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dc.contributor.authorRuske, Liam J.
dc.contributor.authorKursawe, Jochen
dc.contributor.authorTsakiridis, Anestis
dc.contributor.authorWilson, Valerie
dc.contributor.authorFletcher, Alex
dc.contributor.authorBlythe, Richard A.
dc.contributor.authorSchumacher, Linus J.
dc.date.accessioned2020-07-20T12:30:01Z
dc.date.available2020-07-20T12:30:01Z
dc.date.issued2020-06-25
dc.identifier.citationRuske , L J , Kursawe , J , Tsakiridis , A , Wilson , V , Fletcher , A , Blythe , R A & Schumacher , L J 2020 , ' Coupled differentiation and division of embryonic stem cells inferred from clonal snapshots ' , Physical Biology , vol. In press . https://doi.org/10.1088/1478-3975/aba041en
dc.identifier.issn1478-3967
dc.identifier.otherPURE: 269137037
dc.identifier.otherPURE UUID: 1a66b977-4e88-4d21-b435-95e07b895c32
dc.identifier.otherPubMed: 32585646
dc.identifier.urihttp://hdl.handle.net/10023/20288
dc.description.abstractThe deluge of single-cell data obtained by sequencing, imaging and epigenetic markers has led to an increasingly detailed description of cell state. However, it remains challenging to identify how cells transition between different states, in part because data are typically limited to snapshots in time. A prerequisite for inferring cell state transitions from such snapshots is to distinguish whether transitions are coupled to cell divisions. To address this, we present two minimal branching process models of cell division and differentiation in a well-mixed population. These models describe dynamics where differentiation and division are coupled or uncoupled. For each model, we derive analytic expressions for each subpopulation's mean and variance and for the likelihood, allowing exact Bayesian parameter inference and model selection in the idealised case of fully observed trajectories of differentiation and division events. In the case of snapshots, we present a sample path algorithm and use this to predict optimal temporal spacing of measurements for experimental design. We then apply this methodology to an \textit{in vitro} dataset assaying the clonal growth of epiblast stem cells in culture conditions promoting self-renewal or differentiation. Here, the larger number of cell states necessitates approximate Bayesian computation. For both culture conditions, our inference supports the model where cell state transitions are coupled to division. For culture conditions promoting differentiation, our analysis indicates a possible shift in dynamics, with these processes becoming more coupled over time.
dc.language.isoeng
dc.relation.ispartofPhysical Biologyen
dc.rightsCopyright © 2020 IOP Publishing Ltd. As the Version of Record of this article is going to be / has been published on a gold open access basis under a CC BY 3.0 licence, this Accepted Manuscript is available for reuse under a CC BY 3.0 licence immediately. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1088/1478-3975/aba041en
dc.subjectBayesian inferenceen
dc.subjectModel selectionen
dc.subjectStem cellsen
dc.subjectStochastic population dynamicsen
dc.subjectQH301 Biologyen
dc.subjectBiophysicsen
dc.subjectCell Biologyen
dc.subjectMolecular Biologyen
dc.subjectStructural Biologyen
dc.subjectT-NDASen
dc.subject.lccQH301en
dc.titleCoupled differentiation and division of embryonic stem cells inferred from clonal snapshotsen
dc.typeJournal articleen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews.School of Mathematics and Statisticsen
dc.contributor.institutionUniversity of St Andrews.Applied Mathematicsen
dc.identifier.doihttps://doi.org/10.1088/1478-3975/aba041
dc.description.statusPeer revieweden


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