1006 – MOLECULAR TO TISSUE-SCALE ANALYSIS OF HEMATOPOIETIC STEM/PROGENITOR CELL DYNAMICS

Abstract

Natural sciences have long been divided into quantitative and qualitative, with physics an example of the former, and biology an example of the latter. Importantly, a quantitative understanding is essential for accurate prediction of future states including the impact of perturbations. Hematopoiesis research has greatly benefited from the qualitative description of the hematopoietic differentiation tree for over 30 years now, with renewed impetus provided through the addition of quantitative measurements of population dynamics in 2015. However, it is universally recognised that stem/progenitor populations defined by small combinations of cell surface markers are heterogeneous. Their use therefore represents a useful abstraction but cannot capture the true complexity of hematopoiesis. Hematopoiesis has a long track record of embracing single-cell functional and molecular profiling technologies. However, a single-cell resolution quantitative model of native hematopoiesis has never been reported. We have employed persistent labelling of stem cells combined with time-series single cell RNA-Seq to build the first quantitative model of native blood stem and progenitor dynamics in the mouse bone marrow at single cell resolution. We capture compartment-wide flux and net proliferation rates in real-time, thus revealing the distinct ways in which different blood lineages maintain their cellular output. By exploring the molecular layer of single cell RNA-Seq, we pinpoint molecular changes associated with changes in cell behaviour, such as increased differentiation or growth rates. Finally, unlike immunophenotyping data, our models can readily be integrated with external data thus enabling a quantitative comparison of stem cell behaviour during homeostatic versus transplantation settings. We anticipate that this work will not only impact our ability to perform quantitative analysis of perturbed hematopoiesis using mouse models, but also stimulate discussions on how a similarly quantitative understanding of human hematopoiesis might be achieved. Natural sciences have long been divided into quantitative and qualitative, with physics an example of the former, and biology an example of the latter. Importantly, a quantitative understanding is essential for accurate prediction of future states including the impact of perturbations. Hematopoiesis research has greatly benefited from the qualitative description of the hematopoietic differentiation tree for over 30 years now, with renewed impetus provided through the addition of quantitative measurements of population dynamics in 2015. However, it is universally recognised that stem/progenitor populations defined by small combinations of cell surface markers are heterogeneous. Their use therefore represents a useful abstraction but cannot capture the true complexity of hematopoiesis. Hematopoiesis has a long track record of embracing single-cell functional and molecular profiling technologies. However, a single-cell resolution quantitative model of native hematopoiesis has never been reported. We have employed persistent labelling of stem cells combined with time-series single cell RNA-Seq to build the first quantitative model of native blood stem and progenitor dynamics in the mouse bone marrow at single cell resolution. We capture compartment-wide flux and net proliferation rates in real-time, thus revealing the distinct ways in which different blood lineages maintain their cellular output. By exploring the molecular layer of single cell RNA-Seq, we pinpoint molecular changes associated with changes in cell behaviour, such as increased differentiation or growth rates. Finally, unlike immunophenotyping data, our models can readily be integrated with external data thus enabling a quantitative comparison of stem cell behaviour during homeostatic versus transplantation settings. We anticipate that this work will not only impact our ability to perform quantitative analysis of perturbed hematopoiesis using mouse models, but also stimulate discussions on how a similarly quantitative understanding of human hematopoiesis might be achieved.