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Inferring rules of lineage commitment in haematopoiesis

Nature Cell Biology volume 14, pages 287–294 (2012)Cite this article

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Abstract

How the molecular programs of differentiated cells develop as cells transit from multipotency through lineage commitment remains unexplored. This reflects the inability to access cells undergoing commitment or located in the immediate vicinity of commitment boundaries. It remains unclear whether commitment constitutes a gradual process, or else represents a discrete transition. Analyses of in vitro self-renewing multipotent systems have revealed cellular heterogeneity with individual cells transiently exhibiting distinct biases for lineage commitment1,2,3,4,5,6. Such systems can be used to molecularly interrogate early stages of lineage affiliation and infer rules of lineage commitment. In haematopoiesis, population-based studies have indicated that lineage choice is governed by global transcriptional noise, with self-renewing multipotent cells reversibly activating transcriptome-wide lineage-affiliated programs7. We examine this hypothesis through functional and molecular analysis of individual blood cells captured from self-renewal cultures, during cytokine-driven differentiation and from primary stem and progenitor bone marrow compartments. We show dissociation between self-renewal potential and transcriptome-wide activation of lineage programs, and instead suggest that multipotent cells experience independent activation of individual regulators resulting in a low probability of transition to the committed state.

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Figure 1: Individual self-renewing cells do not express transcriptome-wide lineage programs.
Figure 2: Post-commitment assembly of the EML erythroid program is gradual and heterogeneous.
Figure 3: Molecular heterogeneity of early committed cells is observed in the presence of physiological differentation cues.
Figure 4: Self-renewing cells exhibit rare expression of individual erythroid regulators.
Figure 5: Mouse bone marrow stem cells exhibit infrequent and independent expression of erythroid genes.

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Acknowledgements

The authors would like to thank R. Gupta and G. May for conceptual discussions; C. Waugh, K. Clark, A. Pizzey and T. Adejumo for cell sorting; S. McGowan for microarray data accessibility; and J. Wray for critical reading of the manuscript. J.T. is a student of the PhD Program in Computational Biology at Instituto Gulbenkian de Ciencia, Oeiras, Portugal, and was financially supported by Fundacao para a Ciencia e Tecnologia (SFRH/BD/33208/2007). C. Peterson is supported by the Swedish Foundation for Strategic Research (Senior Individual Grant). This work was financially supported by the Medical Research Council of the United Kingdom, Leukaemia and Lymphoma Research, EuroSyStem and STEMEXPAND.

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Authors and Affiliations

  1. Stem Cell Laboratory, UCL Cancer Institute, University College London, Paul O’Gorman Building, 72 Huntley Street, London WC1E 6BT, UK

    Cristina Pina, Cristina Fugazza, Alex J. Tipping, John Brown, Shamit Soneji & Tariq Enver

  2. Molecular Haematology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK

    Alex J. Tipping, John Brown & Shamit Soneji

  3. Department of Theoretical Physics, Computational Biology and Biological Physics, Lund University, Lund SE-223 62, Sweden

    Jose Teles & Carsten Peterson

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  1. Cristina Pina
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Contributions

C. Pina initiated and led the study, conducted all single-cell RT–qPCR experiments, carried out clonal reconstitution assays and divisional tracking experiments, participated in population-based analyses of EML subcompartments, analysed and interpreted experimental data, participated in figure production and wrote the paper. C.F. processed mouse bone marrow samples, carried out western blotting, participated in characterization of EML subcompartments, including clonal reconstitution assays, analysed experimental data and contributed to its interpretation and produced the figures. A.J.T. carried out immunostaining, did cell sorting, participated in population-based analyses of EML subcompartments, analysed experimental data and contributed to its interpretation and contributed to figure production. J.B. carried out non-quantitative single-cell RT–PCR and processed microarray samples. S.S. analysed microarray data, contributed to analysis of single-cell RT–qPCR data and participated in figure production. J.T. and C. Peterson contributed to analysis of single-cell RT–qPCR data and contributed to data interpretation. T.E. supervised all aspects of the study, and wrote the paper. C. Pina, C.F. and A.J.T. contributed equally to this work.

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Correspondence to Tariq Enver.

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The authors declare no competing financial interests.

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Pina, C., Fugazza, C., Tipping, A. et al. Inferring rules of lineage commitment in haematopoiesis. Nat Cell Biol 14, 287–294 (2012). https://doi.org/10.1038/ncb2442

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