Development of a Computational High-Throughput Tool for the Quantitative Examination of Dose-Dependent Histological Features

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Abstract

High-resolution digitalizing of histology slides facilitates the development of computational alternatives to manual quantitation of features of interest. We developed a MATLAB-based quantitative histological analysis tool (QuHAnT) for the high-throughput assessment of distinguishable histological features. QuHAnT validation was demonstrated by comparison with manual quantitation using liver sections from mice orally gavaged with sesame oil vehicle or 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD; 0.001-30 μg/kg) every 4 days for 28 days, which elicits hepatic steatosis with mild fibrosis. A quality control module of QuHAnT reduced the number of quantifiable Oil Red O (ORO)-stained images from 3,123 to 2,756. Increased ORO staining was measured at 10 and 30 μg/kg TCDD with a high correlation between manual and computational volume densities (Vv), although the dynamic range of QuHAnT was 10-fold greater. Additionally, QuHAnT determined the size of each ORO vacuole, which could not be accurately quantitated by visual examination or manual point counting. PicroSirius Red quantitation demonstrated superior collagen deposition detection due to the ability to consider all images within each section. QuHAnT dramatically reduced analysis time and facilitated the comprehensive assessment of features improving accuracy and sensitivity and represents a complementary tool for tissue/cellular features that are difficult and tedious to assess via subjective or semiquantitative methods.

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