Background: Lupus nephritis (LN) is one of the most severe organ manifestations of SLE and better understanding of its mechanisms requires analysis of kidney samples in patients with lupus nephritis. Single cell RNA sequencing (ssRNA-seq) of kidney biopsy material allow for detailed investigation of gene expression in specific cell subsets. It also gives the possibility to identify mechanisms of of cell-cell communication important for the pathogenesis of disease. However, in SLE nephritis this has seldom been investigated due to small sample sizes. In this study we integrate scRNA-seq information not only from public lupus nephritis, and healthy kidney biopsy samples but also biopsy data from patients with other inflammatory kidney pathologies to identify lupus nephritis-specific cell-cell communication pathways.

Objectives: To leverage and analyze public lupus nephritis (LN) and healthy donors (HC) kidney biopsy scRNA-seq data to identify nephritis-enriched cell communication pathways. Further, we examine their LN-specificity using independent scRNA-seq datasets of other inflammatory kidney pathologies.

Methods: We constructed an integrated scRNA-seq dataset (30505 cells) starting from raw gene counts using publicly available lupus nephritis kidney biopsies (AMP SLE, Immport SDY997) [1] and an independent set of kidney samples from 19 healthy adults (GSE202109) [2]. Data was integrated using Harmony, normalized (ScTransform) and reclustered (Louvain). We annotated cell types for the detected clusters using ScType for kidney and immune references. Gene expression in LN and HC was compared between cell clusters. For prediction of potential cell communication (ligand-receptor), expression data was analyzed using CellChat. For cross-disease comparison we have integrated independent scRNA-seq datasets for other inflammation-associated kidney pathologies, including IgA nephropathy (GSE127136, GSE166793 and GSE189481 datasets), diabetic nephropathy (GSE131882) and kidney allograft rejection (GSE245870 and GSE189536 datasets).

Results: We predicted multiple cell-cell communications between cell clusters for HC (80.55 interaction strength) and LN (71.88 strength). Cell type specificity for these communications demonstrated enrichment of interactions involving immune cells (B cells, CD4 and CD8 T cells, monocytes and macrophages), endothelial cells and proximal tubule cells in LN in comparison with HC. Individual ligand-receptor pathway analysis identified a group of LN-enriched pathways including: ICAM, ITGB2, BAFF, IFN-II, IL16 and THY1. BAFF communication was predominantly associated with macrophages and B cells (both signaling receivers and influencers). THY1 signaling in LN predominantly involved proximal tubule cells (senders) and CD8 T cells (receivers). We analyzed cell type specific different expression of genes, involved in the identified differential cell communications, and detected the most prominent upregulation of BAFF, ITGB2 and THY1 pathway genes in type I macrophages and B cells. Coexpression analysis for cells, characterized by nephritis-specific changes in these pathways, showed positive correlation with interferon regulated genes in LN but not in healthy donors macrophages, including IFITM3 (p 1.1E-32) and IFI44L (p 1.4e-29) genes. Differential expression analysis of LN macrophages showed significant enrichment in pathways related to cell adhesion and locomotion, including cell-cell adhesion (GO:1903039, logP -18.76) and Rho GTPases signaling (R-HSA-194315, logP -9.23). Comparative analysis of LN and other kidney pathologies showed common inflammatory pathways, including ICAM and TGFbeta. Interaction strength analysis of cell communications showed an increase for BAFF and THY1 in LN in comparison with diabetic and IgA nephropathies. The IL8 pathway signaling was higher in diabetic nephropathy samples than in LN. Preliminary differential expression analysis of B cells in each pathology showed differences in activated genes and pathways, including increased expression of IKZF1 (p 6.5E-19) and IRF8 (p 1.1E-13) in lupus nephritis in comparison with IgA nephropathy and diabetic kidney B cells.

Conclusion: We demonstrated that leveraging public scRNA-seq data can help to identify LN-enriched cell-cell communication pathways. Integrating diverse datasets validates the robustness of identified transcriptomic changes in patients, and additional comparison with other inflammatory kidney pathologies helps to define which pathological mechanisms are specific to lupus nephritis. Further spatial transcriptomic analysis of SLE nephritis kidney tissue is needed to verify the cell interactions suggested here.

REFERENCES: [1] Arazi et al., Nat Immunol., 2019, DOI: 10.1038/s41590-019-0398-x.

[2] McEvoy et al., Nat Commun., 2022, DOI: 10.1038/s41467-022-35297-z.

[3] Wilson et al., PNAS, 2019, DOI: 10.1073/pnas.1908706116.

[4] Tang et al., Front Immunol., 2021, DOI: 10.3389/fimmu.2021.645988.

Acknowledgements: NIL.

Disclosure of Interests: Nina Oparina: None declared, Maija-leena Eloranta: None declared, Marcus Wallgren: None declared, Elisabeth Skoglund: None declared, Dag Leonard Astrazeneca.

© The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.