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

Concepts and limitations for learning developmental trajectories from single cell genomics

“…Although high-resolution profiling of gene expression in developmental space can provide key insights into a given system, it should be noted that gene expression profiles only yield descriptive information and cannot be used for causal inference of gene regulatory networks. Several methods have been proposed for single cell regulatory network inference (Aibar et al, 2017;Matsumoto et al, 2017;Chan et al, 2017), of which some are even based on pseudotemporally ordered cell states (Hamey et al, 2017). Still, these are merely correlative and based on co-expression networks, and have been shown to perform relatively poorly in predicting accurate network structures (Chen and Mar, 2018).…”

Reconstructing Gene Regulatory Networks That Control Hematopoietic Commitment

“…In this chapter, we discuss one approach for reconstructing Boolean gene regulatory network models of a haematopoietic differentiation process using snapshot single cell gene expression data [12].…”

“…High quality single cell quantitative real-time PCR (qRT-PCR) data fit both of these requirements. In our case study we used data originally published in [7] and supplemented with additional populations in [12]. These data sampled cells from haematopoietic stem and progenitor cells using 12 different sorting strategies to sample cells at different stages of commitment towards mature blood lineages (Fig.…”

“…Nevertheless, our approach provides a valuable way for identifying ways in which known regulators act as part of a network to control cell fate decisions. progenitors [7,12]. These data measured the expression of 42 genes including 32 transcription factors in each cell.…”

Gene regulatory networks and network models in development and evolution

“…One thing that is clear from the papers that follow is that the impact of thinking on gene networks in bioscience is truly broad and deep. The topics span a wide range, from the differentiation of germ cells (1), immunology (2,3), and the molecular basis of celltype differentiation (4)(5)(6)(7)(8)(9)(10)(11), to paleontology (12,13) and behavior (14). The central role of network thinking is revealed in the way it allows for both the design of testable experimental frameworks to probe complex biological processes and the conceptual framework for understanding biological mechanisms.…”