A Smartphone-Fluidic Digital Imaging Analysis System for Pancreatic Islet Mass Quantification

Abstract

Islet beta-cell viability, function, and mass are three decisive attributes that determine the efficacy of human islet transplantation for type 1 diabetes mellitus (T1DM) patients. Islet mass is commonly assessed manually, which often leads to error and bias. Digital imaging analysis (DIA) system has shown its potential as an alternative, but it has some associated limitations. In this study, a Smartphone-Fluidic Digital Imaging Analysis (SFDIA) System, which incorporates microfluidic techniques and Python-based video processing software, was developed for islet mass assessment. We quantified islets by tracking multiple moving islets in a microfluidic channel using the SFDIA system, and we achieved a relatively consistent result. The counts from the SFDIA and manual counting showed an average difference of 2.91 ± 1.50%. Furthermore, our software can analyze and extract key human islet mass parameters, including quantity, size, volume, IEq, morphology, and purity, which are not fully obtainable from traditional manual counting methods. Using SFDIA on a representative islet sample, we measured an average diameter of 99.88 ± 53.91 µm, an average circularity of 0.591 ± 0.133, and an average solidity of 0.853 ± 0.107. Via analysis of dithizone-stained islets using SFDIA, we found that a higher islet tissue percentage is associated with top-layer islets as opposed to middle-layer islets (0.735 ± 0.213 and 0.576 ± 0.223, respectively). Our results indicate that the SFDIA system can potentially be used as a multi-parameter islet mass assay that is superior in accuracy and consistency, when compared to conventional manual techniques.