Project description:Transcriptomic profiling and unsupervised clustering of cMyc+ GC-B cells identify the four subpopulations representing positive-selection stages.
Project description:To determine functional overlap between cMyc and AP4 in CD8+ T cell priming, we retrovirally expressed cMyc or AP4 in cMyc-deficient CD8+ T cells and examined gene expression after activation.
Project description:To identify genes with cell-lineage-specific expression not accessible by experimental micro-dissection, we developed a genome-scale iterative method, in-silico nano-dissection, which leverages high-throughput functional-genomics data from tissue homogenates using a machine-learning framework. This study applied nano-dissection to chronic kidney disease and identified transcripts specific to podocytes, key cells in the glomerular filter responsible for hereditary proteinuric syndromes and acquired CKD. In-silico prediction accuracy exceeded predictions derived from fluorescence-tagged-murine podocytes, identified genes recently implicated in hereditary glomerular disease and predicted genes significantly correlated with kidney function. The nano-dissection method is broadly applicable to define lineage specificity in many functional and disease contexts. We applied a machine-learning framework on high-throughput gene expression data from human kidney biopsy tissue homogenates and predict novel podocyte-specific genes. The prediction was validated by Human Protein Atlas at protein level. Prediction accuracy was compared with predictions derived from experimental approach using fluorescence-tagged-murine podocytes.
Project description:To identify genes with cell-lineage-specific expression not accessible by experimental micro-dissection, we developed a genome-scale iterative method, in-silico nano-dissection, which leverages high-throughput functional-genomics data from tissue homogenates using a machine-learning framework. This study applied nano-dissection to chronic kidney disease and identified transcripts specific to podocytes, key cells in the glomerular filter responsible for hereditary proteinuric syndromes and acquired CKD. In-silico prediction accuracy exceeded predictions derived from fluorescence-tagged-murine podocytes, identified genes recently implicated in hereditary glomerular disease and predicted genes significantly correlated with kidney function. The nano-dissection method is broadly applicable to define lineage specificity in many functional and disease contexts. We applied a machine-learning framework on high-throughput gene expression data from human kidney biopsy tissue homogenates and predict novel podocyte-specific genes. The prediction was validated by Human Protein Atlas at protein level. Prediction accuracy was compared with predictions derived from experimental approach using fluorescence-tagged-murine podocytes.
Project description:To determine functional overlap between cMyc and AP4 in CD8+ T cell priming, we retrovirally expressed cMyc or AP4 in cMyc-deficient CD8+ T cells and examined gene expression after activation. Naive CD8+ T cells from Myc conditional knockout mice with a tamoxifen inducible Cre transgene were retrovirally transduced with Myc or AP4 followed by a treatment with 4-hydroxytamoxifen in the presence of IL-7 for 2 days. RNA was harvested 48 hours after restimulation of transduced cells with anti-CD3 antibody and gene expression was compared by microarray. CD8+ T cells from littermate wildtype mice that were transduced with an empty retrovirus were used as control.
Project description:We generated the raw sample sets of TMT16-labeled for the quantification of single-cell from two cultured murine cell lines, GC-1 spg (GC-1) and GC-2spd (GC-2), and the raw bulk sets of TMT16-labeled cells.