A 3D Bioprinted In Vitro Model of Pulmonary Artery Atresia to Evaluate Endothelial Cell Response to Microenvironment

Targeted Rapamycin Delivery via Magnetic Nanoparticles to Address Stenosis in a 3D Bioprinted in Vitro Model of Pulmonary Veins

Vascular cell overgrowth and lumen size reduction in pulmonary vein stenosis (PVS) can result in elevated PV pressure, pulmonary hypertension, cardiac failure, and death. Administration of chemotherapies such as rapamycin have shown promise by inhibiting the vascular cell proliferation; yet clinical success is limited due to complications such as restenosis and off-target effects. The lack of in vitro models to recapitulate the complex pathophysiology of PVS has hindered the identification of disease mechanisms and therapies. This study integrated 3D bioprinting, functional nanoparticles, and perfusion bioreactors to develop a novel in vitro model of PVS. Bioprinted bifurcated PV constructs are seeded with endothelial cells (ECs) and perfused, demonstrating the formation of a uniform and viable endothelium. Computational modeling identified the bifurcation point at high risk of EC overgrowth. Application of an external magnetic field enabled targeting of the rapamycin-loaded superparamagnetic iron oxide nanoparticles at the bifurcation site, leading to a significant reduction in EC proliferation with no adverse side effects. These results establish a 3D bioprinted in vitro model to study PV homeostasis and diseases, offering the potential for increased throughput, tunability, and patient specificity, to test new or more effective therapies for PVS and other vascular diseases.

Computational Fluid Dynamics (CFD) Analysis of Bioprinting

Regenerative medicine has evolved with the rise of tissue engineering due to advancements in healthcare and technology. In recent years, bioprinting has been an upcoming approach to traditional tissue engineering practices, through the fabrication of functional tissue by its layer-by-layer deposition process. This overcomes challenges such as irregular cell distribution and limited cell density, and it can potentially address organ shortages, increasing transplant options. Bioprinting fully functional organs is a long stretch but the advancement is rapidly growing due to its precision and compatibility with complex geometries. Computational Fluid Dynamics (CFD), a carestone of computer-aided engineering, has been instrumental in assisting bioprinting research and development by cutting costs and saving time. CFD optimizes bioprinting by testing parameters such as shear stress, diffusivity, and cell viability, reducing repetitive experiments and aiding in material selection and bioprinter nozzle design. This review discusses the current application of CFD in bioprinting and its potential to enhance the technology that can contribute to the evolution of regenerative medicine.

Leveraging 3D Bioprinting and Photon-Counting Computed Tomography to Enable Noninvasive Quantitative Tracking of Multifunctional Tissue Engineered Constructs

3D bioprinting is revolutionizing the fields of personalized and precision medicine by enabling the manufacturing of bioartificial implants that recapitulate the structural and functional characteristics of native tissues. However, the lack of quantitative and noninvasive techniques to longitudinally track the function of implants has hampered clinical applications of bioprinted scaffolds. In this study, multimaterial 3D bioprinting, engineered nanoparticles (NPs), and spectral photon-counting computed tomography (PCCT) technologies are integrated for the aim of developing a new precision medicine approach to custom-engineer scaffolds with traceability. Multiple CT-visible hydrogel-based bioinks, containing distinct molecular (iodine and gadolinium) and NP (iodine-loaded liposome, gold, methacrylated gold (AuMA), and Gd2 O3 ) contrast agents, are used to bioprint scaffolds with varying geometries at adequate fidelity levels. In vitro release studies, together with printing fidelity, mechanical, and biocompatibility tests identified AuMA and Gd2 O3 NPs as optimal reagents to track bioprinted constructs. Spectral PCCT imaging of scaffolds in vitro and subcutaneous implants in mice enabled noninvasive material discrimination and contrast agent quantification. Together, these results establish a novel theranostic platform with high precision, tunability, throughput, and reproducibility and open new prospects for a broad range of applications in the field of precision and personalized regenerative medicine.

Vat photopolymerization 3D printed microfluidic devices for organ-on-a-chip applications

Organs-on-a-chip, or OoCs, are microfluidic tissue culture devices with micro-scaled architectures that repeatedly achieve biomimicry of biological phenomena. They are well positioned to become the primary pre-clinical testing modality as they possess high translational value. Current methods of fabrication have facilitated the development of many custom OoCs that have generated promising results. However, the reliance on microfabrication and soft lithographic fabrication techniques has limited their prototyping turnover rate and scalability. Additive manufacturing, known commonly as 3D printing, shows promise to expedite this prototyping process, while also making fabrication easier and more reproducible. We briefly introduce common 3D printing modalities before identifying two sub-types of vat photopolymerization - stereolithography (SLA) and digital light processing (DLP) - as the most advantageous fabrication methods for the future of OoC development. We then outline the motivations for shifting to 3D printing, the requirements for 3D printed OoCs to be competitive with the current state of the art, and several considerations for achieving successful 3D printed OoC devices touching on design and fabrication techniques, including a survey of commercial and custom 3D printers and resins. In all, we aim to form a guide for the end-user to facilitate the in-house generation of 3D printed OoCs, along with the future translation of these important devices.

Novel rare genetic variants of familial and sporadic pulmonary atresia identified by whole-exome sequencing

Pulmonary atresia (PA) is a severe cyanotic congenital heart disease. Although some genetic mutations have been described to be associated with PA, the knowledge of pathogenesis is insufficient. The aim of this research was to use whole-exome sequencing (WES) to determine novel rare genetic variants in PA patients. We performed WES in 33 patients (27 patient-parent trios and 6 single probands) and 300 healthy control individuals. By applying an enhanced analytical framework to incorporate de novo and case-control rare variation, we identified 176 risk genes (100 de novo variants and 87 rare variants). Protein‒protein interaction (PPI) analysis and Genotype-Tissue Expression analysis revealed that 35 putative candidate genes had PPIs with known PA genes with high expression in the human heart. Expression quantitative trait loci analysis revealed that 27 genes that were identified as novel PA genes that could be affected by the surrounding single nucleotide polymorphism were screened. Furthermore, we screened rare damaging variants with a threshold of minor allele frequency at 0.5% in the ExAC_EAS and GnomAD_exome_EAS databases, and the deleteriousness was predicted by bioinformatics tools. For the first time, 18 rare variants in 11 new candidate genes have been identified that may play a role in the pathogenesis of PA. Our research provides new insights into the pathogenesis of PA and helps to identify the critical genes for PA.

Stereo Coverage and Overall Stiffness of Biomaterial Arrays Underly Parts of Topography Effects on Cell Adhesion

Surface topography is a biophysical factor affecting cell behaviors, yet the underlying cues are still not clear. Herein, we hypothesized that stereo coverage and overall stiffness of biomaterial arrays on the scale of single cells underly parts of topography effects on cell adhesion. We fabricated a series of microarrays (micropillar, micropit, and microtube) of poly(l-lactic acid) (PLLA) using mold casting based on pre-designed templates. The characteristic sizes of array units were less than that of a single cell, and thus, each cell could sense the micropatterns with varied roughness. With human umbilical vein endothelial cells (HUVECs) as the model cell type, we examined spreading areas and cell viabilities on different surfaces. "Stereo coverage" was defined to quantify the actual cell spreading fraction on a topographic surface. Particularly in the case of high micropillars, cells were confirmed not able to touch the bottom and had to partially hang among the micropillars. Then, in our opinion, a cell sensed the overall stiffness combining the bulk stiffness of the raw material and the stiffness of the culture medium. Spreading area and single cell viability were correlated to coverage and topographic feature of the prepared microarrays in particular with the significantly protruded geometry feather. Cell traction forces exerted on micropillars were also discussed. These findings provide new insights into the surface modifications toward biomedical implants.

Modelling blood flow in patients with heart valve disease using deep learning: A computationally efficient method to expand diagnostic capabilities in clinical routine

Introduction

The computational modelling of blood flow is known to provide vital hemodynamic parameters for diagnosis and treatment-support for patients with valvular heart disease. However, most diagnosis/treatment-support solutions based on flow modelling proposed utilize time- and resource-intensive computational fluid dynamics (CFD) and are therefore difficult to implement into clinical practice. In contrast, deep learning (DL) algorithms provide results quickly with little need for computational power. Thus, modelling blood flow with DL instead of CFD may substantially enhances the usability of flow modelling-based diagnosis/treatment support in clinical routine. In this study, we propose a DL-based approach to compute pressure and wall-shear-stress (WSS) in the aorta and aortic valve of patients with aortic stenosis (AS).

Methods

A total of 103 individual surface models of the aorta and aortic valve were constructed from computed tomography data of AS patients. Based on these surface models, a total of 267 patient-specific, steady-state CFD simulations of aortic flow under various flow rates were performed. Using this simulation data, an artificial neural network (ANN) was trained to compute spatially resolved pressure and WSS using a centerline-based representation. An unseen test subset of 23 cases was used to compare both methods.

Results

ANN and CFD-based computations agreed well with a median relative difference between both methods of 6.0% for pressure and 4.9% for wall-shear-stress. Demonstrating the ability of DL to compute clinically relevant hemodynamic parameters for AS patients, this work presents a possible solution to facilitate the introduction of modelling-based treatment support into clinical practice.

3D-bioprinted, phototunable hydrogel models for studying adventitial fibroblast activation in pulmonary arterial hypertension

Pulmonary arterial hypertension (PAH) is a progressive disease of the lung vasculature, characterized by elevated pulmonary blood pressure, remodeling of the pulmonary arteries, and ultimately right ventricular failure. Therapeutic interventions for PAH are limited in part by the lack ofin vitroscreening platforms that accurately reproduce dynamic arterial wall mechanical properties. Here we present a 3D-bioprinted model of the pulmonary arterial adventitia comprised of a phototunable poly(ethylene glycol) alpha methacrylate (PEG-αMA)-based hydrogel and primary human pulmonary artery adventitia fibroblasts (HPAAFs). This unique biomaterial emulates PAH pathogenesisin vitrothrough a two-step polymerization reaction. First, PEG-αMA macromer was crosslinked off-stoichiometry by 3D bioprinting an acidic bioink solution into a basic gelatin support bath initiating a base-catalyzed thiol-ene reaction with synthetic and biodegradable crosslinkers. Then, matrix stiffening was induced by photoinitiated homopolymerization of unreacted αMA end groups. A design of experiments approach produced a hydrogel platform that exhibited an initial elastic modulus (E) within the range of healthy pulmonary arterial tissue (E= 4.7 ± 0.09 kPa) that was stiffened to the pathologic range of hypertensive tissue (E= 12.8 ± 0.47 kPa) and supported cellular proliferation over time. A higher percentage of HPAAFs cultured in stiffened hydrogels expressed the fibrotic marker alpha-smooth muscle actin than cells in soft hydrogels (88 ± 2% versus 65 ± 4%). Likewise, a greater percentage of HPAAFs were positive for the proliferation marker 5-ethynyl-2'-deoxyuridine (EdU) in stiffened models (66 ± 6%) compared to soft (39 ± 6%). These results demonstrate that 3D-bioprinted, phototunable models of pulmonary artery adventitia are a tool that enable investigation of fibrotic pathogenesisin vitro.

A 3D Bioprinted in vitro Model of Neuroblastoma Recapitulates Dynamic Tumor-Endothelial Cell Interactions Contributing to Solid Tumor Aggressive Behavior

Neuroblastoma (NB) is the most common extracranial tumor in children resulting in substantial morbidity and mortality. A deeper understanding of the NB tumor microenvironment (TME) remains an area of active research but there is a lack of reliable and biomimetic experimental models. This study utilizes a 3D bioprinting approach, in combination with NB spheroids, to create an in vitro vascular model of NB for exploring the tumor function within an endothelialized microenvironment. A gelatin methacryloyl (gelMA) bioink is used to create multi-channel cubic tumor analogues with high printing fidelity and mechanical tunability. Human-derived NB spheroids and human umbilical vein endothelial cells (HUVECs) are incorporated into the biomanufactured gelMA and cocultured under static versus dynamic conditions, demonstrating high levels of survival and growth. Quantification of NB-EC integration and tumor cell migration suggested an increased aggressive behavior of NB when cultured in bioprinted endothelialized models, when cocultured with HUVECs, and also as a result of dynamic culture. This model also allowed for the assessment of metabolic, cytokine, and gene expression profiles of NB spheroids under varying TME conditions. These results establish a high throughput research enabling platform to study the TME-mediated cellular-molecular mechanisms of tumor growth, aggression, and response to therapy.

4D Printing Applications in the Development of Smart Cardiovascular Implants

Smart materials are able to react to different stimuli and adapt their shape to the environment. Although the development of 3D printing technology increased the reproducibility and accuracy of scaffold fabrication, 3D printed scaffolds can still be further improved to resemble the native anatomy. 4D printing is an innovative fabrication approach combining 3D printing and smart materials, also known as stimuli-responsive materials. Especially for cardiovascular implants, 4D printing can promisingly create programmable, adaptable prostheses, which facilitates implantation and/or create the topology of the target tissue post implantation. In this review, the principles of 4D printing with a focus on the applied stimuli are explained and the underlying 3D printing technologies are presented. Then, according to the type of stimulus, recent applications of 4D printing in constructing smart cardiovascular implants and future perspectives are discussed.

Hydrogels for Tissue Engineering: Addressing Key Design Needs Toward Clinical Translation

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