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果蝇中的神经元细胞类型:单细胞解剖学与单细胞基因组学的交汇。

Neuronal cell types in the fly: single-cell anatomy meets single-cell genomics.

机构信息

Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge CB2 0QH, UK.

VIB Center for Brain & Disease Research, KU Leuven, Leuven 3000, Belgium; Department of Human Genetics KU Leuven, Leuven 3000, Belgium.

出版信息

Curr Opin Neurobiol. 2019 Jun;56:125-134. doi: 10.1016/j.conb.2018.12.012. Epub 2019 Jan 28.

DOI:10.1016/j.conb.2018.12.012
PMID:30703584
Abstract

At around 150 000 neurons, the adult Drosophila melanogaster central nervous system is one of the largest species, for which a complete cellular catalogue is imminent. While numerically much simpler than mammalian brains, its complexity is still difficult to parse without grouping neurons into consistent types, which can number 1-1000 cells per hemisphere. We review how neuroanatomical and gene expression data are being used to discover neuronal types at scale. The correlation among multiple co-varying neuronal properties, including lineage, gene expression, morphology, connectivity, response properties and shared behavioral significance is essential to the definition of neuronal cell type. Initial studies comparing morphological and transcriptomic definitions of neuronal type suggest that these are highly consistent, but there is much to do to match these approaches brain-wide. Matched single-cell transcriptomic and morphological data provide an effective reference point to integrate other data types, including connectomics data. This will significantly enhance our ability to make functional predictions from brain wiring diagrams as well facilitating molecular genetic manipulation of neuronal types.

摘要

在大约 150000 个神经元中,成年黑腹果蝇的中枢神经系统是最大的物种之一,其完整的细胞目录即将面世。尽管与哺乳动物大脑相比,它的数量要简单得多,但如果不将神经元分组为一致的类型,仍然很难解析其复杂性,每个半球的神经元数量可以达到 1-1000 个。我们回顾了如何使用神经解剖学和基因表达数据大规模发现神经元类型。多个共变神经元特性(包括谱系、基因表达、形态、连接性、反应特性和共享行为意义)之间的相关性对于神经元细胞类型的定义至关重要。比较神经元类型的形态学和转录组学定义的初步研究表明,这些定义高度一致,但仍有许多工作要做,以在全脑范围内匹配这些方法。匹配的单细胞转录组学和形态学数据为整合其他数据类型(包括连接组学数据)提供了有效的参考点。这将极大地提高我们从大脑连接图中进行功能预测的能力,并有助于对神经元类型进行分子遗传操作。

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