Project description:Advances in single-cell genomics enable commensurate improvements in methods for uncovering lineage relations among individual cells. Current sequencing based methods for cell lineage analysis depend on low resolution bulk analysis or rely on extensive single cell sequencing which is not scalable and could be biased by functional dependencies. Here we show an integrated biochemical-computational platform for generic single-cell lineage analysis that is retrospective, cost-effective and scalable. It consists of a biochemical-computational pipeline that inputs individual cells, produces targeted single-cell sequencing data and uses it to generate a lineage tree of the input cells. We validated the platform by applying it to cells sampled from an ex vivo grown tree and analyzed its feasibility landscape by computer simulations. We conclude that the platform may serve as a generic tool for lineage analysis and thus pave the way towards large-scale human cell lineage discovery.
Project description:We developed a new single cell sequencing method to simultaneously sequence methylome and transcriptome for mouse DRG neurons Integrative analysis of transcription and methylation at single cell level
Project description:We develop a single cell methylome analysis technique based on RRBS, which works robustly for mouse embryonic stem cells (mESCs), sperm, metaphase II oocytes, and zygotes. In total, 36 samples were analyzed, including 8 single mouse embryonic stem cells (mESCs), pooled-5, 10, 20 mESCs, bulk mESCs, 7 single sperms, 10 single pronuclei from 5 individual zygotes, 2 metaphase II oocytes, 2 the first polar bodies and 3 negative controls.