Digital Image Analysis to Assess Quantity and Morphological Quality of Isolated Pancreatic Islets

IsletSwipe, a mobile platform for expert opinion exchange on islet graft images

We previously developed a deep learning-based web service (IsletNet) for an automated counting of isolated pancreatic islets. The neural network training is limited by the absent consensus on the ground truth annotations. Here, we present a platform (IsletSwipe) for an exchange of graphical opinions among experts to facilitate the consensus formation. The platform consists of a web interface and a mobile application. In a small pilot study, we demonstrate the functionalities and the use case scenarios of the platform. Nine experts from three centers validated the drawing tools, tested precision and consistency of the expert contour drawing, and evaluated user experience. Eight experts from two centers proceeded to evaluate additional images to demonstrate the following two use case scenarios. The Validation scenario involves an automated selection of images and islets for the expert scrutiny. It is scalable (more experts, images, and islets may readily be added) and can be applied to independent validation of islet contours from various sources. The Inquiry scenario serves the ground truth generating expert in seeking assistance from peers to achieve consensus on challenging cases during the preparation for IsletNet training. This scenario is limited to a small number of manually selected images and islets. The experts gained an opportunity to influence IsletNet training and to compare other experts' opinions with their own. The ground truth-generating expert obtained feedback for future IsletNet training. IsletSwipe is a suitable tool for the consensus finding. Experts from additional centers are welcome to participate.

A Multiparametric Assessment of Human Islets Predicts Transplant Outcomes in Diabetic Mice

Prior to transplantation into individuals with type 1 diabetes, in vitro assays are used to evaluate the quality, function and survival of isolated human islets. In addition to the assessments of these parameters in islet, they can be evaluated by multiparametric morphological scoring (0–10 points) and grading (A, B, C, D, and F) based on islet characteristics (shape, border, integrity, single cells, and diameter). However, correlation between the multiparametric assessment and transplantation outcome has not been fully elucidated. In this study, 55 human islet isolations were scored using this multiparametric assessment. The results were correlated with outcomes after transplantation into immunodeficient diabetic mice. In addition, the multiparametric assessment was compared with oxygen consumption rate of isolated islets as a potential prediction factor for successful transplantations. All islet batches were assessed and found to score: 9 points (n = 18, Grade A), 8 points (n = 19, Grade B), and 7 points (n = 18, Grade B). Islets that scored 9 (Grade A), scored 8 (Grade B) and scored 7 (Grade B) were transplanted into NOD/SCID mice and reversed diabetes in 81.2%, 59.4%, and 33.3% of animals, respectively (P < 0.0001). Islet scoring and grading correlated well with glycemic control post-transplantation (P < 0.0001) and reversal rate of diabetes (P < 0.05). Notably, islet scoring and grading showed stronger correlation with transplantation outcome compared to oxygen consumption rate. Taken together, a multiparametric assessment of isolated human islets was highly predictive of transplantation outcome in diabetic mice.

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

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.

Building Biomimetic Potency Tests for Islet Transplantation

Diabetes is a disease of insulin insufficiency, requiring many to rely on exogenous insulin with constant monitoring to avoid a fatal outcome. Islet transplantation is a recent therapy that can provide insulin independence, but the procedure is still limited by both the availability of human islets and reliable tests to assess their function. While stem cell technologies are poised to fill the shortage of transplantable cells, better methods are still needed for predicting transplantation outcome. To ensure islet quality, we propose that the next generation of islet potency tests should be biomimetic systems that match glucose stimulation dynamics and cell microenvironmental preferences and rapidly assess conditional and continuous insulin secretion with minimal manual handing. Here, we review the current approaches for islet potency testing and outline technologies and methods that can be used to arrive at a more predictive potency test that tracks islet secretory capacity in a relevant context. With the development of potency tests that can report on islet secretion dynamics in a context relevant to their intended function, islet transplantation can expand into a more widely accessible and reliable treatment option for individuals with diabetes.

Pancreatic beta cell/islet mass and body mass index

Body mass index (BMI) is widely used to define obesity. In studies of pancreatic beta-cell/islet mass, BMI is also a common standard for matching control subjects in comparative studies along with age and sex, based on the existing dogma of their significant positive correlation reported in the literature. We aimed to test the feasibility of BMI and BSA to assess obesity and predict beta-cell/islet mass. We used National Health and Nutrition Examination Survey (NHANES) data that provided dual-energy Xray absorptiometry (DXA)-measured fat mass (percent body fat; %BF), BMI, and BSA for adult subjects (20-75y; 4,879 males and 4,953 females). We then analyzed 152 cases of islet isolation performed at our center for correlation between islet yields and various donor anthropometric indices. From NHANES, over 50% of male subjects and 60% of female subjects with BMI:20.1-28.1 were obese as defined by %BF, indicating a poor correlation between BMI and %BF. BSA was also a poor indicator of %BF, as broad overlap was observed in different BSA ranges. Additionally, BMI and BSA ranges markedly varied between sex and race/ethnicity groups. From islet isolation, BMI and BSA accounted for only a small proportion of variance in islet equivalent (IEQ; r² = 0.09 and 0.11, respectively). BMI and obesity were strongly correlated in cases of high BMI subjects. However, the critical populations were non-obese subjects with BMI ranging from 20.1-28.1, in which a substantial proportion of individuals may carry excess body fat. Correlations between BMI, BSA, pancreas weight and beta-cell/islet mass were low.

The Flaws and Future of Islet Volume Measurements

When working with isolated islet preparations, measuring the volume of tissue is not a trivial matter. Islets come in a large range of sizes and are often contaminated with exocrine tissue. Many factors complicate the procedure, and yet knowledge of the islet volume is essential for predicting the success of an islet transplant or comparing experimental groups in the laboratory. In 1990, Ricordi presented the islet equivalency (IEQ), defined as one IEQ equaling a single spherical islet of 150 μm in diameter. The method for estimating IEQ was developed by visualizing islets in a microscope, estimating their diameter in 50 μm categories and calculating a total volume for the preparation. Shortly after its introduction, the IEQ was adopted as the standard method for islet volume measurements. It has helped to advance research in the field by providing a useful tool improving the reproducibility of islet research and eventually the success of clinical islet transplants. However, the accuracy of the IEQ method has been questioned for years and many alternatives have been proposed, but none have been able to replace the widespread use of the IEQ. This article reviews the history of the IEQ, and discusses the benefits and failings of the measurement. A thorough evaluation of alternatives for estimating islet volume is provided along with the steps needed to uniformly move to an improved method of islet volume estimation. The lessons learned from islet researchers may serve as a guide for other fields of regenerative medicine as cell clusters become a more attractive therapeutic option.

Fully Automated Islet Cell Counter (ICC) for the Assessment of Islet Mass, Purity, and Size Distribution by Digital Image Analysis

For isolated pancreatic islet cell preparations, it is important to be able to reliably assess their mass and quality, and for clinical applications, it is part of the regulatory requirement. Accurate assessment, however, is difficult because islets are spheroid-like cell aggregates of different sizes (<50 to 500 μm) resulting in possible thousandfold differences between the mass contribution of individual particles. The current standard manual counting method that uses size-based group classification is known to be error prone and operator dependent. Digital image analysis (DIA)-based methods can provide less subjective, more reproducible, and better-documented islet cell mass (IEQ) estimates; however, so far, none has become widely accepted or used. Here we present results obtained using a compact, self-contained islet cell counter (ICC3) that includes both the hardware and software needed for automated islet counting and requires minimal operator training and input; hence, it can be easily adapted at any center and could provide a convenient standardized cGMP-compliant IEQ assessment. Using cross-validated sample counting, we found that for most human islet cell preparations, ICC3 provides islet mass (IEQ) estimates that correlate well with those obtained by trained operators using the current manual SOP method ( r² = 0.78, slope = 1.02). Variability and reproducibility are also improved compared to the manual method, and most of the remaining variability (CV = 8.9%) results from the rearrangement of the islet particles due to movement of the sample between counts. Characterization of the size distribution is also important, and the present digitally collected data allow more detailed analysis and coverage of a wider size range. We found again that for human islet cell preparations, a Weibull distribution function provides good description of the particle size.