Human islet preparations distributed for research exhibit a variety of insulin-secretory profiles.

Human islet research is providing new insights into human islet biology and diabetes, using islets isolated at multiple US centers from donors with varying characteristics. This creates challenges for understanding, interpreting, and integrating research findings from the many laboratories that use these islets. In what is, to our knowledge, the first standardized assessment of human islet preparations from multiple isolation centers, we measured insulin secretion from 202 preparations isolated at 15 centers over 11 years and noted five distinct patterns of insulin secretion. Approximately three quarters were appropriately responsive to stimuli, but one quarter were dysfunctional, with unstable basal insulin secretion and/or an impairment in stimulated insulin secretion. Importantly, the patterns of insulin secretion by responsive human islet preparations (stable Baseline and Fold stimulation of insulin secretion) isolated at different centers were similar and improved slightly over the years studied. When all preparations studied were considered, basal and stimulated insulin secretion did not correlate with isolation center, biological differences of the islet donor, or differences in isolation, such as Cold Ischemia Time. Dysfunctional islet preparations could not be predicted from the information provided by the isolation center and had altered expression of genes encoding components of the glucose-sensing pathway, but not of insulin production or cell death. These results indicate that insulin secretion by most preparations from multiple centers is similar but that in vitro responsiveness of human islets cannot be predicted, necessitating preexperimental human islet assessment. These results should be considered when one is designing, interpreting, and integrating experiments using human islets.

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Will Bush

William S. Bush, Ph.D., is a human geneticist and bioinformatician, and Assistant Professor within the Cleveland Institute for Computational Biology and the Department of Population and Quantitative Health Sciences at Case Western Reserve University.