Signaling pathways and tissue interactions in neural plate border formation

Schille, Carolin; Schambony, Alexandra

doi:10.1080/23262133.2017.1292783

Cited by 13 publications

(15 citation statements)

References 89 publications

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“…Crabp2a, a marker of the neural plate border [16], is more lowly expressed in the cluster with higher chordin relative likelihood values, suggesting that chd loss-of-function inhibits expression of crabp2a. This is consistent with previous studies showing a requirement of chordin for proper gene expression patterning within the neural plate [76, 77].…”

Section: Supplementary Notessupporting

confidence: 94%

Quantifying the effect of experimental perturbations at single-cell resolution

Burkhardt¹,

Stanley

Tong

et al. 2019

Preprint

103

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4Single-cell RNA-sequencing (scRNA-seq) is a powerful tool to quantify transcriptional states in 5 thousands to millions of cells. It is increasingly common for scRNA-seq data to be collected in 6 multiple experimental conditions, yet quantifying differences between scRNA-seq datasets re-7 mains an analytical challenge. Previous efforts at quantifying such differences focus on discrete 8 regions of the transcriptional state space such as clusters of cells. Here, we describe a contin-9 uous measure of the effect of an experiment across the transcriptomic space. First, we use the 10 manifold assumption to model the cellular state space as a graph (or network) with cells as nodes 11 and edges connecting cells with similar transcriptomic profiles. Next, we create an Enhanced 12 Experimental Signal (EES) that estimates the likelihood of observing cells from each condition 13 at every point in the manifold. We show that the EES has useful properties and information that 14 can be extracted. The EES can be used to identify how gene expression is affected by a given 15 perturbation, including identifying non-monotonic changes from only two conditions. We also 16 show that we can use both the magnitude and frequency of the EES, using an algorithm we 17 call vertex frequency clustering, to derive subsets of cells at appropriate levels of granularity 18 (tailored to areas that change) that are enriched in the experimental or control conditions or that 19 are unaffected between conditions. We demonstrate both algorithms using a combination of 20 biological and synthetic datasets. Implementations are provided in the MELD Python package, 21 which is available at https://github.com/KrishnaswamyLab/MELD. 22As single-cell RNA-sequencing (scRNA-seq) has become more accessible, the design of single-cell exper-24 iments has become increasingly complex. Researchers regularly use scRNA-seq to quantify the effect of 25 a drug, gene knockout, or other experimental perturbation on a biological system. However, quantifying 26 the compositional differences between single-cell datasets collected from multiple experimental conditions 27 1 remains an analytical challenge [1] because of the heterogeneity and noise in both the data and the effects 28 of a given perturbation. 29 Previous work has shown the utility of modelling the transcriptomic state space as a continuous low-30 dimensional manifold, or set of manifolds, to characterize cellular heterogeneity and dynamic biological 31 processes [2][3][4][5][6][7][8]. In the manifold model, the biologically valid combinations of gene expression are rep-32 resented as a smooth, low-dimensional surface in a high dimensional space, such as a two-dimensional 33 sheet embedded in three dimensions. The main challenge in developing tools to quantify compositional 34 differences between single-cell datasets is that each dataset comprises several intrinsic structures of hetero-35 geneous cells, and the effect of the experimental condition could be diffuse or isolated to particular areas 36 of...

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Section: Supplementary Notessupporting

confidence: 94%

Quantifying the effect of experimental perturbations at single-cell resolution

Burkhardt¹,

Stanley

Tong

et al. 2019

Preprint

103

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“…Levels of activity and cross-regulations between BMP, FGF, and WNT signaling pathways are particularly important for the induction of NC and PP, as they initiate spatial subdivisions of the dorsal ectoderm during gastrulation (Wilson and Hemmati-Brivanlou, 1995;Streit and Stern, 1999;Monsoro-Burq et al, 2003;Kudoh et al, 2004;Steventon et al, 2009;Stuhlmiller and Garcia-Castro, 2012;Yardley and Garcia-Castro, 2012;Schille and Schambony, 2017). Activity levels are influenced by the source of ligands and their antagonists.…”

Section: Secreted Signaling Pathways Broadly Pattern the Ectodermmentioning

confidence: 99%

Insights Into the Early Gene Regulatory Network Controlling Neural Crest and Placode Fate Choices at the Neural Border

Seal

Monsoro-Burq

2020

Front. Physiol.

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The neural crest (NC) cells and cranial placodes are two ectoderm-derived innovations in vertebrates that led to the acquisition of a complex head structure required for a predatory lifestyle. They both originate from the neural border (NB), a portion of the ectoderm located between the neural plate (NP), and the lateral non-neural ectoderm. The NC gives rise to a vast array of tissues and cell types such as peripheral neurons and glial cells, melanocytes, secretory cells, and cranial skeletal and connective cells. Together with cells derived from the cranial placodes, which contribute to sensory organs in the head, the NC also forms the cranial sensory ganglia. Multiple in vivo studies in different model systems have uncovered the signaling pathways and genetic factors that govern the positioning, development, and differentiation of these tissues. In this literature review, we give an overview of NC and placode development, focusing on the early gene regulatory network that controls the formation of the NB during early embryonic stages, and later dictates the choice between the NC and placode progenitor fates.

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“…It has been proposed that neural crest cells are especially sensitive to perturbation, causing them to undergo apoptosis, as compared to neighboring tissue. This sensitivity is likely due to the multiple genes required for precisely coordinated secreted signals and tissue interactions, at varying stages, from induction to EMT, migration, and ultimately to final differentiation (Betancur et al, 2010; Driever et al, 1996; Neuhauss et al, 1996; Schille and Schambony, 2017). This is supported by the relatively high prevalence of neural crest defects in humans (1 per 300 to 2,500 births) caused by genetic defects or prenatal exposures, many of which result in apoptosis of the neural crest (Chappell et al, 2009; McCarthy and Eberhart, 2014; WHO, 2004; Stanier and Moore, 2004).…”

Section: Discussionmentioning

confidence: 99%

“…Secreted signals from neighboring tissues are required for neural crest induction and differentiation. These secreted factors include Wnts, from the neural and epidermal ectoderm, BMPs from the epidermal ectoderm and FGF family members from the underlying mesoderm (Klymkowsky et al, 2010; Milet and Monsoro-Burq, 2012; Rogers et al, 2012; Sauka-Spengler and Bronner-Fraser, 2008; Schille and Schambony, 2017; Stuhlmiller and Garcia-Castro, 2012). Together, these factors induce the formation of the neural crest domain, a broad progenitor region that expresses distinct molecular markers, such as Pax3, c-myc and Zic1 (Meulemans and Bronner-Fraser, 2004; Pegoraro and Monsoro-Burq, 2013).…”

Section: Introductionmentioning

confidence: 99%

Neural crest development in Xenopus requires Protocadherin 7 at the lateral neural crest border

Bradley¹

2018

Mechanisms of Development

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In vertebrates, the neural crest is a unique population of pluripotent cells whose development is dependent on signaling from neighboring tissues. Cadherin family members, including protocadherins, are emerging as major players in neural crest development, largely through their roles in cell adhesion and sorting in embryonic tissues. Here, we show that Protocadherin 7 (Pcdh7), previously shown to function in sensorial layer integrity and neural tube closure in Xenopus, is also involved in neural crest specification and survival. Pcdh7 expression partly overlaps the neural crest domain at the lateral neural crest border. Pcdh7 knockdown in embryos does not alter neural crest induction; however, neural crest specification markers, including Snail2 and Sox9, are lost, due to apoptosis of the neural crest starting after stage 13. Pcdh7 knockdown also results in downregulation of Wnt11b; both of which are co-expressed in the sensorial layer lateral to the neural crest, suggestive of a role for Wnt11b in the neural crest apoptosis. Confirming this role, apoptosis, Snail2 expression and the developmental fate of the neural crest can be partially rescued by ectopic expression of Wnt11b. These results indicate that Pcdh7 plays an important role in maintaining the sensorial layer at the lateral neural crest border, which is necessary for the secretion of survival factors, including Wnt11b.

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Signaling pathways and tissue interactions in neural plate border formation

Cited by 13 publications

References 89 publications

Quantifying the effect of experimental perturbations at single-cell resolution

Quantifying the effect of experimental perturbations at single-cell resolution

Insights Into the Early Gene Regulatory Network Controlling Neural Crest and Placode Fate Choices at the Neural Border

Neural crest development in Xenopus requires Protocadherin 7 at the lateral neural crest border

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