Pathologist-trained machine learning classifiers developed to quantitate celiac disease features differentiate endoscopic biopsies according to modified marsh score and dietary intervention response.

Background: Histologic evaluation of the mucosal changes associated with celiac disease is important for establishing an accurate diagnosis and monitoring the impact of investigational therapies. While the Marsh-Oberhuber classification has been used to categorize the histologic findings into discrete stages (i.e.

Targeted Quantitative Mass Spectrometry Analysis of Protein Biomarkers From Previously Stained Single Formalin-Fixed Paraffin-Embedded Tissue Sections.

Formalin-fixed, paraffin-embedded tissues represent a majority of all biopsy specimens commonly analyzed by histologic or immunohistochemical staining with adhesive coverslips attached. Mass spectrometry (MS) has recently been used to precisely quantify proteins in samples consisting of multiple unstained formalin-fixed, paraffin-embedded sections. Here, we report an MS method to analyze proteins from a single coverslipped 4-μm section previously stained with hematoxylin and eosin, Masson trichrome, or 3,3'-diaminobenzidine-based immunohistochemical staining.

Accelerated instability testing reveals quantitative mass spectrometry overcomes specimen storage limitations associated with PD-L1 immunohistochemistry.

Immunohistochemistry (IHC) using formalin-fixed, paraffin embedded (FFPE) tissue is limited by epitope masking, posttranslational modification and immunoreactivity loss that occurs in stored tissue by poorly characterized mechanisms. Conformational epitopes recognized by many programmed-death-ligand-1 (PD-L1) IHC assays are particularly susceptible to degradation and provide an ideal model for understanding signal loss in stored FFPE tissue. Here we assessed 1206 tissue sections to evaluate environmental factors impacting immunoreactivity loss.