Allen Institute for Brain Science, 551 N. 34th Street, Seattle, WA 98103, USA.
Neural Netw. 2011 Nov;24(9):933-42. doi: 10.1016/j.neunet.2011.06.012. Epub 2011 Jun 25.
The mammalian brain is best understood as a multi-scale hierarchical neural system, in the sense that connection and function occur on multiple scales from micro to macro. Modern genomic-scale expression profiling can provide insight into methodologies that elucidate this architecture. We present a methodology for understanding the relationship of gene expression and neuroanatomy based on correlation between gene expression profiles across tissue samples. A resulting tool, NeuroBlast, can identify networks of genes co-expressed within or across neuroanatomic structures. The method applies to any data modality that can be mapped with sufficient spatial resolution, and provides a computation technique to elucidate neuroanatomy via patterns of gene expression on spatial and temporal scales. In addition, from the perspective of spatial location, we discuss a complementary technique that identifies gene classes that contribute to defining anatomic patterns.
哺乳动物的大脑被认为是一个多尺度的分层神经网络系统，因为连接和功能在从微观到宏观的多个尺度上发生。现代基因组规模的表达谱分析可以为阐明这种结构的方法提供深入的了解。我们提出了一种基于组织样本中基因表达谱之间相关性来理解基因表达和神经解剖关系的方法。由此产生的工具 NeuroBlast 可以识别在神经结构内或跨神经结构共表达的基因网络。该方法适用于任何可以以足够的空间分辨率进行映射的数据模式，并提供了一种计算技术，通过空间和时间尺度上的基因表达模式来阐明神经解剖结构。此外，从空间位置的角度来看，我们讨论了一种互补技术，该技术可以识别有助于定义解剖模式的基因类别。
