Background/Purpose: Autoimmunity is a hallmark of SSc pathogenesis. Emerging evidence suggests that interferon (IFN) signaling plays a role in predicting SSc patients at risk of progression. Understanding the alterations in the transcriptome of SSc Peripheral Blood Mononuclear Cells (PBMCs) may provide key insights into the role of IFN signaling and improve patient stratification.

Methods: We performed single cell RNA sequencing of PBMCs from 25 early (≤ 5 years from diagnosis), active SSc (ACR/EULAR 2013) patients and 9 age-matched healthy controls using the Chromium Next GEM protocol. Active SSc was defined by progression of skin or lung fibrosis and/or arthritis. Cellranger was used for read alignment, Seurat v5 for analysis. PBMCs were annotated via Azimuth. Differentially expressed genes (DEGs) were identified using FindMarkers (|log2FC| ≥ 0.5, FDR ≤ 0.05). Pathway analysis (MSigDB Hallmark, Reactome) was performed on upregulated DEGs (adj p ≤ 0.05). Z-scores for selected DEGs were calculated from count-per-million normalized pseudocounts. Clinical data included autoantibody status, dcSSc, Interstitial Lung Disease (ILD), disease activity indices, CRP, ESR, mRSS, lung function test, neutrophil (N)/lymphocyte (L) counts, and NL Ratio (NLR). Association between Z-scores and continuous clinical variables were assessed using linear regression. Differences in Z scores between patient groups were tested using Mann–Whitney U test.

Results: In the SSc cohort, 88% had ILD, 36% dcSSc, with a median disease duration of 12 [3-22] months. PBMCs (n = 108’138) were annotated as monocytes, dendritic, NK, B, CD4 T and CD8 T cells. Upregulated DEGs related to IFN-α and γ pathways were detected in all cell types (Figure 1) with overall significantly increased expression of IFI44L, ISG15, EIF2AK2, and XAF1. A prominent heterogeneity in the expression of IFN-related DEGs was observed between SSc samples across all cell types (Figure 1). IFN Z scores further confirmed the substantial heterogeneity between different cell types and individual patients (Figure 2).Linear regression analysis revealed no significant association between IFN Z-scores and disease activity (r-EUSTAR and SCTC activity indices). In contrast, the IFN Z-scores of monocytes (r = 0.41, p = 0.04) and NK cells (r = 0.47, p=0.018) showed a positive, significant association with NLR (Figure 3a). A significant negative association with L counts and the IFN Z scores was found in monocytes (r = -0.66, p = < 0.001), dendritic cells (r = -0.72, p = < 0.001), and NK cells (r = -0.49, p = 0.012) (Figure 3b). Baseline IFN Z-scores in NK cells were higher in skin progressors (n = 5) at one year (p = 0.031) (Figure 3c). No other clinical parameter was predicted by celltype IFN Z score (Figure 2a-b).

Conclusion: Our data highlight the heterogeneity of IFN response both among patients with active SSc and across different PBMC types within the same patient. IFN response associated with NLR — a predictor of organ involvement and poor prognosis — specifically in monocytes and NK cells.Other clinical parameters, including disease activity, did not associate with the IFN response signatures in PBMCs.

Figure 1. IFN-related DEGs across the different PBMCs subtypes. For each subtype (Monocytes, dendritic cells, NK cells, B cells, CD4 T cells and CD8 T cells), pathway analysis showed significant upregulation of the IFN type I and type II related pathways. Monocytes displayed the highest number of IFN-related DEGs (n = 33). Columns represent subjects (P = Patient, H = Healthy control), rows represent genes.

Figure 2. IFN Z scores across different patients (P) (columns) and PBMC subtypes (rows). The heatmap evidences the strong heterogeneity of IFN Z scores both among different patients (columns) and within each patient among different celltypes(rows). For each cell type, the Z-score was calculated using only the IFN-related DEGs specific to that cell type (Figure 1). We assessed for association between categorical (a) and continuous (b) clinical variables. Anti-centromere antibodies (ACA), anti-RNA polymerase III antibodies (RNPIII), anti-topoisomerase I antibodies (Scl70), Revised EUSTAR Activity Index (r-EUSTAR AI), Scleroderma Clinical Trials Consortium Activity Index (SCTC-AI), modified Rodnan Skin Score (mRSS), Functional Vital Capcity (FVC), Diffusing Capacity of the Lung for Carbon Monoxide (DLCO), Carbon monoxide transfer coefficient (KCO).

Figure 3. Significant linear regression analysis between celltypes IFN Z scores and NLR (a), lymphocyte count (b). NK cells IFN Z Score grouped by skin fibrosis (mRSS) progression at 1 year (c).

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ACR Meeting Abstracts - https://acrabstracts.org/abstract/unraveling-the-complexity-of-interferon-responses-in-peripheral-blood-mononuclear-cells-in-systemic-sclerosis-at-single-cell-resolution/