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Deep learning-based image analysis reveals significant differences in the number and distribution of mucosal CD3 and
Elin Synnøve Røyset1,2,3, Henrik P Sahlin Pettersen1,2, Weili Xu4
1Department of Clinical and Molecular Medicine (IKOM), Faculty of Medicine and Health Sciences (MH), NTNU - Norwegian University of Science and Technology, Trondheim, Norway.
Insights
Deep learning reveals distinct immune cell patterns in inflammatory bowel disease (IBD). Ulcerative colitis (UC) and Crohn
Area of Science:
- Gastroenterology and Immunology
- Computational Pathology
- Digital Health
Background:
- Immune cell distribution in colon mucosa differs between ulcerative colitis (UC) and Crohn's disease (CD), posing diagnostic challenges.
- Assessing these differences visually is difficult, necessitating advanced quantitative methods.
- Deep learning on whole slide images (WSIs) offers a powerful approach to analyze complex cellular patterns in IBD.
Purpose of the Study:
- To explore the distribution of CD3 and gamma-delta (γδ) T cells in colon mucosal compartments in active and inactive inflammatory bowel disease (IBD).
- To quantitatively compare these immune cell populations between CD, UC, and healthy controls (HCs) using deep learning.
- To investigate the impact of disease activity and treatment on T cell distribution.
Main Methods:
- Deep learning-based segmentation and cell detection were applied to WSIs from CD (n=37), UC (n=58), and HC (n=33) cohorts.
- Quantification of CD3 and γδ T cells was performed within and beneath the epithelium and in the lamina propria of proximal and distal colon.
- The Nancy histological index was used to define disease activity.
Main Results:
- Inactive CD showed fewer intraepithelial γδ T cells than inactive UC but higher total CD3 cells compared to UC and HCs.
- Active disease led to a significant loss of intraepithelial γδ T cells in UC, but not in CD.
- Intraepithelial CD3 T cells remained constant irrespective of disease activity in both CD and UC.
- Proximal colon mucosa had higher numbers of CD3 and γδ T cells than distal colon.
- Oral corticosteroids impacted γδ T cell numbers, while age, gender, and disease duration did not.
Conclusions:
- Deep learning-based quantification reveals significant differences in CD3 and γδ T cell distribution within colon mucosal compartments between CD, UC, and HCs.
- Distinct inflammatory patterns, particularly in the epithelial compartment, can be identified using this computational approach.
- This method provides a valuable tool for objective assessment and potential subtyping of IBD.
Abstract:
Colon mucosae of ulcerative colitis (UC) and Crohn's disease (CD) display differences in the number and distribution of immune cells that are difficult to assess by eye. Deep learning-based analysis on whole slide images (WSIs) allows extraction of complex quantitative data that can be used to uncover different inflammatory patterns. We aimed to explore the distribution of CD3 and γδ T cells in colon mucosal compartments in histologically inactive and active inflammatory bowel disease. By deep learning-based segmentation and cell detection on WSIs from a well-defined cohort of CD (n = 37), UC (n = 58), and healthy controls (HCs, n = 33), we quantified CD3 and γδ T cells within and beneath the epithelium and in lamina propria in proximal and distal colon mucosa, defined by the Nancy histological index. We found that inactive CD had significantly fewer intraepithelial γδ T cells than inactive UC, but higher total number of CD3 cells in all compartments than UC and HCs. Disease activity was associated with a massive loss of intraepithelial γδ T cells in UC, but not in CD. The total intraepithelial number of CD3 cells remained constant regardless of disease activity in both CD and UC. There were more mucosal CD3 and γδ T cells in proximal versus distal colon. Oral corticosteroids had an impact on γδ T cell numbers, while age, gender, and disease duration did not. Relative abundance of γδ T cells in mucosa and blood did not correlate. This study reveals significant differences in the total number of CD3 and γδ T cells in particularly the epithelial area between CD, UC, and HCs, and demonstrates useful application of deep segmentation to quantify cells in mucosal compartments.
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