Project description:We here systematically studied the interaction network of bone marrow cells. To this end, we micro-dissected many small interacting structures (cell doublets, triplets etc.) into single cells, and sequenced their mRNAs, to infer cell identity. After grouping the cells into cell types (based on the single-cell transcriptomes), we identified actual physical interactions that occurred more, or less, than what would be expected by chance. We compared the micro-dissected data to sorted hematopoietic stem cells. After mild dissociation of the bone marrow, we micro-dissected many small interacting structures (cell doublets, triplets etc.) into single cells, and sequenced their mRNAs. In addition, single hematopoietic stem cells were sorted (Lineage- Kit+ Sca1+ CD150+ CD48-) to sequence their transcriptome. In detail, the bone marrow was mildly flushed. Structures composed of about 10 to 20 cells were set apart. With needles and a micro-dissection microscope, we trimmed of smaller structures from these big structure. These smaller structures are mainly two to four cells attached together. These small structures were then further micro-dissected to single cells. The goal of doing these sequential dissections was to keep track of which cells were interacting with which. Each micro-dissected cell sample contains RNA-seq data for 96 single cells. Each single cells received a different barcode that allow us to entangle them, and to produce the processed .coutt.csv file. Each sorted hematopoietic stem cell sample represents a single cell. The processed data file expdata_BMJhscC.csv contains transcript counts for all analyzed cells.
Project description:We here systematically studied the interaction network of bone marrow cells. To this end, we micro-dissected many small interacting structures (cell doublets, triplets etc.) into single cells, and sequenced their mRNAs, to infer cell identity. After grouping the cells into cell types (based on the single-cell transcriptomes), we identified actual physical interactions that occurred more, or less, than what would be expected by chance. We compared the micro-dissected data to sorted hematopoietic stem cells.
Project description:We used a positron emission tomography (PET) tracer 18F fluorodeoxyglucose ([18F]-FDG) and transcriptomic analysis to detect glucose uptake by cells in the bone marrow micro-environment with an MLL-AF9-induced mouse model. Leukaemic cells had the greatest glucose uptake. To determine whether glucose uptake is driven by intrinsic demand, we applied RNA-seq of sorted leukaemia cells (GFP+) and bone marrow micro-environment myeloid cells (GFP-CD11b+) from the MLL-AF9 transduced mouse model.
Project description:Tumor-associated macrophages (TAMs) are key regulators in tumor progression. Although a role of bone marrow-derived monocytes (Mons) as TAM precursors has been revealed, the dynamic phenotypes of both TAMs and Mons regulated by the tumor microenvironment remain unclear. Here, we constructed an optimized micro-proteomics workflow, which was applicable to the mouse myeloid cells of low cell number. We sorted both TAMs and the corresponding Mons (1×105 per sample) from individual melanoma mouse model in the early stage and the late stage to establish the protein expression profiles by mass spectrum.
Project description:We here systematically studied the interaction network of bone marrow cells. To this end, we micro-dissected many small interacting structures (cell doublets, triplets etc.) into single cells, and sequenced their mRNAs, to infer cell identity. After grouping the cells into cell types (based on the single-cell transcriptomes), we identified actual physical interactions that occurred more, or less, than what would be expected by chance.
Project description:Single cell sequencing of Ins2-Cre;ptdTomato+ FACS sorted cells from micro-dissected hypothalamic arcuate nucleus and median eminence of wildtype and Irx3/5 double heterozygous mice.
Project description:Three Vsx2-GFP mouse retinas were dissected, dissociated and FACS sorted, and single cell RNA-seq libraries were generated for 288 single cells and 3 bulk libraries using Smart-seq2 (~10,000 cells each)
