Single-cell analysis of mixed-lineage states leading to a binary cell fate choice

By A. Olsson, Meenakshi Venkatasubramanian, Virendra K. Chaudhri, Bruce J. Aronow, N. Salomonis, Harinder Singh, H. Grimes, Data A O. Nature, 2016

460 citations recorded by the source corpus.

Delineating hierarchical cellular states, including rare intermediates and the networks of regulatory genes that orchestrate cell-type specification, are continuing challenges for developmental biology. Single-cell RNA sequencing is greatly accelerating such research, given its power to provide comprehensive descriptions of genomic states and their presumptive regulators. Haematopoietic multipotential progenitor cells, as well as bipotential intermediates, manifest mixed-lineage patterns of gene expression at a single-cell level. Such mixed-lineage states may reflect the molecular priming of different developmental potentials by co-expressed alternative-lineage determinants, namely transcription factors. Although a bistable gene regulatory network has been proposed to regulate the specification of either neutrophils or macrophages, the nature of the transition states manifested in vivo, and the underlying dynamics of the cell-fate determinants, have remained elusive. Here we use single-cell RNA sequencing coupled with a new analytic tool, iterative clustering and guide-gene selection, and clonogenic assays to delineate hierarchical genomic and regulatory states that culminate in neutrophil or macrophage specification in mice. We show that this analysis captured prevalent mixed-lineage intermediates that manifested concurrent expression of haematopoietic stem cell/progenitor and myeloid progenitor cell genes. It also revealed rare metastable intermediates that had collapsed the haematopoietic stem cell/progenitor gene expression programme, instead expressing low levels of the myeloid determinants, Irf8 and Gfi1 (refs 9, 10, 11, 12, 13). Genetic perturbations and chromatin immunoprecipitation followed by sequencing revealed Irf8 and Gfi1 as key components of counteracting myeloid-gene-regulatory networks. Combined loss of these two determinants ‘trapped’ the metastable intermediate. We propose that mixed-lineage states are obligatory during cell-fate specification, manifest differing frequencies because of their dynamic instability and are dictated by counteracting gene-regulatory networks.

Subfields: Immunology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f4cfb99ea0ea2e73c36ba3b1cd57e59bd22bd1d3

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Citations in the corpus

Reversed graph embedding resolves complex single-cell trajectories (2017, unknown): “A simulation of differentiation driven by a set of stochastic differential equations controlled by a hypothetical gene regulatory network 15 demonstrated that Monocle 2 robustly and accurately reconstructed trajectories with up to three fates (Supplementary Figure 5–8, Supplementary Data 1, 2) 16 .”
The Human Cell Atlas (2017, unknown): “, 2016), and have helped address open questions about whether myeloid progenitor cells in bone marrow are already skewed towards distinct fates (Olsson et al., 2016; Paul et al., 2015) and when T helper cell commit to their fate (Lönnberg et al.”
Developmental and Functional Heterogeneity of Monocytes. (2018, background): “Unbiased single-cell RNA sequencing combined with indexed FACS-sorting of early myeloid precursor subsets identified cells with a monocytic transcriptional program within the CMP as well as GMP population (Olsson et al., 2016; Paul et al., 2015), indicating the limitation of population analysis base”
Western Diet Triggers NLRP3-Dependent Innate Immune Reprogramming (2018, background): “In addition, we detected signaling pathways in GMPs associated with cell proliferation, anti-apoptosis, loss of stem cell quiescence, and favored lineage differentiation into monocytes (Olsson et al., 2016), which potentially raises GMP lifespan and facilitates memory induction.”
Human haematopoietic stem cell lineage commitment is a continuous process (2017, unknown): “The comparison of the surface marker expression of these populations to the commonly applied gating scheme 5 using our indexed data set showed that immunophenotypically defined oligopotent progenitor populations (megakaryocyte-erythroid progenitors, MEPs; granulocyte-monocyte progenitors, GMPs; B ce”
FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data (2017, methodology): “For data from Olsson et al.(3) target clusters were inferred by FateID, using the following list of markers: Pf4 (megakaryocytes), Epor”
Reversed graph embedding resolves complex single-cell developmental trajectories (2017, methodology): “Mao et al later proposed an extension of SimplePPT that can learn arbitrary graphs, rather than just trees, which describe large datasets embedded in the same space as the input [7].”