{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:43:55Z","timestamp":1760233435030,"version":"build-2065373602"},"reference-count":46,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,1,13]],"date-time":"2021-01-13T00:00:00Z","timestamp":1610496000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Biological tissue consists of populations of cells exhibiting different responses to pharmacological stimuli. To probe the heterogeneity of cell function, we propose a multiplexed approach based on real\u2010time imaging of the secondary messenger levels within each cell of the tissue, followed by extraction of the changes of single\u2010cell fluorescence over time. By utilizing a piecewise baseline correction, we were able to quantify the effects of multiple pharmacological stimuli added and removed sequentially to pancreatic islets of Langerhans, thereby performing a deep functional profiling for each cell within the islet. Cluster analysis based on the functional profile demonstrated dose\u2010dependent changes in statistical inter\u2010relationships between islet cell populations. We therefore believe that the functional cytometric approach can be used for routine quantitative profiling of the tissue for drug screening or pathological testing.<\/jats:p>","DOI":"10.3390\/jimaging7010009","type":"journal-article","created":{"date-parts":[[2021,1,13]],"date-time":"2021-01-13T11:52:32Z","timestamp":1610538752000},"page":"9","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Imaging Meets Cytometry: Analyzing Heterogeneous Functional Microscopic Data from Living Cell Populations"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1146-1246","authenticated-orcid":false,"given":"Matthew","family":"Draper","sequence":"first","affiliation":[{"name":"School of Biomedical Sciences, Ulster University, Cromore Road, Coleraine BT52 1SA, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8442-6348","authenticated-orcid":false,"given":"Mara","family":"Willems","sequence":"additional","affiliation":[{"name":"Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Headington, Oxford OX3 7LE, UK"}]},{"given":"Reshwan K.","family":"Malahe","sequence":"additional","affiliation":[{"name":"Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Headington, Oxford OX3 7LE, UK"}]},{"given":"Alexander","family":"Hamilton","sequence":"additional","affiliation":[{"name":"Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Headington, Oxford OX3 7LE, UK"},{"name":"Lund University Diabetes Centre, Unit of Molecular Metabolism, Clinical Research Centre, Malm\u00f6 University Hospital, 20502 Malm\u00f6, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8883-176X","authenticated-orcid":false,"given":"Andrei I.","family":"Tarasov","sequence":"additional","affiliation":[{"name":"School of Biomedical Sciences, Ulster University, Cromore Road, Coleraine BT52 1SA, UK"},{"name":"Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Headington, Oxford OX3 7LE, UK"},{"name":"Oxford Biomedical Research Centre, National Institute for Health Research, Oxford OX3 7LE, UK"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1038\/s42255-019-0148-2","article-title":"Integrating the inputs that shape pancreatic islet hormone release","volume":"1","author":"Noguchi","year":"2019","journal-title":"Nat. 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