Blood immunophenotyping identifies distinct kidney histopathology and outcomes in patients with lupus nephritis
Alice Horisberger, … , James A. Lederer, Deepak A. Rao
Alice Horisberger, … , James A. Lederer, Deepak A. Rao
Published June 19, 2025
Citation Information: J Clin Invest. 2025;135(16):e181034. https://doi.org/10.1172/JCI181034.
View: Text | PDF
Research Article Autoimmunity Immunology

Blood immunophenotyping identifies distinct kidney histopathology and outcomes in patients with lupus nephritis

Abstract

Lupus nephritis (LN) is a frequent manifestation of systemic lupus erythematosus, and fewer than half of patients achieve complete renal response with standard immunosuppressants. Identifying noninvasive, blood-based immune alterations associated with renal injury could aid therapeutic decisions. Here, we used mass cytometry immunophenotyping of peripheral blood mononuclear cells in 145 patients with biopsy-proven LN and 40 healthy controls to evaluate the heterogeneity of immune activation and identify correlates of renal parameters. Unbiased analysis identified 3 immunologically distinct groups of patients that were associated with different patterns of histopathology, renal cell infiltrates, urine proteomic profiles, and treatment response at 1 year. Patients with enriched circulating granzyme B+ T cells showed more active disease and increased numbers of activated CD8+ T cells in the kidney, yet they had the highest likelihood of treatment response. A second group characterized by a high type I interferon signature had a lower likelihood of response to therapy, while a third group appeared immunologically inactive but with chronic renal injuries. The major immunologic axes of variation could be distilled down to 5 simple cytometric parameters that recapitulate several clinical associations, highlighting the potential for blood immunoprofiling to translate to clinically useful noninvasive metrics to assess immune-mediated disease in LN.

Authors

Alice Horisberger, Alec Griffith, Joshua Keegan, Arnon Arazi, John Pulford, Ekaterina Murzin, Kaitlyn Howard, Brandon Hancock, Andrea Fava, Takanori Sasaki, Tusharkanti Ghosh, Jun Inamo, Rebecca Beuschel, Ye Cao, Katie Preisinger, Maria Gutierrez-Arcelus, Thomas M. Eisenhaure, Joel Guthridge, Paul J. Hoover, Maria Dall’Era, David Wofsy, Diane L. Kamen, Kenneth C. Kalunian, Richard Furie, Michael Belmont, Peter Izmirly, Robert Clancy, David Hildeman, E. Steve Woodle, William Apruzzese, Maureen A. McMahon, Jennifer Grossman, Jennifer L. Barnas, Fernanda Payan-Schober, Mariko Ishimori, Michael Weisman, Matthias Kretzler, Celine C. Berthier, Jeffrey B. Hodgin, Dawit S. Demeke, Chaim Putterman, Accelerating Medicines Partnership Rheumatoid Arthritis and Systemic Lupus Erythematosus (AMP RA/SLE) Network, Michael B. Brenner, Jennifer H. Anolik, Soumya Raychaudhuri, Nir Hacohen, Judith A. James, Anne Davidson, Michelle A. Petri, Jill P. Buyon, Betty Diamond, Fan Zhang, James A. Lederer, Deepak A. Rao

×

Figure 6

Immune cellular signature heterogeneity across and within patients with LN.

Options: View larger image (or click on image) Download as PowerPoint
Immune cellular signature heterogeneity across and within patients with ...
(A) Importance and direction of the effect of demographic, clinical, and renal characteristics on immune cell signatures. The coefficients were defined by a linear model with an elastic net penalization using a 10-fold cross-validation, with the cellular signature as a response variable and the y axis variables as the predictor variables. IS, immunosuppressants; DA, disease activity; low c, low complement. (B) Comparison of immune cell signatures between treated (n = 80) or not treated (n = 59) with MMF at baseline. Statistical significance was determined using Wilcoxon’s rank sum test; ***P < 0.001. (C) Changes in blood-defined group membership over time. Each band represents a patient, and each patient is colored by the baseline group membership. All patients with LN with samples at 3 time points and samples stained with all 4 panels were included in this analysis (n = 21). (D) Longitudinal changes in immune cell signatures, stratified by response status (NR/PR, no response/partial response [n = 13]; CR, complete response [n = 16]) including patients with LN who were treated with MMF throughout the study. Statistical significance was determined using a mixed-effects model including patients as a random effect. *P < 0.05, **P < 0.01, ***P < 0.001. (E) Correlations between simplified immune cell signatures and NIH activity subscores at baseline. Statistical significance was determined using Spearman’s ρ correlation followed by FDR correction for multiple testing. For visualization purposes we showed the inversed value of median CD21 in non-proliferative B cells (written as *–1). (F) Longitudinal changes in simplified immune cell signatures in patients treated with an MMF-based therapy. For visualization purposes we showed the inversed value of median CD21 in non-proliferative B cells (written as *–1). Statistical significance was determined using a mixed-effects model including patients as a random effect. •P = 0.05, *P < 0.05, **P < 0.01, ***P < 0.001.