1. Automated multi-scale computational pathotyping (AMSCP) of inflamed synovial tissue
- Author
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Richard D. Bell, Matthew Brendel, Maxwell A. Konnaris, Justin Xiang, Miguel Otero, Mark A. Fontana, Zilong Bai, Accelerating Medicines Partnership Rheumatoid Arthritis and Systemic Lupus Erythematosus (AMP RA/SLE) Consortium, Daria M. Krenitsky, Nida Meednu, Javier Rangel-Moreno, Dagmar Scheel-Toellner, Hayley Carr, Saba Nayar, Jack McMurray, Edward DiCarlo, Jennifer H. Anolik, Laura T. Donlin, Dana E. Orange, H. Mark Kenney, Edward M. Schwarz, Andrew Filer, Lionel B. Ivashkiv, and Fei Wang
- Subjects
Science - Abstract
Abstract Rheumatoid arthritis (RA) is a complex immune-mediated inflammatory disorder in which patients suffer from inflammatory-erosive arthritis. Recent advances on histopathology heterogeneity of RA synovial tissue revealed three distinct phenotypes based on cellular composition (pauci-immune, diffuse and lymphoid), suggesting that distinct etiologies warrant specific targeted therapy which motivates a need for cost effective phenotyping tools in preclinical and clinical settings. To this end, we developed an automated multi-scale computational pathotyping (AMSCP) pipeline for both human and mouse synovial tissue with two distinct components that can be leveraged together or independently: (1) segmentation of different tissue types to characterize tissue-level changes, and (2) cell type classification within each tissue compartment that assesses change across disease states. Here, we demonstrate the efficacy, efficiency, and robustness of the AMSCP pipeline as well as the ability to discover novel phenotypes. Taken together, we find AMSCP to be a valuable cost-effective method for both pre-clinical and clinical research.
- Published
- 2024
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