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      Genetic programs in human and mouse early embryos revealed by single-cell RNA sequencing

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          Abstract

          Mammalian pre-implantation development is a complex process involving dramatic changes in the transcriptional architecture. We report here a comprehensive analysis of transcriptome dynamics from oocyte to morula in both human and mouse embryos, using single-cell RNA sequencing. Based on single-nucleotide variants in human blastomere messenger RNAs and paternal-specific single-nucleotide polymorphisms, we identify novel stage-specific monoallelic expression patterns for a significant portion of polymorphic gene transcripts (25 to 53%). By weighted gene co-expression network analysis, we find that each developmental stage can be delineated concisely by a small number of functional modules of co-expressed genes. This result indicates a sequential order of transcriptional changes in pathways of cell cycle, gene regulation, translation and metabolism, acting in a step-wise fashion from cleavage to morula. Cross-species comparisons with mouse pre-implantation embryos reveal that the majority of human stage-specific modules (7 out of 9) are notably preserved, but developmental specificity and timing differ between human and mouse. Furthermore, we identify conserved key members (or hub genes) of the human and mouse networks. These genes represent novel candidates that are likely to be key in driving mammalian pre-implantation development. Together, the results provide a valuable resource to dissect gene regulatory mechanisms underlying progressive development of early mammalian embryos.

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          Most cited references 17

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          Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq.

          Our understanding of the development and maintenance of tissues has been greatly aided by large-scale gene expression analysis. However, tissues are invariably complex, and expression analysis of a tissue confounds the true expression patterns of its constituent cell types. Here we describe a novel strategy to access such complex samples. Single-cell RNA-seq expression profiles were generated, and clustered to form a two-dimensional cell map onto which expression data were projected. The resulting cell map integrates three levels of organization: the whole population of cells, the functionally distinct subpopulations it contains, and the single cells themselves-all without need for known markers to classify cell types. The feasibility of the strategy was demonstrated by analyzing the transcriptomes of 85 single cells of two distinct types. We believe this strategy will enable the unbiased discovery and analysis of naturally occurring cell types during development, adult physiology, and disease.
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            Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways.

            Because mouse models play a crucial role in biomedical research related to the human nervous system, understanding the similarities and differences between mouse and human brain is of fundamental importance. Studies comparing transcription in human and mouse have come to varied conclusions, in part because of their relatively small sample sizes or underpowered methodologies. To better characterize gene expression differences between mouse and human, we took a systems-biology approach by using weighted gene coexpression network analysis on more than 1,000 microarrays from brain. We find that global network properties of the brain transcriptome are highly preserved between species. Furthermore, all modules of highly coexpressed genes identified in mouse were identified in human, with those related to conserved cellular functions showing the strongest between-species preservation. Modules corresponding to glial and neuronal cells were sufficiently preserved between mouse and human to permit identification of cross species cell-class marker genes. We also identify several robust human-specific modules, including one strongly correlated with measures of Alzheimer disease progression across multiple data sets, whose hubs are poorly-characterized genes likely involved in Alzheimer disease. We present multiple lines of evidence suggesting links between neurodegenerative disease and glial cell types in human, including human-specific correlation of presenilin-1 with oligodendrocyte markers, and significant enrichment for known neurodegenerative disease genes in microglial modules. Together, this work identifies convergent and divergent pathways in mouse and human, and provides a systematic framework that will be useful for understanding the applicability of mouse models for human brain disorders.
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              RNA-Seq analysis to capture the transcriptome landscape of a single cell.

              We describe here a protocol for digital transcriptome analysis in a single mouse oocyte and blastomere using a deep-sequencing approach. In this method, individual cells are isolated and transferred into lysate buffer by mouth pipette, followed by reverse transcription carried out directly on the whole cell lysate. Free primers are removed by exonuclease I and a poly(A) tail is added to the 3' end of the first-strand cDNAs by terminal deoxynucleotidyl transferase. Single-cell cDNAs are then amplified by 20 + 9 cycles of PCR. The resulting 100-200 ng of amplified cDNAs are used to construct a sequencing library, which can be used for deep sequencing using the SOLiD system. Compared with cDNA microarray techniques, our assay can capture up to 75% more genes expressed in early embryos. This protocol can generate deep-sequencing libraries for 16 single-cell samples within 6 d.
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                Author and article information

                Journal
                Nature
                Nature
                Springer Science and Business Media LLC
                0028-0836
                1476-4687
                August 2013
                July 28 2013
                August 2013
                : 500
                : 7464
                : 593-597
                Article
                10.1038/nature12364
                23892778
                © 2013

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