Researchers at NYU College of Dentistry and NYU Grossman School of Medicine have made significant advances in understanding what drives Sjögren’s disease, publishing findings in Science Translational Medicine in December 2024. The study was supported in part by the Colton Center for Autoimmunity at NYU.
Sjögren’s disease is an autoimmune condition in which the immune system attacks the glands that produce saliva and tears, causing chronic dry mouth and dry eyes. Despite affecting millions of people — predominantly women — its underlying mechanisms have remained poorly understood, and treatment options are limited.
The new study identifies impaired regulatory T cells as a critical contributing factor in Sjögren’s disease, finding this dysfunction present in both mouse models and human patients. Regulatory T cells play a key role in keeping the immune system in check; when they fail to function properly, the immune system can turn on healthy tissue. The researchers also examined the roles of calcium signaling and interferon in the disease, using machine learning to detect and classify different cell types in salivary gland tissue.
Crucially, the team identified an existing rheumatology drug as a promising therapy for Sjögren’s disease — a finding that could accelerate the path to clinical application, since the drug’s safety profile is already established.
The study was a collaboration between NYU College of Dentistry and NYU Grossman School of Medicine, with additional co-authors from the University of Rochester and NYU Abu Dhabi.
Featured Experts

Katsuo Kurabayashi, PhD
Colton Consortium Member
Department Chair, Mechanical and Aerospace Engineering, NYU Tandon School of Engineering
Carla R. Nowosad, PhD
Colton Consortium Member
Assistant Professor, Department of Pathology, NYU Grossman School of Medicine / NYU Langone Health
Jun Wang, PhD
Colton Consortium Member
Associate Professor, Department of Pathology, NYU Grossman School of Medicine / NYU Langone HealthFeatured Projects

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Early Detection and Diagnosis of Autoimmune Diseases Using Foundation AI Models
Applying self-supervised AI to multi-modal electronic health records — integrating clinical notes, labs, and imaging — this project builds scalable diagnostic models to detect autoimmune diseases earlier and more precisely.
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