Novel endothelial progenitor cells populations as biomarkers of damage and remission in systemic lupus erythematosus

Abstract

Abstract Introduction Endothelial progenitor cells (EPCs) are essential for maintenance of vascular homeostasis and stability, key processes in the pathogenesis of systemic lupus erythematosus (SLE). However, the role and phenotypic characterization of EPCs populations in SLE have not been completely elucidated. Objective To identify EPCs specific subpopulations in patients with SLE using a novel flow cytometry tool. Methods Peripheral blood mononuclear cells (PBMCs) were isolated from patients with SLE and healthy controls (HC). mRNA and surface protein expression were determined by quantitative PCR (qPCR) and flow cytometry. Clusters identification and characterization were performed using tSNE-CUDA dimensionality reduction algorithms. Results tSNE-CUDA analysis identified eight different clusters in PBMCs from HC and patients with SLE. Three of these clusters had EPC-like phenotype and the expression was elevated in patients with SLE. Moreover, four SLE-associated subclusters were found mainly expressed in patients with SLE, being only present in patients in remission with SLE and significantly associated with the 2021 Definition of Remission in SLE. Importantly, we also identified specific clusters in SLE patients with organ damage, according to the Systemic Lupus International Collaborating Clinics (SLICC)/American College of Rheumatology damage index (SDI). These clusters showed an EPC-like phenotype, but the expression of angiogenic markers was lower compared to HC or patients without organ damage, suggesting an impaired angiogenic function. Conclusion Our novel approach identified clusters of EPCs in patients with SLE that are associated with remission and damage. Therefore, these clusters might be useful biomarkers to predict disease progression and severity in SLE pathogenesis.

Keywords

• Endothelial progenitor cells
• Systemic lupus erythematosus
• Remission
• Damage
• EPCs clusters