From neural border to migratory stage: A comprehensive single cell roadmap of the timing and regulatory logic driving cranial and vagal neural crest emergence

Abstract

Neural crest cells exemplify cellular diversification from a multipotent progenitor population. However, the full sequence of molecular choices orchestrating the emergence of neural crest heterogeneity from the embryonic ectoderm remains elusive. Gene-regulatory-networks (GRN) govern early development and cell specification towards definitive neural crest. Here, we combine ultra-dense single cell transcriptomes with machine-learning and large-scale experimental validation to provide a comprehensive GRN underlying neural crest fate diversification from induction to early migration stages. During gastrulation, a transient neural border zone state precedes choice between neural crest and placodes following a "dual convergence model". Transcription factor connectome and bifurcation analyses demonstrate the early emergence of neural crest fates at neural plate stage, alongside an unbiased multipotent lineage persisting until after epithelial-mesenchymal transition. We decipher the circuits driving cranial and vagal neural crest formation and provide a broadly applicable strategy for investigating SC transcriptomes in vertebrate GRNs in development, evolution and disease.

scEvoNet: a gradient boosting-based method for prediction of cell state evolution

“…The NC is a multipotent and migratory cell population unique to vertebrates and essential notably for pigment, peripheral and enteric nervous system, and craniofacial structures formation [ 24 ]. For the input data, we used whole embryo scRNA-seq datasets for the Xenopus tropicalis, a non-amniote tetrapod vertebrate, at an early developmental stage (late gastrulation stage 12, and neurulation stages 13 (neural plate) and 14 (neural fold)) and Mus musculus, a mammal, at a similar developmental stage (late gastrulation stage 8.25) [ 20 , 25 ].…”

“…3000 genes were chosen as the optimal number because it strikes a balance between minimizing the batch effect and still maintaining a sufficient number of genes to accurately classify cell types. Thus, this is part of the domain adaptation that we perform to make the resulting model less dependent on the number of genes that are missing in another dataset to which the model will be applied (e.g., 30% of the genes considered crucial for training a NC classifier on frog data were absent from the zebrafish dataset [ 20 ]). Additionally, to reduce the effect of the different biological domains between datasets, and to reduce the effect of the scRNA-seq data sparsity, we apply a sigmoid function that smooths expression units in a more flexible manner than simple binarization, which has been shown to keep enough information for scRNA-seq data analysis [ 21 ].…”

“…This tool predicts potentially co-opted genes together with genes characteristic of each cell state during development across species. Recently we showed the ability of a similar LGBM-based classifier to detect neural crest cells in distantly-related scRNA-seq datasets [ 20 ]. Despite technical batch effects (datasets were made in different laboratories with different technologies) and biological batch effects (datasets were from two evolutionarily distant organisms and at different developmental time points), we have achieved a high AUC score of 0.95 for classifying zebrafish cells with our frog-based NC model [ 20 ].…”

“…Recently we showed the ability of a similar LGBM-based classifier to detect neural crest cells in distantly-related scRNA-seq datasets [ 20 ]. Despite technical batch effects (datasets were made in different laboratories with different technologies) and biological batch effects (datasets were from two evolutionarily distant organisms and at different developmental time points), we have achieved a high AUC score of 0.95 for classifying zebrafish cells with our frog-based NC model [ 20 ]. Here, we have expanded this method: scEvoNet applies to a variety of applications, e.g., between different time points during a given organism’s development, between species, and when comparing primary tumor and metastasis.…”

Vertebrate Cell Differentiation, Evolution, and Diseases: The Vertebrate-Specific Developmental Potential Guardians VENTX/NANOG and POU5/OCT4 Enter the Stage

“…The neuro-epithelial border cells (NEBs) form a transient embryonic cell population. Investigations on the transcriptional state of vertebrate NEBs by scRNA-seq analyses in several vertebrate models (i.e., zebrafish, Xenopus , chicken) have shown that NEBs express the NEB-specific gene regulatory network (GRN: PAX3 / 7 , ZIC1 , MSX1 / 2 , TFAP2 , ZNF703 ) ( Figure 3 A) together with ectodermal-lineage specifiers ( SOX2 / 3 , GMNN , LHX5 ) [ 37 , 96 , 97 ]. These studies thus propose that NEBs initially transit through an ectodermal state.…”