Computer Vision Analysis of Rheumatoid Arthritis Synovium Reveals Lymphocytic Inflammation is Associated with Immunoglobulin Skewing in Blood.
Academic Article
Overview
abstract
OBJECTIVE: We sought to develop computer vision methods to quantify aggregates of cells in synovial tissue and compare these to clinical and gene expression parameters. METHODS: We assembled a computer vision pipeline to quantify 5 features encompassing synovial cell density and aggregates and compared these to pathologist scores, disease classification, autoantibody status and RNA expression in a cohort of 156 patients with rheumatoid arthritis (RA) and 149 patients with osteoarthritis (OA). RESULTS: All 5 features were associated with pathologist scores of synovial lymphocytic inflammation (p<0.0001). Three features that related to the cells per unit of tissue were significantly increased in patients with both seronegative and seropositive RA compared to those with OA, on the other hand, aggregate features (number and diameter) were significantly increased in seropositive, but not seronegative RA compared to OA. Aggregate diameter was associated with gene expression of immunoglobulin heavy chain genes in the synovial tissue. Compared to blood, synovial immunoglobulin isotypes were skewed from IGHM and IGHD to IGHG3 and IGHG1. Further, RA patients with high levels of lymphocytic infiltrates in the synovium demonstrated parallel skewing in their blood with a relative decrease in IGHGM (p<0.002) and IGHD (p<0.03) and an increase in class-switched immunoglobulin genes IGHG3 (p<0.03) and IGHG1 (p<0.002). CONCLUSION: High resolution automated identification and quantification of synovial immune cell aggregates uncovered skewing in the synovium from naive IGHD and IGHM to memory IGHG3 and IGHG1 and revealed that this process is reflected in the blood of patients with high inflammatory synovium. This article is protected by copyright. All rights reserved.
