubMed  Google Scholar 

  97. Farouni, R., Djambazian, H., Ferri, L. E., Ragoussis, J. & Najafabadi, H. S. Model-based analysis of sample index hopping reveals its widespread artifacts in multiplexed single-cell RNA-sequencing. Nat. Commun. 11, 2704 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Kyle, R. A. et al. Clinical course of light-chain smouldering multiple myeloma (idiopathic Bence Jones proteinuria): a retrospective cohort study. Lancet Haematol. 1, e28–e36 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  99. Vander Heiden, J. A. et al. pRESTO: a toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires. Bioinformatics 30, 1930–1932 (2014).

    Article  Google Scholar 

  100. Setliff, I. et al. High-throughput mapping of B cell receptor sequences to antigen specificity. Cell 179, 1636–1646 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank G. Sturm for the useful discussions and D. Maresco-Pennisi and C. Lee for helping to proofread the document. We acknowledge Children’s Hospital Foundation’s philanthropic contributions awarded to the Ian Frazer Centre for Children’s Immunotherapy Research.

Author information

Authors and Affiliations

Authors

Contributions

S.E.I., N.B. and Z.K.T. wrote the original draft. S.E.I. and Z.K.T. synthesized the literature and designed the review structure. M.S.F.S., N.B. and Z.K.T. critically reviewed, revised and edited the manuscript. M.S.F.S. made and synthesized the figures and tables. Z.K.T. conceptualized the review, outlined the structure, provided overall direction and supervised the writing.

Corresponding author

Correspondence to Zewen Kelvin Tuong.

Ethics declarations

Competing interests

N.B. is Head of Computational Biology at Omniscope and has consulted for Starling Biosciences and Santa Ana Bio. Z.K.T. has consulted for Synteny Biotechnology in the last 3 years. All other authors declare no competing interests.

Peer review

Peer review information

Nature Methods thanks Caleb Lareau, Guideng Li, and Tao Wang for their contribution to the peer review of this work. Primary Handling Editor: Madhura Mukhopadhyay, in collaboration with the Nature Methods team.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Irac, S.E., Soon, M.S.F., Borcherding, N. et al. Single-cell immune repertoire analysis. Nat Methods (2024). https://doi.org/10.1038/s41592-024-02243-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41592-024-02243-4

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing