Zonated quantification of steatosis in an entire mouse liver

Computers in Biology and Medicine

Volumn 73, Issue , 2016, Pages 108-118

  Schwen, Lars Ole ^a   Homeyer, André ^a   Schwier, Michael ^a,b   Dahmen, Uta ^c   Dirsch, Olaf ^d   Schenk, Arne ^e,f   Kuepfer, Lars ^e,f   Preusser, Tobias ^a,b   Schenk, Andrea ^a  

^a FRAUNHOFER MEVIS   (Germany)

^b JACOBS UNIVERSITY BREMEN   (Germany)

^c JENA UNIVERSITY HOSPITAL   (Germany)

^d Institute of Pathology   (Germany)

^e BAYER AG   (Germany)

^f RWTH AACHEN UNIVERSITY   (Germany)

Author keywords

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

Indexed keywords

DIAGNOSIS; PHARMACOKINETICS;

DEXTROMETHORPHAN; QUANTITATIVE IMAGE ANALYSIS; SERIAL SECTION; STEATOSIS; WHOLE-SLIDE SCANS; ZONATION;

MAMMALS;

DEXTROMETHORPHAN; GLUTAMATE AMMONIA LIGASE;

ANIMAL EXPERIMENT; ANIMAL MODEL; ARTICLE; CELL VACUOLE; CENTRAL VEIN; DESCRIPTIVE RESEARCH; DRUG CLEARANCE; DRUG METABOLISM; FATTY LIVER; LIPID STORAGE; LIPOPHILICITY; LIVER HISTOLOGY; LIVER LOBULE; MALE; MOUSE; NONHUMAN; PRIORITY JOURNAL; ZONATION; ANIMAL; BIOLOGICAL MODEL; COMPUTER SIMULATION; IMAGE PROCESSING; LIPID METABOLISM; LIVER; LIVER CELL; METABOLISM; PATHOLOGY;

ANIMALS; COMPUTER SIMULATION; FATTY LIVER; HEPATOCYTES; IMAGE PROCESSING, COMPUTER-ASSISTED; LIPID METABOLISM; LIVER; MALE; MICE; MODELS, BIOLOGICAL; VACUOLES;

EID: 84963542029     PISSN: 00104825     EISSN: 18790534     Source Type: Journal    
DOI: 10.1016/j.compbiomed.2016.04.004     Document Type: Article

References (51)

1. Kuntz E., Kuntz H.-D. Hepatology: Textbook and Atlas 2008, Springer, Heidelberg. 3rd edition.
2. Miller D.L., Zanolli C.S., Gumucio J.J. Quantitative morphology of the sinusoids of the hepatic acinus. Gastroenterology 1979, 76(5):965-969. 10.1016/S0016-5085(79)91326-X.
3. Kleiner D.E., Brunt E.M. Nonalcoholic fatty liver disease. pathologic patterns and biopsy evaluation in clinical research. Semin. Liver Dis. 2012, 32(1):3-13. 10.1055/s-0032-1306421.
4. Gebhardt R. Metabolic zonation of the liver. regulation and implications for liver function. Pharmacol. Ther. 1992, 53(3):275-354. 10.1016/0163-7258(92)90055-5.
5. Jungermann K. Zonation of metabolism and gene expression in liver. Histochem. Cell Biol. 1995, 103(2):81-91. 10.1007/BF01454004.
6. Lindros K.O. Zonation of cytochrome P450 expression, drug metabolism and toxicity in liver. Gen. Pharmacol.: Vasc. Syst. 1997, 28(2):191-196. 10.1016/S0306-3623(96)00183-8.
7. Schliess F., Hoehme S., Henkel S.G., Ghallab A., Driesch D., Böttger J., Guthke R., Pfaff M., Hengstler J.G., 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, 60(6):2040-2051. 10.1002/hep.27136.
8. Rappaport A.M., Borowy Z.J., Lougheed W.M., Lotto W.N. Subdivision of hexagonal liver lobules into a structural and functional unit. Role in hepatic physiology and pathology. Anat. Rec. 1954, 119(1):11-33. 10.1002/ar.1091190103.
9. Bedossa P., Dargère D., Paradis V. Sampling variability of liver fibrosis in chronic hepatitis C. Hepatology 2003, 38:1449-1457. 10.1016/j.hep.2003.09.022.
10. Goldstein N.S., Hastah F., Galan M.V., Gordon S.C. Fibrosis heterogeneity in nonalcoholic steatohepatitis and hepatitis C virus needle core biopsy specimens. Am. J. Clin. Pathol. 2005, 123:382-387. 10.1309/EY72F1EN9XCB1KXX.
11. Maharaj B., Leary W.P., Naran A.D., Maharaj R.J., Cooppan R.M., Pirie D., Pudifin D.J. Sampling variability and its influence on the diagnostic yield of percutaneous needle biopsy of the liver. Lancet 1986, 327(8480):523-525. 10.1016/S0140-6736(86)90883-4.
12. Vaupel P., Kallinowski F., Okunieff P. Blood flow, oxygen and nutrient supply, and metabolic microenvironment of human tumors. a review. Cancer Res. 1989, 49(23):6449-6465. 〈http://cancerres.aacrjournals.org/content/49/23/6449〉.
13. Avila M.A., Berasain C., Torres L., Martín-Duce A., Corrales F.J., Yang H., Prieto J., Lu S.C., Caballería J., Rodés J., Mato J.M. Reduced mRNA abundance of the main enzymes involved in methionine metabolism in human liver cirrhosis and hepatocellular carcinoma. J. Hepatol. 2000, 33(6):907-914. 10.1016/S0168-8278(00)80122-1.
14. Ricken T., Werner D., Holzhütter H.-G., König M., Dahmen U., Dirsch O. Modeling function-perfusion behavior in liver lobules including tissue, blood, glucose, lactate and glycogen by use of a coupled two-scale PDE-ODE approach. Biomech. Model. Mechanobiol. 2015, 14(3):515-536. 10.1007/s10237-014-0619-z.
15. Schwen L.O., Krauss M., Niederalt C., Gremse F., Kiessling F., Schenk A., Preusser T., Kuepfer L. Spatio-temporal simulation of first pass drug perfusion in the liver. PLOS Comput. Biol. 2014, 10(3):1-18. e1003499. 10.1371/journal.pcbi.1003499.
16. Bellentani S., Saccoccio G., Masutti F., Crocè L.S., Brandi G., Sasso F., Cristanini G., Tiribelli C. Prevalence of and risk factors for hepatic steatosis in Northern Italy. Ann. Intern. Med. 2000, 132(2):112-117. 10.7326/0003-4819-132-2-200001180-00004.
17. Lonardo A., Lombardini S., Ricchi M., Scaglioni F., Loria P. Review article. hepatic steatosis and insulin resistance. Aliment. Pharmacol. Ther. 2005, 22(s2):64-70. 10.1111/j.1365-2036.2005.02600.x.
18. Hijmans B.S., Grefhorst A., Oosterveer M.H., Groen A.K. Zonation of glucose and fatty acid metabolism in the liver. mechanism and metabolic consequences. Biochimie 2014, 96:121-129. 10.1016/j.biochi.2013.06.007.
19. Capitan V., Petit J.-M., Aho S., Lefevre P.-H., Favelier S., Loffroy R., Hillon P., Krausé D., Cercueil J.-P., Guiu B. Macroscopic heterogeneity of liver fat. an MR-based study in type-2 diabetic patients. Eur. Radiol. 2012, 22(10):2161-2168. 10.1007/s00330-012-2468-4.
20. Levene A.P., Kudo H., Armstrong M.J., Thursz M.R., Gedroyc W.M., Anstee Q.M., Goldin R.D. Quantifying hepatic steatosis-more than meets the eye. Histopathology 2012, 60(6):971-981. 10.1111/j.1365-2559.2012.04193.x.
21. Hourigan S.K., Torbenson M., Tibesar E., Scheimann A.O. The full spectrum of hepatic steatosis in children. Clin. Pediatr. (online before print) 2015, 1-8. 10.1177/0009922814566927.
22. Schwenzer N.F., Springer F., Schraml C., Stefan N., Machann J., Schick F. Non-invasive assessment and quantification of liver steatosis by ultrasound, computed tomography and magnetic resonance. J. Hepatol. 2009, 51(3):433-445. 10.1016/j.jhep.2009.05.023.
23. Idilman I.S., Aniktar H., Idilman R., Kabacam G., Savas B., Elhan A., Celik A., Bahar K., Karcaaltincaba M. Hepatic steatosis. quantification by proton density fat fraction with MR imaging versus liver biopsy. Radiology 2013, 267(3):767-775. 10.1148/radiol.13121360.
24. Hines C.D.G., Yu H., Shimakawa A., McKenzie C.A., Warner T.F., Brittain J.H., Reeder S.B. Quantification of hepatic steatosis with 3-T MR imaging. validation in ob/ob mice. Radiology 2009, 254(1):119-128. 10.1148/radiol.09090131.
25. Teutsch H.F., Schuerfeld D., Groezinger E. Three-dimensional reconstruction of parenchymal units in the liver of the rat. Hepatology 1999, 29:494-505. 10.1002/hep.510290243.
26. Teutsch H.F. The modular microarchitecture of human liver. Hepatology 2005, 42(2):317-325. 10.1002/hep.20764.
27. Friebel A., Neitsch J., Johann T., Hammad S., Hengstler J.G., Drasdo D., Hoehme S. Tiquant: software for tissue analysis, quantification and surface reconstruction. Bioinformatics 2015, 31(19):3234-3236. 10.1093/bioinformatics/btv346.
28. Webster J.D., Dunstan R.W. Whole-slide imaging and automated image analysis considerations and opportunities in the practice of pathology. Veter-. Pathol. Online 2014, 51(1):211-223. 10.1177/0300985813503570.
29. Schwen L.O., Schenk A., Kreutz C., Timmer J., Bartolomé Rodríguez M.M., Küpfer L., Preusser T. Representative sinusoids for hepatic four-scale pharmacokinetics simulations. PLOS ONE 2015, 10(7):e0133653. 10.1371/journal.pone.0133653.
30. Song Y., Treanor D., Bulpitt A.J., Magee D.R. 3D reconstruction of multiple stained histology images. J. Pathol. Inf. 2013, 4(2):7. 10.4103/2153-3539.109864.
31. Roberts N., Magee D., Song Y., Brabazon K., Shires M., Crellin D., Orsi N.M., Quirke R., Quirke P., Treanor D. Toward routine use of 3D histopathology as a research tool. Am. J. Pathol. 2012, 180(5):1835-1842. 10.1016/j.ajpath.2012.01.033.
32. ssniff® experimental diets for laboratory animals, viewed 2015-04-29. URL 〈〉. http://www.ssniff.de/documents/gereinigte_diaeten_experimentaldiaeten.pdf.
33. Schwier M., Böhler T., Hahn H.K., Dahmen U., Dirsch O. Registration of histological whole slide images guided by vessel structures. J. Pathol. Inf. 2013, 4:10. 10.4103/2153-3539.109868.
34. C. Broit, Optimal registration of deformed images (Ph.D. thesis), University of Pennsylvania, 1981, 〈〉. http://www.repository.upenn.edu/dissertations/AAI8207933/.
35. E. Haber, J. Modersitzki, Intensity gradient based registration and fusion of multi-modal images, in: C. Barillot, D.R. Haynor, P. Hellier (Eds.), Medical Image Computing and Computer-Assisted Intervention-MICCAI 2006, Springer Lecture Notes in Computer Science, vol. 3216, 2006, pp. 591-598,. doi:10.1007/11866763_89.
36. Modersitzki J. FAIR: Flexible Algorithms for Image Registration 2009, SIAM, Philadelphia.
37. 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 J.G., 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, 107(23):10371-10376. supporting Information. 10.1073/pnas.0909374107.
38. Marsman H., Matsushita T., Dierkhising R., Kremers W., Rosen C., Burgart L., Nyberg S.L. Assessment of donor liver steatosis. pathologist or automated software?. Human. Pathol. 2004, 35(4):430-435. 10.1016/j.humpath.2003.10.029.
39. Selzner M., Clavien P.-A. Fatty liver in liver transplantation and surgery. Semin. Liver Dis. 2001, 21(1):105-113. 10.1055/s-2001-12933.
40. Homeyer A., Schenk A., Arlt J., Dahmen U., Dirsch O., Hahn H.K. Practical quantification of necrosis in histological whole-slide images. Comput. Med. Imaging Graph. 2013, 37(4):313-322. 10.1016/j.compmedimag.2013.05.002.
41. Aurenhammer F. Voronoi diagrams-a survey of a fundamental geometric data structure. ACM Comput. Surv. 1991, 23(3):345-405. 10.1145/116873.116880.
42. H.K. Hahn, H.-O. Peitgen, IWT-interactive watershed transform: a hierarchical method for efficient interactive and automated segmentation of multidimensional gray-scale images, in: M. Sonka, J.M. Fitzpatrick (Eds.), 2003 Proceedings of SPIE Medical Imaging, vol. 5032, 2003, pp. 643-653,. doi:10.1117/12.481097.
43. Bem J.L., Peck R. Dextromethorphan. Drug Saf. 1992, 7(3):190-199. 10.2165/00002018-199207030-00004.
44. Yu A.-M., Haining R.L. Expression, purification, and characterization of mouse CYP2d22. Drug Metab. Dispos. 2006, 34(7):1167-1174. 10.1124/dmd.105.008870.
45. Blume N., Leonard J., Xu Z.J., Watanabe O., Remotti H., Fishman J. Characterization of Cyp2d22, a novel cytochrome P450 expressed in mouse mammary cells. Arch. Biochem. Biophys. 2000, 381(2):191-204. 10.1006/abbi.2000.1978.
46. Thiel C., Schneckener S., Krauss M., Ghallab A., Hofmann U., Kanacher T., Zellmer S., Gebhardt R., Hengstler J.G., Kuepfer L. A systematic evaluation of the use of physiologically based pharmacokinetic modeling for cross-species extrapolation. J. Pharm. Sci. 2014, 104:191-206. 10.1002/jps.24214.
47. Willmann S., Thelen K., Lippert J. Integration of dissolution into physiologically-based pharmacokinetic models III. PK-Sim®. J. Pharm. Pharmacol. 2012, 64(7):997-1007. 10.1111/j.2042-7158.2012.01534.x.
48. Schwen L.O., Wei W., Gremse F., Ehling J., Wang L., Dahmen U., Preusser T. Algorithmically generated rodent hepatic vascular trees in arbitrary detail. J. Theor. Biol. 2015, 365:289-300. 10.1016/j.jtbi.2014.10.026.
49. Homeyer A., Schenk A., Arlt J., Dahmen U., Dirsch O., Hahn H.K. Fast and accurate identification of fat droplets in histological images. Comput. Methods Prog. Biomed. 2015, 121(2):59-65. 10.1016/j.cmpb.2015.05.009.
50. Holzhütter H.-G., Drasdo D., Preusser T., Lippert J., Henney A.M. The virtual liver. a multidisciplinary, multi-level challenge for systems biology. Wiley Interdiscip. Rev.: Syst. Biol. Med. 2012, 4(3):221-235. 10.1002/wsbm.1158.
51. Drasdo D., Bode J., Dahmen U., Dirsch O., Dooley S., Gebhardt R., Ghallab A., Godoy P., Häussinger D., Hammad S., Hoehme S., Holzhütter H.-G., Klingmüller U., Kuepfer L., Timmer J., Zerial M., Hengstler J.G. The virtual liver. state of the art and future perspectives. Arch. Toxicol. 2014, 88(12):2071-2075. 10.1007/s00204-014-1384-6.