Coupled differentiation and division of embryonic stem cells inferred from clonal snapshots
| Autor: | Richard A. Blythe, Anestis Tsakiridis, Linus J. Schumacher, Alexander G. Fletcher, Valerie Wilson, Liam J. Ruske, Jochen Kursawe |
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| Přispěvatelé: | University of St Andrews. School of Mathematics and Statistics, University of St Andrews. Applied Mathematics |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Cell division
Computer science QH301 Biology Bayesian inference T-NDAS Bayesian probability Population Biophysics FOS: Physical sciences Stem cells Model selection Models Biological Quantitative Biology - Quantitative Methods Stochastic population dynamics Quantitative Biology::Cell Behavior QH301 03 medical and health sciences 0302 clinical medicine Structural Biology Cell Behavior (q-bio.CB) Physics - Biological Physics education Molecular Biology Embryonic Stem Cells Quantitative Methods (q-bio.QM) 030304 developmental biology Branching process Probability 0303 health sciences education.field_of_study Bayes Theorem Cell Differentiation Cell Biology Division (mathematics) 92C15 Biological Physics (physics.bio-ph) FOS: Biological sciences Quantitative Biology - Cell Behavior Approximate Bayesian computation Biological system 030217 neurology & neurosurgery Algorithms Cell Division |
| Zdroj: | Ruske, L, 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 . https://doi.org/10.1088/1478-3975/aba041 University of St Andrews CRIS |
| Popis: | AGF was supported by a Vice-Chancellor's Fellowship from the University of Sheffield, LJS was supported by a Chancellor's Fellowship from the University of Edinburgh. The 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 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. Publisher PDF |
| Databáze: | OpenAIRE |
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