Rheumatoid Arthritis (RA) is a chronic autoimmune disease characterized by autoantibodies, systemic inflammatory features, and joint inflammation. Elevated anti-citrullinated protein auto-antibodies are a predictor of clinical disease that can identify patients “at-risk” for developing clinical RA symptoms prior to diagnosis by a physician. To study the immune state of at-risk patients we generated and analyzed data from several high-resolution protein abundance and genomic assays across multiple time points per person.
For full details about this study, see our preprint on bioRxiv:
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Over the course of the study, a third of at-risk patients progressed to clinically active RA. However, we also identified systemic inflammatory features in at-risk RA subjects, despite a lack of clinical features associated with frank RA disease. We identified these pro-pathogenic features primarily in naive cells, and changes in immune state that tracked with clinical progression were especially apparent in T and B cells populations.
For additional details about the cohort, experimental methods, and citation and contributors, see the subsections of this project listed on the left.
To enable exploration of this unique dataset, we provide the following interactive visualization tools:
View and compare patient metadata, clinical lab results, and markers of autoinflammation in the ALTRA cohort.
Explore Clinical Data
Visualize cross-sectional and longitudinal changes in gene expression and plasma proteomics measurements between control and pre-RA progression stages, as well as over time within individual RA subjects.
Explore Expression Data
Visualize > 2 million scRNA-seq profiles generated from controls and longitudinal sampling of pre-RA and RA subjects from the ALTRA cohort.
Explore scRNA-seq Data
Explore trimodal TEA-seq data (Transcription, surface Epitopes, and chromatin Accessibility) from a subset of RA patients and healthy controls.
Explore Multimodal Data