Abstract

Inflammatory bowel disease (IBD) is characterized by chronic, dysregulated inflammation in the gastrointestinal tract. The heterogeneity of IBD is reflected through two major subtypes, Crohn’s Disease (CD) and Ulcerative Colitis (UC). CD and UC differ across symptomatic presentation, histology, immune responses, and treatment. While colitis mouse models have been influential in deciphering IBD pathogenesis, no single model captures the full heterogeneity of clinical disease. The translational capacity of mouse models may be augmented by shifting to multi-mouse model studies that aggregate analysis across various well-controlled phenotypes. Here, we evaluate the value of histology in multi-mouse model characterizations by building upon a previous pipeline that detects histological disease classes in hematoxylin and eosin (H&E)-stained murine colons. Specifically, we map immune marker positivity across serially-sectioned slides to H&E histological classes across the dextran sodium sulfate (DSS) chemical induction model and the intestinal epithelium-specific, inducible Villin-CreERT2;Klf5fl/fl (Klf5ΔIND) genetic model. In this study, we construct the beginning frameworks to define H&E-patch-based immunophenotypes based on IHC-H&E mappings.

Details

Title
Computational immunohistochemical mapping adds immune context to histological phenotypes in mouse models of colitis
Author
Kobayashi, Soma 1 ; Sullivan, Christopher 2 ; Bialkowska, Agnieszka B. 2 ; Saltz, Joel H. 3 ; Yang, Vincent W. 4 

 Brook University, Department of Biomedical Informatics, Renaissance School of Medicine at Stony, Stony Brook, USA (GRID:grid.36425.36) (ISNI:0000 0001 2216 9681) 
 Renaissance School of Medicine at Stony Brook University, Department of Medicine, Stony Brook, USA (GRID:grid.36425.36) (ISNI:0000 0001 2216 9681) 
 Brook University, Department of Biomedical Informatics, Renaissance School of Medicine at Stony, Stony Brook, USA (GRID:grid.36425.36) (ISNI:0000 0001 2216 9681); Renaissance School of Medicine at Stony Brook University, Department of Pathology, Stony Brook, USA (GRID:grid.36425.36) (ISNI:0000 0001 2216 9681) 
 Brook University, Department of Biomedical Informatics, Renaissance School of Medicine at Stony, Stony Brook, USA (GRID:grid.36425.36) (ISNI:0000 0001 2216 9681); Renaissance School of Medicine at Stony Brook University, Department of Medicine, Stony Brook, USA (GRID:grid.36425.36) (ISNI:0000 0001 2216 9681); Brook University, Department of Physiology and Biophysics, Renaissance School of Medicine at Stony, Stony Brook, USA (GRID:grid.36425.36) (ISNI:0000 0001 2216 9681) 
Pages
14386
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2859762038
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.