Browsing by Author "Department of Medical and Molecular Genetics, Indiana University School of Medicine"

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    Global gene expression of histologically normal primary skin cells from BCNS subjects reveals "single-hit" effects that are influenced by rapamycin
    (Impact Journals, 2019-02-15) Phatak, Amruta; Athar, Mohammad; Crowell, James A.; Leffel, David; Herbert, Brittney-Shea; Bale, Allen E.; Kopelovich, Levy; Department of Medical and Molecular Genetics, Indiana University School of Medicine
    Studies of dominantly heritable cancers enabled insights about tumor progression. BCNS is a dominantly inherited disorder that is characterized by developmental abnormalities and postnatal neoplasms, principally BCCs. We performed an exploratory gene expression profiling of primary cell cultures derived from clinically unaffected skin biopsies of BCNS gene-carriers (PTCH1 +/-) and normal individuals. PCA and HC of untreated keratinocytes or fibroblasts failed to clearly distinguish BCNS samples from controls. These results are presumably due to the common suppression of canonical HH signaling in vitro. We then used a relaxed threshold (p-value <0.05, no FDR cut-off; FC 1.3) that identified a total of 585 and 857 genes differentially expressed in BCNS keratinocytes and fibroblasts samples, respectively. A GSEA identified pancreatic β cell hallmark and mTOR signaling genes in BCNS keratinocytes, whereas analyses of BCNS fibroblasts identified gene signatures regulating pluripotency of stem cells, including WNT pathway. Significantly, rapamycin treatment (FDR<0.05), affected a total of 1411 and 4959 genes in BCNS keratinocytes and BCNS fibroblasts, respectively. In contrast, rapamycin treatment affected a total of 3214 and 4797 genes in normal keratinocytes and normal fibroblasts, respectively. The differential response of BCNS cells to rapamycin involved 599 and 1463 unique probe sets in keratinocytes and fibroblasts, respectively. An IPA of these genes in the presence of rapamycin pointed to hepatic fibrosis/stellate cell activation, and HIPPO signaling in BCNS keratinocytes, whereas mitochondrial dysfunction and AGRN expression were uniquely enriched in BCNS fibroblasts. The gene expression changes seen here are likely involved in the etiology of BCCs and they may represent biomarkers/targets for early intervention.
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    Topological Methods for Visualization and Analysis of High Dimensional Single-Cell RNA Sequencing Data
    (World Scientific Publishing Company, 2019) Wang, Tongxin; Johnson, Travis; Zhang, Jie; Huang, Kun; Department of Medical and Molecular Genetics, Indiana University School of Medicine
    Single-cell RNA sequencing (scRNA-seq) techniques have been very powerful in analyzing heterogeneous cell population and identifying cell types. Visualizing scRNA-seq data can help researchers effectively extract meaningful biological information and make new discoveries. While commonly used scRNA-seq visualization methods, such as t-SNE, are useful in detecting cell clusters, they often tear apart the intrinsic continuous structure in gene expression profiles. Topological Data Analysis (TDA) approaches like Mapper capture the shape of data by representing data as topological networks. TDA approaches are robust to noise and different platforms, while preserving the locality and data continuity. Moreover, instead of analyzing the whole dataset, Mapper allows researchers to explore biological meanings of specific pathways and genes by using different filter functions. In this paper, we applied Mapper to visualize scRNA-seq data. Our method can not only capture the clustering structure of cells, but also preserve the continuous gene expression topologies of cells. We demonstrated that by combining with gene co-expression network analysis, our method can reveal differential expression patterns of gene co-expression modules along the Mapper visualization.