Dataset Information

Towards the Experimentally-Informed In Silico Nozzle Design Optimization for Extrusion-Based Bioprinting of Shear-Thinning Hydrogels.

ABSTRACT: Research in bioprinting is booming due to its potential in addressing several manufacturing challenges in regenerative medicine. However, there are still many hurdles to overcome to guarantee cell survival and good printability. For the 3D extrusion-based bioprinting, cell viability is amongst one of the lowest of all the bioprinting techniques and is strongly influenced by various factors including the shear stress in the print nozzle. The goal of this study is to quantify, by means of in silico modeling, the mechanical environment experienced by the bioink during the printing process. Two ubiquitous nozzle shapes, conical and blunted, were considered, as well as three common hydrogels with material properties spanning from almost Newtonian to highly shear-thinning materials following the power-law behavior: Alginate-Gelatin, Alginate and PF127. Comprehensive in silico testing of all combinations of nozzle geometry variations and hydrogels was achieved by combining a design of experiments approach (DoE) with a computational fluid dynamics (CFD) of the printing process, analyzed through a machine learning approach named Gaussian Process. Available experimental results were used to validate the CFD model and justify the use of shear stress as a surrogate for cell survival in this study. The lower and middle nozzle radius, lower nozzle length and the material properties, alone and combined, were identified as the major influencing factors affecting shear stress, and therefore cell viability, during printing. These results were successfully compared with those of reported experiments testing viability for different nozzle geometry parameters under constant flow rate or constant pressure. The in silico 3D bioprinting platform developed in this study offers the potential to assist and accelerate further development of 3D bioprinting.

SUBMITTER: Reina-Romo E

PROVIDER: S-EPMC8378215 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:3D bioprinting of living cellular constructs with heterogeneity in cell types and extra cellular matrices (ECMs) matching those of biological tissues remains challenging. Here, we demonstrate that, through bioink material design, microextrusion-based (ME) bioprinting techniques have the potential to address this challenge. A new bioink employing alginate (1%), cellulose nanocrystal (CNC) (3%), and gelatin methacryloyl (GelMA) (5%) (namely 135ACG hybrid ink) was formulated for the direct printing of cell-laden and acellular architectures. The 135ACG ink displayed excellent shear-thinning behavior and solid-like properties, leading to high printability without cell damage. After crosslinking, the ACG gel can also provide a stiff ECM ideal for stromal cell growth. By controlling the degree of substitution and polymer concentration, a GelMA (4%) bioink was designed to encapsulate hepatoma cells (hepG2), as GelMA gel possesses the desired low mechanical stiffness matching that of human liver tissue. Four different versions of to-scale liver lobule-mimetic constructs were fabricated via ME bioprinting, with precise positioning of two different cell types (NIH/3T3 and hepG2) embedded in matching ECMs (135ACG and GelMA, respectively). The four versions allowed us to exam effects of mechanical cues and intercellular interactions on cell behaviors. Fibroblasts thrived in stiff 135ACG matrix and aligned at the 135ACG/GelMA boundary due to durotaxis, while hepG2 formed spheroids exclusively in the soft GelMA matrix. Elevated albumin production was observed in the bicellular 3D co-culture of hepG2 and NIH/3T3, both with and without direct intercellular contact, indicating that improved hepatic cell function can be attributed to soluble chemical factors. Overall, our results showed that complex constructs with multiple cell types and varying ECMs can be bioprinted and potentially useful for both fundamental biomedical research and translational tissue engineering.

Dataset Information

Towards the Experimentally-Informed In Silico Nozzle Design Optimization for Extrusion-Based Bioprinting of Shear-Thinning Hydrogels.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets