This allows for discovery of novel cell types and resolution of the transcriptional changes throughout a single cell type's developmental journey. As scRNAseq analysis algorithms allow for a priori identification of individual cells within a population, one can process heterogenous cell populations and unravel the transcriptomic signatures underlying such heterogeneity. It can therefore provide information regarding the various cell types that emerge during developmental processes (e.g., neurogenesis) and elucidate how the transcriptomic landscape changes within stem cells and their progeny as development progresses. scRNAseq enables massively parallel sequencing of transcriptomic libraries prepared from thousands of individual cells and allows for in silico identification and characterization of distinct cell populations ( Trapnell, 2015 Tanay and Regev, 2017). ![]() More recently, single-cell RNA sequencing (scRNAseq) has emerged as a powerful technique to understand the genome-wide transcriptomic landscapes of different cell types ( Tang et al., 2010 Hashimshony et al., 2012 Saliba et al., 2014 Trapnell et al., 2014 Achim et al., 2015, 2018 Satija et al., 2015 Vergara et al., 2017 Svensson et al., 2018 Zhong et al., 2018). ![]() Therefore, to identify related cell types across taxa, it is necessary to compare genomic information, such as shared gene expression profiles or shared enhancers across individual cells from specific developmental regions and stages. (2016), cell types are evolutionary units that can undergo evolutionary change. Furthermore, alterations in gene regulatory networks (GRNs) may have driven diversification of cell types during animal evolution. Therefore, understanding the underlying transcriptional dynamics is of utmost importance to understand developmental processes. Cell fate acquisition and differentiation are directly regulated by changes in transcriptional gene regulation. In general, many embryonic and postembryonic tissues are generated by stem cells that give rise to multipotent precursor cells whose daughters differentiate into tissue-specific, specialized cell types. Proper development of multicellular organisms relies on precise regulation of the cell cycle relative to establishment of cell lineages and cell fate decisions, e.g., the maintenance of proliferating cells vs. This work provides a valuable resource for future functional investigations to better understanding neurogenesis and the transitions from neural precursors to neurons in an annelid. Our data revealed two potentially distinct neural differentiation trajectories including an early trajectory for brain neurosecretory cells. Pseudotime analysis along this trajectory identified temporally-distinct cell states undergoing progressive gene expression changes over time. We examined the neural and neurosecretory clusters more deeply and characterized a differentiation trajectory starting from dividing precursors to neurons using Monocle3 and velocyto. Analysis of coregulatory programs in individual clusters revealed gene interactions that can be used for comparisons of cell types across taxa. We identified eight unique cell clusters (undifferentiated precursors, ectoderm, muscle, ciliary-band, gut, neurons, neurosecretory cells, and protonephridia), thus helping to identify uncharacterized molecular signatures such as previously unknown neurosecretory cell markers in C. Here we use a whole-organism, single-cell transcriptomic approach to map larval cell types in the annelid Capitella teleta at 24- and 48-h post gastrulation (stages 4 and 5). However, gene regulatory mechanisms underlying cell fate acquisition during development remains largely uncharacterized in spiralians. 2Department of Biology, Clark University, Worcester, MA, United StatesĮvolution and diversification of cell types has contributed to animal evolution.1Unit on Cell Specification and Differentiation, National Institute of Child Health and Human Development (NICHD), Bethesda, MD, United States.
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