3D-printed gelatin scaffolds of differing pore geometry modulate hepatocyte function and gene expression.

Three dimensional (3D) printing is highly amenable to the fabrication of tissue-engineered organs of a repetitive microstructure such as the liver. The creation of uniform and geometrically repetitive tissue scaffolds can also allow for the control over cellular aggregation and nutrient diffusion. However, the effect of differing geometries, while controlling for pore size, has yet to be investigated in the context of hepatocyte function. In this study, we show the ability to precisely control pore geometry of 3D-printed gelatin scaffolds. An undifferentiated hepatocyte cell line (HUH7) demonstrated high viability and proliferation when seeded on 3D-printed scaffolds of two different geometries. However, hepatocyte specific functions (albumin secretion, CYP activity, and bile transport) increases in more interconnected 3D-printed gelatin cultures compared to a less interconnected geometry and to 2D controls. Additionally, we also illustrate the disparity between gene expression and protein function in simple 2D culture modes, and that recreation of a physiologically mimetic 3D environment is necessary to induce both expression and function of cultured hepatocytes. STATEMENT OF SIGNIFICANCE: Three dimensional (3D) printing provides tissue engineers the ability spatially pattern cells and materials in precise geometries, however the biological effects of scaffold geometry on soft tissues such as the liver have not been rigorously investigated. In this manuscript, we describe a method to 3D print gelatin into well-defined repetitive geometries that show clear differences in biological effects on seeded hepatocytes. We show that a relatively simple and widely used biomaterial, such as gelatin, can significantly modulate biological processes when fabricated into specific 3D geometries. Furthermore, this study expands upon past research into hepatocyte aggregation by demonstrating how it can be manipulated to enhance protein function, and how function and expression may not precisely correlate in 2D models.