Integrating Experimental and Computational Approaches to Optimize 3D Bioprinting of Cancer Cells

A key feature distinguishing 3D bioprinting from other 3D cell culture techniques is its precise control over created structures. This property allows for the high-resolution fabrication of biomimetic structures with controlled structural and mechanical properties such as porosity, permeability, and stiffness. However, for bioprinting to be successful, a comprehensive understanding of cell behavior is essential, yet challenging. This includes the survivability of cells throughout the printing process, their interactions with the printed structures, and their responses to environmental cues after printing. There are numerous variables in bioprinting which influence the cell behavior, so bioprinting quality during and after the procedure. Thus, to achieve desirable results, it is necessary to consider and optimize these influential variables. So far, these optimizations have been accomplished primarily through trial and error and replicating several experiments, a procedure that is not only time-consuming but also costly. This issue motivated the development of computational techniques in the bioprinting process to more precisely predict and elucidate cells’ function within 3D printed structures during and after printing. During printing, we developed predictive machine learning models to determine the effect of different variables such as cell type, bioink formulation, printing settings parameters, and crosslinking condition on cell viability in extrusion-based bioprinting. To do this, we first created a dataset of these parameters for gelatin and alginate-based bioinks and the corresponding cell viability by integrating data obtained in our laboratory and those derived from the literature. Then, we developed regression and classification neural networks to predict cell viability based on these bioprinting variables. Compared to models that have been developed so far, the performance of our models was superior and showed great prediction results. The study further demonstrated that among the variables investigated in bioprinting, cell type, printing pressure, and crosslinker concentration, respectively, had the most significant impact on the survival of cells. Additionally, we introduced a new optimization strategy that employs the Bayesian optimization model based on the developed regression neural network to determine the optimal combination of the selected bioprinting parameters for maximizing cell viability and eliminating trial-and-error experiments. In our study, this strategy enabled us to identify the optimal crosslinking parameters, within a specified range, including those not previously explored, resulting in optimum cell viability. Finally, we experimentally validated the optimization model's performance. After printing, we developed a cellular automata model for the first time to predict and elucidate the post-printing cell behavior within the 3D bioprinted construct. To improve our model, we bioprinted a 3D construct using cell-laden hydrogel and evaluated cellular functions, including viability and proliferation, in 11 days. The results showed that our model successfully simulated the 3D bioprinted structure and captured in-vitro observations. The proposed model is beneficial for demonstrating complex cellular systems, including cellular proliferation, movement, cell interactions with the environment (e.g., extracellular microenvironment and neighboring cells), and cell aggregation within the scaffold. We also demonstrated that this computational model could predict post-printing biological functions for different initial cell numbers in bioink and different bioink formulations with gelatin and alginate without replicating several in-vitro measurements. Taken all together, this thesis introduces novel bioprinting process design strategies by presenting mathematical and computational frameworks for both during and after bioprinting. We believe such frameworks will substantially impact 3D bioprinting's future application and inspire researchers to further realize how computational methods might be utilized to advance in-vitro 3D bioprinting research

\u3cem\u3eIn vitro\u3c/em\u3e Effect of Graphene Structures as an Osteoinductive Factor in Bone Tissue Engineering: A Systematic Review

Graphene and its derivatives have been well‐known as influential factors in differentiating stem/progenitor cells toward the osteoblastic lineage. However, there have been many controversies in the literature regarding the parameters effect on bone regeneration, including graphene concentration, size, type, dimension, hydrophilicity, functionalization, and composition. This study attempts to produce a comprehensive review regarding the given parameters and their effects on stimulating cell behaviors such as proliferation, viability, attachment and osteogenic differentiation. In this study, a systematic search of MEDLINE database was conducted for in vitro studies on the use of graphene and its derivatives for bone tissue engineering from January 2000 to February 2018, organized according to the PRISMA statement. According to reviewed articles, different graphene derivative, including graphene, graphene oxide (GO) and reduced graphene oxide (RGO) with mass ratio ≤1.5 wt % for all and concentration up to 50 μg/mL for graphene and GO, and 60 μg/mL for RGO, are considered to be safe for most cell types. However, these concentrations highly depend on the types of cells. It was discovered that graphene with lateral size less than 5 µm, along with GO and RGO with lateral dimension less than 1 µm decrease cell viability. In addition, the three‐dimensional structure of graphene can promote cell‐cell interaction, migration and proliferation. When graphene and its derivatives are incorporated with metals, polymers, and minerals, they frequently show promoted mechanical properties and bioactivity. Last, graphene and its derivatives have been found to increase the surface roughness and porosity, which can highly enhance cell adhesion and differentiation

Predicting and elucidating the post-printing behavior of 3D printed cancer cells in hydrogel structures by integrating in-vitro and in-silico experiments

Abstract A key feature distinguishing 3D bioprinting from other 3D cell culture techniques is its precise control over created structures. This property allows for the high-resolution fabrication of biomimetic structures with controlled structural and mechanical properties such as porosity, permeability, and stiffness. However, analyzing post-printing cellular dynamics and optimizing their functions within the 3D fabricated environment is only possible through trial and error and replicating several experiments. This issue motivated the development of a cellular automata model for the first time to simulate post-printing cell behaviour within the 3D bioprinted construct. To improve our model, we bioprinted a 3D construct using MDA-MB-231 cell-laden hydrogel and evaluated cellular functions, including viability and proliferation in 11 days. The results showed that our model successfully simulated the 3D bioprinted structure and captured in-vitro observations. We demonstrated that in-silico model could predict and elucidate post-printing biological functions for different initial cell numbers in bioink and different bioink formulations with gelatine and alginate, without replicating several costly and time-consuming in-vitro measurements. We believe such a computational framework will substantially impact 3D bioprinting's future application. We hope this study inspires researchers to further realize how an in-silico model might be utilized to advance in-vitro 3D bioprinting research

Controlled tumor heterogeneity in a co-culture system by 3D bio-printed tumor-on-chip model

Abstract Cancer treatment resistance is a caused by presence of various types of cells and heterogeneity within the tumor. Tumor cell–cell and cell-microenvironment interactions play a significant role in the tumor progression and invasion, which have important implications for diagnosis, and resistance to chemotherapy. In this study, we develop 3D bioprinted in vitro models of the breast cancer tumor microenvironment made of co-cultured cells distributed in a hydrogel matrix with controlled architecture to model tumor heterogeneity. We hypothesize that the tumor could be represented by a cancer cell-laden co-culture hydrogel construct, whereas its microenvironment can be modeled in a microfluidic chip capable of producing a chemical gradient. Breast cancer cells (MCF7 and MDA-MB-231) and non-tumorigenic mammary epithelial cells (MCF10A) were embedded in the alginate-gelatine hydrogels and printed using a multi-cartridge extrusion bioprinter. Our approach allows for precise control over position and arrangements of cells in a co-culture system, enabling the design of various tumor architectures. We created samples with two different types of cells at specific initial locations, where the density of each cell type was carefully controlled. The cells were either randomly mixed or positioned in sequential layers to create cellular heterogeneity. To study cell migration toward chemoattractant, we developed a chemical microenvironment in a chamber with a gradual chemical gradient. As a proof of concept, we studied different migration patterns of MDA-MB-231 cells toward the epithelial growth factor gradient in presence of MCF10A cells in different ratios using this device. Our approach involves the integration of 3D bioprinting and microfluidic devices to create diverse tumor architectures that are representative of those found in various patients. This provides an excellent tool for studying the behavior of cancer cells with high spatial and temporal resolution

Incorporation of Functionalized Reduced Graphene Oxide/magnesium Nanohybrid to Enhance the Osteoinductivity Capability of 3D Printed Calcium Phosphate-based Scaffolds

Improving bone regeneration is one of the most pressing problems facing bone tissue engineering (BTE) which can be tackled by incorporating different biomaterials into the fabrication of the scaffolds. The present study aims to apply the 3D-printing and freeze-drying methods to design an ideal scaffold for improving the osteogenic capacity of Dental pulp stem cells (DPSCs). To achieve this purpose, hybrid constructs consisted of 3D-printed Beta-tricalcium phosphate (β-TCP)-based scaffolds filled with freeze-dried gelatin/reduced graphene oxide-Magnesium-Arginine (GRMA) matrix were fabricated through a novel green method. The effect of different concentrations of Reduced graphene oxide-Magnesium-Arginine (RMA) (0, 0.25% and 0.75%wt) on the morphology, mechanical properties, and biological activity of the 3D scaffolds were completely evaluated. Our findings show that the incorporation of RMA hybrid into the scaffold can remarkably enhance its mechanical features and improve cell proliferation and differentiation simultaneously. Of all scaffolds, β-TCP/0.25GRMA showed not only the highest ALP activity and cell proliferation after 14 days but it up-regulated bone-related genes and proteins (COL-I, RUNX2, OCN). Hence, the fabricated 3D printed β-TCP/0.25GRMA porous scaffolds can be considered as a high-potential candidate for BTE