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本文引用的文献

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mRNA-Seq whole-transcriptome analysis of a single cell.单细胞的mRNA测序全转录组分析
Nat Methods. 2009 May;6(5):377-82. doi: 10.1038/nmeth.1315. Epub 2009 Apr 6.
2
Determination of tag density required for digital transcriptome analysis: application to an androgen-sensitive prostate cancer model.数字转录组分析所需标签密度的测定:应用于雄激素敏感性前列腺癌模型
Proc Natl Acad Sci U S A. 2008 Dec 23;105(51):20179-84. doi: 10.1073/pnas.0807121105. Epub 2008 Dec 16.
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RNA-Seq: a revolutionary tool for transcriptomics.RNA测序:转录组学的革命性工具。
Nat Rev Genet. 2009 Jan;10(1):57-63. doi: 10.1038/nrg2484.
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Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing.通过高通量测序对人类转录组中可变剪接复杂性进行深度研究。
Nat Genet. 2008 Dec;40(12):1413-5. doi: 10.1038/ng.259. Epub 2008 Nov 2.
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Alternative isoform regulation in human tissue transcriptomes.人类组织转录组中的可变亚型调控
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6
Nature, nurture, or chance: stochastic gene expression and its consequences.天性、 nurture 还是机遇:随机基因表达及其影响。 (注:“nurture”常见释义为“养育;培育;教养” ,这里结合语境更像是与“天性(Nature)”相对的后天因素,可灵活意译为后天因素,但直接保留英文也不影响理解其在该语境下大概是与天性相对的概念)
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Dynamic equilibrium and heterogeneity of mouse pluripotent stem cells with distinct functional and epigenetic states.具有不同功能和表观遗传状态的小鼠多能干细胞的动态平衡和异质性。
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Transcriptome content and dynamics at single-nucleotide resolution.单核苷酸分辨率下的转录组内容与动态变化
Genome Biol. 2008;9(9):234. doi: 10.1186/gb-2008-9-9-234. Epub 2008 Sep 18.
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The stochastic nature of biochemical networks.生化网络的随机性质。
Curr Opin Biotechnol. 2008 Aug;19(4):369-74. doi: 10.1016/j.copbio.2008.06.011. Epub 2008 Aug 9.
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A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome.通过对人类转录组进行深度测序实现对基因活性和可变剪接的全局观察。
Science. 2008 Aug 15;321(5891):956-60. doi: 10.1126/science.1160342. Epub 2008 Jul 3.

RNA-Seq 分析捕获单细胞的转录组全景。

RNA-Seq analysis to capture the transcriptome landscape of a single cell.

机构信息

Wellcome Trust/Cancer Research UK Gurdon Institute of Cancer and Developmental Biology, University of Cambridge, Cambridge, UK.

出版信息

Nat Protoc. 2010 Mar;5(3):516-35. doi: 10.1038/nprot.2009.236. Epub 2010 Feb 25.

DOI:10.1038/nprot.2009.236
PMID:20203668
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3847604/
Abstract

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.

摘要

我们在这里描述了一种使用深度测序方法在单个小鼠卵母细胞和胚胎中进行数字转录组分析的方案。在该方法中,通过移液管逐个分离细胞并转移到裂解缓冲液中,然后直接在整个细胞裂解物上进行逆转录。通过外切核酸酶 I 去除游离引物,并通过末端脱氧核苷酸转移酶在第一链 cDNA 的 3' 端添加 poly(A) 尾巴。然后通过 20+9 个循环的 PCR 扩增单细胞 cDNA。所得的 100-200ng 扩增 cDNA 用于构建测序文库,可用于 SOLiD 系统的深度测序。与 cDNA 微阵列技术相比,我们的检测方法可以捕获多达 75%的早期胚胎中表达的基因。该方案可在 6 天内为 16 个单细胞样本生成深度测序文库。