DESCRIPTION (provided by applicant): Reconstructing lineage-specific gene expression in C. elegans embryos by shotgun single cell RNA-seq. A major question in biology is how cells diversify their transcriptional states to adopt unique and diverse behaviors. Single cell RNA-sequencing methods now allow analysis of many individual cells from a single population. Analysis of such "shotgun" single cell transcriptome data can allow inference of diverse cell states in a heterogeneous population but improved computational methods are needed to accurately reconstruct cell lineage relationships from these data. We propose here to apply shotgun single-cell RNA-seq to define lineage and cell type-specific expression dynamics and variability genome-wide and at single cell resolution in C. elegans embryos. C. elegans is an ideal system to develop methods for lineage reconstruction from single cell RNA-seq data because its invariant cell lineage and reproducible patterns of fate specification and gene expression allow mapping the single cell data to a known lineage. In addition, our previous use of imaging to define cellular resolution expression patterns for over 100 genes provides "landmark" genes that we will use to anchor expression patterns to the lineage. In Aim 1, we will sequence RNA from ~200 single cells from a simple lineage, `ABpxpaaaap' consisting of a mother cell and two daughters that adopt distinct fates. In Aim 2, we will develop and optimize algorithms to align the single cell data to the lineage and estimate the temporal progression and biological noise during these cells' development. The methods we propose to develop are general and could be applied to any developmental system where lineally related cells can be isolated.

PUBLIC HEALTH RELEVANCE: Newly developed single-cell genomics methods have the potential to shed light on both the process of development of different cell types and how this process is altered in cancer and other diseases. One major advantage of these single cell methods is the potential ability to connect cells with each other to infer the lineage relationship between cells, but well-validated software for lineage reconstruction is not available. This projec aims to develop and optimize computational methods for inferring lineage relationships from single cell data and to use this to ask how variably each gene is expressed across developing cells.