Biopsy proteome scoring to determine mucosal remodeling in celiac disease

Anette Johansen,Geir Kjetil F. Sandve, Jostein Holen Ibsen,Knut E.A. Lundin, Ludvig M. Sollid,Jorunn Stamnaes

Gastroenterology(2024)

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摘要
Background & aims Histological evaluation of gut biopsies 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 inter-observer variability. Biopsy proteome measurement reporting on tissue state can be obtained by mass spectrometry (MS) analysis of formalin-fixed paraffin embedded (FFPE) 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 FFPE sections for MS-based proteome analysis was established. Proteome data was converted to numerical scores using two complementary approaches; a rank-based enrichment score and a score based on machine-learning employing logistic regression. The two scoring approaches were compared to each other and to histology analyzing 18 CeD patients with biopsies collected before and after treatment with gluten-free diet as well as biopsies from CeD patients with varying degree of remission (n = 22). Biopsies from non-CeD individuals (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 two approaches are 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 biopsies with poor orientation. Conclusion Biopsy proteome scores give reliable observer and orientation-independent measures of mucosal remodeling in CeD. The proteomic method can readily be implemented by non-expert laboratories in parallel to histology assessment and easily scaled for clinical trial settings.
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关键词
celiac disease,mass spectrometry,clinical proteomics,molecular histology,machine learning
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