RNA-seq based transcriptomic map reveals new insights into mouse salivary gland development and maturation.
RNA-seq based transcriptomic map reveals new insights into mouse salivary gland development and maturation.
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DOI：
10.1186/s12864-016-3228-7
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发表时间：
2016-11-16
期刊：
影响因子：
4.4
通讯作者：
Romano RA
中科院分区：
文献类型：
--
作者：
Gluck C;Min S;Oyelakin A;Smalley K;Sinha S;Romano RA
小鼠模型在破译唾液腺(SG)生物学的各个方面，从正常的发育程序到疾病状态方面发挥了宝贵的作用。为了促进这类研究，已经为SG器官发生的不同阶段生成了基因表达谱图谱。然而，由于以基因为中心的微阵列技术的范围有限，这些先前的研究未能捕捉到转录的复杂性。与基因芯片相比，RNA测序技术(RNA-seq)对新的转录本具有无偏倚检测、更宽的动态范围、高特异性和高灵敏度来检测基因、转录本和差异基因表达。尽管RNA-SEQ数据，特别是在ENCODE项目的支持下，已经涵盖了大量的生物标本，但关于SG的研究一直很少。为了更好地了解基因表达谱的广谱，我们从不同胚胎和成年阶段的小鼠下颌下唾液腺中提取了RNA。同时，我们处理了从小鼠ENCODE联合体获得的24个器官和组织的RNA-SEQ数据，并计算了平均基因表达值。为了确定可能与SG生物学相关的分子成员和途径，我们对从SG发育和成熟的不同阶段获得的RNA-seq数据集以及其他小鼠器官和组织进行了功能基因丰富分析、网络构建和层次聚类。我们基于生物信息学的数据分析不仅重申了已知的SG形态发生的调节因子，还揭示了小鼠SG生物学和功能所独有的新的转录因子和信号通路。最后，我们证明了从我们的小鼠研究中获得的独特的SG基因特征也是非常保守的，并且可以区分不同于其他组织的人类SG转录组的特征。我们的基于RNA-seq的图谱揭示了小鼠SG在不同阶段的动态转录景观的高分辨率地图视图。这些RNA-SEQ数据集将通过提供更广泛的基于系统生物学的观点而不是传统的以基因为中心的观点来补充先前存在的基于微阵列的数据集，包括唾液腺分子解剖学项目。最终，这些资源在提供有用的工具包方面将是有价值的，以便更好地了解在发育和分化期间SG的不同细胞群体是如何组织和控制的。本文的在线版本(doi：10.1186/s12864-016-3228-7)包含补充材料，授权用户可以使用。
Mouse models have served a valuable role in deciphering various facets of Salivary Gland (SG) biology, from normal developmental programs to diseased states. To facilitate such studies, gene expression profiling maps have been generated for various stages of SG organogenesis. However these prior studies fall short of capturing the transcriptional complexity due to the limited scope of gene-centric microarray-based technology. Compared to microarray, RNA-sequencing (RNA-seq) offers unbiased detection of novel transcripts, broader dynamic range and high specificity and sensitivity for detection of genes, transcripts, and differential gene expression. Although RNA-seq data, particularly under the auspices of the ENCODE project, have covered a large number of biological specimens, studies on the SG have been lacking. To better appreciate the wide spectrum of gene expression profiles, we isolated RNA from mouse submandibular salivary glands at different embryonic and adult stages. In parallel, we processed RNA-seq data for 24 organs and tissues obtained from the mouse ENCODE consortium and calculated the average gene expression values. To identify molecular players and pathways likely to be relevant for SG biology, we performed functional gene enrichment analysis, network construction and hierarchal clustering of the RNA-seq datasets obtained from different stages of SG development and maturation, and other mouse organs and tissues. Our bioinformatics-based data analysis not only reaffirmed known modulators of SG morphogenesis but revealed novel transcription factors and signaling pathways unique to mouse SG biology and function. Finally we demonstrated that the unique SG gene signature obtained from our mouse studies is also well conserved and can demarcate features of the human SG transcriptome that is different from other tissues. Our RNA-seq based Atlas has revealed a high-resolution cartographic view of the dynamic transcriptomic landscape of the mouse SG at various stages. These RNA-seq datasets will complement pre-existing microarray based datasets, including the Salivary Gland Molecular Anatomy Project by offering a broader systems-biology based perspective rather than the classical gene-centric view. Ultimately such resources will be valuable in providing a useful toolkit to better understand how the diverse cell population of the SG are organized and controlled during development and differentiation. The online version of this article (doi:10.1186/s12864-016-3228-7) contains supplementary material, which is available to authorized users.
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DOI：
10.1073/pnas.1415739112
发表时间：
2015-02-17
影响因子：
11.1
作者：
Catalan, Marcelo A.;Kondo, Yusuke;Melvin, James E.
通讯作者：
Melvin, James E.
影响因子：
4.6
作者：
Hoffman, MP;Kidder, BL;Larsen, M
通讯作者：
Larsen, M
影响因子：
4.8
作者：
Huang, AY;Castle, AM;Castle, JD
通讯作者：
Castle, JD
DOI：
10.1093/bioinformatics/btp101
发表时间：
2009-04-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Bindea G;Mlecnik B;Hackl H;Charoentong P;Tosolini M;Kirilovsky A;Fridman WH;Pagès F;Trajanoski Z;Galon J
通讯作者：
Galon J
影响因子：
11.8
作者：
Aure, Marit H.;Konieczny, Stephen F.;Ovitt, Catherine E.
通讯作者：
Ovitt, Catherine E.