Dissecting mechanisms of mouse embryonic stem cells heterogeneity through a model-based analysis of transcription factor dynamics

Herberg, Maria; Glauche, Ingmar; Zerjatke, Thomas; Winzi, Maria; Buchholz, Frank; Roeder, Ingo

doi:10.1098/rsif.2016.0167

Cited by 17 publications

(27 citation statements)

References 40 publications

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“…The network that we observe recapitulates many known relationships between pluripotency markers in mouse embryonic stem cells. For instance, positive covariances support the activation of Fgf4 through Nanog and Sox2 27,28 , while negative covariances support the inhibition of Dnmt3a/b/l by Prdm14 17,29 and of Dppa3 by Tbx3 30 . While our data support previous claims that Nanog is positively covarying with Klf4, Sox2, Tet2, and Kat6b 9 , we see little support for a covariance with Esrrb, Zfp42 and Tet1 and we observe a significant negative covariance with Pou5f1 and Dnmt3a in single cells.…”

Section: Resultsmentioning

confidence: 99%

Nuclear gene proximity and protein interactions shape transcript covariances in mammalian single cells

Tarbier

Mackowiak

Frade

et al. 2019

Preprint

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Single-cell RNA sequencing studies into gene co-expression patterns could yield important new regulatory and functional insights, but have so far been limited by the confounding effects of cell differentiation and the cell cycle. We apply a tailored experimental design that eliminates these confounders, and report >80,000 intrinsically covarying gene pairs in mouse embryonic stem cells. These covariances form a network with biological properties, outlining known and novel gene interactions. We provide the first evidence that miRNAs naturally induce transcriptome-wide covariances, and compare the relative importance of nuclear organization, transcriptional and post-transcriptional regulation in defining covariances. We find that nuclear organization has the greatest impact, and that genes encoding for physically interacting proteins specifically tend to covary, suggesting importance for protein complex stoichiometry. Our results lend support to the concept of post-transcriptional ‘RNA operons’, but we further present evidence that nuclear proximity of genes on the same or even distinct chromosomes also provides substantial functional regulation in mammalian single cells.

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Section: Resultsmentioning

confidence: 99%

Nuclear gene proximity and protein interactions shape transcript covariances in mammalian single cells

Tarbier

Mackowiak

Frade

et al. 2019

Preprint

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“…In these conditions, ES cells are heterogeneous, displaying mosaic and dynamic 3 expression of various transcription factors (Chambers et al, 2007;Filipczyk et al, 2015;Hayashi et al, 2008;Toyooka et al, 2008) . Notably, ES cells in serum and LIF switch between Nanog high and low states (Herberg et al, 2016) . Comparisons of sister cells show a greater similarity during self-renewal than during differentiation in both Nanog-GFP (green fluorescent protein) expression levels (Nakamura et al, 2018) and in single-cell qPCR of microdissected sister cells (Jasnos et al, 2013) .…”

Section: Introductionmentioning

confidence: 99%

“…Although ES cell populations in 2i are substantially homogeneous and described as residing in a ground state, exit is observed to occur asynchronously over a Rex1 (gene name Zfp42 ) is a transcription factor whose expression is tightly linked to the naïve pluripotency network (Wray et al, 2010) . Rex1 linked fluorescent reporters have been widely used as tools that demarcate naïve pluripotency (Guo et al, 2010;Herberg et al, 2016; Nakai-Futatsugi and Niwa, 2016;Toyooka et al, 2008;Wray et al, 2010) . Use of destabilised GFP with a 2 hour half-life (Rex1-GFPd2) allows for accurate estimation of exit from naïve pluripotency (Betschinger et al, 2013;Leeb et al, 2014;Mulas et al, 2017Mulas et al, , 2017) .…”

Section: Introductionmentioning

confidence: 99%

Embryonic stem cells commit to differentiation by symmetric divisions following a variable lag period

Strawbridge

Blanchard

Smith

et al. 2020

Preprint

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Mouse embryonic stem (ES) cells are derived from the epiblast of the preimplantation embryo and retain the capacity to give rise to all embryo lineages. ES cells can be released into differentiation from a near-homogeneous maintenance condition. Exit from the ES cell state can be accurately monitored using the Rex1-GFPd2 transgenic reporter, providing a powerful system for examining a mammalian cell fate transition. Here, we performed live-cell imaging and tracking of ES cells during entry into differentiation for 48 hours in defined conditions. We observed a greater cell surface area and a modest shortening of the cell cycle prior to exit and subsequently a reduction in cell size and increase in motility. We did not see any instance of cells regaining ES cell identity, consistent with unidirectional developmental progression. Transition occurred asynchronously across the population but genealogical tracking revealed a high correlation in cell-cycle length and Rex1-GFPd2 expression between daughter cells. A population dynamics model was consistent with symmetric divisions during exit from naive pluripotency. Collapse of ES cell identity occurred acutely in individual cells but after a variable delay. The variation in lag period can extend up to three generations, creating marked population asynchrony.2

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“…Multicellular systems are not only found at the core of many fundamental biomedical processes like cell differentiation [1] and wound healing, but also play an essential role in a wide spectrum of biotechnological processes ranging from pharmaceutical manufacturing [2,3] to biopolymer production [4] to biological waste-water treatment [5]. The individual cells do not only interact with each other, but also with their environment.…”

Section: Introductionmentioning

confidence: 99%

A Novel Framework for Parameter and State Estimation of Multicellular Systems Using Gaussian Mixture Approximations

Dürr

Waldherr

2018

Processes

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Multicellular systems play an important role in many biotechnological processes. Typically, these exhibit cell-to-cell variability, which has to be monitored closely for process control and optimization. However, some properties may not be measurable due to technical and financial restrictions. To improve the monitoring, model-based online estimators can be designed for their reconstruction. The multicellular dynamics is accounted for in the framework of population balance models (PBMs). These models are based on single cell kinetics, and each cellular state translates directly into an additional dimension of the obtained partial differential equations. As multicellular dynamics often require detailed single cell models and feature a high number of cellular components, the resulting population balance equations are often high-dimensional. Therefore, established state estimation concepts for PBMs based on discrete grids are not recommended due to the large computational effort. In this contribution a novel approach is proposed, which is based on the approximation of the underlying number density functions as the weighted sum of Gaussian distributions. Thus, the distribution is described by the characteristic properties of the individual Gaussians, like the mean and covariance. Thereby, the complex infinite dimensional estimation problem can be reduced to a finite dimension. The characteristic properties are estimated in a recursive approach. The method is evaluated for two academic benchmark examples, and the results indicate its potential for model-based online reconstruction for multicellular systems.

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Dissecting mechanisms of mouse embryonic stem cells heterogeneity through a model-based analysis of transcription factor dynamics

Cited by 17 publications

References 40 publications

Nuclear gene proximity and protein interactions shape transcript covariances in mammalian single cells

Nuclear gene proximity and protein interactions shape transcript covariances in mammalian single cells

Embryonic stem cells commit to differentiation by symmetric divisions following a variable lag period

A Novel Framework for Parameter and State Estimation of Multicellular Systems Using Gaussian Mixture Approximations

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