Islets-on-Chip: A Tool for Real-Time Assessment of Islet Function Prior to Transplantation.

Area of Science:

• Endocrinology
• Transplantation Biology
• Bioelectronics

Background:

• Islet transplantation is a key therapy for unstable type 1 diabetes, with outcomes improving.
• Current methods lack pre-transplant criteria to predict islet quality and clinical success.
• Evaluating donor islet quality before transplantation is crucial for optimizing patient outcomes.

Purpose of the Study:

• To investigate if donor islet electrical activity can serve as a predictive criterion for islet transplantation clinical outcomes.
• To assess the correlation between islet electrical activity (CHIP-score) and established clinical outcome measures (beta-score).

Main Methods:

• Human donor islets (n=8) were analyzed for purity, glucose-induced insulin secretion, and electrical activity using multi-electrode arrays.
• Electrical activity was characterized across varying glucose concentrations, hormone challenges, and drug effects.
• A novel CHIP-score (1-6) was developed based on electrical islet activity patterns.

Main Results:

• Grouping of beta-scores and CHIP-scores into high, intermediate, and low categories was observed.
• A trend towards correlation between CHIP-score and beta-score was identified (R = 0.51, p = 0.1), though not statistically significant.
• The electrical activity analysis, performed online or offline, demonstrated potential for real-time assessment.

Conclusions:

• Characterization of donor islet electrical activity presents a novel, potentially predictive criterion for islet transplantation.
• This easily implementable approach could aid in predicting clinical outcomes, complementing existing assessment methods.
• Further studies with larger cohorts are needed to confirm the significance of the observed correlation and validate the CHIP-score.