Functional identification of islet cell types by electrophysiological fingerprinting
Year: 2017
Type: article
Abstract: = 288 recordings) to investigate whether it is possible to reliably identify cell type (α, β or δ) based on their electrophysiological characteristics. We quantified 15 electrophysiological variables in each recorded cell. Individually, none of the variables could reliably distinguish the cell types. We therefore constructed a logistic regression model that included all quantified variables, to determine whether they could together identify cell type. The model identified cell type with 94% accuracy. This model was applied to a dataset of cells recorded from hyperglycaemic βV59M mice; it correctly identified cell type in all cells and was able to distinguish cells that co-expressed insulin and glucagon. Based on this revised functional identification, we were able to improve conductance-based models of the electrical activity in α-cells and generate a model of δ-cell electrical activity. These new models could faithfully emulate α- and δ-cell electrical activity recorded experimentally.
Language: English
Subfield: Endocrinology, Diabetes and Metabolism
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $2,579
Awards: G1260843, G2817659175, Safety and efficacy of anti-arrhythmic drug therapy in acute myocardial ischaemia in human. An integrative, multiscale and mechanistic investigation., Research Excellence (renewal), A Centre of Excellence in Computational Biomedicine, G5285785796, G6384498975, Metabolic and hormonal regulation of pancreatic hormone secretion., Functional and computational analyses of the regulation of glucagon secretion in health and disease, Metabolic and hormonal regulation of pancreatic hormone secretion., Functional studies of the ATP-sensitive potassium channel.
OpenAlex ID: W2592904886