Elena Pontarini

Dr. Elena Pontarini is a Lecturer in Immunology at Queen Mary University of London (QMUL), based at the William Harvey Research Institute (WHRI), part of the Faculty of Medicine and Dentistry. She leads research focused on autoimmune diseases, particularly Sjogren’s syndrome, investigating T cell subsets (T follicular helper (Tfh) and T peripheral helper (Tph) cells) and their roles in ectopic lymphoid structures and B cell lymphomagenesis. Her work aims to identify therapeutic targets for immunomodulatory agents, with collaboration in the EU IMI-funded NECESSITY consortium for Sjogren’s syndrome research.

Elena holds a Biomedical Scientist degree from the University of Udine (Italy), a PhD in Immunology and Autoimmune Diseases from the Humanitas Research Institute (Milan), and completed postdoctoral training at QMUL under Prof. Maurizio Bombardieri. She has secured grants including the Versus Arthritis Foundation Fellowship (2018–2021) and Barts Charity Seed Grant (2021).

Research Interests: Her studies explore mechanisms of ectopic lymphoid structure formation in autoimmune target tissues, T cell–B cell interactions, and biomarkers predicting therapeutic responses. Key projects include analyzing Tfh/Tph cell roles in Sjogren’s syndrome and rheumatoid arthritis, and validating biomarkers for clinical trials targeting B/T cell co-stimulation.

Awards & Grants: Notable awards include the EULAR Scientific Training Grant (2021), EFIS/EMBO Travel Fellowships (2013), and the Italian Society of Cytometry Thesis Award (2012). Active grants fund her work on transcriptomic stratification, AI-driven diagnostics, and therapeutic biomarkers.

Teaching & Collaboration: She teaches the BSc module BMD375 (Translational Pharmacology). Collaborators include Dr. Gwenny Verstappen (University of Groningen) and Dr. Silvia della Bella (University of Milan). Her lab (ARGOs Lab) integrates high-dimensional flow cytometry, histology, and transcriptomics.

Future Work: Ongoing projects include validating Tph cell expansion as a biomarker for disease activity and refining AI tools for histological analysis of Sjogren’s biopsies.