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

Nature Medicine volume 25, pages 1885–1893 (2019)Cite this article

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Abstract

Early disease diagnosis is key to the effective treatment of diseases. Histopathological analysis of human biopsies is the gold standard to diagnose tissue alterations. However, this approach has low resolution and overlooks 3D (three-dimensional) structural changes resulting from functional alterations. Here, we applied multiphoton imaging, 3D digital reconstructions and computational simulations to generate spatially resolved geometrical and functional models of human liver tissue at different stages of non-alcoholic fatty liver disease (NAFLD). We identified a set of morphometric cellular and tissue parameters correlated with disease progression, and discover profound topological defects in the 3D bile canalicular (BC) network. Personalized biliary fluid dynamic simulations predicted an increased pericentral biliary pressure and micro-cholestasis, consistent with elevated cholestatic biomarkers in patients’ sera. Our spatially resolved models of human liver tissue can contribute to high-definition medicine by identifying quantitative multiparametric cellular and tissue signatures to define disease progression and provide new insights into NAFLD pathophysiology.

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Fig. 1: 3D reconstruction and quantitative analysis of human liver morphology.
Fig. 2: Quantitative characterization of LD along the CV–PV axis.
Fig. 3: Cell based analysis of NAFLD.
Fig. 4: Structural and topological defects of bile canaliculi revealed by spatial 3D analysis.
Fig. 5: Individual-based model prediction of bile pressure p and bile fluid flux profiles based on measured bile canalicular geometries.

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Data Availability

The data supporting the findings of this study are available within the paper and its Supplementary Tables 1 and 3.

Code Availability

Code of the shooting solver is available from https://github.com/MichaelKuecken/bileflow.

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Acknowledgements

We are grateful to O. Ostrenko, J. Francisco Miquel Poblete and S. Nehring for fruitful discussions, and S. Bundschuh for helping setting up the multiphoton microscope. We thank the Center for Information Services and High Performance Computing (ZIH) of the TU Dresden for the generous provision of computing power. We would also like to thank the following Services and Facilities of the Max Planck Institute of Molecular Cell Biology and Genetics for their support: Light Microscopy Facility (LMF) and the Electron Microscopy Facility. This work was financially supported by the German Federal Ministry of Education and Research (BMBF) (LiSyM, grant no. 031L0038 to M.Z.; grant no. 031L0033 to L.B.; grant no. 031L0031 to J.H.; DYNAFLOW, grant no. 031L0082B to M.Z.; grant no. 031L008A to L.B.; and SYSBIO II, grant no. 031L0044 to M.Z.), European Research Council (ERC) (grant no. 695646 to M.Z.) and the Max Planck Society (MPG).

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Authors and Affiliations

  1. Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany

    Fabián Segovia-Miranda, Hernán Morales-Navarrete, Sarah Seifert, Urska Repnik, Yannis Kalaidzidis & Marino Zerial

  2. Center for Information Services and High Performance Computing, Technische Universität Dresden, Dresden, Germany

    Michael Kücken, Fabian Rost & Lutz Brusch

  3. Department of Medicine I, Gastroenterology and Hepatology, University Hospital Carl-Gustav-Carus, Technische Universität Dresden (TU Dresden), Dresden, Germany

    Vincent Moser, Mario Brosch & Jochen Hampe

  4. Max Planck Institute for the Physics of Complex Systems, Dresden, Germany

    Fabian Rost

  5. Center for Regenerative Therapies Dresden (CRTD), Technische Universität Dresden (TU Dresden), Dresden, Germany

    Mario Brosch & Jochen Hampe

  6. Department of General Surgery, University Hospital Rostock, Rostock, Germany

    Alexander Hendricks, Sebastian Hinz & Clemens Schafmayer

  7. University Hospital Schleswig-Holstein, Kiel, Germany

    Christoph Röcken

  8. Institute of Clinical Chemistry and Clinical Pharmacology, University Hospital Bonn, Bonn, Germany

    Dieter Lütjohann

  9. Faculty of Bioengineering and Bioinformatics, Moscow State University, Moscow, Russia

    Yannis Kalaidzidis

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  1. Fabián Segovia-Miranda
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Contributions

F.S.-M., J.H. and M.Z. conceived the project. F.S.-M., V.M. and S.S. performed the immunofluorescence experiments and imaging. H.M.-N. and Y.K. developed the image analysis algorithms. F.S-M., V.M. and H.M.-N. performed the 3D tissue reconstructions. H.M-N. and F.S.-M. performed the data analysis and interpretation of the results. U.R. performed the electron microscopy. A.H., S.H., C.R., C.S and M.B. obtained the samples and characterized the patients. D.L. measured bile acids. M.K., F.R., Y.K. and L.B. conceived and developed the mathematical model. M.K and F.R. programmed and simulated the mathematical model and performed statistical analysis. M.K. and L.B. interpreted results and wrote the model description. F.S.-M., H.M.-N., M.K., Y.K., L.B., J.H. and M.Z. wrote the manuscript.

Corresponding authors

Correspondence to Jochen Hampe or Marino Zerial.

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Competing interests

The authors declare no competing interests.

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Peer review information Joao Monteiro was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Immunofluorescence of human liver tissue.

a, Human liver sections were stained for glutathione synthetase (GS) to visualize CV and DAPI. Scale bar, 1,000 µm. Representative images from NC = 4 samples and eNASH = 5 samples. b, 2D analysis of liver lobule radius represented by box-plots (median values as red lines, 25th and 75th percentiles as blue bottom and top edges of the boxes, extreme data points by whiskers). NC = 4 samples, HO = 4 samples, STEA = 7 samples, eNASH = 5 samples. One-sided Wilcoxon rank-sum test. *P < 0.05, **P < 0.01, ***P < 0.001. c–f, Liver sections (~100 µm thick) were stained for bile canaliculi (CD13), sinusoids (fibronectin), nucleus (DAPI), LDs (BODIPY) and cell border (LDLR), optically cleared with SeeDB and imaged at high resolution using multiphoton microscopy (0.3 µm x 0.3 µm x 0.3 µm per voxel). Orthogonal view of NC (c), HO (d), STEA (e) and eNASH (f). Scale bar, 50 µm. Representative images from NC = 5 samples, HO = 3 samples, STEA = 4 samples, eNASH = 4 samples.

Extended Data Fig. 2 Morphometric features of the nuclei.

a, Representative IF images of fixed human liver tissue sections stained with DAPI. Shown is a single-plane covering an entire CV–PV axis. Arrowhead indicates some examples of vacuolated nuclei. Representative images from NC = 5 samples, HO = 3 samples, STEA = 4 samples, eNASH = 4 samples. b,c, Quantitative characterization of hepatocytes nuclei with respect to the proportion of mono/binuclear cells (b) and ploidy (c). Only the four major populations (1 × 2n, 1 × 4n, 2 × 2n and 2 × 4n), which account for >90% of the hepatocytes, are shown. d, Definition of the regions within the liver lobule. The CV–PV axis was divided in 10 equidistant regions. Regions 1 and 10 are adjacent to the CV and PV, respectively. Quantitative characterization of hepatocytes nuclear elongation (e) and texture based on their DAPI intensity (see Methods for details): nuclear vacuolation (f), homogeneity (Angular Second Moment) (g), local homogeneity (inverse difference moment) (h), contrast (i) and entropy (j). NC = 5 samples, HO = 3 samples, STEA = 4 samples, eNASH = 4 samples. Spatially resolved quantification represented by median ± MAD per region and overall quantifications by box plots (median values as red lines, 25th and 75th percentiles as blue bottom and top edges of the boxes, extreme data points by whiskers). One-tailed Wilcoxon rank-sum test. *P < 0.05, **P < 0.01, ***P < 0.001.

Extended Data Fig. 3 Mislocalization of DPPIV in pericentral hepatocytes in STEA and eNASH.

a–c, Representative confocal microscopy images of human liver sections stained for the apical markers BSEP (a), MRP2 (b) and DPPIV (c). Merged images of the apical markers, phalloidin and DAPI are shown in the right panels. Arrowhead indicates the lateral membrane. Scale bar, 10 µm. NC = 3 samples, STEA = 4 samples, eNASH = 4 samples were repeated independently with similar results. d,e, Large field images of a single-plane of liver tissue stained with DPPIV (d). Scale bar, 50 µm. Apical, basal and lateral membrane of the hepatocytes were segmented based on BSEP (not shown), DPPIV and phalloidin (not shown) in an area covering a radius of 125 µm around the CV and PV. DPPIV intensity was quantified and normalized to the area covered by the different sub-domains (e). NC = 3 samples, STEA = 4 samples, eNASH = 4 samples. Quantifications by box plots (median values as red lines, 25th and 75th percentiles as blue bottom and top edges of the boxes, extreme data points by whiskers). One-tailed Wilcoxon rank-sum test. *P < 0.05, **P < 0.01, ***P < 0.001.

Extended Data Fig. 4 Structural and topological characterization of the sinusoidal network.

a, Representative IF images of fixed human liver tissue sections stained with fibronectin after CAAR. Shown is a maximum projection of a 30-µm z-stack covering an entire CV–PV axis. Representative images from NC = 5 samples, HO = 3 samples, STEA = 5 samples, eNASH = 3 samples. b–h, Quantification of the tissue volume fraction occupied by the sinusoids (b), radius (c), number of junctions (d), total length per unit tissue volume (e), fraction of connected network (f) connectivity density (g) and branches crossing regions (h) for the sinusoidal network along the CV–PV axis and overall. NC = 5 samples, HO = 3 samples, STEA = 5 samples, eNASH = 3 samples. Spatially resolved quantification represented by median ± MAD per region and overall quantifications by box plots (median values as red lines, 25th and 75th percentiles as blue bottom and top edges of the boxes, extreme data points by whiskers). Two-tailed Wilcoxon rank-sum test. *P < 0.05, **P < 0.01, ***P < 0.001.

Extended Data Fig. 5 Geometric and topological variability of the BC network among liver lobules.

BC network was reconstructed from three CV-PV axes from different lobules for each patient. NC = 4 patients, HO = 3 patients STEA = 3 patients, eNASH = 3 patients. Quantification of the tissue volume fraction occupied by the BC, radius, total length per unit tissue volume and fraction of connected network (a–d) along the CV–PV axis and overall. Spatially resolved quantification represented by median ± MAD per region and overall quantifications by box plots (median values as red lines, 25th and 75th percentiles as blue bottom and top edges of the boxes, extreme data points by whiskers). Two-tailed Kruskal–Wallis test. *P < 0.05, **P < 0.01, ***P < 0.001.

Extended Data Fig. 6 Estimates for a fraction of free lumen in total volume of a bile canaliculus.

a, Representative images of bile canaliculi for NC and eNASH liver tissue samples, used for making the estimates. Microvilli are well preserved. A red dashed line indicates lumen of a bile canaliculus. TJ, tight junction. NC = 3 samples, HO = 3 samples, STEA = 3 samples, eNASH = 3 samples. Scalebar, 500 nm. b, Estimation of fraction of free lumen by stereological point counting (the Cavalieri estimator). For each set of samples and each region (central / portal vein) a minimum of five EM images was used. NC = 3 samples, HO = 3 samples, STEA = 3 samples, eNASH = 3 samples, median ± MAD.

Extended Data Fig. 7 Profile of serum cholestatic and liver injury biomarkers as well as bile acids during disease progression.

a–j, The levels of bilirubin (a), GGT (b), AP (c), AST (d), ALT (e), BA precursors (cholesterol, 7α-hydroxycholesterol and 27-hydroxycholesterol) (f), individual (CA, CDCA) and total primary BAs (g), individual (DCA, LCA, UDCA) and total secondary BAs (h), total BAs (i) and ratio secondary to primary BAs (j) were measured in the serum of the subjects and represented in box plots (median values as red lines, 25th and 75th percentiles as blue bottom and top edges of the boxes, extreme data points by whiskers). NC = 22 samples, HO = 27 samples, STEA = 31 samples, eNASH = 24 samples. One-tailed Wilcoxon rank-sum test. *P < 0.05, **P < 0.01, ***P < 0.001.

Extended Data Fig. 8 Scatter plots and regression analysis of measured liver biomarkers and bile acids.

a–g, Bilirubin (a), AP (b), total BAs (c), primary BAs (d), AST (e), ALT (f) and ratio secondary to primary BAs (g) measured in the serum versus the model-derived pericentral pressure in individual patients from all groups. Arrow indicates an outlier for primary BAs (h7252). NC = 6 samples, HO = 4 samples, STEA = 8 samples, eNASH = 7 samples. P values and Spearman correlation coefficient are indicated in the plot.

Supplementary information

Supplementary Tables

Supplementary Tables 1–3.

Reporting Summary

Supplementary Video 1

3D reconstruction of human liver tissue from NC samples. Central vein (light blue), portal vein (orange), bile canaliculus (green), sinusoids (magenta), lipid droplets (red), nuclei (random colours) and hepatocytes (random colours).

Supplementary Video 2

3D reconstruction of human liver tissue from eNASH samples. Central vein (light blue), portal vein (orange), bile canaliculus (green), sinusoids (magenta), lipid droplets (red), nuclei (random colours) and hepatocytes (random colours).

Supplementary Video 3

Representative pericentral and periportal hepatocytes from NC and eNASH liver tissue samples. Apical (green), basal (magenta) and lateral (grey) plasma membrane domains, nuclei (random grey shades) and lipid droplets (red).

Supplementary Video 4

Representative 3D reconstruction of the bile canaliculi (green) and the sinusoidal (magenta) networks from the pericentral zone in STEA.

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Segovia-Miranda, F., Morales-Navarrete, H., Kücken, M. et al. Three-dimensional spatially resolved geometrical and functional models of human liver tissue reveal new aspects of NAFLD progression. Nat Med 25, 1885–1893 (2019). https://doi.org/10.1038/s41591-019-0660-7

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