A Microfluidic Hanging-Drop-Based Islet Perifusion System for Studying Glucose-Stimulated Insulin Secretion From Multiple Individual Pancreatic Islets

Unveiling islet heterogeneity using an automated microfluidic imaging system

Islets of Langerhans are a therapeutic target for diabetes and prediabetes. Measurements of therapeutic inhibitory or excitatory concentrations are often performed using large groups of islets, however, the population heterogeneity cannot be observed when examining the ensemble response. Normal islet function and islet response to therapeutic treatment can be affected by islet heterogeneity, influencing the progression of diabetes mellitus. To identify heterogeneity in an islet population, we developed a simple microfluidic device capable of delivering a stimulant to four independent chambers, allowing measurements of individual responses from a population of 20-25 islets. The device enabled accurate delivery of the same stimulant concentration to all four chambers, with an error <1% between chambers. To demonstrate the capability of this system, ensemble and individual EC/IC[Formula: see text] measurements of glucose and diazoxide were performed on murine islets. Results showed little heterogeneity of glucose EC[Formula: see text] values with all 21 islets within ± 0.6 mM of the ensemble value of 7.4 mM. In contrast, application of diazoxide to 24 islets in the presence of 20 mM glucose resulted in 37% of islets having an IC[Formula: see text] greater than 10% from the ensemble value of 10.2 μM. The simple system developed here is amenable to further studies on islet heterogeneity, and is applicable to investigate heterogeneity in other cell types.

On the design and fabrication of nanoliter-volume hanging drop networks

Hanging drop cultures provide a favorable environment for the gentle, gel-free formation of highly uniform three-dimensional cell cultures often used in drug screening applications. Initial cell numbers can be limited, as with primary cells provided by minimally invasive biopsies. Therefore, it can be beneficial to divide cells into miniaturized arrays of hanging drops to supply a larger number of samples. Here, we present a framework for the miniaturization of hanging drop networks to nanoliter volumes. The principles of a single hanging drop are described and used to construct the fundamental equations for a microfluidic system composed of multiple connected drops. Constitutive equations for the hanging drop as a nonlinear capacitive element are derived for application in the electronic-hydraulic analogy, forming the basis for more complex, time-dependent numerical modeling of hanging drop networks. This is supplemented by traditional computational fluid dynamics simulation to provide further information about flow conditions within the wells. A fabrication protocol is presented and demonstrated for creating transparent, microscale arrays of pinned hanging drops. A custom interface, pressure-based fluidic system, and environmental chamber have been developed to support the device. Finally, fluid flow on the chip is demonstrated to align with expected behavior based on the principles derived for hanging drop networks. Challenges with the system and potential areas for improvement are discussed. This paper expands on the limited body of hanging drop network literature and provides a framework for designing, fabricating, and operating these systems at the microscale.

A static glucose-stimulated insulin secretion (sGSIS) assay that is significantly predictive of time to diabetes reversal in the human islet bioassay

INTRODUCTION

Static incubation (static glucose-stimulated insulin secretion, sGSIS) is a measure of islet secretory function. The Stimulation Index (SI; insulin produced in high glucose/insulin produced in low glucose) is currently used as a product release criterion of islet transplant potency.

RESEARCH DESIGN AND METHODS

Our hypothesis was that the Delta, insulin secreted in high glucose minus insulin secreted in low glucose, would be more predictive. To evaluate this hypothesis, sGSIS was performed on 32 consecutive human islet preparations, immobilizing the islets in a slurry of Sepharose beads to minimize mechanical perturbation. Simultaneous full-mass subrenal capsular transplants were performed in chemically induced diabetic immunodeficient mice. Logistic regression analysis was used to determine optimal cut-points for diabetes reversal time and the Fisher Exact Test was used to assess the ability of the Delta and the SI to accurately classify transplant outcomes. Receiver operating characteristic curve analysis was performed on cut-point grouped data, assessing the predictive power and optimal cut-point for each sGSIS potency metric. Finally, standard Kaplan-Meier-type survival analysis was conducted.

RESULTS

In the case of the sGSIS the Delta provided a superior islet potency metric relative to the SI.ConclusionsThe sGSIS Delta value is predicitive of time to diabetes reversal in the full mass human islet transplant bioassay.

Twenty years of islet-on-a-chip: microfluidic tools for dissecting islet metabolism and function

Pancreatic islets are metabolically active micron-sized tissues responsible for controlling blood glucose through the secretion of insulin and glucagon. A loss of functional islet mass results in type 1 and 2 diabetes. Islet-on-a-chip devices are powerful microfluidic tools used to trap and study living ex vivo human and murine pancreatic islets and potentially stem cell-derived islet organoids. Devices developed over the past twenty years offer the ability to treat islets with controlled and dynamic microenvironments to mimic in vivo conditions and facilitate diabetes research. In this review, we explore the various islet-on-a-chip devices used to immobilize islets, regulate the microenvironment, and dynamically detect islet metabolism and insulin secretion. We first describe and assess the various methods used to immobilize islets including chambers, dam-walls, and hydrodynamic traps. We subsequently describe the surrounding methods used to create glucose gradients, enhance the reaggregation of dispersed islets, and control the microenvironment of stem cell-derived islet organoids. We focus on the various methods used to measure insulin secretion including capillary electrophoresis, droplet microfluidics, off-chip ELISAs, and on-chip fluorescence anisotropy immunoassays. Additionally, we delve into the various multiparametric readouts (NAD(P)H, Ca2+-activity, and O2-consumption rate) achieved primarily by adopting a microscopy-compatible optical window into the devices. By critical assessment of these advancements, we aim to inspire the development of new devices by the microfluidics community and accelerate the adoption of islet-on-a-chip devices by the wider diabetes research and clinical communities.

Controlling bead and cell mobility in a recirculating hanging-drop network

Integrating flowing cells, such as immune cells or circulating tumour cells, within a microphysiological system is crucial for body-on-a-chip applications. However, ensuring unimpeded recirculation of cells is a significant challenge. Closed microfluidic devices have a no-slip boundary condition along channel walls and a defined chip geometry (laminar flow) that hinders the ability to freely control cell flow. Open microfluidic devices, where the bottom device boundary is an air-liquid interface (ALI), e.g., hanging drop networks (HDNs), offer the advantage of an easily-actuatable fluid-phase geometry, where cells can either flow or stagnate. In this paper, we optimized a hanging-drop-integrated pneumatic-pump system for closed-loop recirculation of particles (i.e., beads or cells). Experiments with both beads and cells in cell culture medium initially resulted in particle stagnation, which was suggestive of a pseudo-no-slip boundary condition at the ALI. Transmission electron microscopy and dynamic light scattering measurements of the ALI suggested that aggregation of submicron-scale cell-culture-medium components is the cause of the pseudo-no-slip boundary condition. We used the finite element method to study the forces on particles at the ALI and to optimize HDN design (drop aperture) and operation (drop height) parameters. Based on this analysis, we report a phase diagram delineating the conditions for free flow or stagnation of particles at the ALI of hanging drops. Using our experimental setup with 3.5 mm drop apertures, we conducted particle flow experiments while actuating drop heights. We confirmed the ability to control the flow or stagnation of particles by actuating the height of hanging drops: a drop height over 300 μm led to particle stagnation and a drop height under 300 μm allowed for particle flow. This particle-flow control, combined with the ease of integrating scaffold-free organ models (microtissues or organoids) in HDNs, constitutes the basis for an experimental setup enabling the control of the residence time of single cells around 3D organ models.

Insulin C-peptide secretion on-a-chip to measure the dynamics of secretion and metabolism from individual islets

First-phase glucose-stimulated insulin secretion is mechanistically linked to type 2 diabetes, yet the underlying metabolism is difficult to discern due to significant islet-to-islet variability. Here, we miniaturize a fluorescence anisotropy immunoassay onto a microfluidic device to measure C-peptide secretion from individual islets as a surrogate for insulin (InsC-chip). This method measures secretion from up to four islets at a time with ∼7 s resolution while providing an optical window for real-time live-cell imaging. Using the InsC-chip, we reveal two glucose-dependent peaks of insulin secretion (i.e., a double peak) within the classically defined 1st phase (<10 min). By combining real-time secretion and live-cell imaging, we show islets transition from glycolytic to oxidative phosphorylation (OxPhos)-driven metabolism at the nadir of the peaks. Overall, these data validate the InsC-chip to measure glucose-stimulated insulin secretion while revealing new dynamics in secretion defined by a shift in glucose metabolism.

Automated cellular stimulation with integrated pneumatic valves and fluidic capacitors

Microfluidic technologies have proven to be a reliable tool in profiling dynamic insulin secretion from islets of Langerhans. Most of these systems rely on external pressure sources to induce flow, leading to difficulties moving to more elaborate systems. To reduce complexity, a microfluidic system was developed that used a single vacuum source at the outlet to drive fluidic transport of immunoassay reagents and stimulation solutions throughout the device. A downside to this approach is the lack of flow control over the reagents delivered to the islet chamber. To address this challenge, 4-layer pneumatic valves were integrated into the perfusion lines to automate and control the delivery of stimulants; however, it was found that as the valves closed, spikes in the flow would lead to abnormal insulin secretion profiles. Fluidic capacitors were then incorporated after the valves and found to remove the spikes. The combination of the valves and capacitors resulted in automated collection of insulin secretion profiles from single murine islets that were similar to those previously reported in the literature. In the future, these integrated fluidic components may enable more complex channel designs to be used with a relatively simple flow control solution.

Integrated experimental-computational analysis of a HepaRG liver-islet microphysiological system for human-centric diabetes research

Microphysiological systems (MPS) are powerful tools for emulating human physiology and replicating disease progression in vitro. MPS could be better predictors of human outcome than current animal models, but mechanistic interpretation and in vivo extrapolation of the experimental results remain significant challenges. Here, we address these challenges using an integrated experimental-computational approach. This approach allows for in silico representation and predictions of glucose metabolism in a previously reported MPS with two organ compartments (liver and pancreas) connected in a closed loop with circulating medium. We developed a computational model describing glucose metabolism over 15 days of culture in the MPS. The model was calibrated on an experiment-specific basis using data from seven experiments, where HepaRG single-liver or liver-islet cultures were exposed to both normal and hyperglycemic conditions resembling high blood glucose levels in diabetes. The calibrated models reproduced the fast (i.e. hourly) variations in glucose and insulin observed in the MPS experiments, as well as the long-term (i.e. over weeks) decline in both glucose tolerance and insulin secretion. We also investigated the behaviour of the system under hypoglycemia by simulating this condition in silico, and the model could correctly predict the glucose and insulin responses measured in new MPS experiments. Last, we used the computational model to translate the experimental results to humans, showing good agreement with published data of the glucose response to a meal in healthy subjects. The integrated experimental-computational framework opens new avenues for future investigations toward disease mechanisms and the development of new therapies for metabolic disorders.

Microphysiological systems (MPS) are powerful tools to unravel biological knowledge underlying disease. MPS provide a physiologically relevant, human-based in vitro setting, which can potentially yield better translatability to humans than current animal models and traditional cell cultures. However, mechanistic interpretation and extrapolation of the experimental results to human outcome remain significant challenges. In this study, we confront these challenges using an integrated experimental-computational approach. We present a computational model describing glucose metabolism in a previously reported MPS integrating liver and pancreas. This MPS supports a homeostatic feedback loop between HepaRG/HHSteC spheroids and pancreatic islets, and allows for detailed investigations of mechanisms underlying type 2 diabetes in humans. We show that the computational model captures the complex dynamics of glucose-insulin regulation observed in the system, and can provide mechanistic insight into disease progression features, such as insulin resistance and β-cell dynamics. Furthermore, the computational model can explain key differences in temporal dynamics between MPS and human responses, and thus provides a tool for translating experimental insights into human outcome. The integrated experimental-computational framework opens new avenues for future investigations toward disease mechanisms and the development of new therapies for metabolic disorders.

Liraglutide protects β-cells in novel human islet spheroid models of type 1 diabetes

To enable accurate, high-throughput and longer-term studies of the immunopathogenesis of type 1 diabetes (T1D), we established three in-vitro islet-immune injury models by culturing spheroids derived from primary human islets with proinflammatory cytokines, activated peripheral blood mononuclear cells or HLA-A2-restricted preproinsulin-specific cytotoxic T lymphocytes. In all models, β-cell function declined as manifested by increased basal and decreased glucose-stimulated insulin release (GSIS), and decreased intracellular insulin content. Additional hallmarks of T1D progression such as loss of the first-phase insulin response (FFIR), increased proinsulin-to-insulin ratios, HLA-class I expression, and inflammatory cytokine release were also observed. Using these models, we show that liraglutide, a glucagon-like peptide 1 receptor agonist, prevented loss of GSIS under T1D-relevant stress, by preserving the FFIR and decreasing immune cell infiltration and cytokine secretion. Our results corroborate that liraglutide mediates an anti-inflammatory effect that aids in protecting β-cells from the immune-mediated attack that leads to T1D.

Circuit-Based Design of Microfluidic Drop Networks

Microfluidic-drop networks consist of several stable drops—interconnected through microfluidic channels—in which organ models can be cultured long-term. Drop networks feature a versatile configuration and an air–liquid interface (ALI). This ALI provides ample oxygenation, rapid liquid turnover, passive degassing, and liquid-phase stability through capillary pressure. Mathematical modeling, e.g., by using computational fluid dynamics (CFD), is a powerful tool to design drop-based microfluidic devices and to optimize their operation. Although CFD is the most rigorous technique to model flow, it falls short in terms of computational efficiency. Alternatively, the hydraulic–electric analogy is an efficient “first-pass” method to explore the design and operation parameter space of microfluidic-drop networks. However, there are no direct electric analogs to a drop, due to the nonlinear nature of the capillary pressure of the ALI. Here, we present a circuit-based model of hanging- and standing-drop compartments. We show a phase diagram describing the nonlinearity of the capillary pressure of a hanging drop. This diagram explains how to experimentally ensure drop stability. We present a methodology to find flow rates and pressures within drop networks. Finally, we review several applications, where the method, outlined in this paper, was instrumental in optimizing design and operation.

Advances in microfluidics devices and its applications in personalized medicines

Microfluidics is an exponentially growing area and is being used for numerous applications from basic science to advanced biotechnology and medicines. Microfluidics provides a platform to the research community for studying and building new strategies for the diagnosis and therapeutics applications. In the last decade, microfluidic have enriched the field of diagnostics by providing new solutions which was not possible with conventional detection and treatment methods. Microfluidics has the ability to precisely control and perform high-throughput functions. It has been proven as an efficient and rapid method for biological sample preparation, analysis and controlled drug delivery system. Microfluidics plays significant role in personalized medicine. These personalized medicines are used for medical decisions, practices and other interventions as well as for individual patients based on their predicted response or risk of disease. This chapter highlights microfluidics in developing personalized medical applications for its applications in diseases such as cancer, cardiovascular disease, diabetes, pulmonary disease and several others.

Smartphone-microfluidic fluorescence imaging system for studying islet physiology

Smartphone technology has been recently applied for biomedical image acquisition and data analysis due to its high-quality imaging capability, and flexibility to customize multi-purpose apps. In this work, we developed and characterized a smartphone-microfluidic fluorescence imaging system for studying the physiology of pancreatic islets. We further evaluated the system capability by performing real-time fluorescence imaging on mouse islets labeled with either chemical fluorescence dyes or genetically encoded fluorescent protein indicators (GEFPIs). Our results showed that the system was capable of analyzing key beta-cell insulin stimulator-release coupling factors in response to various stimuli with high-resolution dynamics. Furthermore, the integration of a microfluidics allowed high-resolution detection of insulin secretion at single islet level. When compared to conventional fluorescence microscopes and macro islet perifusion apparatus, the system has the advantages of low cost, portable, and easy to operate. With all of these features, we envision that this smartphone-microfluidic fluorescence imaging system can be applied to study islet physiology and clinical applications.

Modeling and measuring glucose diffusion and consumption by colorectal cancer spheroids in hanging drops using integrated biosensors

As 3D in vitro tissue models become more pervasive, their built-in nutrient, metabolite, compound, and waste gradients increase biological relevance at the cost of analysis simplicity. Investigating these gradients and the resulting metabolic heterogeneity requires invasive and time-consuming methods. An alternative is using electrochemical biosensors and measuring concentrations around the tissue model to obtain size-dependent metabolism data. With our hanging-drop-integrated enzymatic glucose biosensors, we conducted current measurements within hanging-drop compartments hosting spheroids formed from the human colorectal carcinoma cell line HCT116. We developed a physics-based mathematical model of analyte consumption and transport, considering (1) diffusion and enzymatic conversion of glucose to form hydrogen peroxide (H₂O₂) by the glucose-oxidase-based hydrogel functionalization of our biosensors at the microscale; (2) H₂O₂ oxidation at the electrode surface, leading to amperometric H₂O₂ readout; (3) glucose diffusion and glucose consumption by cancer cells in a spherical tissue model at the microscale; (4) glucose and H₂O₂ transport in our hanging-drop compartments at the macroscale; and (5) solvent evaporation, leading to glucose and H₂O₂ upconcentration. Our model relates the measured currents to the glucose concentrations generating the currents. The low limit of detection of our biosensors (0.4 ± 0.1 μM), combined with our current-fitting method, enabled us to reveal glucose dynamics within our system. By measuring glucose dynamics in hanging-drop compartments populated by cancer spheroids of various sizes, we could infer glucose distributions within the spheroid, which will help translate in vitro 3D tissue model results to in vivo.