Characterizing the developing human brain transcriptome at single-base resolution

 描述(由申请人提供)：人类尸检脑组织为确定大脑疾病中基于表达的发育决定因素和随后的失调提供了宝贵的资源(Kleinman 2011)。RNA测序(RNA-seq)产生潜在的无偏见的转录组特征，现在已经在我们的实验室(LIBD)内和通过BrainSpan项目(www.braincan.org)对1,000多个非精神疾病的人脑样本进行了测试。我们建议将这些数据合并和重新处理在一起，以使它们更具可比性，从原始测序读数开始。我们的目标是进一步询问大脑发育过程中发育动态基因表达区域的临床相关性。我们的初步RNAseq数据表明，在人类和小鼠的多个大脑区域中，先前未注释的基因内和基因间序列具有广泛的发育调节作用。对这些组合的RNA-seq数据进行基本水平的分析可以极大地改进现有的基因注释数据库，如EnSembl和UCSC，这些数据库目前缺乏许多我们在样本中识别和表征的胎儿大脑特定转录本。我们将对整个数据集进行基本级别的分辨率分析，从胎儿生命的早期三个月到老年人(&gt；85岁)，然后进行二次分析，探索不同大脑区域的差异表达，以确定动态表达序列。识别的差异表达区域(DER)将通过关于预定义的临床基因集的浓缩分析来询问临床意义， 例如，来自全基因组关联研究(GWAS)的重要基因座对精神分裂症等大脑疾病的研究，以及通过将遗传风险与已识别的DERs中的表达水平直接关联。我们将进一步提供基因组工具，允许对其他基因和基因座感兴趣的研究人员在细胞系和/或初级脑组织中设计自己的验证实验，以确定这些临床相关基因座的有效性。更全面地描述人类大脑转录组的特征，利用两个互补数据集的“大数据”，对于研究广泛的发育过程和大脑疾病的科学家将是有价值的。

 DESCRIPTION (provided by applicant): Human post-mortem brain tissue provides a valuable resource for identifying expression-based determinants of development and subsequent dysregulation in brain disorders (Kleinman 2011). RNA sequencing (RNA-seq) generates potentially unbiased characterization of the transcriptome, and has now been performed on over 1,000 non-psychiatric human brain samples across the lifespan within our lab (LIBD) and via the BrainSpan project (www.brainspan.org). We propose to combine and re-process these data together to make them more comparable, starting from raw sequencing reads. Our goal is further interrogate the clinical relevance of developmentally dynamic regions of gene expression across brain development. Our preliminary RNAseq data demonstrates extensive developmental regulation of previously unannotated intra- and inter-genic sequence conserved across multiple brain regions in both humans and mice. Base-level analysis of these combined RNA-seq data can greatly improve existing gene annotation databases like Ensembl and UCSC, which currently lack many fetal brain-specific transcripts that we have identified and characterized in our samples. We will perform base-level resolution analyses on both the entire dataset, from first trimester of fetal life through the aged (>85 years), and then secondary analyses exploring differential expression across different brain regions, to identify dynamic expressed sequence. Identified differentially expressed regions (DERs) will be interrogated for clinical significance through enrichment analysis with regard to predefined clinical gene sets, for examples significant loci from genome-wide association studies (GWAS) for brain disorders like schizophrenia, and also by directly associating genetic risk with expression levels within identified DERs. We will further provide the genomic tools to allow researchers with interests in other genes and loci to design their own validation experiments in cell lines and/or primary brain tissue for these clinically relevant loci. A more comprehensive characterization of the human brain transcriptome, leveraging "big data" across two complementary datasets, will be valuable for scientists studying a wide range of developmental processes and brain disorders.