Cell Sorting and Noise-Induced Cell Plasticity Coordinate to Sharpen Boundaries between Gene Expression Domains

Wang, Qixuan; Holmes, William R.; Sosnik, Julian; Schilling, Thomas F.; Nie, Qing

doi:10.1371/journal.pcbi.1005307

Cited by 21 publications

(40 citation statements)

References 66 publications

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“…Boundary formation has a crucial role in the organisation of neurons and neurogenesis in the hindbrain. Through a combination of cell identity regulation (Addison et al, 2018;Wang et al, 2017) and Eph-ephrin-mediated cell segregation (Batlle and Wilkinson, 2012;Cayuso et al, 2015;Fagotto, 2014), each rhombomere is demarcated by sharp borders and has a homogeneous segmental identity. Specialised boundary cells form at each rhombomere border , which express specific molecular markers (Cheng et al, 2004;Cooke et al, 2005;Heyman et al, 1995;Letelier et al, 2018;Xu et al, 1995).…”

Section: Introductionmentioning

confidence: 99%

A single cell transcriptome atlas of the developing zebrafish hindbrain

2020

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Segmentation of the vertebrate hindbrain leads to the formation of rhombomeres, each with a distinct anteroposterior identity. Specialised boundary cells form at segment borders that act as a source or regulator of neuronal differentiation. In zebrafish, there is spatial patterning of neurogenesis in which non-neurogenic zones form at boundaries and segment centres, in part mediated by Fgf20 signalling. To further understand the control of neurogenesis, we have carried out single cell RNA sequencing of the zebrafish hindbrain at three different stages of patterning. Analyses of the data reveal known and novel markers of distinct hindbrain segments, of cell types along the dorsoventral axis, and of the transition of progenitors to neuronal differentiation. We find major shifts in the transcriptome of progenitors and of differentiating cells between the different stages analysed. Supervised clustering with markers of boundary cells and segment centres, together with RNA-seq analysis of Fgf-regulated genes, has revealed new candidate regulators of cell differentiation in the hindbrain. These data provide a valuable resource for functional investigations of the patterning of neurogenesis and the transition of progenitors to neuronal differentiation.

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Section: Introductionmentioning

confidence: 99%

A single cell transcriptome atlas of the developing zebrafish hindbrain

2020

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“…Calculation of gradient (23) and Hessian (24). The calculation of ∇f for f (λ) given by (22) involves the following ingredients:…”

Section: Appendix Amentioning

confidence: 99%

Computing the quasipotential for nongradient SDEs in 3D

Yang

Potter

Cameron

2019

Journal of Computational Physics

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Nongradient SDEs with small white noise often arise when modeling biological and ecological time-irreversible processes. If the governing SDE were gradient, the maximum likelihood transition paths, transition rates, expected exit times, and the invariant probability distribution would be given in terms of its potential function. The quasipotential plays a similar role for nongradient SDEs. Unfortunately, the quasipotential is the solution of a functional minimization problem that can be obtained analytically only in some special cases. We propose a Dijkstra-like solver for computing the quasipotential on regular rectangular meshes in 3D. This solver results from a promotion and an upgrade of the previously introduced ordered line integral method with the midpoint quadrature rule for 2D SDEs. The key innovations that have allowed us to keep the CPU times reasonable while maintaining good accuracy are (i) a new hierarchical update strategy, (ii) the use of Karush-Kuhn-Tucker theory for rejecting unnecessary simplex updates, and (iii) pruning the number of admissible simplexes and a fast search for them. An extensive numerical study is conducted on a series of linear and nonlinear examples where the quasipotential is analytically available or can be found at transition states by other methods. In particular, the proposed solver is applied to Tao's examples where the transition states are hyperbolic periodic orbits, and to a genetic switch model by Lv et al. (2014). The C source code implementing the proposed algorithm is available at M. Cameron's web page.

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“…This mechanism is regulated by effectors, such as cadherins or Eph receptors and ephrins, whose expression is related to cell identity [91]. Coupling cell-based models with gene expression models, the multi-scale hybrid models show that selective intercellular adhesions are crucial to sharpening boundaries in the segmental zebrafish hindbrain pattern formation [68] and stratification in skin epidermis [92]. Moreover, the asymmetric cell division contributes to better stratified level in epidermis than the symmetric division [93].…”

Section: Cell-cell Interactions To Reduce Variability In Boundary Formentioning

confidence: 99%

Noise control and utility: From regulatory network to spatial patterning

et al. 2020

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Stochasticity (or noise) at cellular and molecular levels has been observed extensively as a universal feature for living systems. However, how living systems deal with noise while performing desirable biological functions remains a major mystery. Regulatory network configurations, such as their topology and timescale, are shown to be critical in attenuating noise, and noise is also found to facilitate cell fate decision. Here we review major recent findings on noise attenuation through regulatory control, the benefit of noise via noise-induced cellular plasticity during developmental patterning, and summarize key principles underlying noise control. 2 Nie Q et al. Sci China Math studies investigate how to control or utilize noise in living systems [7,10,[10][11][12][13][14][15][16][17][18]. While noise may induce heterogeneity within the cell population, contributing to diversity in cell fate choice [19][20][21], it usually causes uncertainty in information transmission in the cell and impairs robustness of cellular functions, which is one of the main reasons for the difficulty in robust circuit functions [4,22]. A natural question at hand is how cells deal with noise effectively.Since cellular functions, such as bistability, oscillation and adaptation, have been found to link to regulatory network architectures [23], the network property is naturally important in noise control [18,[24][25][26][27][28][29][30][31]. What are basic constraints on the regulatory networks for noise attenuation? How noise is controlled in function-specific systems, such as adaptive systems or oscillatory systems? In addition, can noise be utilized to achieve specific biological functions? How does noise affect spatial organization and morphogen-mediated patterning? How can a precise and robust readout be generated from the noisy spatial morphogen gradient?In this work, we first review major noise attenuation mechanisms in regulatory networks, and then explore key strategies to combat noise in morphogens during spatial patterning. We conclude by summarizing the major mechanisms in noise control.

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Cell Sorting and Noise-Induced Cell Plasticity Coordinate to Sharpen Boundaries between Gene Expression Domains

Cited by 21 publications

References 66 publications

A single cell transcriptome atlas of the developing zebrafish hindbrain

A single cell transcriptome atlas of the developing zebrafish hindbrain

Computing the quasipotential for nongradient SDEs in 3D

Noise control and utility: From regulatory network to spatial patterning

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