Defining an essential transcription factor program for naïve pluripotency

Dunn, Sara-Jane; Martello, Graziano; Yordanov, Boyan; Emmott, Stephen; Smith, Austin

doi:10.1126/science.1248882

Cited by 388 publications

(552 citation statements)

References 42 publications

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“…ES cells in 2iLIF were compared to cells cultured without MEK inhibition in medium containing GSK3 inhibitor, Chir99021 (CH), plus LIF (CHLIF). This allows analysis of the ERK‐responsive phosphoproteome independent of a change in identity because ES cells withstand ERK signalling and remain undifferentiated in CHLIF 7, 19, 20.…”

Section: Resultsmentioning

confidence: 99%

Negative feedback via RSK modulates Erk‐dependent progression from naïve pluripotency

et al. 2018

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Mitogen‐activated protein kinase (MAPK)/extracellular signal‐regulated kinase (ERK) signalling is implicated in initiation of embryonic stem (ES) cell differentiation. The pathway is subject to complex feedback regulation. Here, we examined the ERK‐responsive phosphoproteome in ES cells and identified the negative regulator RSK1 as a prominent target. We used CRISPR/Cas9 to create combinatorial mutations in RSK family genes. Genotypes that included homozygous null mutations in Rps6ka1, encoding RSK1, resulted in elevated ERK phosphorylation. These RSK‐depleted ES cells exhibit altered kinetics of transition into differentiation, with accelerated downregulation of naïve pluripotency factors, precocious expression of transitional epiblast markers and early onset of lineage specification. We further show that chemical inhibition of RSK increases ERK phosphorylation and expedites ES cell transition without compromising multilineage potential. These findings demonstrate that the ERK activation profile influences the dynamics of pluripotency progression and highlight the role of signalling feedback in temporal control of cell state transitions.

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Section: Resultsmentioning

confidence: 99%

Negative feedback via RSK modulates Erk‐dependent progression from naïve pluripotency

et al. 2018

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“…Variation in gene expression dynamics between the two differentiation trajectories suggested that these orderings could be used to help understand differences between the regulatory programs controlling differentiation toward alternative blood fates. Previous studies have successfully used Boolean abstraction to model transcriptional regulatory networks in HSCs (23), embryonic blood development (10), and embryonic stem cells (8,24).…”

Section: Resultsmentioning

confidence: 99%

“…An advantage of Boolean network modeling, as used in this work, is that it does not suffer from this limitation. A recent study used Boolean abstraction to model the pluripotency network in embryonic stem cells (24), and was able to predict the results of experimental network perturbations. However, this study relied on performing gene expression profiling in multiple experimental conditions, which is not always feasible, and was limited to bulk rather than single-cell data.…”

Section: Discussionmentioning

confidence: 99%

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

Hamey

Nestorowa

Kinston

et al. 2017

Experimental Hematology

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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

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“…It would be tempting to explore the different logical rules in a more unsupervised way by building all possible models with all combinations of regulations and testing their accuracy in silico. Although this approach has been used previously (37), it can be applied only to a subset of possible combinations (e.g., testing the addition or removal of regulations under a general logical rule, such as requiring all activators but none of the inhibitors to enable the activation of a component) and impose certain technical constraints (e.g., limitation to Boolean variables or to synchronous updating). In this study, we first built a model based on published data and then used it to identify caveats in our current knowledge; these caveats then were addressed by exploiting relevant highthroughput datasets.…”

Section: Discussionmentioning

confidence: 99%

Logical modeling of lymphoid and myeloid cell specification and transdifferentiation

Collombet

Oevelen

Sardina

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

123

101

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Blood cells are derived from a common set of hematopoietic stem cells, which differentiate into more specific progenitors of the myeloid and lymphoid lineages, ultimately leading to differentiated cells. This developmental process is controlled by a complex regulatory network involving cytokines and their receptors, transcription factors, and chromatin remodelers. Using public data and data from our own molecular genetic experiments (quantitative PCR, Western blot, EMSA) or genome-wide assays (RNA-sequencing, ChIP-sequencing), we have assembled a comprehensive regulatory network encompassing the main transcription factors and signaling components involved in myeloid and lymphoid development. Focusing on B-cell and macrophage development, we defined a qualitative dynamical model recapitulating cytokine-induced differentiation of common progenitors, the effect of various reported gene knockdowns, and the reprogramming of pre-B cells into macrophages induced by the ectopic expression of specific transcription factors. The resulting network model can be used as a template for the integration of new hematopoietic differentiation and transdifferentiation data to foster our understanding of lymphoid/myeloid cell-fate decisions.

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Defining an essential transcription factor program for naïve pluripotency

Cited by 388 publications

References 42 publications

Negative feedback via RSK modulates Erk‐dependent progression from naïve pluripotency

Negative feedback via RSK modulates Erk‐dependent progression from naïve pluripotency

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

Logical modeling of lymphoid and myeloid cell specification and transdifferentiation

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