Smartphone-Fluidic Imaging System for Cell-Based Therapy in Diabetes

Diabetic nephropathy is a serious complication of both Type 1 and 2 diabetes. Islet transplantation, a cell-based therapy, has become a successful therapy for T1D that is able to achieve tight glycemic control without the needs of insulin injection.
In order to achieve long-term glycemic control, it is essential to have better evaluation of isolated human islet mass and function prior to islet transplantation. In Aim1 of the thesis, we developed a smartphone-fluidic Digital Imaging Analysis (SFDIA) System, in combination with a microfluidic technique for islet mass assessment. With the system, we quantified islets by tracking multiple moving islets in a microfluidic channel and received a relatively consistent result. Furthermore, the software can analyze and extract key human islet mass parameters, including quantity, size, volume, IEq, morphology, and purity, which are not fully obtainable from the traditional manual counting methods. In Aim2, we equipped the SFDIA system with fluorescence imaging capability, and used the system to study the islets’ functionality. We evaluated the system capability by performing real-time fluorescence imaging on mouse islets labelled with either chemical fluorescence dyes or genetically encoded fluorescent protein indicators (GEFPIs). The 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 and good signal to noise ratio.
Creatinine is a waste product of muscle metabolism that is filtered out of the blood by the kidneys. High levels of creatinine in the blood indicates impaired renal function. As a result, creatinine has been an important indicator to monitor post-islet-transplant. In Aim3, we attempted to build a point-of-care (POC) paper-based device for whole blood creatinine measurement. We tested the device with mice whole blood samples, and proved that the device was able to take quantifiable readings to reflect creatinine concentration. Furthermore, we developed a multi-well enzymatic fluorescence assay with the help of the SFDIA system to measure creatinine in serum. We validated the assay using 30 human samples, and proved that the measurement from the assay aligned with the clinical readings.