Th115 - Utilizing High-dimensionality Mass Cytometry to Decode Complex Immunopathogenic Network for Potential Theragnostic Applications in Early Systemic Sclerosis

Pavanish Kumar, PhD – SingHealth Duke-National University of Singapore Academic Medical Centre, Translational Immunology Institute, Singapore; Vasuki Ranjani Chellamuthu, PhD – SingHealth Duke-National University of Singapore Academic Medical Centre, Translational Immunology Institute, Singapore; Ahmad Lajam – SingHealth Duke-National University of Singapore Academic Medical Centre, Translational Immunology Institute, Singapore; Alfred Yu Ting Chia – SingHealth Duke-National University of Singapore Academic Medical Centre, Translational Immunology Institute, Singapore; Katherine Nay Yaung, PhD – SingHealth Duke-National University of Singapore Academic Medical Centre, Translational Immunology Institute, Singapore; Martin Wasser, PhD – SingHealth Duke-National University of Singapore Academic Medical Centre, Translational Immunology Institute, Singapore; Joo Guan Yeo, MBBS, PhD – SingHealth Duke-National University of Singapore Academic Medical Centre, Translational Immunology Institute, Singapore; Andrea Hsiu Ling Low, ​BmedSci (UK), BMBS (UK), MCI – Singapore General Hospital, Department of Rheumatology and Immunology, Singapore; Salvatore Albani – SingHealth Duke-National University of Singapore Academic Medical Centre, Translational Immunology Institute, Singapore

Abstract Text: Background
Systemic sclerosis (SSc) is a heterogenous disease characterised by autoimmunity, vasculopathy, and fibrosis. This study aimed to characterize the peripheral immunome associated with active disease in SSc

Methods
Peripheral blood mononuclear cells were collected from early SSc patients (n=13 active, n=12 inactive), and healthy controls (n=10). Single-cell proteomic data was acquired using mass cytometry; data was analysed using the Extended Polydimensional Immunome Characterization platform, an unsupervised machine learning algorithm. Using the identified cell subset frequencies, we built multiple logistic regression models to classify active diseases. Correlation network between nodes was constructed by performing pairwise correlations between each node pair.

Results
Dysregulated peripheral immunome was observed in active SSc. CD4 T cell subsets expressing immune checkpoints were decreased, while MAIT cells expressing IL-22, TGF-b were increased in active SSc, compared to inactive SSc. NK cell subsets expressing proinflammatory cytokines and B cell subsets expressing CXCR4 were increased in active SSc, compared to inactive SSc. Using the identified immune cell subsets to evaluate active and inactive disease, receiver operating characteristic curve analyses showed an area under the curve of 0.8939. Immune cell network from inactive SSc showed more negatively connected edges and higher modularity compared to network from active SSc.

Conclusions
We identified dysregulated immune cell subsets and network in active SSc. Future studies to investigate their functional significance would shed light into the disease pathogenesis. The identified immune signatures could potentially be used as theragnostic tools to evaluate disease activity in SSc.