AngioMT: A MATLAB based 2D image-to-physics tool to predict oxygen transport in vascularized microphysiological systems.

Microphysiological models (MPS) are increasingly getting recognized as in vitro preclinical systems of pathophysiology and drug discovery. However, there is also a growing need to adapt and advance MPS to include the physiological contributions of the capillary vascular dynamics, because they undergo angiogenesis or vasculogenesis to deliver soluble oxygen and nutrients to its organs. Currently, the process of formation of microvessels in MPS is measured arbitrarily, and vascularized MPS do not include oxygen measurements in their analysis. Sensing and measuring tissue oxygen delivery is extremely difficult because it requires access to opaque and deep tissue, and/or requires extensive integration of biosensors that makes such systems impractical to use in the real world. Here, a finite element method-based oxygen transport program, called AngioMT, is built in MATLAB. AngioMT processes the routinely acquired 2D confocal images of microvascular networks in vitro and solves physical equations of diffusion-reaction dominated oxygen transport phenomena. This user-friendly image-to-physics transition in AngioMT is an enabling tool of MPS analysis because unlike the averaged morphological measures of vessels, it provides information of the spatial transport of oxygen both within the microvessels and the surrounding tissue regions. Further, it solves the more complex higher order reaction mechanisms which also improve the physiological relevance of this tool when compared directly against in vivo measurements. Finally, the program is applied in a multicellular vascularized MPS by including the ability to define additional organ/tissue subtypes in complex co-cultured systems. Therefore, AngioMT serves as an analytical tool to enhance the predictive power and performance of MPS that incorporate microcirculation.

Granular Biphasic Colloidal Hydrogels for 3D Bioprinting.

Granular hydrogels composed of hydrogel microparticles are promising candidates for 3D bioprinting due to their ability to protect encapsulated cells. However, to achieve high print fidelity, hydrogel microparticles need to jam to exhibit shear-thinning characteristics, which is crucial for 3D printing. Unfortunately, this overpacking can significantly impact cell viability, thereby negating the primary advantage of using hydrogel microparticles to shield cells from shear forces. To overcome this challenge, a novel solution: a biphasic, granular colloidal bioink designed to optimize cell viability and printing fidelity is introduced. The biphasic ink consists of cell-laden polyethylene glycol (PEG) hydrogel microparticles embedded in a continuous gelatin methacryloyl (GelMA)-nanosilicate colloidal network. Here, it is demonstrated that this biphasic bioink offers outstanding rheological properties, print fidelity, and structural stability. Furthermore, its utility for engineering complex tissues with multiple cell types and heterogeneous microenvironments is demonstrated, by incorporating ß-islet cells into the PEG microparticles and endothelial cells in the GelMA-nanosilicate colloidal network. Using this approach, it is possible to induce cell patterning, enhance vascularization, and direct cellular function. The proposed biphasic bioink holds significant potential for numerous emerging biomedical applications, including tissue engineering and disease modeling.

Angiogenesis-Enabled Human Ovarian Tumor Microenvironment-Chip Evaluates Pathophysiology of Platelets in Microcirculation.

The tumor microenvironment (TME) promotes angiogenesis for its growth through the recruitment of multiple cells and signaling mechanisms. For example, TME actively recruits and activates platelets from the microcirculation to facilitate metastasis, but platelets may simultaneously also support tumor angiogenesis. Here, to model this complex pathophysiology within the TME that involves a signaling triad of cancer cells, sprouting endothelial cells, and platelets, an angiogenesis-enabled tumor microenvironment chip (aTME-Chip) is presented. This platform recapitulates the convergence of physiology of angiogenesis and platelet function within the ovarian TME and describes the contribution of platelets in promoting angiogenesis within an ovarian TME. By including three distinct human ovarian cancer cell-types, the aTME-Chip quantitatively reveals the following outcomes-first, introduction of platelets significantly increases angiogenesis; second, the temporal dynamics of angiogenic signaling is dependent on cancer cell type; and finally, tumor-educated platelets either activated exogenously by cancer cells or derived clinically from a cancer patient accelerate tumor angiogenesis. Further, analysis of effluents available from aTME-Chip validate functional outcomes by revealing changes in cytokine expression and several angiogenic and metastatic signaling pathways due to platelets. Collectively, this tumor microphysiological system may be deployed to derive antiangiogenic targets combined with antiplatelet treatments to arrest cancer metastasis.

Engineering Lymphangiogenesis-On-Chip: The Independent and Cooperative Regulation by Biochemical Factors, Gradients, and Interstitial Fluid Flow.

Despite the crucial role of lymphangiogenesis during development and in several diseases with implications for tissue regeneration, immunity, and cancer, there are significantly fewer tools to understand this process relative to angiogenesis. While there has been a major surge in modeling angiogenesis with microphysiological systems, they have not been rigorously optimized or standardized to enable the recreation of the dynamics of lymphangiogenesis. Here, a Lymphangiogenesis-Chip (L-Chip) is engineered, within which new sprouts form and mature depending upon the imposition of interstitial flow, growth factor gradients, and pre-conditioning of endothelial cells with growth factors. The L-Chip reveals the independent and combinatorial effects of these mechanical and biochemical determinants of lymphangiogenesis, thus ultimately resulting in sprouts emerging from a parent vessel and maturing into tubular structures up to 1 mm in length within 4 days, exceeding prior art. Further, when the constitution of the pre-conditioning cocktail and the growth factor cocktail used to initiate and promote lymphangiogenesis are dissected, it is found that endocan (ESM-1) results in more dominant lymphangiogenesis relative to angiogenesis. Therefore, The L-Chip provides a foundation for standardizing the microfluidics assays specific to lymphangiogenesis and for accelerating its basic and translational science at par with angiogenesis.

Machine learning chained neural network analysis of oxygen transport amplifies the physiological relevance of vascularized microphysiological systems.

Since every biological system requires capillaries to support its oxygenation, design of engineered preclinical models of such systems, for example, vascularized microphysiological systems (vMPS) have gained attention enhancing the physiological relevance of human biology and therapies. But the physiology and function of formed vessels in the vMPS is currently assessed by non-standardized, user-dependent, and simple morphological metrics that poorly relate to the fundamental function of oxygenation of organs. Here, a chained neural network is engineered and trained using morphological metrics derived from a diverse set of vMPS representing random combinations of factors that influence the vascular network architecture of a tissue. This machine-learned algorithm outputs a singular measure, termed as vascular network quality index (VNQI). Cross-correlation of morphological metrics and VNQI against measured oxygen levels within vMPS revealed that VNQI correlated the most with oxygen measurements. VNQI is sensitive to the determinants of vascular networks and it consistently correlates better to the measured oxygen than morphological metrics alone. Finally, the VNQI is positively associated with the functional outcomes of cell transplantation therapies, shown in the vascularized islet-chip challenged with hypoxia. Therefore, adoption of this tool will amplify the predictions and enable standardization of organ-chips, transplant models, and other cell biosystems.

Evaluation of the Morphological and Biological Functions of Vascularized Microphysiological Systems with Supervised Machine Learning.

Vascularized microphysiological systems and organoids are contemporary preclinical experimental platforms representing human tissue or organ function in health and disease. While vascularization is emerging as a necessary physiological organ-level feature required in most such systems, there is no standard tool or morphological metric to measure the performance or biological function of vascularized networks within these models. Further, the commonly reported morphological metrics may not correlate to the network's biological function-oxygen transport. Here, a large library of vascular network images was analyzed by the measure of each sample's morphology and oxygen transport potential. The oxygen transport quantification is computationally expensive and user-dependent, so machine learning techniques were examined to generate regression models relating morphology to function. Principal component and factor analyses were applied to reduce dimensionality of the multivariate dataset, followed by multiple linear regression and tree-based regression analyses. These examinations reveal that while several morphological data relate poorly to the biological function, some machine learning models possess a relatively improved, but still moderate predictive potential. Overall, random forest regression model correlates to the biological function of vascular networks with relatively higher accuracy than other regression models.

Evaluation of the morphological and biological functions of vascularized microphysiological systems with supervised machine learning.

Vascularized microphysiological systems and organoids are contemporary preclinical experimental platforms representing human tissue or organ function in health and disease. While vascularization is emerging as a necessary physiological organ-level feature required in most such systems, there is no standard tool or morphological metric to measure the performance or biological function of vascularized networks within these models. Further, the commonly reported morphological metrics may not correlate to the network's biological function - oxygen transport. Here, a large library of vascular network images was analyzed by the measure of each sample's morphology and oxygen transport potential. The oxygen transport quantification is computationally expensive and user-dependent, so machine learning techniques were examined to generate regression models relating morphology to function. Principal component and factor analyses were applied to reduce dimensionality of the multivariate dataset, followed by multiple linear regression and tree-based regression analyses. These examinations reveal that while several morphological data relate poorly to the biological function, some machine learning models possess a relatively improved, but still moderate predictive potential. Overall, random forest regression model correlates to the biological function of vascular networks with relatively higher accuracy than other regression models.

AngioMT: An in silico platform for digital sensing of oxygen transport through heterogenous microvascular networks.

Measuring the capacity of microvascular networks in delivering soluble oxygen and nutrients to its organs is essential in health, disease, and surgical interventions. Here, a finite element method-based oxygen transport program, AngioMT, is designed and validated to predict spatial oxygen distribution and other physiologically relevant transport metrics within both the vascular network and the surrounding tissue. The software processes acquired images of microvascular networks and produces a digital mesh which is used to predict vessel and tissue oxygenation. The image-to-physics translation by AngioMT correlated with results from commercial software, however only AngioMT could provide predictions within the solid tissue in addition to vessel oxygenation. AngioMT predictions were sensitive and positively correlated to spatial heterogeneity and extent of vascularization of 500 different vascular networks formed with variable vasculogenic conditions. The predictions of AngioMT cross-correlate with experimentally-measured oxygen distributions in vivo. The computational power of the software is increased by including calculations of higher order reaction mechanisms, and the program includes defining additional organ and tissue structures for a more physiologically relevant analysis of tissue oxygenation in complex co-cultured systems, or in vivo. AngioMT may serve as a digital performance measuring tool of vascular networks in microcirculation, experimental models of vascularized tissues and organs, and in clinical applications, such as organ transplants.

Comparative Analysis of Blood-Derived Endothelial Cells for Designing Next-Generation Personalized Organ-on-Chips.

Background Organ-on-chip technology has accelerated in vitro preclinical research of the vascular system, and a key strength of this platform is its promise to impact personalized medicine by providing a primary human cell-culture environment where endothelial cells are directly biopsied from individual tissue or differentiated through stem cell biotechniques. However, these methods are difficult to adopt in laboratories, and often result in impurity and heterogeneity of cells. This limits the power of organ-chips in making accurate physiological predictions. In this study, we report the use of blood-derived endothelial cells as alternatives to primary and induced pluripotent stem cell-derived endothelial cells. Methods and Results Here, the genotype, phenotype, and organ-chip functional characteristics of blood-derived outgrowth endothelial cells were compared against commercially available and most used primary endothelial cells and induced pluripotent stem cell-derived endothelial cells. The methods include RNA-sequencing, as well as criterion standard assays of cell marker expression, growth kinetics, migration potential, and vasculogenesis. Finally, thromboinflammatory responses under shear using vessel-chips engineered with blood-derived endothelial cells were assessed. Blood-derived endothelial cells exhibit the criterion standard hallmarks of typical endothelial cells. There are differences in gene expression profiles between different sources of endothelial cells, but blood-derived cells are relatively closer to primary cells than induced pluripotent stem cell-derived. Furthermore, blood-derived endothelial cells are much easier to obtain from individuals and yet, they serve as an equally effective cell source for functional studies and organ-chips compared with primary cells or induced pluripotent stem cell-derived cells. Conclusions Blood-derived endothelial cells may be used in preclinical research for developing more robust and personalized next-generation disease models using organ-on-chips.

Human tumor microenvironment chip evaluates the consequences of platelet extravasation and combinatorial antitumor-antiplatelet therapy in ovarian cancer.

Platelets extravasate from the circulation into tumor microenvironment, enable metastasis, and confer resistance to chemotherapy in several cancers. Therefore, arresting tumor-platelet cross-talk with effective and atoxic antiplatelet agents in combination with anticancer drugs may serve as an effective cancer treatment strategy. To test this concept, we create an ovarian tumor microenvironment chip (OTME-Chip) that consists of a platelet-perfused tumor microenvironment and which recapitulates platelet extravasation and its consequences. By including gene-edited tumors and RNA sequencing, this organ-on-chip revealed that platelets and tumors interact through glycoprotein VI (GPVI) and tumor galectin-3 under shear. Last, as proof of principle of a clinical trial, we showed that a GPVI inhibitor, Revacept, impairs metastatic potential and improves chemotherapy. Since GPVI is an antithrombotic target that does not impair hemostasis, it represents a safe cancer therapeutic. We propose that OTME-Chip could be deployed to study other vascular and hematological targets in cancer.

Engineering new microvascular networks on-chip: ingredients, assembly, and best practices.

Tissue engineered grafts show great potential as regenerative implants for diseased or injured tissues within the human body. However, these grafts suffer from poor nutrient perfusion and waste transport, thus decreasing their viability post-transplantation. Graft vascularization is therefore a major area of focus within tissue engineering because biologically relevant conduits for nutrient and oxygen perfusion can improve viability post-implantation. Many researchers utilize microphysiological systems as testing platforms for potential grafts due to an ability to integrate vascular networks as well as biological characteristics such as fluid perfusion, 3D architecture, compartmentalization of tissue-specific materials, and biophysical and biochemical cues. While many methods of vascularizing these systems exist, microvascular self-assembly has great potential for bench-to-clinic translation as it relies on naturally occurring physiological events. In this review, we highlight the past decade of literature and critically discuss the most important and tunable components yielding a self-assembled vascular network on chip: endothelial cell source, tissue-specific supporting cells, biomaterial scaffolds, biochemical cues, and biophysical forces. This article discusses the bioengineered systems of angiogenesis, vasculogenesis, and lymphangiogenesis, and includes a brief overview of multicellular systems. We conclude with future avenues of research to guide the next generation of vascularized microfluidic models and future tissue engineered grafts.

Pumpless, modular, microphysiological systems enabling tunable perfusion for long-term cultivation of endothelialized lumens.

Given the increased recognition of the importance of physiologically relevant microenvironments when designing in vitro assays, microphysiological systems (MPS) that mimic the critical function and structure of tissues and organs have gained considerable attention as alternatives to traditional experimental models. Accordingly, the field is growing rapidly, and some promising MPS are being tested for use in pharmaceutical development and toxicological testing. However, most MPS are complex and require additional infrastructure, which limits their successful translation. Here, we present a pumpless, modular MPS consisting of 1) a resistance module that controls flow rate and 2) a physiologically relevant, three-dimensional blood vessel module. Flow is provided by an attached reservoir tank that feeds fluid into the resistance channel via hydrostatic pressure. The flow rate is controlled by the height of the media in the tank and the resistance channel's dimensions. The flow from the resistance module is streamed into the blood vessel module using a liquid bridge. We utilize optical coherence tomography (OCT) to measure fluid velocity at regions of interest. The endothelial cells cultured in the MPS remain viable for up to 14 days and demonstrate the functional characteristics of the human blood vessels verified by tight junction expression and diffusion assay. Our results show that a modular MPS can simulate a functional endothelium in vitro while simplifying the operation of the MPS. The simplicity of the system allows for modifications to incorporate other microenvironmental components and to build other organ-modeling systems easily.

Electric Field Assisted Self-Assembly of Viruses into Colored Thin Films.

Filamentous viruses called M13 bacteriophages are promising materials for devices with thin film coatings because phages are functionalizable, and they can self-assemble into smectic helicoidal nanofilament structures. However, the existing "pulling" approach to align the nanofilaments is slow and limits potential commercialization of this technology. This study uses an applied electric field to rapidly align the nanostructures in a fixed droplet. The electric field reduces pinning of the three-phase contact line, allowing it to recede at a constant rate. Atomic force microscopy reveals that the resulting aligned structures resemble those produced via the pulling method. The field-assisted alignment results in concentric color bands quantified with image analysis of red, green, and blue line profiles. The alignment technique shown here could reduce self-assembly time from hours to minutes and lend itself to scalable manufacturing techniques such as inkjet printing.