Automated Digital Image Analysis of Islet Cell Mass Using Nikon's Inverted Eclipse Ti Microscope and Software to Improve Engraftment May Help to Advance the Therapeutic Efficacy and Accessibility of Islet Transplantation Across Centers

摘要

Reliable assessment of islet viability, mass, and purity must be met prior to transplanting an islet preparation into patients with type 1 diabetes. The standard method for quantifying human islet preparations is by direct microscopic analysis of dithizone-stained islet samples, but this technique may be susceptible to inter-/intraobserver variability, which may induce false positiveegative islet counts. Here we describe a simple, reliable, automated digital image analysis (ADIA) technique for accurately quantifying islets into total islet number, islet equivalent number (IEQ), and islet purity before islet transplantation. Islets were isolated and purified from n = 42 human pancreata according to the automated method of Ricordi et al. For each preparation, three islet samples were stained with dithizone and expressed as IEQ number. Islets were analyzed manually by microscopy or automatically quantified using Nikon's inverted Eclipse Ti microscope with built-in MS-Elements Advanced Research (AR) software. The AIDA method significantly enhanced the number of islet preparations eligible for engraftment compared to the standard manual method (p < 0.001). Comparisons of individual methods showed good correlations between mean values of IEQ number (r(2)=0.91) and total islet number (r(2)=0.88) and thus increased to r(2)=0.93 when islet surface area was estimated comparatively with IEQ number. The ADIA method showed very high intraobserver reproducibility compared to the standard manual method (p <0.001). However, islet purity was routinely estimated as significantly higher with the manual method versus the ADIA method (p <0.001). The ADIA method also detected small islets between 10 and 50 mu m in size. Automated digital image analysis utilizing the Nikon Instruments software is an unbiased, simple, and reliable teaching tool to comprehensively assess the individual size of each islet cell preparation prior to transplantation. Implementation of this technology to improve engraftment may help to advance the therapeutic efficacy and accessibility of islet transplantation across centers.