3D spatially-resolved geometrical and functional models of human liver tissue reveal new aspects of NAFLD progression

Abstract

25Early disease diagnosis is key for the effective treatment of diseases. It relies on the 26 identification of biomarkers and morphological inspection of organs and tissues. 27Histopathological analysis of human biopsies is the gold standard to diagnose tissue 28 alterations. However, this approach has low resolution and overlooks 3D structural changes 29 that are consequence of functional alterations. Here, we applied multiphoton imaging, 3D 30 digital reconstructions and computational simulations to generate spatially-resolved 31 geometrical and functional models of human liver tissue at different stages of non-alcoholic 32 fatty liver disease (NAFLD). We identified a set of new morphometric cellular parameters 33 correlated with disease progression. Moreover, we found profound topological defects in the 34 3D bile canaliculi (BC) network. Personalized biliary fluid dynamic simulations predicted an 35 increased pericentral biliary pressure and zonated cholestasis, consistent with elevated 36 cholestatic biomarkers in patients' sera. Our spatially-resolved models of human liver tissue 37 can contribute to high-definition medicine by identifying quantitative multi-parametric cellular 38 and tissue signatures to define disease progression and provide new insights into NAFLD 39 pathophysiology. 42High definition medicine is emerging as an integrated approach to profile and restore 43 the health of an individual using a pipeline of multi-parametric analytical and therapeutic 44 technologies 1 . High-definition medicine relies on large data sets, e.g. genomics, 45 metabolomics, to characterize human health at the molecular level. It also relies on imaging, 46 image analysis and computational modelling approaches to identify structural and functional 47 abnormalities in organs and tissues associated with a disease state. Histology has been 48 classically used to characterize tissue structure and remains the method of choice to 49 describe and monitor a large variety of pathologies 2 . However, this technique has several 50 disadvantages, e.g. it is subjective (depends on the pathologist's skills), is often semi-51 quantitative and provides only two-dimensional (2D) information, i.e. does not account for 52 the three-dimensional (3D) complexity of tissues 3 . In recent years, an increasing number of 53 studies have highlighted the importance of considering 3D information for the 54 histopathological examination of tissues 4-7 . This is particularly crucial for the analysis of 3D 55 structures. The liver is a pertinent example of an organ with a complex 3D tissue 56 organization 8 . It consists of functional units, the liver lobule 9,10 , containing two intertwined 57 networks, the sinusoids for blood flow and the bile canaliculi (BC) for bile secretion and flux 9 . 58 Sinusoids and BC run antiparallel along the central vein (CV)-portal vein (PV) axis. The 59 hepatocytes are the major parenchymal cells and display a peculiar and unique type of cell 60 polarity distinct from that of simple epithelia 11 . Whereas in epit...

Role of FXR in Bile Acid and Metabolic Homeostasis in NASH: Pathogenetic Concepts and Therapeutic Opportunities

“…1). 112 Computational modeling of bile flow in these patients predicted higher pericentral biliary pressure which was correlated with GGT levels as a typical serum biochemical finding in NAFLD/NASH, underscoring the concept of microcholestasis. 112 Hepatocyte morphometry revealed increased number and size of lipid droplets pericentrally correlating with disease progression and potentially contributing to apical protein trafficking disruption.…”

“…112 Hepatocyte morphometry revealed increased number and size of lipid droplets pericentrally correlating with disease progression and potentially contributing to apical protein trafficking disruption. 112 More specifically, dipeptidyl peptidase 4 which follows a transcytotic route for apical expression was mislocalized while multidrug resistanceassociated protein (MRP2) and BSEP localization remained intact. Contrasting this finding and in line with preclinical studies in rodent models, 113,114 altered protein trafficking and mislocalization of MRP2 and MRP3 have been found in NASH patients, further substantiating the concept of microcholestasis in NASH.…”

“…This lipotoxic stress response subsequently activates Kupffer cells involving damage-associated molecular patterns (DAMPs) leading to upregulation of proinflammatory markers such as tumor necrosis factor-α (TNF-α), interleukin 6 (IL6), and monocyte chemotactic protein 1 (MCP-1) as well as activation of hepatic stellate cells (HSCs) through transforming growth factor β (TGFb) and Sonic hedgehog (SHH) signaling, leading to development of lobular inflammation and fibrosis, respectively. These metabolic perturbations are associated with profound changes in BA metabolism in NASH patients where microcholestasis encompassing molecular but also structural alterations as a result of increased pericentral biliary pressure leading to dilatation and reduced connectivity of the bile canalicular (BC) network 112 promise in therapeutically managing NASH. 22,23,25 This review provides a summary of the pathophysiological rationale and therapeutic opportunities of targeting FXR NASH.…”

Training of Deep Convolutional Neural Networks to Identify Critical Liver Alterations in Histopathology Image Samples

“…Segovia-Miranda et al [11] applied a three-dimensional imaging technique, to generate spatially-resolved geometrical and functional models for the diagnosis of liver tissue specimens at different NAFLD stages. The methodological approach identified a set of morphological changes associated with NAFLD progression.…”

“…These approaches utilize a combination of image processing techniques (including regions of interest segmentation) with supervised machine learning techniques, using manually annotated features. Τhe risk of hepatic obstruction, which refers to the blockage of the bile ducts, sinusoids, portal veins, etc., has led researchers to develop fat detection systems [6][7][8][9], combined with trained classifiers for the separation of fat tissue from other histological structures [10,11]. Thanks to the effectiveness of these diagnostic systems, histopathology has focused on more complex identification problems, including hepatocellular ballooning and tissue inflammation.…”