Seung H Sebastian, Sümbül Uygar
Princeton Neuroscience Institute and Computer Science Department, Princeton University, Princeton, NJ 08544, USA.
Grossman Center for the Statistics of Mind and Department of Statistics, Columbia University, New York, NY 10027, USA.
Neuron. 2014 Sep 17;83(6):1262-72. doi: 10.1016/j.neuron.2014.08.054.
We describe recent progress toward defining neuronal cell types in the mouse retina and attempt to extract lessons that may be generally useful in the mammalian brain. Achieving a comprehensive catalog of retinal cell types now appears within reach, because researchers have achieved consensus concerning two fundamental challenges. The first is accuracy-defining pure cell types rather than settling for neuronal classes that are mixtures of types. The second is completeness-developing methods guaranteed to eventually identify all cell types, as well as criteria for determining when all types have been found. Case studies illustrate how these two challenges are handled by combining state-of-the-art molecular, anatomical, and physiological techniques. Progress is also being made in observing and modeling connectivity between cell types. Scaling up to larger brain regions, such as the cortex, will require not only technical advances but also careful consideration of the challenges of accuracy and completeness.
我们描述了在定义小鼠视网膜神经元细胞类型方面的最新进展,并试图从中汲取可能对哺乳动物大脑普遍有用的经验教训。现在看来,实现视网膜细胞类型的全面目录已指日可待,因为研究人员已就两个基本挑战达成共识。第一个挑战是准确性——定义纯细胞类型,而不是满足于由多种类型混合而成的神经元类别。第二个挑战是完整性——开发能够最终识别所有细胞类型的方法,以及确定何时已找到所有类型的标准。案例研究说明了如何通过结合最先进的分子、解剖和生理技术来应对这两个挑战。在观察和模拟细胞类型之间的连接方面也取得了进展。扩大到更大的脑区,如皮层,不仅需要技术进步,还需要仔细考虑准确性和完整性方面的挑战。