Dataset Information

MAPPING NEURONAL CELL TYPES USING INTEGRATIVE MULTI-SPECIES MODELING OF HUMAN AND MOUSE SINGLE CELL RNA SEQUENCING.

ABSTRACT: Mouse brain transcriptomic studies are important in the understanding of the structural heterogeneity in the brain. However, it is not well understood how cell types in the mouse brain relate to human brain cell types on a cellular level. We propose that it is possible with single cell granularity to find concordant genes between mouse and human and that these genes can be used to separate cell types across species. We show that a set of concordant genes can be algorithmically derived from a combination of human and mouse single cell sequencing data. Using this gene set, we show that similar cell types shared between mouse and human cluster together. Furthermore we find that previously unclassified human cells can be mapped to the glial/vascular cell type by integrating mouse cell type expression profiles.

SUBMITTER: Johnson T

PROVIDER: S-EPMC6956653 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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MAPPING NEURONAL CELL TYPES USING INTEGRATIVE MULTI-SPECIES MODELING OF HUMAN AND MOUSE SINGLE CELL RNA SEQUENCING.

Johnson Travis T Abrams Zachary Z Zhang Yan Y Huang Kun K

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing 20170101

Mouse brain transcriptomic studies are important in the understanding of the structural heterogeneity in the brain. However, it is not well understood how cell types in the mouse brain relate to human brain cell types on a cellular level. We propose that it is possible with single cell granularity to find concordant genes between mouse and human and that these genes can be used to separate cell types across species. We show that a set of concordant genes can be algorithmically derived from a com ...[more]

PMID: 27897010

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Project description:BACKGROUND:It is a long established fact that sex is an important factor that influences the transcriptional regulatory processes of an organism. However, understanding sex-based differences in gene expression has been limited because existing studies typically sequence and analyze bulk tissue from female or male individuals. Such analyses average cell-specific gene expression levels where cell-to-cell variation can easily be concealed. We therefore sought to utilize data generated by the rapidly developing single cell RNA sequencing (scRNA-seq) technology to explore sex dimorphism and its functional consequences at the single cell level. METHODS:Our study included scRNA-seq data of ten well-defined cell types from the brain and heart of female and male young adult mice in the publicly available tissue atlas dataset, Tabula Muris. We combined standard differential expression analysis with the identification of differential distributions in single cell transcriptomes to test for sex-based gene expression differences in each cell type. The marker genes that had sex-specific inter-cellular changes in gene expression formed the basis for further characterization of the cellular functions that were differentially regulated between the female and male cells. We also inferred activities of transcription factor-driven gene regulatory networks by leveraging knowledge of multidimensional protein-to-genome and protein-to-protein interactions and analyzed pathways that were potential modulators of sex differentiation and dimorphism. RESULTS:For each cell type in this study, we identified marker genes with significantly different mean expression levels or inter-cellular distribution characteristics between female and male cells. These marker genes were enriched in pathways that were closely related to the biological functions of each cell type. We also identified sub-cell types that possibly carry out distinct biological functions that displayed discrepancies between female and male cells. Additionally, we found that while genes under differential transcriptional regulation exhibited strong cell type specificity, six core transcription factor families responsible for most sex-dimorphic transcriptional regulation activities were conserved across the cell types, including ASCL2, EGR, GABPA, KLF/SP, RXR?, and ZF. CONCLUSIONS:We explored novel gene expression-based biomarkers, functional cell group compositions, and transcriptional regulatory networks associated with sex dimorphism with a novel computational pipeline. Our findings indicated that sex dimorphism might be widespread across the transcriptomes of cell types, cell type-specific, and impactful for regulating cellular activities.

Project description:Background & aimsHuman primary liver cancer is classified into biologically distinct subgroups based on cellular origin. Liver cancer stem cells (CSCs) have been recently described. We investigated the ability of distinct lineages of hepatic cells to become liver CSCs and the phenotypic and genetic heterogeneity of primary liver cancer.MethodsWe transduced mouse primary hepatic progenitor cells, lineage-committed hepatoblasts, and differentiated adult hepatocytes with transgenes encoding oncogenic H-Ras and SV40LT. The CSC properties of transduced cells and their ability to form tumors were tested by standard in vitro and in vivo assays and transcriptome profiling.ResultsIrrespective of origin, all transduced cells acquired markers of CSC/progenitor cells, side populations, and self-renewal capacity in vitro. They also formed a broad spectrum of liver tumors, ranging from cholangiocarcinoma to hepatocellular carcinoma, which resembled human liver tumors, based on genomic and histologic analyses. The tumor cells coexpressed hepatocyte (hepatocyte nuclear factor 4α), progenitor/biliary (keratin 19, epithelial cell adhesion molecule, A6), and mesenchymal (vimentin) markers and showed dysregulation of genes that control the epithelial-mesenchymal transition. Gene expression analyses could distinguish tumors of different cellular origin, indicating the contribution of lineage stage-dependent genetic changes to malignant transformation. Activation of c-Myc and its target genes was required to reprogram adult hepatocytes into CSCs and for tumors to develop. Stable knockdown of c-Myc in transformed adult hepatocytes reduced their CSC properties in vitro and suppressed growth of tumors in immunodeficient mice.ConclusionsAny cell type in the mouse hepatic lineage can undergo oncogenic reprogramming into a CSC by activating different cell type-specific pathways. Identification of common and cell of origin-specific phenotypic and genetic changes could provide new therapeutic targets for liver cancer.

Project description:BackgroundBreast cancer is a complex and heterogeneous disease with distinct subtypes and molecular profiles corresponding to different clinical outcomes. Mouse models of breast cancer are widely used, but their relevance in capturing the heterogeneity of human disease is unclear. Previous studies have shown the heterogeneity at the gene expression level for the MMTV-Myc model, but have only speculated on the underlying genetics.MethodsTumors from the microacinar, squamous, and EMT histological subtypes of the MMTV-Myc mouse model of breast cancer underwent whole genome sequencing. The genomic data obtained were then integrated with previously obtained matched sample gene expression data and extended to additional samples of each histological subtype, totaling 42 gene expression samples. High correlation was observed between genetic copy number events and resulting gene expression by both Spearman's rank correlation coefficient and the Kendall rank correlation coefficient. These same genetic events are conserved in humans and are indicative of poor overall survival by Kaplan-Meier analysis. A supervised machine learning algorithm trained on METABRIC gene expression data was used to predict the analogous human breast cancer intrinsic subtype from mouse gene expression data.ResultsHerein, we examine three common histological subtypes of the MMTV-Myc model through whole genome sequencing and have integrated these results with gene expression data. Significantly, key genomic alterations driving cell signaling pathways were well conserved within histological subtypes. Genomic changes included frequent, co-occurring mutations in KIT and RARA in the microacinar histological subtype as well as SCRIB mutations in the EMT subtype. EMT tumors additionally displayed strong KRAS activation signatures downstream of genetic activating events primarily ascribed to KRAS activating mutations, but also FGFR2 amplification. Analogous genetic events in human breast cancer showed stark decreases in overall survival. In further analyzing transcriptional heterogeneity of the MMTV-Myc model, we report a supervised machine learning model that classifies MMTV-Myc histological subtypes and other mouse models as being representative of different human intrinsic breast cancer subtypes.ConclusionsWe conclude the well-established MMTV-Myc mouse model presents further opportunities for investigation of human breast cancer heterogeneity.

Dataset Information

MAPPING NEURONAL CELL TYPES USING INTEGRATIVE MULTI-SPECIES MODELING OF HUMAN AND MOUSE SINGLE CELL RNA SEQUENCING.

Publications

MAPPING NEURONAL CELL TYPES USING INTEGRATIVE MULTI-SPECIES MODELING OF HUMAN AND MOUSE SINGLE CELL RNA SEQUENCING.

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