Molecular phenotyping of retinal ganglion cells
Molecular phenotyping of retinal ganglion cells
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DOI：
10.1523/jneurosci.22-02-00413.2002
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发表时间：
2002-01-15
影响因子：
5.3
通讯作者：
Jones, BW
中科院分区：
文献类型：
--
作者：
Marc, RE;Jones, BW
对哺乳动物视网膜中的所有神经节细胞进行分类一直是解剖学家、生理学家和细胞生物学家的目标。兔视网膜神经节细胞层的表型使用固有的小分子信号（天冬氨酸，谷氨酸，甘氨酸，谷氨酰胺，GABA，和牛磺酸）和谷氨酸受体门控1-氨基-4-胍丁烷激发信号作为正式分类的聚类维度。内源性信号单独产生7个神经节细胞超类和1个无长突细胞超类;此外，激发信号最终解决了14个自然神经节细胞类和3个无长突细胞类。神经节细胞占神经节细胞层中细胞的三分之二至四分之三，并表现出不同的代谢、偶联和兴奋表型，以及特征性大小、群体分数和模式。代谢特征（谷氨酸、天冬氨酸、谷氨酰胺和GABA的混合物）在化学上区分神经节和无长突细胞。偶联特征反映了跨神经节细胞的异源偶联状态：（1）未偶联，（2）偶联至GABA能无长突细胞，和（3）偶联至甘氨酸能无长突细胞。激发签名反映了AMPA激活后不同类别的通道渗透率。从数据集中提取独特的尺寸和图案特征进一步验证了分类的鲁棒性。由于分类是明确盲结构，这是强有力的证据，分子表型类是自然类。对应的分子表型类功能类推断大小，耦合，遇到，和生理属性。神经节细胞类显示显着不同的离子驱动器，这可能部分解释了生理brisk-slowing光谱的神经节细胞尖峰模式。
Classifying all of the ganglion cells in the mammalian retina has long been a goal of anatomists, physiologists, and cell biologists. The rabbit retinal ganglion cell layer was phenotyped using intrinsic small molecule signals (aspartate, glutamate, glycine, glutamine, GABA, and taurine) and glutamate receptor-gated 1-amino-4-guanidobutane excitation signals as the clustering dimensions for formal classification. Intrinsic signals alone yielded 7 ganglion cell superclasses and 1 amacrine cell superclass; the addition of excitation signals ultimately resolved 14 natural ganglion cell classes and 3 amacrine cell classes. Ganglion cells comprise two-thirds to three-quarters of the cells in the ganglion cell layer and exhibited distinct metabolic, coupling, and excitation phenotypes, as well as characteristic sizes, population fractions, and patterns. Metabolic signatures (mixtures of glutamate, aspartate, glutamine, and GABA) chemically discriminated ganglion from amacrine cells. Coupling signatures reflected heterologous coupling states across ganglion cells: (1) uncoupled, (2) coupled to GABAergic amacrine cells, and (3) coupled to glycinergic amacrine cells. Excitation signatures reflected differential channel permeation rates across classes after AMPA activation. Extraction of unique size and patterning features from the data sets further validated the robustness of the classification. Because the classifications were explicitly blinded to structure, this is strong evidence that molecular phenotype classes are natural classes. Correspondences of molecular phenotype classes to functional classes were inferred from size, coupling, encounter, and physiological attributes. Ganglion cell classes display markedly different ionotropic drives, which may partly explain the physiological brisk-sluggish spectrum of ganglion cell spiking patterns.