title: Functional identification of islet cell types by electrophysiological fingerprinting.
abstract: The α-, β- and δ-cells of the pancreatic islet exhibit different electrophysiological features. We used a large dataset of whole-cell patch-clamp recordings from cells in intact mouse islets (N= 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.
authors.affiliation: Oxford Centre for Diabetes, Endocrinology, and Metabolism, Radcliffe Department of Medicine, University of Oxford, Churchill Hospital, Oxford OX3 7LE, UK linford.briant@ocdem.ox.ac.uk.|Department of Computer Science, University of Oxford, Oxford OX1 3QD, UK.
authors.forename: Linford J B
authors.identifier: 0000-0003-3619-3177
authors.initials: LJ
authors.lastname: Briant
authors.affiliation: Oxford Centre for Diabetes, Endocrinology, and Metabolism, Radcliffe Department of Medicine, University of Oxford, Churchill Hospital, Oxford OX3 7LE, UK.
authors.forename: Quan
authors.initials: Q
authors.lastname: Zhang
authors.affiliation: Oxford Centre for Diabetes, Endocrinology, and Metabolism, Radcliffe Department of Medicine, University of Oxford, Churchill Hospital, Oxford OX3 7LE, UK.
authors.forename: Elisa
authors.initials: E
authors.lastname: Vergari
authors.affiliation: Oxford Centre for Diabetes, Endocrinology, and Metabolism, Radcliffe Department of Medicine, University of Oxford, Churchill Hospital, Oxford OX3 7LE, UK.
authors.forename: Joely A
authors.initials: JA
authors.lastname: Kellard
authors.affiliation: Department of Computer Science, University of Oxford, Oxford OX1 3QD, UK.
authors.forename: Blanca
authors.initials: B
authors.lastname: Rodriguez
authors.affiliation: Department of Physiology, Anatomy, and Genetics, University of Oxford, South Parks Road, Oxford OX1 3PT, UK.
authors.forename: Frances M
authors.initials: FM
authors.lastname: Ashcroft
authors.affiliation: Oxford Centre for Diabetes, Endocrinology, and Metabolism, Radcliffe Department of Medicine, University of Oxford, Churchill Hospital, Oxford OX3 7LE, UK.|Metabolic Research, Department of Physiology, Institute of Neuroscience and Physiology, University of Göteborg, SE-405 30 Göteborg, Sweden.
authors.forename: Patrik
authors.initials: P
authors.lastname: Rorsman
mesh_terms: D000818:Animals; D055724:Electrophysiological Phenomena; D006943:Hyperglycemia / Q000235:genetics / Q000503:physiopathology*; D007515:Islets of Langerhans / Q000503:physiopathology*; D051379:Mice; D018345:Mice, Knockout; D008954:Models, Biological
keywords: conductance-based models; islet electrophysiology; logistic regression; α-cell; β-cell; δ-cell