# NIH grant: Development of multimodal multiscale imaging platform for high-content molecular profiling of mucosal healing to predict the risk of relapse in ulcerative colitis (1R01EB037445-01A1) — retained record

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[original source ↗](https://reporter.nih.gov/project-details/1R01EB037445-01A1)

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NIH grant 1R01EB037445-01A1: Development of multimodal multiscale imaging platform for high-content molecular profiling of mucosal healing to predict the risk of relapse in ulcerative colitis
Institute: {'code': 'EB', 'abbreviation': 'NIBIB', 'name': 'National Institute of Biomedical Imaging and Bioengineering', 'admin_org_id': '12500035', 'admin_funding_url': ''}. PI: Yang  Liu. Award: $629808.
Start: 2026-09-11T00:00:00.
PROJECT SUMMARY
Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized by unpredictable periods of
remission and flare-ups, presenting significant management challenges. Current methods for assessing
mucosal healing, including clinical, endoscopic, and histopathological scoring systems, are limited by inter-
observer variability and inconsistent patient-reported symptoms. Non-invasive biomarkers from blood and stool
lack reliability, and confocal endoscopy cannot reliably predict relapse risks. Spatial omics technologies offer
the potential to revolutionize mucosal tissue biopsies by enabling comprehensive molecular profiling and
providing insights into the immune microenvironment. However, due to the complexity and inherent
heterogeneity of UC, hyper-plexed imaging involving hundreds of proteins and thousands of mRNA targets is
often needed to capture the functional states of immune cells and tissues, which is costly and labor-intensive.
We hypothesize that combining common structural features of super-resolved cellular architectures with
multiplexed molecular phenotyping of a small set of key markers can provide precise assessment of mucosal
healing in UC. We propose developing a whole-slide multiscale multimodal imaging platform capable of
mapping nuclear ultrastructure and tissue microenvironment. This imaging platform integrates super-resolution
microscopy with quantitative phase and multiplexed fluorescence imaging, enabling comprehensive
visualization of endoscopic biopsies. We will then establish baseline structural and molecular profiles in UC
patients and healthy controls to assess spatial heterogeneity and develop a computational image analysis
pipeline. We will further perform structural and molecular profiling of mucosal healing in UC patients in
remission to predict relapse risks, identifying molecular and structural characteristics that differentiate patients
who maintain remission from those who experience flare-ups. The success of this project holds great potential
to advance personalized care in UC management by providing a comprehensive and clinically actionable
approach to assessing mucosal healing.
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