Cellular diversity in mouse neocortex revealed by multispectral analysis of amino acid immunoreactivity
Cellular diversity in mouse neocortex revealed by multispectral analysis of amino acid immunoreactivity
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DOI：
10.1093/cercor/11.8.679
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发表时间：
2001-08-01
期刊：
影响因子：
3.7
通讯作者：
Tan, SS
中科院分区：
文献类型：
--
作者：
Hill, E;Kalloniatis, M;Tan, SS
使用基于谷氨酸、γ-氨基丁酸(GABA)、天冬氨酸、谷氨酰胺和牛磺酸的定量和组合免疫反应性的非监督聚类分析对皮质细胞进行分类。总体而言，发现了12种细胞类型的特定氨基酸特征：7个GABA免疫反应(GABA-IR)群体(GABA1-7)，3个含有高谷氨酸水平的群体(GLUT1-3)和2个假定的胶质细胞(GLIA1)。2)细胞类型。从它们巨大的体细胞、相关的垂直突起和高谷氨酸含量来看，Glut类很可能对应于锥体神经元。其中两个GLUT类别在不同的皮质层中显示出互补的分布，这表明在氨基酸免疫反应性上不同的细胞在空间上是分开的。在七个GABA类别中，有两个类别的细胞具有较大的体细胞，并显示出中等到低的谷氨酸水平。以大小为基础。这两个群体可能对应于大型篮细胞中间神经元。胶质细胞群可分为两类：GLIA1细胞更多地与血管相关，GLIA2细胞更常见于皮质下层。这项工作表明，基于氨基酸含量的特征识别可以用于将皮层细胞分成不同的类别，并揭示这些类别中的更多子类。这种方法是对其他使用生理学和分子工具的方法的补充，最终将增强我们对神经元异质性的理解。
Cortical cells were classified using an unsupervised cluster analysis based upon their quantitative and combinatorial immunoreactivity for glutamate, gamma -aminobutyric acid (GABA), aspartate, glutamine and taurine. Overall, cell class-specific amino acid signatures were found for 12 cellular types: seven GABA-immunoreactive (GABA-IR) populations (GABA1-7), three classes containing high glutamate levels (GLUT1-3) and two putative glial (GLIA1. 2) cell types. From their large somata, associated vertical processes and high glutamate content, the GLUT classes most probably correspond to pyramidal neurons. Two of the GLUT classes demonstrated complementary distributions in different cortical layers, suggesting spatial separation of cells differing in amino acid immunoreactivity. Of the seven GABA classes, two comprised cells with large somata and displayed medium to low glutamate levels. On the basis of size. these two populations may correspond to large basket cell interneurons. Glial populations could be divided into two classes: GLIA1 cells were more frequently associated with blood vessels and GLIA2 cells were more commonly seen in the lower cortical layers. This work demonstrates that signature recognition based upon amino acid content can be used to separate cortical cells into different categories and reveal further subclasses within these categories. This approach is complementary to other methods using physiological and molecular tools and ultimately will enhance our understanding of neuronal heterogeneity.