Non-alcoholic steatohepatitis (NASH) is a severe chronic liver disease that affects 3 to 5 percent of the world population. It is characterized by hepatic lipid accumulation and inflammation and can progress towards fibrosis, cirrhosis and hepatocellular carcinoma. Until today, no drug has been approved for the treatment of NASH. This delay relates to the complex pathogenesis of NASH and also to a lack of appropriate predictive preclinical testing systems. Furthermore, the human specificity of the NASH pathology hampers a fortiori clinical translation of animal studies. Therefore, we recently employed human skin-derived precursors (hSKP) differentiated to hepatocyte-like cells (hSKP-HPC) as a human-relevant cell source for modelling NASH in vitro. Using this in vitro NASH model, it was possible to test novel drugs being developed for anti-NASH therapy, such as elafibranor. Since steatosis is an important aspect of NASH and multiple drugs are being developed to decelerate and reduce lipid accumulation in the liver, we optimized a flow cytometric method for quantifying neutral lipids in 'NASH'-triggered hSKP-HPC. This methodology enables efficient identification of anti-steatotic properties of new medicines. • NASH-triggered hSKP-HPC robustly accumulate lipids intracellularly. • Flow cytometric quantification of neutral lipids in NASH-triggered hSKP-HPC allows for accurate determination of the steatotic response. • This method enables efficient identification of potential anti-steatotic drugs in a human-specific model.

Original languageEnglish
Article number101068
Number of pages7
Publication statusPublished - 2020

Bibliographical note

© 2020 The Authors. Published by Elsevier B.V.


  • Adult stem cells
  • Flow cytometry
  • Human skin-derived precursors (HSKP)
  • In vitro
  • Non-alcoholic steatohepatitis (NASH)
  • Preclinical drug testing
  • Steatosis

Fingerprint Dive into the research topics of 'Flow cytometric quantification of neutral lipids in a human skin stem cell-derived model of NASH'. Together they form a unique fingerprint.

Cite this