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Single-cell lineage tracking strategies enabled by recent experimentaltechnologies have produced significant insights into cell fate decisions, butlack the quantitative framework necessary for rigorous statistical analysis ofmechanistic models describing cell division and differentiation. In this paper,we develop such a framework with corresponding moment-based parameterestimation techniques for continuous-time, multi-type branching processes. Suchprocesses provide a probabilistic model of how cells divide and differentiate,and we apply our method to study hematopoiesis, the mechanism of blood cellproduction. We derive closed-form expressions for higher moments in a generalclass of such models. These analytical results allow us to efficiently estimateparameters of much richer statistical models of hematopoiesis than those usedin previous statistical studies. After validating the methodology in simulationstudies, we apply our estimator to hematopoietic lineage tracking data fromrhesus macaques. Our analysis provides a more complete understanding of cellfate decisions during hematopoiesis in non-human primates, which may be morerelevant to human biology and clinical strategies than previous findings frommurine studies. For example, in addition to previously estimated hematopoieticstem cell self-renewal rate, we are able to estimate fate decisionprobabilities and to compare structurally distinct models of hematopoiesisusing cross validation. These estimates of fate decision probabilities and ourmodel selection results should help biologists compare competing hypothesesabout how progenitor cells differentiate. The methodology is transferrable to alarge class of stochastic compartmental models and multi-type branching models,commonly used in studies of cancer progression, epidemiology, and many otherfields.

Statistical inference in partially observed branching processes with application to cell lineage tracking of in vivo hematopoiesis