Biopsy Proteome Scoring to Determine Mucosal Remodeling in Celiac Disease

Gastroenterology. 2024 Mar 11:S0016-5085(24)00286-5. doi: 10.1053/j.gastro.2024.03.006. Online ahead of print.


Background & aims: Histologic evaluation of gut biopsy specimens is a cornerstone for diagnosis and management of celiac disease (CeD). Despite its wide use, the method depends on proper biopsy orientation, and it suffers from interobserver variability. Biopsy proteome measurement reporting on the tissue state can be obtained by mass spectrometry analysis of formalin-fixed paraffin-embedded tissue. Here we aimed to transform biopsy proteome data into numerical scores that give observer-independent measures of mucosal remodeling in CeD.

Methods: A pipeline using glass-mounted formalin-fixed paraffin-embedded sections for mass spectrometry-based proteome analysis was established. Proteome data were converted to numerical scores using 2 complementary approaches: a rank-based enrichment score and a score based on machine-learning using logistic regression. The 2 scoring approaches were compared with each other and with histology analyzing 18 patients with CeD with biopsy specimens collected before and after treatment with a gluten-free diet as well as biopsy specimens from patients with CeD with varying degree of remission (n = 22). Biopsy specimens from individuals without CeD (n = 32) were also analyzed.

Results: The method yielded reliable proteome scoring of both unstained and H&E-stained glass-mounted sections. The scores of the 2 approaches were highly correlated, reflecting that both approaches pick up proteome changes in the same biological pathways. The proteome scores correlated with villus height-to-crypt depth ratio. Thus, the method is able to score biopsy specimens with poor orientation.

Conclusions: Biopsy proteome scores give reliable observer and orientation-independent measures of mucosal remodeling in CeD. The proteomic method can readily be implemented by nonexpert laboratories in parallel to histology assessment and easily scaled for clinical trial settings.

Keywords: Celiac Disease; Clinical Proteomics; Machine Learning; Mass Spectrometry; Molecular Histology.