Tissue imaging and analysis of structural changes and functional
consequences of the liver lobular/sinusoidal system during fibrosis
Iryna Ilkavets, Honglei Weng, Steven Dooley
Molecular Hepatology-Alcohol dependent Diseases, II. Medical Clinic, Faculty of
Medicine at Mannheim, University of Heidelberg, Germany
Chronic liver injury from different etiologies, including hepatitis viral infection,
cholestasis, alcoholic or non alcoholic steatohepatitis and drug intoxication, is
characterized by inflammed and fibrotic liver tissue that regenerates or progresses to
cirrhosis and end stage disease, a major cause of mortality and morbidity worldwide.
Liver damage is characterized by the presence of inflammatory cells and activated
hepatic stellate cells (HSCs) and loss of hepatocyte shape and function. The 3D
architecture and therewith spatiotemporal processes (cell morphology and extracellular
matrix (ECM) remodelling, e.g., expression of, among others, collagens and matrix
metalloproteinases, as well as function of the hepatic sinusoid/liver lobule) is transiently
or chronically altered. We aim at characterizing acute or chronically injured liver tissue
using high resolution tissue imaging (Leica bright field and confocal scanning
microscopes) and digital texture analysis to monitor spatiotemporal changes of liver
architecture and relate these to functional/pathophysiological consequences. Although
many therapeutic approaches show antiinflammatory/antifibrotic activity in animal
disease models, there is no effective pharmaceutical intervention at present, mainly
because of disease stage dependently differing functions of the targeted components. To
overcome this limitation, a spatio-temporal resolution of liver damage is required. We
optimized and standardized tissue processing for quantitative IHC/IF estimating (i) cell
populations of the liver sinusoid/lobule, e.g., hepatocytes (transferrin), hepatic stellate
cells (SMA), Kupffer (CD68), and progenitor cells (CD19), (ii) cell proliferation (Ki67,
PCNA), (iii) epithelial-to-mesenchymal transition (S100A4, Twist, Snail, ZO-1, ECadherin, Catenin) and (iv) TGFβ- (pSmad1/2/3) and Notch- (Jag1) signaling and (v)
cell activation (SMA, CTGF, Wisp1) in mouse and human liver tissues. We performed
systematic statistical analyses of micrographs from bright field and confocal laser
scanning microscopy on multilobular, lobular and sublobular scales. Quantification of
different cell types and their properties was done with ImageJ. In image processing, we
defined criteria encompassing variations in cell morphology. Further, we applied a scene
segmentation approach to detect and quantify changes in cell size, cell shape, and
specific marker signature. A catalogue and SOPs of imaging markers for liver (human and
mouse) were generated. This will serve us as background for machine learning and
multiscale tissue modelling purposes. Data will be further applied to identify novel digital
tissue markers for improved diagnosis, prognosis and to predict responses to therapy of
liver chronic diseases.