title: Large-scale mapping of mammalian transcriptomes identifies conserved genes associated with different cell states.
abstract: Distinguishing cell states based only on gene expression data remains a challenging task. This is true even for analyses within a species. In cross-species comparisons, the results obtained by different groups have varied widely. Here, we integrate RNA-seq data from more than 40 cell and tissue types of four mammalian species to identify sets of associated genes as indicators for specific cell states in each species. We employ a statistical method, TROM, to identify both protein-coding and non-coding indicators. Next, we map the cell states within each species and also between species using these indicator genes. We recapitulate known phenotypic similarity between related cell and tissue types and reveal molecular basis for their similarity. We also report novel associations between several tissues and cell types with functional support. Moreover, our identified conserved associated genes are found to be a good resource for studying cell differentiation and reprogramming. Lastly, long non-coding RNAs can serve well as associated genes to indicate cell states. We further infer the biological functions of those non-coding associated genes based on their co-expressed protein-coding genes. This study demonstrates that combining statistical modeling with public RNA-seq data can be powerful for improving our understanding of cell identity control.
authors.affiliation: PKU-Tsinghua-NIBS Graduate Program, School of Life Sciences, Peking University, Beijing 100871, China.|MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, Center for Plant Biology and Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing 100084, China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Yang
authors.affiliation: MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, Center for Plant Biology and Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing 100084, China.
authors.forename: Yu-Cheng T
authors.initials: YT
authors.lastname: Yang
authors.affiliation: MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, Center for Plant Biology and Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing 100084, China.
authors.forename: Jiapei
authors.initials: J
authors.lastname: Yuan
authors.affiliation: MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, Center for Plant Biology and Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing 100084, China.
authors.forename: Zhi John
authors.initials: ZJ
authors.lastname: Lu
authors.affiliation: Department of Statistics, University of California, Los Angeles, CA 90095-1554, USA.|Department of Human Genetics, University of California, Los Angeles, CA 90095-7088, USA.
authors.forename: Jingyi Jessica
authors.initials: JJ
authors.lastname: Li
mesh_terms: D000465:Algorithms; D000818:Animals; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D020451:Contig Mapping; D019143:Evolution, Molecular; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D018507:Gene Expression Regulation, Developmental; D063990:Gene Ontology; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D008322:Mammals / Q000235:genetics*; D051379:Mice; D058977:Molecular Sequence Annotation; D005810:Multigene Family; D009928:Organ Specificity; D059467:Transcriptome