Islets-on-Chip: A Tool for Real-Time Assessment of Islet Function Prior to Transplantation.

Islet transplantation improves metabolic control in patients with unstable type 1 diabetes. Clinical outcomes have been improving over the last decade, and the widely used beta-score allows the evaluation of transplantation results. However, predictive pre-transplantation criteria of islet quality for clinical outcomes are lacking. In this proof-of-concept study, we examined whether characterization of the electrical activity of donor islets could provide a criterion. Aliquots of 8 human donor islets from the STABILOT study, sampled from islet preparations before transplantation, were characterized for purity and split for glucose-induced insulin secretion and electrical activity using multi-electrode-arrays. The latter tests glucose concentration dependencies, biphasic activity, hormones, and drug effects (adrenalin, GLP-1, glibenclamide) and provides a ranking of CHIP-scores from 1 to 6 (best) based on electrical islet activity. The analysis was performed online in real time using a dedicated board or offline. Grouping of beta-scores and CHIP-scores with high, intermediate, and low values was observed. Further analysis indicated correlation between CHIP-score and beta-score, although significance was not attained (R = 0.51, p = 0.1). This novel approach is easily implantable in islet isolation units and might provide means for the prediction of clinical outcomes. We acknowledge the small cohort size as the limitation of this pilot study.

Towards the Integration of an Islet-Based Biosensor in Closed-Loop Therapies for Patients With Type 1 Diabetes.

In diabetes mellitus (DM) treatment, Continuous Glucose Monitoring (CGM) linked with insulin delivery becomes the main strategy to improve therapeutic outcomes and quality of patients' lives. However, Blood Glucose (BG) regulation with CGM is still hampered by limitations of algorithms and glucose sensors. Regarding sensor technology, current electrochemical glucose sensors do not capture the full spectrum of other physiological signals, i.e., lipids, amino acids or hormones, relaying the general body status. Regarding algorithms, variability between and within patients remains the main challenge for optimal BG regulation in closed-loop therapies. This work highlights the simulation benefits to test new sensing and control paradigms which address the previous shortcomings for Type 1 Diabetes (T1D) closed-loop therapies. The UVA/Padova T1DM Simulator is the core element here, which is a computer model of the human metabolic system based on glucose-insulin dynamics in T1D patients. That simulator is approved by the US Food and Drug Administration (FDA) as an alternative for pre-clinical testing of new devices and closed-loop algorithms. To overcome the limitation of standard glucose sensors, the concept of an islet-based biosensor, which could integrate multiple physiological signals through electrical activity measurement, is assessed here in a closed-loop insulin therapy. This investigation has been addressed by an interdisciplinary consortium, from endocrinology to biology, electrophysiology, bio-electronics and control theory. In parallel to the development of an islet-based closed-loop, it also investigates the benefits of robust control theory against the natural variability within a patient population. Using 4 meal scenarios, numerous simulation campaigns were conducted. The analysis of their results then introduces a discussion on the potential benefits of an Artificial Pancreas (AP) system associating the islet-based biosensor with robust algorithms.

Vertical Organic Electrochemical Transistors and Electronics for Low Amplitude Micro-Organ Signals.

Electrical signals are fundamental to key biological events such as brain activity, heartbeat, or vital hormone secretion. Their capture and analysis provide insight into cell or organ physiology and a number of bioelectronic medical devices aim to improve signal acquisition. Organic electrochemical transistors (OECT) have proven their capacity to capture neuronal and cardiac signals with high fidelity and amplification. Vertical PEDOT:PSS-based OECTs (vOECTs) further enhance signal amplification and device density but have not been characterized in biological applications. An electronic board with individually tuneable transistor biases overcomes fabrication induced heterogeneity in device metrics and allows quantitative biological experiments. Careful exploration of vOECT electric parameters defines voltage biases compatible with reliable transistor function in biological experiments and provides useful maximal transconductance values without influencing cellular signal generation or propagation. This permits successful application in monitoring micro-organs of prime importance in diabetes, the endocrine pancreatic islets, which are known for their far smaller signal amplitudes as compared to neurons or heart cells. Moreover, vOECTs capture their single-cell action potentials and multicellular slow potentials reflecting micro-organ organizations as well as their modulation by the physiological stimulator glucose. This opens the possibility to use OECTs in new biomedical fields well beyond their classical applications.

Integrating an Islet-Based Biosensor in the Artificial Pancreas: In Silico Proof-of-Concept.

OBJECTIVE: Current treatment of type 1 diabetes by closed-loop therapy depends on continuous glucose monitoring. However, glucose readings alone are insufficient for an artificial pancreas to truthfully restore nutrient homeostasis where additional physiological regulators of insulin secretion play a considerable role. Previously, we have developed an electrophysiological biosensor of pancreatic islet activity, which integrates these additional regulators through electrical measurements. This work aims at investigating the performance of the biosensor in a blood glucose control loop as potential in silico proof-of-concept. METHODS: Two islet algorithm models were identified on experimental data recorded with the biosensor. First, we validated electrical measurement as a means to exploit the inborn regulation capabilities of islets for intravenous glucose measurement and insulin infusion. Subsequently, an artificial pancreas integrating the islet-based biosensor was compared to standard treatment approaches using subcutaneous routes. The closed-loop simulations were performed in the UVA/Padova T1DM Simulator where a series of realistic meal scenarios were applied to virtual diabetic patients. RESULTS: With intravenous routes, the endogenous islet algorithms successfully restored glucose homeostasis for all patient categories (mean time in range exceeds 90%) while mitigating the risk of adverse glycaemic events (mean BGI < 2). Using subcutaneous routes, the biosensor-based artificial pancreas was as efficient as standard treatments, and outperformed them under challenging conditions. CONCLUSION: This work validates the concept of using inborn pancreatic islets algorithms in an artificial pancreas in silico. SIGNIFICANCE: Pancreatic islet endogenous algorithms obtained via an electrophysiological biosensor successfully regulate blood glucose levels of virtual type 1 diabetic patients.

Dynamic Uni- and Multicellular Patterns Encode Biphasic Activity in Pancreatic Islets.

Biphasic secretion is an autonomous feature of many endocrine micro-organs to fulfill physiological demands. The biphasic activity of islet ß-cells maintains glucose homeostasis and is altered in type 2 diabetes. Nevertheless, underlying cellular or multicellular functional organizations are only partially understood. High-resolution noninvasive multielectrode array recordings permit simultaneous analysis of recruitment, of single-cell, and of coupling activity within entire islets in long-time experiments. Using this unbiased approach, we addressed the organizational modes of both first and second phase in mouse and human islets under physiological and pathophysiological conditions. Our data provide a new uni- and multicellular model of islet ß-cell activation: during the first phase, small but highly active ß-cell clusters are dominant, whereas during the second phase, electrical coupling generates large functional clusters via multicellular slow potentials to favor an economic sustained activity. Postprandial levels of glucagon-like peptide 1 favor coupling only in the second phase, whereas aging and glucotoxicity alter coupled activity in both phases. In summary, biphasic activity is encoded upstream of vesicle pools at the micro-organ level by multicellular electrical signals and their dynamic synchronization between ß-cells. The profound alteration of the electrical organization of islets in pathophysiological conditions may contribute to functional deficits in type 2 diabetes.

Tuning of an Artificial Pancreas Controller: an in silico methodology based on clinically-relevant criteria.

This paper presents a methodology to tune an artificial pancreas controller by minimizing the time spent in endangering glycaemic ranges (hypo- and hyperglycaemia). The risk associated to the patient's glycaemia is evaluated with an objective metric (the blood glucose risk index), which has an established clinical relevance. The tuned controller is validated in the UVA/Padova environment where the resulting artificial pancreas achieves minimal glucose risk index in realistic 24-hour long scenarios with unannounced glucose intake.

Bioluminescence of Vibrio fischeri: bacteria respond quickly and sensitively to pulsed microwave electric (but not magnetic) fields.

Biological systems with intrinsic luminescent properties serve as powerful and noninvasive bioreporters for real-time and label-free monitoring of cell physiology. This study employs the bioluminescent marine bacterium Vibrio fischeri to investigate the effects of separated microwave electric (E) and magnetic (H) fields. Using a cylindrical TM010 mode aluminum resonant cavity, designed to spatially separate E and H fields of a pulsed microwave (2.45 GHz) input, we sampled at 100-ms intervals the 490-nm emission of bioluminescence from suspensions of the V. fischeri. E-field exposure (at 4.24 and 13.4 kV/m) results in rapid and sensitive responses to 100-ms pulses. H-field excitation elicits no measurable responses, even at 100-fold higher power input levels (equivalent to 183 A/m). The observed effects on bacterial light output partially correlate with measured E-field-induced temperature increases. In conclusion, the endogenous bioluminescence of V. fischeri provides a sensitive and noninvasive method to assess the biological effects of microwave fields.

Multimed: An Integrated, Multi-Application Platform for the Real-Time Recording and Sub-Millisecond Processing of Biosignals.

Enhanced understanding and control of electrophysiology mechanisms are increasingly being hailed as key knowledge in the fields of modern biology and medicine. As more and more excitable cell mechanics are being investigated and exploited, the need for flexible electrophysiology setups becomes apparent. With that aim, we designed Multimed, which is a versatile hardware platform for the real-time recording and processing of biosignals. Digital processing in Multimed is an arrangement of generic processing units from a custom library. These can freely be rearranged to match the needs of the application. Embedded onto a Field Programmable Gate Array (FPGA), these modules utilize full-hardware signal processing to lower processing latency. It achieves constant latency, and sub-millisecond processing and decision-making on 64 channels. The FPGA core processing unit makes Multimed suitable as either a reconfigurable electrophysiology system or a prototyping platform for VLSI implantable medical devices. It is specifically designed for open- and closed-loop experiments and provides consistent feedback rules, well within biological microseconds timeframes. This paper presents the specifications and architecture of the Multimed system, then details the biosignal processing algorithms and their digital implementation. Finally, three applications utilizing Multimed in neuroscience and diabetes research are described. They demonstrate the system’s configurability, its multi-channel, real-time processing, and its feedback control capabilities.

Simultaneous monitoring of single cell and of micro-organ activity by PEDOT:PSS covered multi-electrode arrays.

Continuous and long-term monitoring of cellular and micro-organ activity is required for new insights into physiology and novel technologies such as Organs-on-Chip. Moreover, recent advances in stem cell technology and especially in the field of diabetes call for non-invasive approaches in quality testing of the large quantities of surrogate pancreatic islets to be generated. Electrical activity of such a micro-organ results in single cell action potentials (APs) of high frequency and in low frequency changes in local field potentials (slow potentials or SPs), reflecting coupled cell activity and overall organ physiology. Each of them is indicative of different physiological stages in islet activation. Action potentials in islets are of small amplitude and very difficult to detect. The use of PEDOT:PSS to coat metal electrodes is expected to reduce noise and results in a frequency-dependent change in impedance, as shown here. Whereas detection of high-frequency APs improves, low frequency SPs are less well detected which is, however, an acceptable trade off in view of the strong amplitude of SPs. Using a dedicated software, recorded APs and SPs can be automatically diagnosed and analyzed. Concomitant capture of the two signals will considerably increase the diagnostic power of monitoring islets and islet surrogates in fundamental research as well as drug screening or the use of islets as biosensors.

Guiding pancreatic beta cells to target electrodes in a whole-cell biosensor for diabetes.

We are developing a cell-based bioelectronic glucose sensor that exploits the multi-parametric sensing ability of pancreatic islet cells for the treatment of diabetes. These cells sense changes in the concentration of glucose and physiological hormones and immediately react by generating electrical signals. In our sensor, signals from multiple cells are recorded as field potentials by a micro-electrode array (MEA). Thus, cell response to various factors can be assessed rapidly and with high throughput. However, signal quality and consequently overall sensor performance rely critically on close cell-electrode proximity. Therefore, we present here a non-invasive method of further exploiting the electrical properties of these cells to guide them towards multiple micro-electrodes via electrophoresis. Parameters were optimized by measuring the cell's zeta potential and modeling the electric field distribution. Clonal and primary mouse or human ß-cells migrated directly to target electrodes during the application of a 1 V potential between MEA electrodes for 3 minutes. The morphology, insulin secretion, and electrophysiological characteristics were not altered compared to controls. Thus, cell manipulation on standard MEAs was achieved without introducing any external components and while maintaining the performance of the biosensor. Since the analysis of the cells' electrical activity was performed in real time via on-chip recording and processing, this work demonstrates that our biosensor is operational from the first step of electrically guiding cells to the final step of automatic recognition. Our favorable results with pancreatic islets, which are highly sensitive and fragile cells, are encouraging for the extension of this technique to other cell types and microarray devices.

Slow potentials encode intercellular coupling and insulin demand in pancreatic beta cells.

AIMS/HYPOTHESIS: Ion fluxes constitute a major integrative signal in beta cells that leads to insulin secretion and regulation of gene expression. Understanding these electrical signals is important for deciphering the endogenous algorithms used by islets to attain homeostasis and for the design of new sensors for monitoring beta cell function. METHODS: Mouse and human islets were cultured on multielectrode arrays (MEAs) for 3-13 days. Extracellular electrical activities received on each electrode were continuously amplified and recorded for offline characterisation. RESULTS: Differential band-pass filtering of MEA recordings of mouse islets showed two extracellular voltage waveforms: action potentials (lasting 40-60 ms) and very robust slow potentials (SPs, lasting 800-1,500 ms), the latter of which have not been described previously. The frequency of SPs directly correlated with glucose concentration, peaked at 10 mmol/l glucose and was further augmented by picomolar concentrations of glucagon-like peptide-1. SPs required the closure of ATP-dependent potassium channels as they were induced by glucose or glibenclamide but were not elicited by KCl-induced depolarisation. Pharmacological tools and the use of beta cell specific knockout mice showed that SPs reflected cell coupling via connexin 36. Moreover, increasing and decreasing glucose ramps showed hysteresis with reduced glucose sensitivity during the decreasing phase. SPs were also observed in human islets and could be continuously recorded over 24 h. CONCLUSIONS/INTERPRETATION: This novel electrical signature reflects the syncytial function of the islets and is specific to beta cells. Moreover, the observed hysteresis provides evidence for an endogenous algorithm naturally present in islets to protect against hypoglycaemia.