Combining in-depth immune profiling and multi-omics approaches identifies distinct signatures in the early stage of Systemic Sclerosis

Background

Systemic sclerosis (SSc) is a complex and severe autoimmune disease characterized by immune dysregulation, inflammation and progressive fibrosis affecting multiple organs. Despite advances in clinical classification, the immunological mechanisms underlying disease initiation and progression remain poorly understood. It is increasingly recognized that immune alterations begin long before the onset of overt clinical symptoms. In particular, early immune changes detectable during the preclinical phase, termed Very Early Diagnosis of Systemic Sclerosis (VEDOSS), may hold the key to understanding disease pathogenesis and identifying effective points for therapeutic intervention. Regulatory T cells (Treg) and other immune cell subsets are believed to play a critical role in the breakdown of immune tolerance but the dynamics and functional consequences of these disturbances during early disease stages are not fully elucidated. Furthermore, emerging technologies such as in-depth immunophenotyping and machine learning-based analysis offer powerful tools to identify early immune signatures and support stratified, targeted approaches to diagnosis and treatment.

Objectives

(i) To define immunophenotypic changes from VEDOSS to established SSc.

(ii) To investigate the contribution of IL-2 deprivation to early Treg dysfunction and its role in disease progression.

(iii) To assess the diagnostic potential of integrated multi-omics and machine learning models for early classification of SSc.

(iv) To evaluate the capacity of low-dose IL-2 therapy to restore Treg homeostasis and prevent progression of immune dysregulation in the early stage of SSc.

Work Program

Peripheral blood samples from patients across the disease spectrum, from VEDOSS to established SSc, will be used for an in-depth immunophenotyping approach to characterize alterations in both innate and adaptive immune compartments. Multi-color flow cytometry will be employed to analyze a broad range of immune cell subsets with a particular focus on phenotypic and functional markers associated with immune activation and dysregulation. In parallel, serum cytokine and chemokine profiling will be conducted to assess the inflammatory and tissue-remodeling milieu, providing insights into immune signaling pathways involved in disease progression. Additionally, titers of SSc-associated autoantibodies will be measured to evaluate their correlation with immune alterations and disease stage. Using the combined immunological, serological and cytokine data, machine learning models will be developed to identify predictive immune signatures and stratify patients based on their risk of progressing from VEDOSS to established SSc. These models aim to support early diagnosis, improve patient classification, and guide preventive therapeutic strategies.