Orchestrating the Single-cell Transcriptome: From Cells to Genes

Single-cell RNA-sequencing (scRNA-seq) is a technique that measures the transcriptional profile of the samples at single-cell resolution. Such a breakthrough technique brings the genomic study to an entirely new level, providing potential to understand some of the fundamental problems in biology, including gene expression, cell differentiation, cancer progression, and many more. With the help from statisticians, bioinformaticians and computer scientists, pipelines have been developed to process and analyze the huge amount of information from scRNA-seq experiments. However, challenges remain as customization is still needed depending on specific input dataset. In this study, we applied scRNA-seq to mouse embryos at early organogenesis to understand the cell type dynamics and gene regulations during development, using a variety of data analysis tools with experimental validation. In Chapter 1, we introduced our data analysis pipeline for analyzing large-scale high-heterogeneity dataset obtained from mouse whole embryos at E11.5, and presented a complete catalog of cell types using an integrated annotation approach. In Chapter 2, we extended such a pipeline to time-series datasets from mouse embryos at different stages, by successful removal of batch effects and integration of multiple datasets we provided insights into how cell types emerge and disappear during embryogenesis. In Chapter 3, we focused on the gene expression pattern revealed by scRNA-seq datasets, and presented a novel approach to identify housekeeping genes from these datasets. In Chapter 4, we have investigated the scRNA-seq datasets from a completely different perspective, treating genes as samples and clustered genes into groups with similar biological functions named ‘metagene’, and successfully using metagene as a novel approach to understand genes with unknown functions. Our results showed the possibility of using scRNA-seq as a powerful tool to understand mouse embryonic development from different perspectives, and provided valuable data that could be used for more detailed studies on specific cell types or genes.