Spatial-temporal modelling; intravital imaging

This site shows some two-photon based intravital videos to visualize the possibilities of this technique, published e.g. in Jansen et al., 2017 and Reif et al., 2016. The lower part of this site contains some spatio-temporal models simulating the damage and regeneration process after administration of the hepatotoxic compounds.

Video 1: Intravital imaging of natural killer cells in healthy mouse liver by intravenous administration of NK1.1 antibody (red) using two-photon microscopy.

Video 2:Intravital imaging of Natural Killer cells in fibrotic mouse liver by intravenous administration of NK1.1 antibody (red) using two-photon microscopy.

Video 3:Immune mediated liver damage: CD8 positive T- cells (red) were adoptively transferred into mice overexpressing albumen in hepatocytes. The CD8 positive T- cells roll in the sinusoids, stuck to and finally invade the affected hepatocytes.

Video 4:Immune mediated liver damage: CD8 positive T- cells (red) were adoptively transferred into mice overexpressing albumen in hepatocytes. The CD8 positive T- cells roll in the sinusoids, stuck to and finally invade the affected hepatocytes.

Video 5:Intravital imaging of Kupffer cells in mouse liver after intravenous administration of F4/80 antibody (red) using two-photon microscopy.

Video 6:Intravital imaging of Kupffer cells and granulocytes using Lysozyme-M reporter mouse (green).

Video 7:Intravital imaging of infiltrating monocytes in the liver after acetaminophen intoxication by intravenous administration of cd11b antibody (red) using two-photon microscopy.

Video 8:Intravital imaging of hepatic blood flow at the sinusoids level by intravenous administration of dextran 10 KDa (green).

Video 9:Intravital imaging of liver sinusoidal endothelial cells using Tie-2 reporter mouse (green).

Systems biology approaches for spatial-temporal modelling of liver toxicity and regeneration allowidentification of new key mechanisms. An example is the identification of daughter hepatocytes along liver sinusoids as an order-principle during liver regeneration (Höhme et al., 2010). Disturbing this mechanism compromises liver regeneration.

Fig. 1: Reconstruction of liver tissue from confocal laser scans. Immunostaining of DPPIV (green), ICAM (red) as well as DAPI nuclear staining (blue), and confocal laser scanning (B) microscopy allow reconstruction of sinusoidal networks (E) and of complete liver lobules (A). An advantage of this type of reconstruction is that the data can be used for further calculations. For example, the percentage of the hepatocyte surface that is in contact with another hepatocyte or with sinusoidal endothelial cells can be quantified. Using these techniques, measurements of cell relevant aspects of the liver microarchitecture can be performed.
Fig. 1: Reconstruction of liver tissue from confocal laser scans. Immunostaining of DPPIV (green), ICAM (red) as well as DAPI nuclear staining (blue), and confocal laser scanning (B) microscopy allow reconstruction of sinusoidal networks (E) and of complete liver lobules (A). An advantage of this type of reconstruction is that the data can be used for further calculations. For example, the percentage of the hepatocyte surface that is in contact with another hepatocyte or with sinusoidal endothelial cells can be quantified. Using these techniques, measurements of cell relevant aspects of the liver microarchitecture can be performed.
Fig. 2: Centrilobular liver damage induced by administration of CCl4 to mice. Two days after intoxication, a central dead cell area is observed that can be clearly distinguished from the darker surviving cells. After four days the central dead cell area becomes smaller and is no longer visible after eight days. Experimentally determined process parameters, such as time resolved data on proliferation and cell death at certain positions of the liver lobule are required for establishment of the models.
Fig. 2: Centrilobular liver damage induced by administration of CCl4 to mice. Two days after intoxication, a central dead cell area is observed that can be clearly distinguished from the darker surviving cells. After four days the central dead cell area becomes smaller and is no longer visible after eight days. Experimentally determined process parameters, such as time resolved data on proliferation and cell death at certain positions of the liver lobule are required for establishment of the models.
Fig. 3: Experimental validation of the order principle “oriented cell division”. Reconstruction of regenerating liver tissue where daughter cells have been visualised by BrdU incorporation (green nuclei). Daughter cells after mitosis are oriented in the direction of the closest sinusoid.
Fig. 3: Experimental validation of the order principle “oriented cell division”. Reconstruction of regenerating liver tissue where daughter cells have been visualised by BrdU incorporation (green nuclei). Daughter cells after mitosis are oriented in the direction of the closest sinusoid.

Video 1: In dieses Modell wurden alle experimentell bestimmten Prozessparameter einprogrammiert. Allerdings fehlt den Hepatozyten der Prozessparameter „oriented cell division“ OCD, der es Tochterhepatozyten ermöglicht, sich unmittelbar nach der Mitose in Richtung des nahesten Sinusoids zu orientieren. Das Ergebnis ist eine Störung der Mikroarchitektur des Leberläppchens während der Regeneration.

Video 2: Regeneration mit „oriented cell division“. Erst durch Einführung dieses Prozessparameters gelingt die Modellierung des Regenerationsprozesses in einer Weise, dass er der experimentell bestimmten Situation der Leber in vivo entspricht.

Video 3: Im Zeitrafferfilm von kokultivierten Hepatozyten und sinusoidalen Endothelzellen wird deutlich, dass Hepatozyten zu den Endothelzellen migrieren und bestrebt sind, die Kontaktfläche zu maximieren.

Selected publications1

Ghallab A, Cellière G, Henkel SG, Driesch D, Hoehme S, Hofmann U, Zellmer S, Godoy P, Sachinidis A, Blaszkewicz M, Reif R, Marchan R, Kuepfer L, Häussinger D, Drasdo D, Gebhardt R, Hengstler JG. Model-guided identification of a therapeutic strategy to reduce hyperammonemia in liver diseases. J Hepatol. 2016 Apr;64(4):860-71.

Jansen PL, Ghallab A, Vartak N, Reif R, Schaap FG, Hampe J, Hengstler JG. The ascending pathophysiology of cholestatic liver disease. Hepatology. 2016 Dec 16. [Epub ahead of print] Review.

Vartak N, Damle-Vartak A, Richter B, Dirsch O, Dahmen U, Hammad S, Hengstler JG.Cholestasis-induced adaptive remodeling of interlobular bile ducts. Hepatology. 2016 Mar;63(3):951-64. doi: 10.1002/hep.28373.

Drasdo D, Hoehme S, Hengstler JG. How predictive quantitative modelling of tissue organisation can inform liver disease pathogenesis. J Hepatol. 2014 Oct;61(4):951-6.

Schliess F, Hoehme S, Henkel SG, Ghallab A, Driesch D, Böttger J, Guthke R, Pfaff M, Hengstler JG, Gebhardt R, Häussinger D, Drasdo D, Zellmer S. Integrated metabolic spatial-temporal model for the prediction of ammonia detoxification during liver damage and regeneration. Hepatology. 2014 Dec;60(6):2040-51.

Zeigerer A, Gilleron J, Bogorad RL, Marsico G, Nonaka H, Seifert S, Epstein-Barash H, Kuchimanchi S, Peng CG, Ruda VM, Del Conte-Zerial P, Hengstler JG, Kalaidzidis Y, Koteliansky V, Zerial M. Rab5 is necessary for the biogenesis of the endolysosomal system in vivo. Nature. 2012 May 23;485(7399):465-70.

Hoehme S, Brulport M, Bauer A, Bedawy E, Schormann W, Hermes M, Puppe V, Gebhardt R, Zellmer S, Schwarz M, Bockamp E, Timmel T, Hengstler JG, Drasdo D. Prediction and validation of cell alignment along microvessels as order principle to restore tissue architecture in liver regeneration. Proc Natl Acad Sci U S A. 2010 Jun 8;107(23):10371-6.

Brulport M, Schormann W, Bauer A, Hermes M, Elsner C, Hammersen FJ, Beerheide W, Spitkovsky D, Härtig W, Nussler A, Horn LC, Edelmann J, Pelz-Ackermann O, Petersen J, Kamprad M, von Mach M, Lupp A, Zulewski H, Hengstler JG: Fate of extrahepatic human stem and precursor cells after transplantation into mouse livers.Hepatology 2007;46:861-70.

1underlined: members of the Toxicology group