The Cranial Neural Crest in a Multiomics Era

Abstract

Neural crest ontogeny plays a prominent role in craniofacial development. In this Perspective article, we discuss recent advances to the understanding of mechanisms underlying the cranial neural crest gene regulatory network (cNC-GRN) stemming from omics-based studies. We briefly summarize how parallel considerations of transcriptome, interactome, and epigenome data significantly elaborated the roles of key players derived from pre-omics era studies. Furthermore, the growing cohort of cNC multiomics data revealed contribution of the non-coding genomic landscape. As technological improvements are constantly being developed, we reflect on key questions we are poised to address by taking advantage of the unique perspective a multiomics approach has to offer.

Keywords: epigenome; gene regulatory network; interactome; multiomics; neural crest; non-coding; transcriptome.

Figure 1

A multiomics approach for construction of the cranial neural crest gene regulatory network (cNC-GRN). CNCCs from in vivo non-human embryo models and human pluripotent stem cell differentiation in vitro model were subjected to multiomics interrogation for global-level information. Interactome analyses resolve TF interactions to the genome (TF-ChIP-seq, TF CUT&RUN), other TFs (TF-TF-μMassSpec), or CRs (TF-CR-μMassSpec). Epigenome analyses reveal enhancers and promoters defined by regions of accessible chromatin (ATAC-seq) and/or specific histone modifications (Histone-ChIP-seq, Histone CUT&RUN). CUT&RUN is an alternative method to ChIP-seq that has its utility demonstrated in the chick embryo NC (Skene and Henikoff, ; Rothstein and Simoes-Costa, 2020). Direct epigenomic relationships between promoters and enhancers are obtained by profiling their physical proximity (Chromatin capture). Transcriptome analysis provides snapshot of expressed genes. Parsing of all the datasets results in substantial number of gene modules to elaborate on the cNC-GRN, especially if coupled with single cell technologies for subpopulation resolution. CNCC, cranial neural crest cell; TF, transcription factor; CR, chromatin remodeler; μMassSpec, micro mass spectrophotometry.

Figure 2

Subcellular profiling increases resolution of the non-coding landscape. The transcriptome consists of a mixed population of protein-coding and non-coding RNAs, including but not limited to enhancer RNAs (eRNAs), long non-coding RNAs (lncRNAs) and microRNAs (miRNAs). Previous transcriptomic studies on populations of neural crest (NC) cells focused on polyadenylated mRNAs constituting mostly of protein-coding mRNAs. NC-specific subcellular profiling achieved by in vivo biotinylation of nuclei and ribosomes (i.e., polysomes) enables enrichment of RNA species subtypes already present in the whole cell transcriptome. The nuclear transcriptome provided higher definition of non-coding RNAs while the polysomal translatome minimized the “noise” of non-coding RNAs to inform on proteins being made (suggestive of dominant biological processes occurring at that stage). In depth exploration of non-coding RNAs' putative roles within the context of the cNC-GRN is currently underexplored but well-suited to the advantages provided by multiomics.