THE IMMUNE MAP OF LUPUS NEPHRITIS: A SPATIALLY-RESOLVED KIDNEY PROTEOMIC APPROACH

O059 / #244 Topic:AS16 - Lupus Nephritis-Pathogenesis ABSTRACT CONCURRENT SESSION 10: INTEGRATING PROTEOMIC & TRANSCRIPTOMICS IN SLE 24-05-2025 10:40 AM - 11:40 AM Background/Purpose Treatment response in lupus nephritis (LN) remain inadequately low, highlighting the need for better understanding of LN pathogenesis to improve management. Single-cell transcriptomic studies are providing an unprecedented catalog of cell states in LN, yet their spatial organization is not well understood. Since structure underlies function, we aim to map the spatial organization of immune cells in LN. Methods We developed a serial immunohistochemistry (sIHC) workflow (18-plex), followed by imaging and destaining cycles. Image processing was performed using HALO (Indica Labs), including AI-assisted tissue classification. PCA was used for dimensional reduction, and KNN and SNN algorithms were applied to identify immune cell types based on their markers’ fluorescent intensity. Immune cell aggregates in the tissues were defined using DBSCAN as a minimum of 3 cells within a radius (epsilon) of 50 μm to infer interactions between cells. Aggregate sizes were categorized into small (3-29 cells), medium (30-99 cells), and large (>100 cells) based on the frequency distribution (Figure 1A,B). The proportion of immune cell types in each aggregate was used for K-means clustering to determine aggregate subtypes. Clinical features were correlated with each aggregate subtype using Pearson’s correlation coefficient (Figure 2). Figure 1. Demographics of intrarenal immune cell aggregates. (A) Digitalized biopsy showing an example of the distribution of immune cells in LN. (B) Examples of intrarenal immune cell aggregates of different sizes. (C) Distribution of aggregates by size and by total cells. (D) Distribution of aggregates by size and region. (E) Density of aggregate types according to size and class. Figure 2. Correlation between aggregate subtypes and clinical features. Left heatmaps show aggregates subtypes. Middle heatmaps display the average density of aggregate subtypes (average number of aggregates/mm²). Right heatmaps show the correlation matrices between the aggregate subtypes and clinical features. (A) Glomerular small aggregate (B) Tubulointerstitial small aggregate (C) Tubulointerstitial medium aggregate (D) Tubulointerstitial large aggregate. Act: NIH activity index; Chr: NIH Chronicity Index. Results In this analysis, we included 29 kidney biopsies of LN resulting in 1,913,845 cells (182,783 immune cells). We identified 12,371 cellular aggregates. Most (97%) aggregates were small (<30 cells) (Figure 1C,D); however, medium and large aggregates included 33.7% of immune cells. Glomerular aggregates were numerically increased in proliferative and mixed classes (Figure 1E). These were small and primarily composed of CD68+ myeloid cells (Figure 2A). Glomerular aggregates rich in CD68+ cells negatively correlated with UPCR, while aggregates rich in lymphocytes negatively correlated with chronicity (Figure 2A). In contrast, tubulointerstitial (TI) aggregate density was similar across LN classes (Figure 1E) and negatively correlated with GFR. Significant heterogeneity in aggregate composition revealed >10 aggregate subtypes according to composition and size (Figure 2). Small aggregates tended to be restricted to 1-2 cell types each, while medium and large aggregates included mixed proportions of CD4+ T, CD8+ T, B, dendritic, myeloid, and plasma cells, suggesting germinal center-like structures (Figure 2). Distinct TI aggregate subtypes associated with specific clinical and pathological features (Figure 2B,C). Conclusions We describe the heterogeneity in glomerular and TI immune cell structures in LN, offering insights into LN pathological processes and potential cellular interactions based on proximity. TI inflammation appears similar in membranous and proliferative LN, yet specific immune structures are linked to distinct clinical and pathological features.

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