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Zonated quantification of immunohistochemistry in normal and steatotic livers

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Abstract

Immunohistochemical stains (IHC) reveal differences between liver lobule zones in health and disease, including nonalcoholic fatty liver disease (NAFLD). However, such differences are difficult to accurately quantify. In NAFLD, the presence of lipid vacuoles from macrovesicular steatosis further hampers interpretation by pathologists. To resolve this, we applied a zonal image analysis method to measure the distribution of hypoxia markers in the liver lobule of steatotic livers.

The hypoxia marker pimonidazole was assessed with IHC in the livers of male C57BL/6 J mice on standard diet or choline-deficient L-amino acid-defined high-fat diet mimicking NAFLD. Another hypoxia marker, carbonic anhydrase IX, was evaluated by IHC in human liver tissue. Liver lobules were reconstructed in whole slide images, and staining positivity was quantified in different zones in hundreds of liver lobules. This method was able to quantify the physiological oxygen gradient along hepatic sinusoids in normal livers and panlobular spread of the hypoxia in NAFLD and to overcome the pronounced impact of macrovesicular steatosis on IHC. In a proof-of-concept study with an assessment of the parenchyma between centrilobular veins in human liver biopsies, carbonic anhydrase IX could be quantified correctly as well.

The method of zonated quantification of IHC objectively quantifies the difference in zonal distribution of hypoxia markers (used as an example) between normal and NAFLD livers both in whole liver as well as in liver biopsy specimens. It constitutes a tool for liver pathologists to support visual interpretation and estimate the impact of steatosis on IHC results.

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Data availability

The whole slide image data used to support the findings of this study are available from the corresponding author at cedric.peleman@uantwerpen.be upon request.

Abbreviations

ABC :

Avidin-biotin complex

CAIX :

Carbonic anhydrase IX

CDAHFD :

Choline-deficient L-amino acid-defined high-fat diet

CL :

Centrilobular

DAB :

3,3′-Diaminobenzidine

FFPE :

Formalin-fixed paraffin-embedded

GS :

Glutamine synthetase

H&E :

Haematoxylin-eosin

HRP :

Horse radish peroxidase

ICC :

Intraclass correlation coefficient

IHC :

Immunohistochemistry

NAFLD :

Nonalcoholic fatty liver disease

NASH :

Nonalcoholic steatohepatitis

Pimo :

Pimonidazole

PP :

Periportal

References

  1. Droin C, El KJ, Bahar Halpern K et al (2021) Space-time logic of liver gene expression at sub-lobular scale. Nat Metab 3:43–58. https://doi.org/10.1038/s42255-020-00323-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Seki S, Kitada T, Yamada T et al (2002) In situ detection of lipid peroxidation and oxidative DNA damage in non-alcoholic fatty liver diseases. J Hepatol 37:56–62. https://doi.org/10.1016/S0168-8278(02)00073-9

    Article  CAS  PubMed  Google Scholar 

  3. Meyerholz DK, Beck AP (2018) Principles and approaches for reproducible scoring of tissue stains in research. Lab Investig 98:844–855. https://doi.org/10.1038/s41374-018-0057-0

    Article  PubMed  Google Scholar 

  4. Walker RA (2006) Quantification of immunohistochemistry - issues concerning methods, utility and semiquantitative assessment I. Histopathology 49:406–410. https://doi.org/10.1111/j.1365-2559.2006.02514.x

    Article  CAS  PubMed  Google Scholar 

  5. Lau C, Kalantari B, Batts KP et al (2021) The Voronoi theory of the normal liver lobular architecture and its applicability in hepatic zonation. Sci Rep 11:9343. https://doi.org/10.1038/s41598-021-88699-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Taylor-Weiner A, Pokkalla H, Han L et al (2021) A machine learning approach enables quantitative measurement of liver histology and disease monitoring in NASH. Hepatology 74:133–147. https://doi.org/10.1002/hep.31750

    Article  PubMed  Google Scholar 

  7. Setiawan VW, Stram DO, Porcel J et al (2016) Prevalence of chronic liver disease and cirrhosis by underlying cause in understudied ethnic groups: the multiethnic cohort. Hepatology 64:1969–1977. https://doi.org/10.1002/hep.28677

    Article  PubMed  Google Scholar 

  8. Goldberg D, Ditah IC, Saeian K et al (2017) Changes in the prevalence of hepatitis C virus infection, nonalcoholic steatohepatitis, and alcoholic liver disease among patients with cirrhosis or liver failure on the waitlist for liver transplantation. Gastroenterology 152:1090–1099. https://doi.org/10.1053/j.gastro.2017.01.003

    Article  PubMed  Google Scholar 

  9. Wong RJ, Aguilar M, Cheung R et al (2015) Nonalcoholic steatohepatitis is the second leading etiology of liver disease among adults awaiting liver transplantation in the United States. Gastroenterology 148:547–555. https://doi.org/10.1053/j.gastro.2014.11.039

    Article  PubMed  Google Scholar 

  10. Younossi ZM, Koenig AB, Abdelatif D et al (2016) Global epidemiology of nonalcoholic fatty liver disease—meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology 64:73–84. https://doi.org/10.1002/hep.28431

    Article  PubMed  Google Scholar 

  11. Ghallab A, Myllys M, Friebel A et al (2021) Spatio-temporal multiscale analysis of western diet-fed mice reveals a translationally relevant sequence of events during NAFLD progression. Cells 10:2516. https://doi.org/10.3390/cells10102516

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Raleigh JA, Koch CJ (1990) Importance of thiols in the reductive binding of 2-nitroimidazoles to macromolecules. Biochem Pharmacol 40:2457–2464. https://doi.org/10.1016/0006-2952(90)90086-Z

    Article  CAS  PubMed  Google Scholar 

  13. Percie N, Hurst V, Ahluwalia A, et al (2020) The ARRIVE guidelines 2.0: updated guidelines for reporting animal research. BMJ Open Sci 1–7. https://doi.org/10.1136/bmjos-2020-100115

  14. Schindelin J, Arganda-Carreras I, Frise E et al (2012) Fiji: an open-source platform for biological-image analysis. Nat Methods 9:676–682. https://doi.org/10.1038/nmeth.2019

    Article  CAS  PubMed  Google Scholar 

  15. Ruifrok AC, Johnston DA (2001) Quantification of histochemical staining by color deconvolution. Anal Quant Cytol Histol 23(291–299):10–27

    Google Scholar 

  16. Doyle W (1962) Operations useful for similarity-invariant pattern recognition. J ACM 9:259–267. https://doi.org/10.1145/321119.321123

    Article  Google Scholar 

  17. Munsterman ID, van Erp M, Weijers G et al (2019) A novel automatic digital algorithm that accurately quantifies steatosis in NAFLD on histopathological whole-slide images. Cytom Part B 96:521–528. https://doi.org/10.1002/cyto.b.21790

    Article  CAS  Google Scholar 

  18. Schwen LO, Homeyer A, Schwier M et al (2016) Zonated quantification of steatosis in an entire mouse liver. Comput Biol Med 73:108–118. https://doi.org/10.1016/j.compbiomed.2016.04.004

    Article  PubMed  Google Scholar 

  19. Panday R, Monckton CP, Khetani SR (2022) The role of liver zonation in physiology, regeneration, and disease. Semin Liver Dis 42:1–16. https://doi.org/10.1055/s-0041-1742279

    Article  PubMed  Google Scholar 

  20. R Core Team (2018) R: a language and environment for statistical computinng. In: R Found. Stat. Comput. Vienna. https://www.r-project.org

  21. Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33:159–174. https://doi.org/10.2307/2529310

    Article  CAS  PubMed  Google Scholar 

  22. Matsumoto M, Hada N, Sakamaki Y et al (2013) An improved mouse model that rapidly develops fibrosis in non-alcoholic steatohepatitis. Int J Exp Pathol 94:93–103. https://doi.org/10.1111/iep.12008

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Kietzmann T (2019) Liver zonation in health and disease: hypoxia and hypoxia-inducible transcription factors as concert masters. Int J Mol Sci 20:2347. https://doi.org/10.3390/ijms20092347

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Mantena SK, Vaughn DP, Andringa KK et al (2009) High fat diet induces dysregulation of hepatic oxygen gradients and mitochondrial function in vivo. Biochem J 417:183–193. https://doi.org/10.1042/BJ20080868

    Article  PubMed  Google Scholar 

  25. Meng L, Goto M, Tanaka H et al (2021) Decreased portal circulation augments fibrosis and ductular reaction in nonalcoholic fatty liver disease in mice. Am J Pathol 191:1580–1591. https://doi.org/10.1016/j.ajpath.2021.06.001

    Article  CAS  PubMed  Google Scholar 

  26. Ben-Moshe S, Shapira Y, Moor AE et al (2019) Spatial sorting enables comprehensive characterization of liver zonation. Nat Metab 1:899–911. https://doi.org/10.1038/s42255-019-0109-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Paris J, Henderson NC (2022) Liver zonation, revisited. Hepatology 76:1219–1230. https://doi.org/10.1002/hep.32408

    Article  PubMed  Google Scholar 

  28. Cunningham RP, Porat-Shliom N (2021) Liver zonation – revisiting old questions with new technologies. Front Physiol 12:1–17. https://doi.org/10.3389/fphys.2021.732929

    Article  Google Scholar 

  29. Czaja AJ, Carpenter HA (1993) Sensitivity, specificity, and predictability of biopsy interpretations in chronic hepatitis. Gastroenterology 105:1824–1832. https://doi.org/10.1016/0016-5085(93)91081-R

    Article  CAS  PubMed  Google Scholar 

  30. Meyerholz DK, Beck AP (2018) Fundamental concepts for semiquantitative tissue scoring in translational research. ILAR J 59:13–17. https://doi.org/10.1093/ilar/ily025

    Article  CAS  PubMed  Google Scholar 

  31. McCarty K, Szabo E, Flowers J et al (1986) Use of a monoclonal anti-estrogen receptor antibody in the immunohistochemical evaluation of human tumors. Cancer Res 46:4244–4248

    Google Scholar 

  32. Gavrielides MA, Gallas BD, Lenz P et al (2011) Observer variability in the interpretation of HER2/neu immunohistochemical expression with unaided and computer-aided digital microscopy. Arch Pathol Lab Med 135:233–242. https://doi.org/10.5858/135.2.233

    Article  PubMed  PubMed Central  Google Scholar 

  33. Skaland I, Øvestad I, Janssen EAM et al (2008) Digital image analysis improves the quality of subjective HER-2 expression scoring in breast cancer. Appl Immunohistochem Mol Morphol 16:185–190. https://doi.org/10.1097/PAI.0b013e318059c20c

    Article  CAS  PubMed  Google Scholar 

  34. Rimm DL, Giltnane JM, Moeder C et al (2007) Bimodal population or pathologist artifact? [1]. J Clin Oncol 25:2487–2488. https://doi.org/10.1200/JCO.2006.07.7537

    Article  PubMed  Google Scholar 

  35. Camp RL, Dolled-Filhart M, King BL, Rimm DL (2003) Quantitative analysis of breast cancer tissue microarrays shows that both high and normal levels of HER2 expression are associated with poor outcome. Cancer Res 63:1445–1448

    CAS  PubMed  Google Scholar 

  36. Liu F, Goh GBB, Tiniakos D et al (2020) qFIBS: an automated technique for quantitative evaluation of fibrosis, inflammation, ballooning, and steatosis in patients with nonalcoholic steatohepatitis. Hepatology 71:1953–1966. https://doi.org/10.1002/hep.30986

    Article  CAS  PubMed  Google Scholar 

  37. Brunt EM, Clouston AD, Goodman Z et al (2022) Complexity of ballooned hepatocyte feature recognition: defining a training atlas for artificial intelligence-based imaging in NAFLD. J Hepatol 76:1030–1041. https://doi.org/10.1016/j.jhep.2022.01.011

    Article  CAS  PubMed  Google Scholar 

  38. Forlano R, Mullish BH, Giannakeas N et al (2020) High-throughput, machine learning–based quantification of steatosis, inflammation, ballooning, and fibrosis in biopsies from patients with nonalcoholic fatty liver disease. Clin Gastroenterol Hepatol 18:2081–2090. https://doi.org/10.1016/j.cgh.2019.12.025

    Article  PubMed  PubMed Central  Google Scholar 

  39. Bosch J, Chung C, Carrasco-Zevallos OM et al (2021) A machine learning approach to liver histological evaluation predicts clinically significant portal hypertension in NASH cirrhosis. Hepatology 74:3146–3160. https://doi.org/10.1002/hep.32087

    Article  CAS  PubMed  Google Scholar 

  40. Naoumov NV, Brees D, Loeffler J et al (2022) Digital pathology with artificial intelligence analyses provides greater insights into treatment-induced fibrosis regression in NASH. J Hepatol. https://doi.org/10.1016/j.jhep.2022.06.018.10.1016/j.jhep.2022.06.018

    Article  PubMed  Google Scholar 

  41. Davison BA, Harrison SA, Cotter G et al (2020) Suboptimal reliability of liver biopsy evaluation has implications for randomized clinical trials. J Hepatol 73:1322–1332. https://doi.org/10.1016/j.jhep.2020.06.025

    Article  CAS  PubMed  Google Scholar 

  42. Arteel GE, Iimuro Y, Yin M et al (1997) Chronic enteral ethanol treatment causes hypoxia in rat liver tissue in vivo. Hepatology 25:920–926. https://doi.org/10.1002/hep.510250422

    Article  CAS  PubMed  Google Scholar 

  43. Zaidi M, Fu F, Cojocari D et al (2019) Quantitative visualization of hypoxia and proliferation gradients within histological tissue sections. Front Bioeng Biotechnol 7:1–9. https://doi.org/10.3389/fbioe.2019.00397

    Article  Google Scholar 

  44. Swartz JE, Smits HJG, Philippens MEP et al (2022) Correlation and colocalization of HIF-1α and pimonidazole staining for hypoxia in laryngeal squamous cell carcinomas: a digital, single-cell-based analysis. Oral Oncol 128:105862. https://doi.org/10.1016/j.oraloncology.2022.105862

    Article  CAS  PubMed  Google Scholar 

  45. Podszun MC, Chung JY, Ylaya K et al (2020) 4-HNE immunohistochemistry and image analysis for detection of lipid peroxidation in human liver samples using vitamin e treatment in NAFLD as a proof of concept. J Histochem Cytochem 68:635–643. https://doi.org/10.1369/0022155420946402

    Article  CAS  PubMed  Google Scholar 

  46. Francque S, Verrijken A, Mertens I et al (2010) Noncirrhotic human nonalcoholic fatty liver disease induces portal hypertension in relation to the histological degree of steatosis. Eur J Gastroenterol Hepatol 22:1449–1457. https://doi.org/10.1097/MEG.0b013e32833f14a1

    Article  PubMed  Google Scholar 

  47. Parthasarathy G, Revelo X, Malhi H (2020) Pathogenesis of nonalcoholic steatohepatitis: an overview. Hepatol Commun 4:478–492. https://doi.org/10.1002/hep4.1479

    Article  PubMed  PubMed Central  Google Scholar 

  48. Peiseler M, Schwabe R, Hampe J et al (2022) Immune mechanisms linking metabolic injury to inflammation and fibrosis in fatty liver disease – novel insights into cellular communication circuits. J Hepatol 77:1136–1160. https://doi.org/10.1016/j.jhep.2022.06.012

    Article  CAS  PubMed  Google Scholar 

  49. Doi Y, Tamura S, Nammo T et al (2007) Development of complementary expression patterns of E- and N-cadherin in the mouse liver. Hepatol Res 37:230–237. https://doi.org/10.1111/j.1872-034X.2007.00028.x

    Article  CAS  PubMed  Google Scholar 

  50. Kisseleva T, Brenner D (2021) Molecular and cellular mechanisms of liver fibrosis and its regression. Nat Rev Gastroenterol Hepatol 18:151–166. https://doi.org/10.1038/s41575-020-00372-7

    Article  PubMed  Google Scholar 

  51. Machado MV, Michelotti GA, Xie G et al (2015) Mouse models of diet-induced nonalcoholic steatohepatitis reproduce the heterogeneity of the human disease. PLoS ONE 10:1–16. https://doi.org/10.1371/journal.pone.0127991

    Article  CAS  Google Scholar 

  52. Sabattini E, Bisgaard K, Ascani S et al (1998) The EnVision(TM)+ system: a new immunohistochemical method for diagnostics and research. Critical comparison with the APAAP, ChemMate(TM), CSA, LABC, and SABC techniques. J Clin Pathol 51:506–511. https://doi.org/10.1136/jcp.51.7.506

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank Sofie Thys for her technical assistance with scanning the slides. This study was funded by the Fund for Scientific Research (FWO) Flanders (1171121N), research grants from the University of Antwerp (GOA project: FFB180348/36572), and the Belgian Association for the Study of the Liver (BASL basic research award 2020 supported by Gilead).

Funding

C.P. received funding from the Fund for Scientific Research (FWO) Flanders (1171121 N). This study was funded by research grants from the University of Antwerp (GOA project: FFB180348/36572) and the Belgian Association for the Study of the Liver (BASL basic research award 2020 supported by Gilead). The funders had no role in the study’s design, collection, analysis or interpretation of data, or writing of the report.

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Authors and Affiliations

  1. Laboratory of Experimental Medicine and Pediatrics, Infla-Med Centre of Excellence, University of Antwerp, Universiteitsplein 1, 2610, Antwerp, Belgium

    Cédric Peleman, Annelies Van Eyck, Christophe Van Steenkiste, Luisa Vonghia, Joris De Man, Benedicte Y. De Winter, Sven M. Francque & Wilhelmus J. Kwanten

  2. Department of Gastroenterology and Hepatology, Antwerp University Hospital, Drie Eikenstraat 655, 2650, Edegem, Belgium

    Cédric Peleman, Christophe Van Steenkiste, Luisa Vonghia, Benedicte Y. De Winter, Sven M. Francque & Wilhelmus J. Kwanten

  3. Laboratory of Cell Biology & Histology, Department Veterinary Sciences, University of Antwerp, Universiteitsplein 1, 2610, Antwerp, Belgium

    Winnok H. De Vos & Isabel Pintelon

  4. Antwerp Centre for Advanced Microscopy (ACAM), University of Antwerp, Universiteitsplein 1, 2610, Antwerp, Belgium

    Winnok H. De Vos & Isabel Pintelon

  5. µNEURO Research Excellence Consortium On Multimodal Neuromics, University of Antwerp, Universiteitsplein 1, 2610, Antwerp, Belgium

    Winnok H. De Vos & Isabel Pintelon

  6. Department of Pathology, Antwerp University Hospital, Drie Eikenstraat 655, 2650, Edegem, Belgium

    Ann Driessen

  7. Laboratory of Pathophysiology, University of Antwerp, Universiteitsplein 1, 2610, Antwerp, Belgium

    Tom Vanden Berghe

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Contributions

C.P., W.H.D., T.V., and W.J.K. conceptualisation and design of research; C.P., J.D., and A.V. performed experiments; C.P. and W.H.D. software and formal analysis; C.P. drafted manuscript and prepared figures; I.P. methodology; W.H.D., I.P., A.D., A.V., C.V., L.V., J.D., B.D., T.V., S.M.F., and W.J.K. edited and revised manuscript; C.P., W.H.D., I.P., A.D., A.V., C.V., L.V., J.D., B.D., T.V., S.M.F., and W.J.K. seen and approved the final version of the manuscript.

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Correspondence to Cédric Peleman.

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All animal experiments presented in this work were approved by the Ethical Committee of Animal Experimentation of the University of Antwerp (Protocol number: 2019–42). All animals received humane care in accordance with the “Guide for the Care and Use of Laboratory Animals (Eighth Edition)” prepared by the National Academy of Sciences and published by the National Institutes of Health. Human data reported in this study was obtained from patients who gave written consent for the collection of material; the protocols conformed to the ethical guidelines of the latest version of the Declaration of Helsinki. The study was approved by the Ethical Committee of the Antwerp University Hospital (references 6/25/125 and 15/21/227).

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Peleman, C., De Vos, W.H., Pintelon, I. et al. Zonated quantification of immunohistochemistry in normal and steatotic livers. Virchows Arch 482, 1035–1045 (2023). https://doi.org/10.1007/s00428-023-03496-8

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