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The tissue-specific transcriptomic landscape of the mid-gestational mouse embryo

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Werber,  Martin
Sequencing (Head: Bernd Timmermann), Scientific Service (Head: Christoph Krukenkamp), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Wittler,  Lars
Dept. of Developmental Genetics (Head: Bernhard G. Herrmann), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Timmermann,  Bernd
Sequencing (Head: Bernd Timmermann), Scientific Service (Head: Christoph Krukenkamp), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Grote,  Phillip
Dept. of Developmental Genetics (Head: Bernhard G. Herrmann), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Herrmann,  Bernhard G.
Dept. of Developmental Genetics (Head: Bernhard G. Herrmann), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Citation

Werber, M., Wittler, L., Timmermann, B., Grote, P., & Herrmann, B. G. (2014). The tissue-specific transcriptomic landscape of the mid-gestational mouse embryo. Development, 141(11), 2325-2330. doi:10.1242/dev.105858.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0025-7661-0
Abstract
Differential gene expression is a prerequisite for the formation of multiple cell types from the fertilized egg during embryogenesis. Understanding the gene regulatory networks controlling cellular differentiation requires the identification of crucial differentially expressed control genes and, ideally, the determination of the complete transcriptomes of each individual cell type. Here, we have analyzed the transcriptomes of six major tissues dissected from mid-gestational (TS12) mouse embryos. Approximately one billion reads derived by RNA-seq analysis provided extended transcript lengths, novel first exons and alternative transcripts of known genes. We have identified 1375 genes showing tissue-specific expression, providing gene signatures for each of the six tissues. In addition, we have identified 1403 novel putative long noncoding RNA gene loci, 439 of which show differential expression. Our analysis provides the first complete transcriptome data for the mouse embryo. It offers a rich data source for the analysis of individual genes and gene regulatory networks controlling mid-gestational development.