Reconstructing blood stem cell regulatory network models from single-cell molecular profiles

Abstract

Adult blood contains a mixture of mature cell types, each with specialized functions. Single hematopoietic stem cells (HSCs) have been functionally shown to generate all mature cell types for the lifetime of the organism. Differentiation of HSCs toward alternative lineages must be balanced at the population level by the fate decisions made by individual cells. Transcription factors play a key role in regulating these decisions and operate within organized regulatory programs that can be modeled as transcriptional regulatory networks. As dysregulation of single HSC fate decisions is linked to fatal malignancies such as leukemia, it is important to understand how these decisions are controlled on a cell-bycell basis. Here we developed and applied a network inference method, exploiting the ability to infer dynamic information from single-cell snapshot expression data based on expression profiles of 48 genes in 2,167 blood stem and progenitor cells. This approach allowed us to infer transcriptional regulatory network models that recapitulated differentiation of HSCs into progenitor cell types, focusing on trajectories toward megakaryocyteerythrocyte progenitors and lymphoid-primed multipotent progenitors. By comparing these two models, we identified and subsequently experimentally validated a difference in the regulation of nuclear factor, erythroid 2 (Nfe2) and core-binding factor, runt domain, alpha subunit 2, translocated to, 3 homolog (Cbfa2t3h) by the transcription factor Gata2. Our approach confirms known aspects of hematopoiesis, provides hypotheses about regulation of HSC differentiation, and is widely applicable to other hierarchical biological systems to uncover regulatory relationships.gene regulatory networks | hematopoiesis | single cell | Boolean network | stem progenitor cells

Fat for fuel: lipid metabolism in haematopoiesis

“…Traditionally, the haematopoietic hierarchy involves the differentiation of the CMP into megakaryocyte–erythroid progenitors (MEPs) and granulocyte–macrophage progenitors (GMPs), while the CLPs further differentiate to produce B cells, T cells and NK cells . While some studies suggest that alternative pathways of lineage commitment may exist – including the identification of lymphoid‐primed multipotent progenitors (LMPPs) that are proposed to differentiate either into CLPs or directly into GMPs as well as CMP‐ and/or MEP‐independent platelet production – the majority of lineage tracing studies support the traditional model of haematopoiesis with minor variations . The regulation of haematopoiesis across the different stages of differentiation and commitment is crucial to modulating the balance of the mature blood cell compartments.…”

“…7 While some studies suggest that alternative pathways of lineage commitment may exist including the identification of lymphoid-primed multipotent progenitors (LMPPs) 12 that are proposed to differentiate either into CLPs or directly into GMPs as well as CMP-and/or MEP-independent platelet productionthe majority of lineage tracing studies support the traditional model of haematopoiesis with minor variations. 13 The regulation of haematopoiesis across the different stages of differentiation and commitment is crucial to modulating the balance of the mature blood cell compartments. Haematopoiesis is intrinsically regulated by stem and progenitor cell metabolism, with different metabolic parameters required for both proliferation (through symmetric mitosis) and differentiation (through asymmetric mitosis) across the various progenitors.…”

Kinetic models of hematopoietic differentiation

“…The procedures and goals with this approach are similar to the ones using continuous-valued models above. Early work here within hematopoiesis was done in Krumsiek, Marr, Schroeder, & Theis (2011) and later followed by Bonzanni et al (2013), Hamey et al (2017) and Collombet et al (2017). These studies were based upon static data-no measured expression time series.…”

“…The increase of single-cell studies in the hematopoietic system, reviewed in Cvejic (2015) and Povinelli, Rodriguez-Meira, & Mead (2018) led to development of computational and statistical approaches, including Boolean models, which contribute to gaining a better understanding of mechanisms governing lineage commitment and cell fate decisions (Pina et al, 2012;Moignard et al, 2015;Trapnell et al, 2014). When only static measurements are available, one can nevertheless construct pseudo-time trajectories in the case of single cell expression measurements as was done in Hamey et al (2017). First, one creates diffusion maps (Haghverdi, Buettner, & Theis, 2015;Setty et al, 2016) that follow the trajectories to different cell fates.…”

“…In the next step one infers a pseudo-time ordering based upon similarities between individual cell expressions (Trapnell et al, 2014)-adjacency is established. In Hamey et al (2017), networks are reconstructed from correlation measures along the pseudo-time trajectories, which are subsequently subject to Boolean modeling. Predictions for Gata2 regulation of Cbfa2t3h and Nfe2 in two different differentiation trajectories are subsequently verified with chip-seq techniques.…”