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

Nature

Volumn 500, Issue 7464, 2013, Pages 593-597

  Xue, Zhigang ^a   Huang, Kevin ^b   Cai, Chaochao ^b   Cai, Lingbo ^c   Jiang, Chun Yan ^c   Feng, Yun ^a   Liu, Zhenshan ^a   Zeng, Qiao ^a   Cheng, Liming ^a   Sun, Yi E ^a   Liu, Jia Yin ^c   Horvath, Steve ^b   Fan, Guoping ^b  

^a TONGJI UNIVERSITY SCHOOL OF MEDICINE   (China)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c NANJING MEDICAL UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

MESSENGER RNA;

DEVELOPMENTAL STAGE; EMBRYO; FUNCTIONAL ROLE; GENE EXPRESSION; GENETIC ANALYSIS; GENETIC ENGINEERING; GENETICS; GENOMICS; HOMINID; METABOLISM; RODENT;

ARTICLE; BLASTOCYST; BLASTOMA; CELL CYCLE; DEVELOPMENTAL STAGE; DNA SEQUENCE; EMBRYO; EMBRYO DEVELOPMENT; GENE CONTROL; GENE EXPRESSION; GENE REGULATORY NETWORK; GENETIC TRANSCRIPTION; GENOTYPE; HAPLOTYPE; HUMAN; MOUSE; NONHUMAN; PREIMPLANTATION EMBRYO; PRIORITY JOURNAL; PROTEIN TRANSPORT; RNA PROCESSING; RNA SEQUENCE; RNA TRANSLATION; SINGLE NUCLEOTIDE POLYMORPHISM; SPECIES COMPARISON;

ALLELES; ANIMALS; BLASTOCYST; CELL CYCLE; EMBRYO, MAMMALIAN; EMBRYONIC DEVELOPMENT; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION, DEVELOPMENTAL; HUMANS; MICE; MORULA; OOCYTES; SEQUENCE ANALYSIS, RNA; SINGLE-CELL ANALYSIS;

MAMMALIA;

EID: 84883134780     PISSN: 00280836     EISSN: 14764687     Source Type: Journal    
DOI: 10.1038/nature12364     Document Type: Article

References (37)

1. Niakan, K. K., Han, J., Pedersen, R. A., Simon, C. & Pera, R. A. Human preimplantation embryo development. Development 139, 829-841 (2012)
2. Hamatani, T., Carter, M. G., Sharov, A. A. & Ko, M. S. Dynamics of global gene expression changes during mouse preimplantation development. Dev. Cell 6, 117-131 (2004)
3. Wang,Q.T. et al.Agenome-wide study of gene activity reveals developmental signaling pathways in the preimplantation mouse embryo. Dev. Cell 6, 133-144 (2004)
4. Zeng, F., Baldwin, D. A. & Schultz, R. M. Transcript profiling during preimplantation mouse development. Dev. Biol. 272, 483-496 (2004)
5. Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).
6. Zhang, B. & Horvath, S. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. http://dx.doi.org/10.2202/1544-6115.1128 (2005).
7. Walser, C. B. & Lipshitz, H. D. Transcript clearance during the maternal-to-zygotic transition. Curr. Opin. Genet. Dev. 21, 431-443 (2011).
8. Schier, A. F.Thematernal-zygotic transition: death and birth ofRNAs. Science 316, 406-407 (2007)
9. Schultz, R. M. The molecular foundations of the maternal to zygotic transition in the preimplantation embryo. Hum. Reprod. Update 8, 323-331 (2002)
10. Vassena, R. et al. Waves of early transcriptional activation and pluripotency program initiation during human preimplantation development. Development 138, 3699-3709 (2011)
11. Xie, D. et al. Rewirable gene regulatory networks in the preimplantationembryonic development of three mammalian species. Genome Res. 20, 804-815 (2010)
12. Zhang, P. et al. Transcriptome profiling of human pre-implantation development. PLoS ONE 4, e7844 (2009).
13. Dobson, A. T. et al. The unique transcriptome through day 3 of human preimplantation development. Hum. Mol. Genet. 13, 1461-1470 (2004)
14. Tang, F. et al.RNA-Seq analysis to capture the transcriptomelandscape of a single cell. Nature Protocols 5, 516-535 (2010).
15. Islam, S. et al. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res. 21, 1160-1167 (2011)
16. Ramskold, D. et al. Full-length mRNA-seq from single-cell levels of RNA and individual circulating tumor cells. Nature Biotech. 30, 777-782 (2012)
17. Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-Seq: single-cell RNA-seq by multiplexed linear amplification. Cell Rep. 2, 666-673 (2012)
18. The 1000 Genomes Project Consortium. A map of human genome variation from population-scale sequencing. Nature 467, 1061-1073 (2010)
19. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297-1303 (2010)
20. Li, Y. et al. Resequencing of 200 human exomes identifies an excess of lowfrequency non-synonymous coding variants. Nature Genet. 42, 969-972 (2010)
21. Gabriel,S. B. et al. Thestructure of haplotype blocks in the humangenome.Science 296, 2225-2229 (2002)
22. Altshuler, D. M. et al. Integrating common and rare genetic variation in diverse human populations. Nature 467, 52-58 (2010)
23. Kumar, P., Henikoff, S.&Ng, P. C. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nature Protocols 4, 1073-1081 (2009)
24. Langfelder, P., Luo, R., Oldham, M. C.&Horvath, S. Ismynetworkmodule preserved and reproducible? PLOS Comput. Biol. 7, e1001057 (2011)
25. Tang, F. et al. Deterministic and stochastic allele specific gene expression in single mouse blastomeres. PLoS ONE 6, e21208 (2011)
26. Hu, J. et al. Novel importin-a family member Kpna7 is required for normal fertility and fecundity in the mouse. J. Biol. Chem. 285, 33113-33122 (2010)
27. Dannenberg, J. H. et al. mSin3A corepressor regulates diverse transcriptional networks governing normal and neoplastic growth and survival. Genes Dev. 19, 1581-1595 (2005).
28. Miller, J. A., Horvath, S. & Geschwind, D. H. Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways. Proc. Natl Acad. Sci. USA 107, 12698-12703 (2010).
29. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754-1760 (2009).
30. Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature Methods 5, 621-628 (2008).
31. Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variantsfromhigh-throughput sequencingdata. NucleicAcids Res.38,e164(2010).
32. Yip, A. M. & Horvath, S. Gene network interconnectedness and the generalized topological overlap measure. BMC Bioinformatics 8, 22 (2007).
33. Langfelder, P., Zhang, B. & Horvath, S. Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. Bioinformatics 24, 719-720 (2008).
34. Eppig, J. T., Blake, J. A., Bult, C. J., Kadin, J. A. & Richardson, J. E. The Mouse Genome Database (MGD): comprehensive resource for genetics and genomics of the laboratory mouse. Nucleic Acids Res. 40, D881-D886 (2012).
35. Horvath, S. & Dong, J. Geometric interpretation of gene coexpression network analysis. PLOS Comput. Biol. 4, e1000117 (2008).
36. Hu, Z., Mellor, J., Wu, J. & DeLisi, C. VisANT: an online visualization and analysis tool for biological interaction data. BMC Bioinformatics 5, 17 (2004).
37. Huang, D. W., Sherman, B. T. & Lempicki, R. A., Systematic and integrative analysis of large gene lists usingDAVIDBioinformatics Resources. Nature Protoc. 4, 44-57 (2009).