Functional identification of islet cell types by electrophysiological fingerprinting
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...
| Main Authors: | , , , , , , |
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| Format: | Journal article |
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Royal Society
2017
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| _version_ | 1826279347435077632 |
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| author | Briant, L Zhang, Q Vergari, E Kellard, J Rodriguez, B Ashcroft, F Rorsman, O |
| author_facet | Briant, L Zhang, Q Vergari, E Kellard, J Rodriguez, B Ashcroft, F Rorsman, O |
| author_sort | Briant, L |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-06T23:57:20Z |
| format | Journal article |
| id | oxford-uuid:74b14b66-2a1e-4df7-981b-90a1733d1c4f |
| institution | University of Oxford |
| last_indexed | 2024-03-06T23:57:20Z |
| publishDate | 2017 |
| publisher | Royal Society |
| record_format | dspace |
| spelling | oxford-uuid:74b14b66-2a1e-4df7-981b-90a1733d1c4f2022-03-26T20:04:37ZFunctional identification of islet cell types by electrophysiological fingerprintingJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:74b14b66-2a1e-4df7-981b-90a1733d1c4fSymplectic Elements at OxfordRoyal Society2017Briant, LZhang, QVergari, EKellard, JRodriguez, BAshcroft, FRorsman, OThe 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. |
| spellingShingle | Briant, L Zhang, Q Vergari, E Kellard, J Rodriguez, B Ashcroft, F Rorsman, O Functional identification of islet cell types by electrophysiological fingerprinting |
| title | Functional identification of islet cell types by electrophysiological fingerprinting |
| title_full | Functional identification of islet cell types by electrophysiological fingerprinting |
| title_fullStr | Functional identification of islet cell types by electrophysiological fingerprinting |
| title_full_unstemmed | Functional identification of islet cell types by electrophysiological fingerprinting |
| title_short | Functional identification of islet cell types by electrophysiological fingerprinting |
| title_sort | functional identification of islet cell types by electrophysiological fingerprinting |
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