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. 2022 Dec 28;97(2):523–545. doi: 10.1007/s00204-022-03436-6

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© The Author(s) 2022

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 2 — Basal transcriptomic differences across cell models a Principal Component Analysis (PCA) using rlog transformed expression values of mRNA counts. Untreated samples (medium controls) from each cell system were used as input for PCA. Each dot represented a sample, color-coded by cell type: HUVEC/TERT2, HepaRG, RPTEC/TERT1, LUHMES, UKN5, BCC42. b mRNA baseline expression of genes representative of each cell model from untreated samples: CD31 protein-coding gene (PECAM1), Von Willebrand factor (VWF), kinase domain receptor (KDR), albumin (ALB), hepatocyte nuclear factor 4 alpha (HNF4A), carboxylesterase 1 (CES1), claudin 2 (CLDN2), mucin 1 (MUC1), Adhesion Molecule With Ig Like Domain 2 (AMIGO2), neurofilament heave polypeptide gene (NEFH), solute carrier family 6, A3 (SLC6A3), insulin gene enhancer protein (ISL1), paired-box homeotic gene 3 (PAX3), aquaporin-4 (AQP4), and glial fibrillary acidic protein (GFAP)