+
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:

Immune-cell profiling to guide stratification and treatment of patients with rheumatic diseases

Abstract

Methods for high-dimensional immune-cell profiling have advanced dramatically in the past decade. Studies of tissue and blood samples from patients with rheumatic diseases have revealed stereotyped features of immune dysregulation in individual diseases and in subsets of patients who share diagnosis of a heterogeneous disease. Translating immunological patterns into clinically implementable, actionable biomarkers has the potential to improve detection and quantification of pathological immune activity and selection of appropriate treatments for autoimmune rheumatic diseases. For example, cytometric features can be used to distinguish the various forms of inflammatory arthritis, stratify subsets of patients with rheumatoid arthritis or subsets of patients with systemic lupus erythematosus and predict treatment responses. Cellular immune profiling also enables the identification of specific features of immune dysregulation in individuals with rare, undiagnosed, inflammatory diseases. Several paths might lead to translation of discoveries from broad immune profiling into clinical tests to interrogate immune activation in people with rheumatic diseases.

Key points

  • Profiling of immune cells in blood and tissue from patients with rheumatic diseases has helped to define populations of activated immune cells that are characteristically expanded in specific diseases, highlighting both unique and shared features across diseases.

  • Immune profiling of patients with SLE has identified specific axes of immune dysregulation, including activation of type I IFN pathways, proliferation of lymphocytes, expression of cytotoxic molecules on T cells and upregulation of myeloid cell- and neutrophil-associated signatures; these features vary across patients and help to delineate subgroups of patients that differ in immune activity.

  • Longitudinal evaluation of cellular profiles of patients receiving treatments targeting rheumatic disease helps to associate immunological features with treatment effects and predict response to treatment.

  • Incorporation of immune profiling into clinical evaluation of patients with rheumatic diseases might enable improved patient stratification, assessment of disease activity and prediction of treatment response.

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: Focused immune assessments to identify immune dysregulation in patients with suspected systemic lupus erythematosus.
Fig. 2: Broad immune profiling to identify immune abnormalities in rheumatic diseases.

Similar content being viewed by others

References

  1. Bray, C. et al. Erythrocyte sedimentation rate and C-reactive protein measurements and their relevance in clinical medicine. WMJ 115, 317–321 (2016).

    PubMed  Google Scholar 

  2. Weinstein, A., Alexander, R. V. & Zack, D. J. A review of complement activation in SLE. Curr. Rheumatol. Rep. 23, 16 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. Sanjabi, S. & Lear, S. New cytometry tools for immune monitoring during cancer immunotherapy. Cytom. B Clin. Cytom. 100, 10–18 (2021).

    Article  CAS  Google Scholar 

  4. Hartmann, F. J. & Bendall, S. C. Immune monitoring using mass cytometry and related high-dimensional imaging approaches. Nat. Rev. Rheumatol. 16, 87–99 (2020).

    Article  PubMed  Google Scholar 

  5. Stubbington, M. J. T., Rozenblatt-Rosen, O., Regev, A. & Teichmann, S. A. Single-cell transcriptomics to explore the immune system in health and disease. Science 358, 58–63 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Peterson, V. M. et al. Multiplexed quantification of proteins and transcripts in single cells. Nat. Biotechnol. 35, 936–939 (2017).

    Article  PubMed  CAS  Google Scholar 

  8. Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. https://doi.org/10.1038/nbt.4314 (2018).

    Article  PubMed  Google Scholar 

  9. Zhang, S., Li, X., Lin, J., Lin, Q. & Wong, K. C. Review of single-cell RNA-seq data clustering for cell-type identification and characterization. RNA 29, 517–530 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Fonseka, C. Y. et al. Mixed-effects association of single cells identifies an expanded effector CD4+ T cell subset in rheumatoid arthritis. Sci. Transl. Med. 10, eaaq0305 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Reshef, Y. A. et al. Co-varying neighborhood analysis identifies cell populations associated with phenotypes of interest from single-cell transcriptomics. Nat. Biotechnol. 40, 355–363 (2022).

    Article  PubMed  CAS  Google Scholar 

  12. 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  PubMed  CAS  Google Scholar 

  13. Stone, J. H. et al. Trial of tocilizumab in giant-cell arteritis. N. Engl. J. Med. 377, 317–328 (2017).

    Article  PubMed  CAS  Google Scholar 

  14. Devauchelle-Pensec, V. et al. Effect of tocilizumab on disease activity in patients with active polymyalgia rheumatica receiving glucocorticoid therapy: a randomized clinical trial. JAMA 328, 1053–1062 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Dima, A., Opris, D., Jurcut, C. & Baicus, C. Is there still a place for erythrocyte sedimentation rate and C-reactive protein in systemic lupus erythematosus? Lupus 25, 1173–1179 (2016).

    Article  PubMed  CAS  Google Scholar 

  16. Aringer, M. Inflammatory markers in systemic lupus erythematosus. J. Autoimmun. 110, 102374 (2020).

    Article  PubMed  CAS  Google Scholar 

  17. Bennett, L. et al. Interferon and granulopoiesis signatures in systemic lupus erythematosus blood. J. Exp. Med. 197, 711–723 (2003).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Morand, E. F. et al. Trial of anifrolumab in active systemic lupus erythematosus. N. Engl. J. Med. 382, 211–221 (2020).

    Article  PubMed  CAS  Google Scholar 

  19. Cooles, F. A. H. & Isaacs, J. D. The interferon gene signature as a clinically relevant biomarker in autoimmune rheumatic disease. Lancet Rheumatol. 4, e61–e72 (2022).

    Article  PubMed  CAS  Google Scholar 

  20. Lipsky, P. E. et al. Biological impact of iberdomide in patients with active systemic lupus erythematosus. Ann. Rheum. Dis. 81, 1136–1142 (2022).

    Article  PubMed  CAS  Google Scholar 

  21. Tanaka, H. et al. Extracting immunological and clinical heterogeneity across autoimmune rheumatic diseases by cohort-wide immunophenotyping. Ann. Rheum. Dis. 83, 242–252 (2024).

    Article  PubMed  CAS  Google Scholar 

  22. Burns, M. et al. Dysregulated CD38 expression on peripheral blood immune cell subsets in SLE. Int. J. Mol. Sci. 22, 2424 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Katsuyama, E. et al. The CD38/NAD/SIRTUIN1/EZH2 axis mitigates cytotoxic CD8 T cell function and identifies patients with SLE prone to infections. Cell Rep. 30, 112–123.e4 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Wang, R. et al. Clonally expanded CD38hi cytotoxic CD8 T cells define the T cell infiltrate in checkpoint inhibitor-associated arthritis. Sci. Immunol. 8, eadd1591 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Bocharnikov, A. V. et al. PD-1hi CXCR5 T peripheral helper cells promote B cells responses in lupus via MAF and IL-21. JCI Insight 4, e130062 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  26. He, J. et al. Circulating precursor CCR7loPD-1hi CXCR5+ CD4+ T cells indicate Tfh cell activity and promote antibody responses upon antigen reexposure. Immunity 39, 770–781 (2013).

    Article  PubMed  CAS  Google Scholar 

  27. Lin, J., Yu, Y., Ma, J., Ren, C. & Chen, W. PD-1+CXCR5CD4+T cells are correlated with the severity of systemic lupus erythematosus. Rheumatology 58, 2188–2192 (2019).

    Article  PubMed  CAS  Google Scholar 

  28. Makiyama, A. et al. Expanded circulating peripheral helper T cells in systemic lupus erythematosus: association with disease activity and B cell differentiation. Rheumatology 58, 1861–1869 (2019).

    PubMed  CAS  Google Scholar 

  29. Wang, S. et al. IL-21 drives expansion and plasma cell differentiation of autoreactive CD11chiT-bet+ B cells in SLE. Nat. Commun. 9, 1758 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Jenks, S. A. et al. Distinct effector B cells induced by unregulated toll-like receptor 7 contribute to pathogenic responses in systemic lupus erythematosus. Immunity 49, 725–739.e6 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Tipton, C. M. et al. Diversity, cellular origin and autoreactivity of antibody-secreting cell population expansions in acute systemic lupus erythematosus. Nat. Immunol. 16, 755–765 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Marks, K. E. & Rao, D. A. T peripheral helper cells in autoimmune diseases. Immunol. Rev. 307, 191–202 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Balog, J. et al. Comparative single-cell multiplex immunophenotyping of therapy-naive patients with rheumatoid arthritis, systemic sclerosis, and systemic lupus erythematosus shed light on disease-specific composition of the peripheral immune system. Front. Immunol. 15, 1376933 (2024).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Ota, M. et al. Dynamic landscape of immune cell-specific gene regulation in immune-mediated diseases. Cell 184, 3006–3021.e17 (2021).

    Article  PubMed  CAS  Google Scholar 

  35. Chen, S. et al. Interleukin 17A and IL-17F expression and functional responses in rheumatoid arthritis and peripheral spondyloarthritis. J. Rheumatol. 47, 1606–1613 (2020).

    Article  PubMed  CAS  Google Scholar 

  36. Edwards, J. C. et al. Efficacy of B-cell-targeted therapy with rituximab in patients with rheumatoid arthritis. N. Engl. J. Med. 350, 2572–2581 (2004).

    Article  PubMed  CAS  Google Scholar 

  37. Mease, P. J. Is there a role for rituximab in the treatment of spondyloarthritis and psoriatic arthritis? J. Rheumatol. 39, 2235–2237 (2012).

    Article  PubMed  CAS  Google Scholar 

  38. Penkava, F. et al. Single-cell sequencing reveals clonal expansions of pro-inflammatory synovial CD8 T cells expressing tissue-homing receptors in psoriatic arthritis. Nat. Commun. 11, 4767 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Zhang, F. et al. Deconstruction of rheumatoid arthritis synovium defines inflammatory subtypes. Nature 623, 616–624 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Zhang, F. et al. Defining inflammatory cell states in rheumatoid arthritis joint synovial tissues by integrating single-cell transcriptomics and mass cytometry. Nat. Immunol. 20, 928–942 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Yager, N. et al. Ex vivo mass cytometry analysis reveals a profound myeloid proinflammatory signature in psoriatic arthritis synovial fluid. Ann. Rheum. Dis. 80, 1559–1567 (2021).

    Article  PubMed  CAS  Google Scholar 

  42. Rao, D. A. et al. Pathologically expanded peripheral T helper cell subset drives B cells in rheumatoid arthritis. Nature 542, 110–114 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Fortea-Gordo, P. et al. Two populations of circulating PD-1hiCD4 T cells with distinct B cell helping capacity are elevated in early rheumatoid arthritis. Rheumatology 58, 1662–1673 (2019).

    Article  PubMed  CAS  Google Scholar 

  44. Sowerby, J. M. & Rao, D. A. T cell-B cell interactions in human autoimmune diseases. Curr. Opin. Immunol. 93, 102539 (2025).

    Article  PubMed  CAS  Google Scholar 

  45. Povoleri, G. A. M. et al. Psoriatic and rheumatoid arthritis joints differ in the composition of CD8+ tissue-resident memory T cell subsets. Cell Rep. 42, 112514 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Steel, K. J. A. et al. Polyfunctional, proinflammatory, tissue-resident memory phenotype and function of synovial interleukin-17A+CD8+ T cells in psoriatic arthritis. Arthritis Rheumatol. 72, 435–447 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Mease, P. J. et al. Secukinumab inhibition of interleukin-17A in patients with psoriatic arthritis. N. Engl. J. Med. 373, 1329–1339 (2015).

    Article  PubMed  CAS  Google Scholar 

  48. McInnes, I. B. et al. Secukinumab, a human anti-interleukin-17A monoclonal antibody, in patients with psoriatic arthritis (FUTURE 2): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet 386, 1137–1146 (2015).

    Article  PubMed  CAS  Google Scholar 

  49. Qaiyum, Z., Gracey, E., Yao, Y. & Inman, R. D. Integrin and transcriptomic profiles identify a distinctive synovial CD8+ T cell subpopulation in spondyloarthritis. Ann. Rheum. Dis. 78, 1566–1575 (2019).

    Article  PubMed  CAS  Google Scholar 

  50. Jonsson, A. H. et al. Granzyme K+ CD8 T cells form a core population in inflamed human tissue. Sci. Transl. Med. 14, eabo0686 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Reece, R. J., Canete, J. D., Parsons, W. J., Emery, P. & Veale, D. J. Distinct vascular patterns of early synovitis in psoriatic, reactive, and rheumatoid arthritis. Arthritis Rheum. 42, 1481–1484 (1999).

    Article  PubMed  CAS  Google Scholar 

  52. Floudas, A. et al. Distinct stromal and immune cell interactions shape the pathogenesis of rheumatoid and psoriatic arthritis. Ann. Rheum. Dis. 81, 1224–1242 (2022).

    Article  PubMed  CAS  Google Scholar 

  53. Abji, F. et al. Proteinase-mediated macrophage signaling in psoriatic arthritis. Front. Immunol. 11, 629726 (2020).

    Article  PubMed  CAS  Google Scholar 

  54. Fragoulis, G. E. et al. Distinct innate and adaptive immunity phenotypic profile at the circulating single-cell level in psoriatic arthritis. Clin. Immunol. 253, 109679 (2023).

    Article  PubMed  CAS  Google Scholar 

  55. Li, B. et al. Differential immunological profiles in seronegative versus seropositive rheumatoid arthritis: Th17/Treg dysregulation and IL-4. Front. Immunol. 15, 1447213 (2024).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Bhamidipati, K. et al. Spatial patterning of fibroblast TGFβ signaling underlies treatment resistance in rheumatoid arthritis. Preprint at bioRxiv, https://doi.org/10.1101/2025.03.14.642821 (2025).

  57. Naidoo, J. et al. Toxicities of the anti-PD-1 and anti-PD-L1 immune checkpoint antibodies. Ann. Oncol. 26, 2375–2391 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Ghosh, N. & Bass, A. R. Rheumatic complications of immune checkpoint inhibitors. Rheum. Dis. Clin. North. Am. 48, 411–428 (2022).

    Article  PubMed  Google Scholar 

  59. Ghosh, N. et al. Identification of outcome domains in immune checkpoint inhibitor-induced inflammatory arthritis and polymyalgia rheumatica: a scoping review by the OMERACT irAE working group. Semin. Arthritis Rheum. 58, 152110 (2023).

    Article  PubMed  CAS  Google Scholar 

  60. Kim, S. T. et al. Distinct molecular and immune hallmarks of inflammatory arthritis induced by immune checkpoint inhibitors for cancer therapy. Nat. Commun. 13, 1970 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. Maschmeyer, P. et al. Antigen-driven PD-1+ TOX+ BHLHE40+ and PD-1+ TOX+ EOMES+ T lymphocytes regulate juvenile idiopathic arthritis in situ. Eur. J. Immunol. 51, 915–929 (2021).

    Article  PubMed  CAS  Google Scholar 

  62. Barturen, G., Beretta, L., Cervera, R., Van Vollenhoven, R. & Alarcón-Riquelme, M. E. Moving towards a molecular taxonomy of autoimmune rheumatic diseases. Nat. Rev. Rheumatol. 14, 75–93 (2018).

    Article  PubMed  CAS  Google Scholar 

  63. Pitzalis, C., Kelly, S. & Humby, F. New learnings on the pathophysiology of RA from synovial biopsies. Curr. Opin. Rheumatol. 25, 334–344 (2013).

    Article  PubMed  Google Scholar 

  64. Lewis, M. J. et al. Molecular portraits of early rheumatoid arthritis identify clinical and treatment response phenotypes. Cell Rep. 28, 2455–2470.e5 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  65. Pitzalis, C., Jones, G. W., Bombardieri, M. & Jones, S. A. Ectopic lymphoid-like structures in infection, cancer and autoimmunity. Nat. Rev. Immunol. 14, 447–462 (2014).

    Article  PubMed  CAS  Google Scholar 

  66. Humby, F. et al. Synovial cellular and molecular signatures stratify clinical response to csDMARD therapy and predict radiographic progression in early rheumatoid arthritis patients. Ann. Rheum. Dis. 78, 761–772 (2019).

    Article  PubMed  CAS  Google Scholar 

  67. Dunlap, G. et al. Clonal associations between lymphocyte subsets and functional states in rheumatoid arthritis synovium. Nat. Commun. 15, 4991 (2024).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  68. Durham, L. E. et al. Substantive similarities between synovial fluid and synovial tissue t cells in inflammatory arthritis via single-cell RNA and T cell receptor sequencing. Arthritis Rheumatol. 76, 1594–1601 (2024).

    Article  PubMed  CAS  Google Scholar 

  69. Kubo, S. et al. Peripheral blood immunophenotypic diversity in patients with rheumatoid arthritis and its impact on therapeutic responsiveness. Ann. Rheum. Dis. 84, 210–220 (2025).

    Article  PubMed  CAS  Google Scholar 

  70. Goto, M. et al. Age-associated CD4+ T cells with B cell-promoting functions are regulated by ZEB2 in autoimmunity. Sci. Immunol. 9, eadk1643 (2024).

    Article  PubMed  CAS  Google Scholar 

  71. Horisberger, A. et al. Blood immunophenotyping identifies distinct kidney histopathology and outcomes in patients with lupus nephritis. J. Clin. Invest. https://doi.org/10.1172/jci181034 (2025).

    Article  PubMed  PubMed Central  Google Scholar 

  72. Sasaki, T. et al. Clonal relationships between Tph and Tfh cells in patients with SLE and in murine lupus. Preprint at bioRxiv, https://doi.org/10.1101/2025.01.27.635189 (2025).

  73. Perez, R. K. et al. Single-cell RNA-seq reveals cell type-specific molecular and genetic associations to lupus. Science 376, eabf1970 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  74. Nehar-Belaid, D. et al. Mapping systemic lupus erythematosus heterogeneity at the single-cell level. Nat. Immunol. 21, 1094–1106 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  75. Billi, A. C. et al. Nonlesional lupus skin contributes to inflammatory education of myeloid cells and primes for cutaneous inflammation. Sci. Transl. Med. 14, eabn2263 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  76. Dunlap, G. S. et al. Single-cell transcriptomics reveals distinct effector profiles of infiltrating T cells in lupus skin and kidney. JCI Insight 7, e156341 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  77. Arazi, A. et al. The immune cell landscape in kidneys of patients with lupus nephritis. Nat. Immunol. 20, 902–914 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  78. Der, E. et al. Tubular cell and keratinocyte single-cell transcriptomics applied to lupus nephritis reveal type I IFN and fibrosis relevant pathways. Nat. Immunol. 20, 915–927 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  79. Danaher, P. et al. Childhood-onset lupus nephritis is characterized by complex interactions between kidney stroma and infiltrating immune cells. Sci. Transl. Med. 16, eadl1666 (2024).

    Article  PubMed  CAS  Google Scholar 

  80. Banchereau, R. et al. Personalized immunomonitoring uncovers molecular networks that stratify lupus patients. Cell 165, 551–565 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  81. Guthridge, J. M. et al. Adults with systemic lupus exhibit distinct molecular phenotypes in a cross-sectional study. eClinicalMedicine 20, 100291 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  82. Hubbard, E. L. et al. Analysis of transcriptomic features reveals molecular endotypes of SLE with clinical implications. Genome Med. 15, 84 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  83. Garantziotis, P. et al. Molecular taxonomy of systemic lupus erythematosus through data-driven patient stratification: molecular endotypes and cluster-tailored drugs. Front. Immunol. 13, 860726 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  84. Rincon-Arevalo, H. et al. Deep phenotyping of CD11c+ B cells in systemic autoimmunity and controls. Front. Immunol. 12, 635615 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  85. Wehr, C. et al. A new CD21low B cell population in the peripheral blood of patients with SLE. Clin. Immunol. 113, 161–171 (2004).

    Article  PubMed  CAS  Google Scholar 

  86. Kubo, S. et al. Peripheral immunophenotyping identifies three subgroups based on T cell heterogeneity in lupus patients. Arthritis Rheumatol. 69, 2029–2037 (2017).

    Article  PubMed  CAS  Google Scholar 

  87. Sasaki, T. et al. Longitudinal immune cell profiling in early systemic lupus erythematosus. Arthritis Rheumatol. 74, 1808–1821 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  88. Choi, J. Y. et al. Circulating follicular helper-like T cells in systemic lupus erythematosus: association with disease activity. Arthritis Rheumatol. 67, 988–999 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  89. Jenks, S. A., Cashman, K. S., Woodruff, M. C., Lee, F. E. & Sanz, I. Extrafollicular responses in humans and SLE. Immunol. Rev. 288, 136–148 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  90. Rao, D. A. T cells that help B cells in chronically inflamed tissues. Front. Immunol. 9, 1924 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  91. Skougaard, M. et al. Four emerging immune cellular blood phenotypes associated with disease duration and activity established in psoriatic arthritis. Arthritis Res. Ther. 24, 262 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  92. Urbanski, G. et al. Single-cell RNA sequencing of peripheral blood defines two immunological subtypes of Sjögren’s disease. Preprint at bioRxiv, 2025.2002.2027.640483, https://doi.org/10.1101/2025.02.27.640483 (2025).

  93. Aletaha, D. & Smolen, J. S. Diagnosis and management of rheumatoid arthritis: a review. JAMA 320, 1360–1372 (2018).

    Article  PubMed  Google Scholar 

  94. Navarra, S. V. et al. Efficacy and safety of belimumab in patients with active systemic lupus erythematosus: a randomised, placebo-controlled, phase 3 trial. Lancet 377, 721–731 (2011).

    Article  PubMed  CAS  Google Scholar 

  95. Rovin, B. H. et al. Efficacy and safety of voclosporin versus placebo for lupus nephritis (AURORA 1): a double-blind, randomised, multicentre, placebo-controlled, phase 3 trial. Lancet 397, 2070–2080 (2021).

    Article  PubMed  CAS  Google Scholar 

  96. Mulhearn, B., Barton, A. & Viatte, S. Using the immunophenotype to predict response to biologic drugs in rheumatoid arthritis. J. Pers. Med. 9, 46 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  97. Wientjes, M. H. M., den Broeder, A. A., Welsing, P. M. J., Verhoef, L. M. & van den Bemt, B. J. F. Prediction of response to anti-TNF treatment using laboratory biomarkers in patients with rheumatoid arthritis: a systematic review. RMD Open. 8, e002570 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  98. Cohen, S. et al. A molecular signature response classifier to predict inadequate response to tumor necrosis factor-α inhibitors: the NETWORK-004 prospective observational study. Rheumatol. Ther. 8, 1159–1176 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  99. Bergman, M. J. et al. Clinical utility and cost savings in predicting inadequate response to anti-TNF therapies in rheumatoid arthritis. Rheumatol. Ther. 7, 775–792 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  100. Aldridge, J. et al. Blood PD-1+TFh and CTLA-4+CD4+ T cells predict remission after CTLA-4Ig treatment in early rheumatoid arthritis. Rheumatology 61, 1233–1242 (2022).

    Article  PubMed  CAS  Google Scholar 

  101. Monjo-Henry, I. et al. Circulating Tfh cells are differentially modified by abatacept or TNF blockers and predict treatment response in rheumatoid arthritis. Rheumatology 64, 517–525 (2025).

    Article  PubMed  CAS  Google Scholar 

  102. Edner, N. M. et al. Follicular helper T cell profiles predict response to costimulation blockade in type 1 diabetes. Nat. Immunol. 21, 1244–1255 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  103. Sellam, J. et al. Blood memory B cells are disturbed and predict the response to rituximab in patients with rheumatoid arthritis. Arthritis Rheum. 63, 3692–3701 (2011).

    Article  PubMed  CAS  Google Scholar 

  104. Möller, B. et al. Class-switched B cells display response to therapeutic B-cell depletion in rheumatoid arthritis. Arthritis Res. Ther. 11, R62 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  105. Roll, P., Dörner, T. & Tony, H. P. Anti-CD20 therapy in patients with rheumatoid arthritis: predictors of response and B cell subset regeneration after repeated treatment. Arthritis Rheum. 58, 1566–1575 (2008).

    Article  PubMed  CAS  Google Scholar 

  106. Vital, E. M., Dass, S., Buch, M. H., Rawstron, A. C. & Emery, P. An extra dose of rituximab improves clinical response in rheumatoid arthritis patients with initial incomplete B cell depletion: a randomised controlled trial. Ann. Rheum. Dis. 74, 1195–1201 (2015).

    Article  PubMed  CAS  Google Scholar 

  107. Baker, K. F. et al. Single-cell insights into immune dysregulation in rheumatoid arthritis flare versus drug-free remission. Nat. Commun. 15, 1063 (2024).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  108. Orange, D. E. et al. RNA identification of PRIME cells predicting rheumatoid arthritis flares. N. Engl. J. Med. 383, 218–228 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  109. Rivellese, F. et al. Rituximab versus tocilizumab in rheumatoid arthritis: synovial biopsy-based biomarker analysis of the phase 4 R4RA randomized trial. Nat. Med. 28, 1256–1268 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  110. Nerviani, A. et al. A pauci-immune synovial pathotype predicts inadequate response to TNFα-blockade in rheumatoid arthritis patients. Front. Immunol. 11, 845 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  111. Rivellese, F. et al. Stratification of biological therapies by pathobiology in biologic-naive patients with rheumatoid arthritis (STRAP and STRAP-EU): two parallel, open-label, biopsy-driven, randomised trials. Lancet Rheumatol. 5, e648–e659 (2023).

    Article  PubMed  CAS  Google Scholar 

  112. Humby, F. et al. Rituximab versus tocilizumab in anti-TNF inadequate responder patients with rheumatoid arthritis (R4RA): 16-week outcomes of a stratified, biopsy-driven, multicentre, open-label, phase 4 randomised controlled trial. Lancet 397, 305–317 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  113. Alivernini, S. et al. Distinct synovial tissue macrophage subsets regulate inflammation and remission in rheumatoid arthritis. Nat. Med. 26, 1295–1306 (2020).

    Article  PubMed  CAS  Google Scholar 

  114. MacDonald, L. et al. Synovial tissue myeloid dendritic cell subsets exhibit distinct tissue-niche localization and function in health and rheumatoid arthritis. Immunity 57, 2843–2862.e12 (2024).

    Article  PubMed  CAS  Google Scholar 

  115. Thomas, T. et al. A longitudinal single-cell atlas of anti-tumour necrosis factor treatment in inflammatory bowel disease. Nat. Immunol. 25, 2152–2165 (2024).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  116. Carter, L. M., Wigston, Z., Laws, P. & Vital, E. M. Rapid efficacy of anifrolumab across multiple subtypes of recalcitrant cutaneous lupus erythematosus parallels changes in discrete subsets of blood transcriptomic and cellular biomarkers. Br. J. Dermatol. 189, 210–218 (2023).

    Article  PubMed  CAS  Google Scholar 

  117. Baker, T. et al. Type I interferon blockade with anifrolumab in patients with systemic lupus erythematosus modulates key immunopathological pathways in a gene expression and proteomic analysis of two phase 3 trials. Ann. Rheum. Dis. 83, 1018–1027 (2024).

    Article  PubMed  CAS  Google Scholar 

  118. Casey, K. A. et al. Type I interferon receptor blockade with anifrolumab corrects innate and adaptive immune perturbations of SLE. Lupus Sci. Med. 5, e000286 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  119. Law, C. et al. Interferon subverts an AHR-JUN axis to promote CXCL13+ T cells in lupus. Nature 631, 857–866 (2024).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  120. Vital, E. M. et al. Anifrolumab efficacy and safety by type I interferon gene signature and clinical subgroups in patients with SLE: post hoc analysis of pooled data from two phase III trials. Ann. Rheum. Dis. 81, 951–961 (2022).

    Article  PubMed  CAS  Google Scholar 

  121. Moysidou, G. S. et al. Molecular basis for the disease-modifying effects of belimumab in systemic lupus erythematosus and molecular predictors of early response: blood transcriptome analysis implicates the innate immunity and DNA damage response pathways. Ann. Rheum. Dis. 84, 262–273 (2025).

    Article  PubMed  CAS  Google Scholar 

  122. Hoffman, R. W. et al. Gene expression and pharmacodynamic changes in 1,760 systemic lupus erythematosus patients from two phase III trials of BAFF blockade with tabalumab. Arthritis Rheumatol. 69, 643–654 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  123. Nakano, M. et al. Distinct transcriptome architectures underlying lupus establishment and exacerbation. Cell 185, 3375–3389.e21 (2022).

    Article  PubMed  CAS  Google Scholar 

  124. Sun, W. et al. Heterogeneity of peripheral immune cell landscape in systemic lupus erythematosus patients after belimumab treatment. Clin. Exp. Rheumatol. 43, 1259–1276 (2025).

    PubMed  Google Scholar 

  125. Maeda, S. et al. High-dimensional analysis of T-cell profiling variations following belimumab treatment in systemic lupus erythematosus. Lupus Sci. Med. 10, e000976 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  126. Prete, M. et al. Belimumab restores Treg/Th17 balance in patients with refractory systemic lupus erythematosus. Lupus 27, 1926–1935 (2018).

    Article  PubMed  CAS  Google Scholar 

  127. Merrill, J. T. et al. Obexelimab in systemic lupus erythematosus with exploration of response based on gene pathway co-expression patterns: a double-blind, randomized, placebo-controlled, phase 2 trial. Arthritis Rheumatol. 75, 2185–2194 (2023).

    Article  PubMed  CAS  Google Scholar 

  128. Schafer, P. H. et al. Cereblon modulator iberdomide induces degradation of the transcription factors Ikaros and Aiolos: immunomodulation in healthy volunteers and relevance to systemic lupus erythematosus. Ann. Rheum. Dis. 77, 1516–1523 (2018).

    Article  PubMed  CAS  Google Scholar 

  129. Müller, F. et al. CD19 CAR T-cell therapy in autoimmune disease — a case series with follow-up. N. Engl. J. Med. 390, 687–700 (2024).

    Article  PubMed  Google Scholar 

  130. Hagen, M. et al. BCMA-targeted T-cell-engager therapy for autoimmune disease. N. Engl. J. Med. 391, 867–869 (2024).

    Article  PubMed  Google Scholar 

  131. Alexander, T., Krönke, J., Cheng, Q., Keller, U. & Krönke, G. Teclistamab-induced remission in refractory systemic lupus erythematosus. N. Engl. J. Med. 391, 864–866 (2024).

    Article  PubMed  Google Scholar 

  132. Wilhelm, A. et al. Selective CAR T cell-mediated B cell depletion suppresses IFN signature in SLE. JCI Insight 9, e179433 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  133. Ramoni, R. B. et al. The Undiagnosed Diseases Network: accelerating discovery about health and disease. Am. J. Hum. Genet. 100, 185–192 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  134. Mueller, A. A. et al. High-dimensional immunophenotyping reveals immune cell aberrations in patients with undiagnosed inflammatory and autoimmune diseases. J. Clin. Invest. 133, e169619 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  135. Boycott, K. M. et al. International cooperation to enable the diagnosis of all rare genetic diseases. Am. J. Hum. Genet. 100, 695–705 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  136. Frésard, L. et al. Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts. Nat. Med. 25, 911–919 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  137. Montgomery, S. B., Bernstein, J. A. & Wheeler, M. T. Toward transcriptomics as a primary tool for rare disease investigation. Cold Spring Harb. Mol. Case Stud. 8, a006198 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  138. Liu, C. et al. Multi-lineage transcriptional and cell communication signatures define pathways in individuals at-risk for developing rheumatoid arthritis that initiate and perpetuate disease. Preprint at bioRxiv, https://doi.org/10.1101/2025.02.08.619913 (2025).

  139. Inamo, J. et al. Deep immunophenotyping reveals circulating activated lymphocytes in individuals at risk for rheumatoid arthritis. J. Clin. Investig. 135, e185217 (2025).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  140. Sasaki, T. et al. A CD57+ cytotoxic CD8 T cell subset associated with fibrotic lung disease in systemic sclerosis. Preprint at bioRxiv, 2025.2001.2027.635121, https://doi.org/10.1101/2025.01.27.635121 (2025).

  141. Biesen, R. et al. Sialic acid-binding Ig-like lectin 1 expression in inflammatory and resident monocytes is a potential biomarker for monitoring disease activity and success of therapy in systemic lupus erythematosus. Arthritis Rheum. 58, 1136–1145 (2008).

    Article  PubMed  CAS  Google Scholar 

  142. Rose, T. et al. IFNα and its response proteins, IP-10 and SIGLEC-1, are biomarkers of disease activity in systemic lupus erythematosus. Ann. Rheum. Dis. 72, 1639–1645 (2013).

    Article  PubMed  CAS  Google Scholar 

  143. Fang, H., Wang, S. A., Medeiros, L. J. & Wang, W. Application of flow cytometry immunophenotypic analysis for the diagnosis of mature B-cell lymphomas/leukemias. Hum. Pathol. 156, 105711 (2025).

    Article  PubMed  CAS  Google Scholar 

  144. Nguyen, A. A. & Platt, C. D. Flow cytometry-based immune phenotyping of T and B lymphocytes in the evaluation of immunodeficiency and immune dysregulation. Immunol. Allergy Clin. North. Am. 45, 189–203 (2025).

    Article  PubMed  Google Scholar 

  145. LaBere, B. et al. Clinical utility of measuring CD4+ T follicular cells in patients with immune dysregulation. J. Autoimmun. 140, 103088 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  146. Kotliar, D. et al. Reproducible single cell annotation of programs underlying T-cell subsets, activation states, and functions. Preprint at bioRxiv, https://doi.org/10.1101/2024.05.03.592310 (2024).

  147. Kang, J. B. et al. Efficient and precise single-cell reference atlas mapping with Symphony. Nat. Commun. 12, 5890 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  148. De Simone, M. et al. Comparative analysis of commercial single-cell RNA sequencing technologies. Preprint at bioRxiv, 2024.2006.2018.599579, https://doi.org/10.1101/2024.06.18.599579 (2024).

  149. Liu, J. et al. Combined single cell transcriptome and surface epitope profiling identifies potential biomarkers of psoriatic arthritis and facilitates diagnosis via machine learning. Front. Immunol. 13, 835760 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  150. Scott, D. L., Smith, C. & Kingsley, G. Joint damage and disability in rheumatoid arthritis: an updated systematic review. Clin. Exp. Rheumatol. 21, S20–S27 (2003).

    PubMed  CAS  Google Scholar 

  151. Wakefield, R. J. et al. After treat-to-target: can a targeted ultrasound initiative improve RA outcomes? Ann. Rheum. Dis. 71, 799–803 (2012).

    Article  PubMed  Google Scholar 

  152. Raychaudhuri, S. et al. Five amino acids in three HLA proteins explain most of the association between MHC and seropositive rheumatoid arthritis. Nat. Genet. 44, 291–296 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  153. Ishigaki, K. et al. Multi-ancestry genome-wide association analyses identify novel genetic mechanisms in rheumatoid arthritis. Nat. Genet. 54, 1640–1651 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  154. McDermott, G. C. et al. Polygenic risk scores for rheumatoid arthritis and idiopathic pulmonary fibrosis and associations with RA, interstitial lung abnormalities, and quantitative interstitial abnormalities among smokers. Semin. Arthritis Rheum. 72, 152708 (2025).

    Article  PubMed  CAS  Google Scholar 

  155. Yarwood, A. et al. A weighted genetic risk score using all known susceptibility variants to estimate rheumatoid arthritis risk. Ann. Rheum. Dis. 74, 170–176 (2015).

    Article  PubMed  Google Scholar 

  156. Krenn, V. et al. Grading of chronic synovitis — a histopathological grading system for molecular and diagnostic pathology. Pathol. Res. Pract. 198, 317–325 (2002).

    Article  PubMed  CAS  Google Scholar 

  157. Kuo, D. et al. HBEGF+ macrophages in rheumatoid arthritis induce fibroblast invasiveness. Sci. Transl. Med. 11, eaau8587 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  158. Croft, A. P. et al. Distinct fibroblast subsets drive inflammation and damage in arthritis. Nature 570, 246–251 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  159. Wei, K. et al. Notch signalling drives synovial fibroblast identity and arthritis pathology. Nature 582, 259–264 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  160. Bao, W., Xie, M. & Ye, Y. Age-associated B cells indicate disease activity in rheumatoid arthritis. Cell Immunol. 377, 104533 (2022).

    Article  PubMed  CAS  Google Scholar 

  161. Cooles, F. A. H. et al. Phenotypic and transcriptomic analysis of peripheral blood plasmacytoid and conventional dendritic cells in early drug naïve rheumatoid arthritis. Front. Immunol. 9, 755 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

D.A.R. has been supported in part by the Burroughs Wellcome Fund Career Award in Medical Sciences and the Doris Duke Clinical Scientist Development Award. I thank Kevin Wei, Craig Platt, Kathryne Marks and Daimon Simmons for helpful discussions.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Deepak A. Rao.

Ethics declarations

Competing interests

D.A.R. reports grant support from Janssen, Merck and Bristol Myers Squibb outside of the current report, and reports personal fees from AstraZeneca, Merck, AbbVie, Biogen, Simcere, Epana, HiFiBio and Bristol Myers Squibb. He is co-inventor on a patent using TPH cells as a biomarker of autoimmune diseases.

Peer review

Peer review information

Nature Reviews Rheumatology thanks Elena Hsieh, Yoshiya Tanaka, George Kalliolias and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Rao, D.A. Immune-cell profiling to guide stratification and treatment of patients with rheumatic diseases. Nat Rev Rheumatol 21, 657–670 (2025). https://doi.org/10.1038/s41584-025-01291-0

Download citation

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41584-025-01291-0

This article is cited by

Search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research
点击 这是indexloc提供的php浏览器服务,不要输入任何密码和下载