title: Inferring average generation via division-linked labeling.
abstract: For proliferating cells subject to both division and death, how can one estimate the average generation number of the living population without continuous observation or a division-diluting dye? In this paper we provide a method for cell systems such that at each division there is an unlikely, heritable one-way label change that has no impact other than to serve as a distinguishing marker. If the probability of label change per cell generation can be determined and the proportion of labeled cells at a given time point can be measured, we establish that the average generation number of living cells can be estimated. Crucially, the estimator does not depend on knowledge of the statistics of cell cycle, death rates or total cell numbers. We explore the estimator's features through comparison with physiologically parameterized stochastic simulations and extrapolations from published data, using it to suggest new experimental designs.
authors.affiliation: Hamilton Institute, Maynooth University, Maynooth, Ireland.
authors.forename: Tom S
authors.initials: TS
authors.lastname: Weber
authors.affiliation: Division of Immunology, Netherlands Cancer Institute, Amsterdam, The Netherlands.|Department of Theoretical Biology and Bioinformatics, Utrecht University, Utrecht, The Netherlands.|Institut Curie, PSL Research University, CNRS UMR168, Paris, France.
authors.forename: Leïla
authors.initials: L
authors.lastname: Perié
authors.affiliation: Hamilton Institute, Maynooth University, Maynooth, Ireland. ken.duffy@nuim.ie.
authors.forename: Ken R
authors.initials: KR
authors.lastname: Duffy
mesh_terms: D002452:Cell Count; D002453:Cell Cycle; D002455:Cell Division; D002477:Cells / Q000166:cytology* / Q000378:metabolism; D003198:Computer Simulation; D008954:Models, Biological; D011336:Probability; D013194:Staining and Labeling
keywords: Average generation inference; Branching processes; Stochastically decorated trees