Project description:Purpose: The goals of this study are to investigate how the transcriptome of colon cancer cells responds to genotoxic 5FU treatments utilizing single cell RNA-seq (scRNA-seq) technology. Methods: The collection of the single cell transcriptomes obtained from three colon cancer cell lines, RKO (CRL-2577), HCT116 (CCL-247) and SW480 (CCL-228), was generated by Drop-seq method followed by sequencing through Illumina HiSeq-4000 High-Output system. Results: A total of 10 datasets was generated for further analysis.
Project description:BRAF and IKKα are damage-responsive kinases required for 53BP1 and RIF1 co-recruitment to sites of DNA breaks and for timely resolution of DNA lesions. This provides a novel therapeutic opportunity based on combining BRAF or IKKα inhibitors with therapeutic agents that induce DNA damage
Project description:Endogenous and exogenous chemical modifications in DNA have profound influences on genome function. We have developed a novel technology, Nick-seq, for mapping variety of DNA chemical modifications across the genomes at single-nucleotide resolution, by which we achieved quantitative profiling of single-strand breaks, phosphorothioate modifications, and oxidative DNA damage sites. This method is applicable for mapping of any DNA metabolism events that are involved in or can be converted, enzymatically or chemically, to DNA strand breaks such as DNA modifications, damages, genome replication, and chromatin structures.
Project description:Current gene-expression databases for the haematopoietic system provide information on gene expression profiles present in bulk populations. Although informative, these studies lack the resolution that can be gained at a single-cell level. In particular, population-average data assumes homogeneity within the population and may as such obscure the ability to detect the heterogeneity of decision-making processes in individual cells. Here we report 1656 single cell transcriptomes analysed by single-cell RNA sequencing. Cells were FACS sorted on broad gates encompassing haematopoietic stem and progenitor populations (HSPCs), with index sorting data collected to permit retrospective identification of populations by surface marker expression. Our dataset thus represents the gene expression landscape of HSPCs at single-cell resolution, capturing the heterogeneity in and between cell populations. Pseudotime analysis visualized haematopoietic stem (HSC) to progenitor transitions, identified HSC as well as lineage-specific transcriptional programs, and also highlighted putative lineage branching points. To provide access to the wider scientific community, a user-friendly website was developed with intuitive search and display functionality. Single cell RNA sequencing of haematopoeitic stem and progenitor cells