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BackgroundThe speed of insulin absorption after subcutaneous delivery is highly variable. Incorrect assumptions about insulin pharmacokinetics compromise effective glycemic regulation. Our ultimate goal is to develop a system to monitor insulin levels in vivo continuously, allowing pharmacokinetic parameters to be calculated in real time. We hypothesize that a bead-based detection system can be run on a flow-through microfluidic platform to measure insulin in subcutaneous fluid sampled via microdialysis. As a first step in development, we focused on microsphere-based measurement of insulin.MethodsPolystyrene microspheres coated with an anti-insulin monoclonal antibody were exposed to insulin-containing solutions, and after addition of a fluorescently labeled anti-insulin monoclonal antibody with a distinct epitope, bead-associated fluorescence was detected by fluorescence microscopy in 96-well plates or in a flow-through, microfluidic platform.ResultsThe bead detection system in plates had a linear range in buffer for regular human insulin (RHI), insulin lispro, and insulin aspart of 15-1115 µIU/ml, 14-976 µIU/ml, and 25-836 µIU/ml, respectively. Measurement on plasma samples demonstrated proportionality between basal and peak insulin levels similar to the laboratory reference method. Preliminary results in a polydimethylsiloxane-based, flow-through, microfluidic platform showed a strong signal at peak insulin levels.ConclusionsWe have developed a microsphere-based system to rapidly measure levels of insulin and insulin analogs. We have further demonstrated proof of concept that this bead detection system can be implemented in a lab-on-a-chip format, which will be further developed and combined with microdialysis for real-time monitoring of insulin in vivo.

We report an all-in-one platform - ScanDrop - for the rapid and specific capture, detection, and identification of bacteria in drinking water. The ScanDrop platform integrates droplet microfluidics, a portable imaging system, and cloud-based control software and data storage. The cloud-based control software and data storage enables robotic image acquisition, remote image processing, and rapid data sharing. These features form a "cloud" network for water quality monitoring. We have demonstrated the capability of ScanDrop to perform water quality monitoring via the detection of an indicator coliform bacterium, Escherichia coli, in drinking water contaminated with feces. Magnetic beads conjugated with antibodies to E. coli antigen were used to selectively capture and isolate specific bacteria from water samples. The bead-captured bacteria were co-encapsulated in pico-liter droplets with fluorescently-labeled anti-E. coli antibodies, and imaged with an automated custom designed fluorescence microscope. The entire water quality diagnostic process required 8 hours from sample collection to online-accessible results compared with 2-4 days for other currently available standard detection methods.

We report an all-in-one platform - ScanDrop - for the rapid and specific capture, detection, and identification of bacteria in drinking water. The ScanDrop platform integrates droplet microfluidics, a portable imaging system, and cloud-based control software and data storage. The cloud-based control software and data storage enables robotic image acquisition, remote image processing, and rapid data sharing. These features form a "cloud" network for water quality monitoring. We have demonstrated the capability of ScanDrop to perform water quality monitoring via the detection of an indicator coliform bacterium, Escherichia coli, in drinking water contaminated with feces. Magnetic beads conjugated with antibodies to E. coli antigen were used to selectively capture and isolate specific bacteria from water samples. The bead-captured bacteria were co-encapsulated in pico-liter droplets with fluorescently-labeled anti-E. coli antibodies, and imaged with an automated custom designed fluorescence microscope. The entire water quality diagnostic process required 8 hours from sample collection to online-accessible results compared with 2-4 days for other currently available standard detection methods.