Ascidian embryos highlight the importance of cell lineages in animal development. As simple proto-vertebrates they also provide insights into the evolutionary origins of novel cell types, such as cranial placodes and neural crest. To build upon these efforts we have determined single cell transcriptomes for more than 90,000 cells spanning the entirety of Ciona intestinalis development, from the onset of gastrulation to swimming tadpoles. This represents an average of over 12-fold coverage for every cell at every stage of development, owing to the small cell numbers of ascidian embryos. Single cell transcriptome trajectories were used to construct "virtual" cell lineage maps and provisional gene networks for nearly 40 different neuronal subtypes comprising the larval nervous system. We summarize several applications of these datasets, including annotating the synaptome of swimming tadpoles and tracing the evolutionary origin of novel cell types such as the vertebrate telencephalon. Single cell RNA sequencing (scRNA-seq) methods are revolutionizing our understanding of how cells are specified to become definitive tissues during development 1-5. These studies Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:

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Rare or de novo variants have substantial contribution to human diseases, but the statistical power to identify risk genes by rare variants is generally low due to rarity of genotype data. Previous studies have shown that risk genes usually have high expression in relevant cell types, although for many conditions the identity of these cell types are largely unknown. Recent efforts in single cell atlas in human and model organisms produced large amount of gene expression data. Here we present VBASS, a Bayesian method that integrates single-cell expression and de novo variant (DNV) data to improve power of disease risk gene discovery. VBASS models disease risk prior as a function of expression profiles, approximated by deep neural networks. It learns the weights of neural networks and parameters of Gamma-Poisson likelihood models of DNV counts jointly from expression and genetics data. On simulated data, VBASS shows proper error rate control and better power than state-of-the-art methods. We applied VBASS to published datasets and identified more candidate risk genes with supports from literature or data from independent cohorts. VBASS can be generalized to integrate other types of functional genomics data in statistical genetics analysis.

We present VBASS, a Bayesian method that integrates single-cell expression and de novo variant (DNV) data to improve power of disease risk gene discovery. VBASS models disease risk prior as a function of expression profiles, approximated by deep neural networks. It learns the weights of neural networks and parameters of Poisson likelihood models of DNV counts jointly from expression and genetics data. On simulated data, VBASS shows proper error rate control and better power than state-of-the-art methods. We applied VBASS to published datasets and identified more candidate risk genes with supports from literature or data from independent cohorts.