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 non-invasive, blood-based pathologic 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 in patients with LN and to identify correlates of renal parameters and treatment response. Unbiased analysis identified 3 immunologically distinct groups of patients with LN that were associated with different patterns of histopathology, renal cell infiltrates, urine proteomic profiles, and treatment response at one year. Patients with enriched circulating granzyme B+ T cells at baseline showed more severe 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 primarily by a high type I interferon signature had a lower likelihood of response to therapy, while a third group appeared immunologically inactive by immunophenotyping at enrollment but with chronic renal injuries. Main immune profiles could be distilled down to 5 simple cytometric parameters that recapitulate several of the associations, highlighting the potential for blood immune profiling to translate to clinically useful non-invasive metrics to assess immune-mediated disease in LN.

Immune disease dialogue of chemokine-based cell communications as revealed by single-cell RNA sequencing meta-analysis

“…We selected diseases based on the availability of public scRNA-seq datasets across skin, lung, colon and kidney healthy control tissue, and their respective diseases (encompassing AD, PSO, COPD, IPF, UC, IgAN, LN) and at least two samples per group. We collected and analyzed the following 15 scRNA-seq datasets in our analysis: 1) E-MTAB-8142 (Reynolds et al, 2021) for healthy skin, AD lesional skin, and AD nonlesional skin, PSO lesional skin and PSO nonlesional skin, 2) GSE147424 (He et al, 2020) for healthy skin, AD lesional skin, and AD nonlesional skin, 3) GSE153760 (Rindler et al, 2021) for healthy skin and AD lesional skin, 4) GSE173706 (Merleev et al, 2022) for healthy skin, PSO lesional skin, and PSO nonlesional skin, 5) GSE220116 (Kim et al, 2023) for healthy skin and PSO lesional skin, 6) EGAS00001004344 (Travaglini et al, 2020) for healthy lung, 7) GSE136831 (Adams et al, 2020) for healthy lung, COPD lung and IPF lung, 8) GSE171541 (Huang et al, 2022) for healthy lung and IPF lung, 9) GSE122960 (Reyfman et al, 2018) and 10) GSE135893 (Habermann et al, 2020) for healthy lung and IPF lung, 11) SCP259 (Smillie et al, 2019) and 12) GSE116222 (Parikh et al, 2019), and 13) GSE231993 (Du et al, 2023) for healthy colon, UC inflamed colon, and UC uninflamed colon, 14) GSA: HRA000342 (Zheng et al, 2020) for healthy kidney and IgAN kidney, and 15) LN_Kidney_AMP (from the Accelerating Medicines Partnership (AMP) SLE phase 2 consortium) for healthy kidney and LN kidney (Arazi et al, 2019;Hoover et al, 2023;Horisberger et al, 2024;Izmirly et al, 2024).…”