Cell fate clusters in ICM organoids arise from cell fate heredity and division: a modelling approach

Liebisch, Tim; Drusko, Armin; Mathew, Biena; Stelzer, Ernst H. K.; Fischer, Sabine; Matthäus, Franziska

doi:10.1038/s41598-020-80141-3

Cited by 11 publications

(14 citation statements)

References 62 publications

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“…For our following simulations, we use a two-dimensional representation of a cell tissue with 177 cells that was created based on an existing tissue-growth model [17,18,19] (Fig. 4).…”

Section: Cell Arrangementmentioning

confidence: 99%

NANOG/GATA6 Interactions Revisited: A Statistical Mechanics Approach towards Cell Fate Decisions

Schardt¹,

Fischer²

2021

Preprint

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In preimplantation mammalian embryos, the second cell fate decision introduces spatial patterns of embryonic and extra-embryonic precursor cells. The transcription factors NANOG and GATA6 are the earliest markers for the two cell types and interact between cells via the fibroblast growth factor signaling pathway. Computational models have been used to mimic the patterns and cell type proportions found in experimental studies. However, these models are always phenomenological in nature and lack a proper physical explanation. We derive a cell fate decision model motivated by the ideas of statistical mechanics. The model incorporates intra-and intercellular interactions of NANOG and GATA6. A detailed mathematical analysis on the resulting dynamical system is presented. We find that our model is capable of generating tissue wide spatial patterns of the two cell types. Its advantages are revealed in the simple physical and biological interpretation of the parameters and their interactions. In numerical simulations, we showcase the ability to replicate checkerboard patterns of different cell type proportions varying only a single parameter. The tight control of the system as well as the ease of use and the direct expandability to other signaling types provide solid reasons for the continued use of our model. We are convinced that our approach presents an exciting perspective in relation to cell fate decisions. Moreover, the concepts are generalizable to questions regarding cell signaling beyond the mammalian embryo.

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“…For our following simulations, we use a two-dimensional representation of a cell tissue with 177 cells that was created based on an existing tissue-growth model [17,18,19] (Fig. 4).…”

Section: Cell Arrangementmentioning

confidence: 99%

NANOG/GATA6 Interactions Revisited: A Statistical Mechanics Approach towards Cell Fate Decisions

Schardt¹,

Fischer²

2021

Preprint

Self Cite

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“…The stability interval for −∆ε g then becomes ∆ε min < −∆ε g < ∆ε max (15) with ∆ε min := −∆ε n + ln 1 + e −∆εs−∆εns min…”

Section: Cell Type Proportionsmentioning

confidence: 99%

“…We are certain that mismatches for small distances can be resolved by including cell division in our system. It has already been proven that cell division leads to a clustering of a given cell type [15]. Since clusters are an accumulation of directly connected cells of equal type, the corresponding PCFs must increase for small distances especially at a distance of 1.…”

Section: Simulations Replicate Patterns Found In Icm Organoidsmentioning

confidence: 99%

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Global cell-cell communication enables spatial segregation of cells in organoids of the inner cell mass

Schardt¹,

Fischer²

2021

Preprint

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During development, cell fates are determined through a combination of intracellular transcriptional regulations and extracellular signaling. As a result, spatial patterns of different cell types arise. We investigate the decision between epiblast and primitive endoderm cells in the inner cell mass of the preimplantation mouse embryo. Our computational model uses global cell signaling for the pattern formation. By varying the signal dispersion, cell type arrangements ranging from a checkerboard to an engulfing pattern can be generated. Pair correlation functions provide a well-suited way of characterizing the model output. With these, we established a quantitative comparison between the simulation results and experimental data of inner cell mass organoids. We obtained an astonishing agreement. Thus, our model proves its capability to replicate the cell differentiation patterns, making global signaling a strong contender to explain pattern formation in the preimplantation embryo.

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“…Molecular noise as well as cell-tocell variability in kinetic parameters may thus play a critical role in the initiation of cell specification [7,8]. The range of possible sources of noise is a priori limited because, in vivo, the global outcome of the lineage decision processes is robustly reproducible in terms of the sizes of the resulting cell populations, the temporal characteristics of cell fate changes and the spatial patterning [9][10][11]. In the present work, we show how mathematical modelling can be used to assess various possible origins of symmetry-breaking scenarios.…”

Section: Introductionmentioning

confidence: 99%

Initial source of heterogeneity in a model for cell fate decision in the early mammalian embryo

et al. 2022

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During development, cells from a population of common progenitors evolve towards different fates characterized by distinct levels of specific transcription factors, a process known as cell differentiation. This evolution is governed by gene regulatory networks modulated by intercellular signalling. In order to evolve towards distinct fates, cells forming the population of common progenitors must display some heterogeneity. We applied a modelling approach to obtain insights into the possible sources of cell-to-cell variability initiating the specification of cells of the inner cell mass into epiblast or primitive endoderm cells in early mammalian embryo. At the single-cell level, these cell fates correspond to three possible steady states of the model. A combination of numerical simulations and bifurcation analyses predicts that the behaviour of the model is preserved with respect to the source of variability and that cell–cell coupling induces the emergence of multiple steady states associated with various cell fate configurations, and to a distribution of the levels of expression of key transcription factors. Statistical analysis of these time-dependent distributions reveals differences in the evolutions of the variance-to-mean ratios of key variables of the system, depending on the simulated source of variability, and, by comparison with experimental data, points to the rate of synthesis of the key transcription factor NANOG as a likely initial source of heterogeneity.

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Cell fate clusters in ICM organoids arise from cell fate heredity and division: a modelling approach

Cited by 11 publications

References 62 publications

NANOG/GATA6 Interactions Revisited: A Statistical Mechanics Approach towards Cell Fate Decisions

NANOG/GATA6 Interactions Revisited: A Statistical Mechanics Approach towards Cell Fate Decisions

Global cell-cell communication enables spatial segregation of cells in organoids of the inner cell mass

Initial source of heterogeneity in a model for cell fate decision in the early mammalian embryo

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