Automated multi-scale computational pathotyping (AMSCP) of inflamed synovial tissue

Richard D. Bell (), Matthew Brendel, Maxwell A. Konnaris, Justin Xiang, Miguel Otero, Mark A. Fontana, Zilong Bai, 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
Additional contact information
Richard D. Bell: Hospital for Special Surgery
Matthew Brendel: Weill Cornell Medical College
Maxwell A. Konnaris: State College
Justin Xiang: Horace Greely High School
Miguel Otero: Weill Cornell Medical College
Mark A. Fontana: Hospital for Special Surgery
Zilong Bai: Weill Cornell Medical College
Daria M. Krenitsky: University of Rochester Medical Center
Nida Meednu: University of Rochester Medical Center
Javier Rangel-Moreno: University of Rochester Medical Center
Dagmar Scheel-Toellner: Queen Elizabeth Hospital
Hayley Carr: Queen Elizabeth Hospital
Saba Nayar: Queen Elizabeth Hospital
Jack McMurray: Queen Elizabeth Hospital
Edward DiCarlo: Hospital for Special Surgery
Jennifer H. Anolik: University of Rochester Medical Center
Laura T. Donlin: Hospital for Special Surgery
Dana E. Orange: Hospital for Special Surgery
H. Mark Kenney: University of Rochester Medical Center
Edward M. Schwarz: University of Rochester Medical Center
Andrew Filer: Queen Elizabeth Hospital
Lionel B. Ivashkiv: Hospital for Special Surgery
Fei Wang: Weill Cornell Medical College

Nature Communications, 2024, vol. 15, issue 1, 1-17

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.

Date: 2024
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DOI: 10.1038/s41467-024-51012-6

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