4.7 Article

Zonated quantification of steatosis in an entire mouse liver

Journal

COMPUTERS IN BIOLOGY AND MEDICINE
Volume 73, Issue -, Pages 108-118

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2016.04.004

Keywords

Histological serial sections; Whole-slide scans; Zonation; Steatosis; Dextromethorphan; Quantitative image analysis; Pharmacokinetics simulations; Virtual Liver

Funding

  1. German Federal Ministry of Education and Research (BMBF), systems biology network Virtual Liver [0315747, 0315765, 0315769]

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Many physiological processes and pathological conditions in livers are spatially heterogeneous, forming patterns at the lobular length scale or varying across the organ. Steatosis, a common liver disease characterized by lipids accumulating in hepatocytes, exhibits heterogeneity at both these spatial scales. The main goal of the present study was to provide a method for zonated quantification of the steatosis patterns found in an entire mouse liver. As an example application, the results were employed in a pharmacokinetics simulation. For the analysis, an automatic detection of the lipid vacuoles was used in multiple slides of histological serial sections covering an entire mouse liver. Lobuli were determined semi-automatically and zones were defined within the lobuli. Subsequently, the lipid content of each zone was computed. The steatosis patterns were found to be predominantly periportal, with a notable organ-scale heterogeneity. The analysis provides a quantitative description of the extent of steatosis in unprecedented detail. The resulting steatosis patterns were successfully used as a perturbation to the liver as part of an exemplary whole-body pharmacokinetics simulation for the antitussive drug dextromethorphan. The zonated quantification is also applicable to other pathological conditions that can be detected in histological images. Besides being a descriptive research tool, this quantification could perspectively complement diagnosis based on visual assessment of histological images. (C) 2016 Elsevier Ltd. All rights reserved.

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