Population snapshots predict early haematopoietic and erythroid hierarchies

Betsabeh Khoramian Tusi, Samuel L. Wolock, Caleb Weinreb, Yung Hwang, Daniel Hidalgo, Rapolas Zilionis, Ari Waisman, Jun R. Huh, Allon M. Klein () and Merav Socolovsky ()
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Betsabeh Khoramian Tusi: Cell and Cancer Biology, University of Massachusetts Medical School
Samuel L. Wolock: Harvard Medical School
Caleb Weinreb: Harvard Medical School
Yung Hwang: Cell and Cancer Biology, University of Massachusetts Medical School
Daniel Hidalgo: Cell and Cancer Biology, University of Massachusetts Medical School
Rapolas Zilionis: Harvard Medical School
Ari Waisman: Institute for Molecular Medicine, University Medical Center of the Johannes Gutenberg-University Mainz
Jun R. Huh: Harvard Medical School and Brigham and Women’s Hospital
Allon M. Klein: Harvard Medical School
Merav Socolovsky: Cell and Cancer Biology, University of Massachusetts Medical School

Nature, 2018, vol. 555, issue 7694, 54-60

Abstract: Abstract The formation of red blood cells begins with the differentiation of multipotent haematopoietic progenitors. Reconstructing the steps of this differentiation represents a general challenge in stem-cell biology. Here we used single-cell transcriptomics, fate assays and a theory that allows the prediction of cell fates from population snapshots to demonstrate that mouse haematopoietic progenitors differentiate through a continuous, hierarchical structure into seven blood lineages. We uncovered coupling between the erythroid and the basophil or mast cell fates, a global haematopoietic response to erythroid stress and novel growth factor receptors that regulate erythropoiesis. We defined a flow cytometry sorting strategy to purify early stages of erythroid differentiation, completely isolating classically defined burst-forming and colony-forming progenitors. We also found that the cell cycle is progressively remodelled during erythroid development and during a sharp transcriptional switch that ends the colony-forming progenitor stage and activates terminal differentiation. Our work showcases the utility of linking transcriptomic data to predictive fate models, and provides insights into lineage development in vivo.

Date: 2018
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DOI: 10.1038/nature25741

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