3D-Printed Tumor-on-a-Chip Model for Investigating the Effect of Matrix Stiffness on Glioblastoma Tumor Invasion.

Glioblastoma multiform (GBM) tumor progression has been recognized to be correlated with extracellular matrix (ECM) stiffness. Dynamic variation of tumor ECM is primarily regulated by a family of enzymes which induce remodeling and degradation. In this paper, we investigated the effect of matrix stiffness on the invasion pattern of human glioblastoma tumoroids. A 3D-printed tumor-on-a-chip platform was utilized to culture human glioblastoma tumoroids with the capability of evaluating the effect of stiffness on tumor progression. To induce variations in the stiffness of the collagen matrix, different concentrations of collagenase were added, thereby creating an inhomogeneous collagen concentration. To better understand the mechanisms involved in GBM invasion, an in silico hybrid mathematical model was used to predict the evolution of a tumor in an inhomogeneous environment, providing the ability to study multiple dynamic interacting variables. The model consists of a continuum reaction-diffusion model for the growth of tumoroids and a discrete model to capture the migration of single cells into the surrounding tissue. Results revealed that tumoroids exhibit two distinct patterns of invasion in response to the concentration of collagenase, namely ring-type and finger-type patterns. Moreover, higher concentrations of collagenase resulted in greater invasion lengths, confirming the strong dependency of tumor behavior on the stiffness of the surrounding matrix. The agreement between the experimental results and the model's predictions demonstrates the advantages of this approach in investigating the impact of various extracellular matrix characteristics on tumor growth and invasion.

Immunohistochemistry (IHC) staining of in-vitro cancer cell-generated tumoroids.

Targeting different pathways in combinational therapy may lead to synergistic effects with higher drug efficiency. Due to a large number of candidate drugs and the variability in the genomic landscape of the disease, conventional cell culture models have limited success. Three-dimensional (3D) cell culture platforms such as tumoroids not only provide a pathophysiological relevant condition but also allow for low-cost and high-throughput drug screening strategies. Immunostaining of targeted proteins within a tumoroid is challenging as the interior cells are difficult to access via a non-destructive method. Immunohistochemistry (IHC) is an important technique in clinical research to explore the expression of various biomarkers. IHC staining of tumoroids allows non-destructive detection of unstable proteins by direct fixation of cells at the state of tumor microenvironment (TME) context, providing two main advantages. First, the target protein can be fixed without dissociating cells and disintegration of tumoroids into a single-cell suspension. Second, staining the preserved structure of tumoroids helps identify the location of the target proteins as well as the spatial distribution throughout the tumoroid geometry. In this protocol, we describe the detailed methodology of a non-destructive IHC staining of cancer biomarkers which minimizes the manipulation of tumoroids prior to fixation by eliminating multiple centrifugations and shaking steps typically required for removing excess hydrogel and collecting tumoroids. The protocol can be used in studies involving prognostic and predictive biomarker investigations in new anti-tumor drug development strategies.

In-silico study of asymmetric remodeling of tumors in response to external biochemical stimuli.

Among different hallmarks of cancer, understanding biomechanics of tumor growth and remodeling benefits the most from the theoretical framework of continuum mechanics. Tumor remodeling initiates when cancer cells seek new homeostasis in response to the microenvironmental stimuli. Cells within a growing tumor are capable to remodel their inter- and intra-connections and become more mobile to achieve a new homeostasis. This mobility enables the tumor to undergo large deformation. In this work, we studied the remodeling of homogeneous tumors, at their early stage of growth, in the context of continuum mechanics. We developed an evolution law for the remodeling-associated deformation which correlates the remodeling to a characteristic tensor of external stimuli. The asymmetric remodeling and the induced mechanical stresses were analyzed for different types of biochemical distributions. To experimentally investigate the model, we studied the remodeling of human glioblastoma (hGB) tumoroids in response to the gradient of nutrients. Using a tumoroid-on-a-chip platform, the degree of remodeling was estimated for the ellipsoidal tumoroids over time. It was observed that higher gradient of nutrients induces higher degree of ellipticity suggesting that the gradient of nutrient is a characteristic property of nutrient distribution that derives the remodeling. We also showed that remodeling gives rise to heterogeneity in cell distribution forming circumferentially aligned cells within the tumors. Compared to the existing studies on tumor growth, our work provides a biomechanical module that relates the remodeling to biochemical stimuli, and allows for large deformation. It also includes experimental component, a necessary but challenging step, that connects the theory and reality to evaluate the practicability of the model.

In Vitro Brain Organoids and Computational Models to Study Cell Death in Brain Diseases.

Understanding the mechanisms underlying the formation and progression of brain diseases is challenging due to the vast variety of involved genetic/epigenetic factors and the complexity of the environment of the brain. Current preclinical monolayer culture systems fail to faithfully recapitulate the in vivo complexities of the brain. Organoids are three-dimensional (3D) culture systems that mimic much of the complexities of the brain including cell-cell and cell-matrix interactions. Complemented with a theoretical framework to model the dynamic interactions between different components of the brain, organoids can be used as a potential tool for studying disease progression, transport of therapeutic agents in tissues, drug screening, and toxicity analysis. In this chapter, we first report on the fabrication and use of a novel self-filling microwell arrays (SFMWs) platform that is self-filling and enables the formation of organoids with uniform size distributions. Next, we will introduce a mathematical framework that predicts the organoid growth, cell death, and the therapeutic responses of the organoids to different therapeutic agents. Through systematic investigations, the computational model can identify shortcomings of in vitro assays and reduce the time and effort required to improve preclinical tumor models' design. Lastly, the mathematical model provides new testable hypotheses and encourages mathematically driven experiments.

Non-destructive mechanical assessment for optimization of 3D bioprinted soft tissue scaffolds.

Characterizing the mechanical properties of engineered tissue constructs provides powerful insight into the function of engineered tissues for their desired application. Current methods of mechanical characterization of soft hydrogels used in tissue engineering are often destructive and ignore the effect of 3D bioprinting on the overall mechanical properties of a whole tissue construct. This work reports on using a non-destructive method of viscoelastic analysis to demonstrate the influence of bioprinting strategy on mechanical properties of hydrogel tissue scaffolds. Structure-function relationships are developed for common 3D bioprinting parameters such as printed fiber size, printed scaffold pattern, and bioink formulation. Further studies include mechanical properties analysis during degradation, real-time monitoring of crosslinking, mechanical characterization of multi-material scaffolds, and monitoring the effect of encapsulated cell growth on the mechanical strength of 3D bioprinted scaffolds. We envision this method of characterization opening a new wave of understanding and strategy in tissue engineering.

In-Silico Modeling of Tumor Spheroid Formation and Growth.

Mathematical modeling has significant potential for understanding of biological models of cancer and to accelerate the progress in cross-disciplinary approaches of cancer treatment. In mathematical biology, solid tumor spheroids are often studied as preliminary in vitro models of avascular tumors. The size of spheroids and their cell number are easy to track, making them a simple in vitro model to investigate tumor behavior, quantitatively. The growth of solid tumors is comprised of three main stages: transient formation, monotonic growth and a plateau phase. The last two stages are extensively studied. However, the initial transient formation phase is typically missing from the literature. This stage is important in the early dynamics of growth, formation of clonal sub-populations, etc. In the current work, this transient formation is modeled by a reaction-diffusion partial differential equation (PDE) for cell concentration, coupled with an ordinary differential equation (ODE) for the spheroid radius. Analytical and numerical solutions of the coupled equations were obtained for the change in the radius of tumor spheroids over time. Human glioblastoma (hGB) cancer cells (U251 and U87) were spheroid cultured to validate the model prediction. Results of this study provide insight into the mechanism of development of solid tumors at their early stage of formation.

The role of biomaterials and three dimensional (3D) in vitro tissue models in fighting against COVID-19.

Over the past century, viral respiratory pandemics have been a leading cause of infectious disease worldwide. A deep understanding of the underlying mechanisms of the viral interactions with host cells at the target sites is necessary for a rapid response to such pandemics. To meet this aim, various testing platforms are required to recapitulate the pathophysiological behavior of the virus within the respiratory tract. These bioengineered platforms can effectively be used for the development of different therapeutics and vaccines. This paper briefly reviews the progress in the areas of biomaterial use for pulmonary tissue regeneration and integration with current bioengineered platforms including engineered tissues, organoids, and organs-on-a-chip platforms for viral respiratory disease studies. Finally, a brief overview of the opportunities presented by organ-on-a-chip systems for studying COVID-19 and subsequent drug development is introduced.

Microfluidic-Based Oxygen (O2) Sensors for On-Chip Monitoring of Cell, Tissue and Organ Metabolism.

Oxygen (O2) quantification is essential for assessing cell metabolism, and its consumption in cell culture is an important indicator of cell viability. Recent advances in microfluidics have made O2 sensing a crucial feature for organ-on-chip (OOC) devices for various biomedical applications. OOC O2 sensors can be categorized, based on their transducer type, into two main groups, optical and electrochemical. In this review, we provide an overview of on-chip O2 sensors integrated with the OOC devices and evaluate their advantages and disadvantages. Recent innovations in optical O2 sensors integrated with OOCs are discussed in four main categories: (i) basic luminescence-based sensors; (ii) microparticle-based sensors; (iii) nano-enabled sensors; and (iv) commercial probes and portable devices. Furthermore, we discuss recent advancements in electrochemical sensors in five main categories: (i) novel configurations in Clark-type sensors; (ii) novel materials (e.g., polymers, O2 scavenging and passivation materials); (iii) nano-enabled electrochemical sensors; (iv) novel designs and fabrication techniques; and (v) commercial and portable electrochemical readouts. Together, this review provides a comprehensive overview of the current advances in the design, fabrication and application of optical and electrochemical O2 sensors.

Bioengineered tissue models for the development of dynamic immuno-associated tumor models and high-throughput immunotherapy cytotoxicity assays.

Cancer immunotherapy is rapidly developing, with numerous therapies approved over the past decade and more therapies expected to gain approval in the future. However, immunotherapy of solid tumors has been less successful because immunosuppressive barriers limit immune cell trafficking and function against cancer cells. Interactions between suppressive immune cells, cytokines, and inhibitory factors are central to cancer immunotherapy approaches. In this review, we discuss recent advances in utilizing microfluidic platforms for understanding cancer-suppressive immune system interactions. Dendritic cell (DC)-mediated tumor models, infiltrated lymphocyte-mediated tumor models [e.g., natural killer (NK) cells, T cells, chimeric antigen receptor (CAR) T cells, and macrophages], monocyte-mediated tumor models, and immune checkpoint blockade (ICB) tumor models are among the various bioengineered immune cell-cancer cell interactions that we reviewed herein.

Stimuli-Responsive Hydrogels for Local Post-Surgical Drug Delivery.

Currently, surgical operations, followed by systemic drug delivery, are the prevailing treatment modality for most diseases, including cancers and trauma-based injuries. Although effective to some extent, the side effects of surgery include inflammation, pain, a lower rate of tissue regeneration, disease recurrence, and the non-specific toxicity of chemotherapies, which remain significant clinical challenges. The localized delivery of therapeutics has recently emerged as an alternative to systemic therapy, which not only allows the delivery of higher doses of therapeutic agents to the surgical site, but also enables overcoming post-surgical complications, such as infections, inflammations, and pain. Due to the limitations of the current drug delivery systems, and an increasing clinical need for disease-specific drug release systems, hydrogels have attracted considerable interest, due to their unique properties, including a high capacity for drug loading, as well as a sustained release profile. Hydrogels can be used as local drug performance carriers as a means for diminishing the side effects of current systemic drug delivery methods and are suitable for the majority of surgery-based injuries. This work summarizes recent advances in hydrogel-based drug delivery systems (DDSs), including formulations such as implantable, injectable, and sprayable hydrogels, with a particular emphasis on stimuli-responsive materials. Moreover, clinical applications and future opportunities for this type of post-surgery treatment are also highlighted.

Antibacterial efficiency assessment of polymer-nanoparticle composites using a high-throughput microfluidic platform.

Over the past decades, inorganic nanoparticles (NPs), particularly metal oxide NPs, have attracted great attention due to their strong bactericidal effects. Researchers have used NPs to fabricate nanocomposite materials which have innate antibacterial capability. Herein, we present a straightforward method to fabricate antibacterial nanocomposites. Ag, TiO2, and ZnO NPs were dispersed within liquid silicone rubber (LSR) structure in four concentrations. Three different methods were used to evaluate the antibacterial efficiency of the NPs forming the nanocomposite materials: (I) the diffusion method, (II) agar counting plate, and (III) a live/dead assay of E. coli. The mechanical properties and hydrophobicity of the nanocomposites were characterized and correlated to the antibacterial efficiency of the NPs. In order to test the antibacterial efficiency in a high-throughput, cost-effective and efficient manner, a microfluidic device fabricated by 3D printing and soft-lithography methods was used. The LSR-15 wt% TiO2 nanocomposites showed the best antibacterial efficiency. In addition, TiO2 NPs formed the stiffest nanocomposites with very fine, even surface which increased the hydrophobicity of the surface where bacteria attach to grow, preventing bacteria from further growth.

3D bioprinting models of neural tissues: The current state of the field and future directions.

3D bioprinting can potentially revolutionize the field of neural tissue engineering by increasing its throughput and reproducibility. However, many obstacles must be overcome to realize this immense potential. This review first discusses how 3D hydrogels can serve as powerful tools for engineering neural tissue, especially when combined with different types of cells. These tools enable us to gain a better understanding of neural tissue development and its associated disease states. Next, we define 3D bioprinting and detail the necessary tools for using this technique to produce neural tissue, along with reviewing relevant recent work in the area. We also compare with other approaches to generating 3D neural tissues while identifying key areas for future developments in the field of bioprinting.

3D Bioprinting Stem Cell Derived Tissues.

Stem cells offer tremendous promise for regenerative medicine as they can become a variety of cell types. They also continuously proliferate, providing a renewable source of cells. Recently, it has been found that 3D printing constructs using stem cells, can generate models representing healthy or diseased tissues, as well as substitutes for diseased and damaged tissues. Here, we review the current state of the field of 3D printing stem cell derived tissues. First, we cover 3D printing technologies and discuss the different types of stem cells used for tissue engineering applications. We then detail the properties required for the bioinks used when printing viable tissues from stem cells. We give relevant examples of such bioprinted tissues, including adipose tissue, blood vessels, bone, cardiac tissue, cartilage, heart valves, liver, muscle, neural tissue, and pancreas. Finally, we provide future directions for improving the current technologies, along with areas of focus for future work to translate these exciting technologies into clinical applications.