Login / Signup

The importance of inter-individual Kupffer cell variability in the governance of hepatic toxicity in a 3D primary human liver microtissue model.

Ali KermanizadehDavid M BrownWolfgang MoritzVicki Stone
Published in: Scientific reports (2019)
The potential for nanomaterial (NM) translocation to secondary organs is a realistic prospect, with the liver one of the most important target organs. Traditional in vitro or ex vivo hepatic toxicology models are often limiting and/or troublesome (i.e. short life-span reduced metabolic activity, lacking important cell populations, high inter-individual variability, etc.). Building on previous work, this study utilises a 3D human liver microtissue (MT) model (MT composed of mono-culture of hepatocytes or two different co-culture MT systems with non-parenchymal cell (NPC) fraction sourced from different donors) to investigate the importance of inter-donor variability of the non-parenchymal cell population in the overall governance of toxicological response following exposure to a panel of NMs. To the best of our knowledge, this is the first study of its kind to investigate inter-donor variability in hepatic NPC population. The data showed that the Kupffer cells were crucial in dictating the overall hepatic toxicity following exposure to the materials. Furthermore, a statistically significant difference was noted between the two co-culture MT models. However, the trend for particle-induced biological responses was similar between the co-cultures (cytotoxicity, cytokine production and caspase activity). Therefore, despite the recognition of some discrepancies in the absolute values between the co-culture models, the fact that the trends and patterns of biological responses were comparable between the multi-cellular models we propose the 3D liver MT to be a valuable tool in particle toxicology.
Keyphrases
  • single cell
  • cell therapy
  • induced apoptosis
  • healthcare
  • stem cells
  • cell death
  • machine learning
  • liver injury
  • drug induced
  • big data
  • climate change
  • artificial intelligence
  • deep learning
  • stress induced
  • current status