Effect of Hepatic Differentiation on Fatty Acid Composition of Induced Pluripotent Stem Cells Derived from Human Dermal Fibroblasts

Abstract

Introduction: Human induced pluripotent stem cells (hiPSCs) have been recognized as key progenitor cells for liver regeneration, which could potentially be used in the repair of liver injury. Differentiation of stem cells involves specific changes in lipid metabolism to form competent hepatic cells. However, the relationship between the hepatic differentiation of hiPSCs and change in cellular lipids has not been well characterized. The aim of the present study was to determine the fatty acid pattern during hepatic differentiation of hiPSCs derived from dermal fibroblasts. Methods: Hepatocyte differentiation was induced in three stages using Wnt family member 3A, hepatocyte growth factor, and oncostatin M and was impaired by a chemical extracellular signal-regulated kinase signaling inhibitor. Hepatocyte-specific metabolic markers including gamma-glutamyltransferase and aminotransferases were evaluated using kinetic chromogenic assay kits. During the hepatic differentiation of hiPSCs, changes in individual fatty acids were determined at multiple time points using gas-liquid chromatography. Results: Significant increases were observed in hepatic enzyme markers during hepatic differentiation. Endodermal induction produced a transient increase in saturated fatty acids (33%, p<0.01) and decrease in monounsaturated fatty acids (-15%, p<0.01). Total n-6 polyunsaturated fatty acid was elevated in undifferentiated hiPSCs, and a gradual downward trend was observed after endodermal induction (-10%, p=0.08) and hepatic lineage commitment (-19%, p<0.01). All of these changes in metabolic differentiation markers and individual fatty acids were suppressed by impaired induced hepatic differentiation. Conclusion: Our findings indicate that the pattern of cellular fatty acids is dynamic and changes with the progress of hepatic differentiation, which includes both transient fluctuations and linear trends.