Columbia University, New York City, NY, USA.
Allen Institute for Brain Science, Seattle, WA, USA.
Nat Neurosci. 2020 Dec;23(12):1456-1468. doi: 10.1038/s41593-020-0685-8.
To understand the function of cortical circuits, it is necessary to catalog their cellular diversity. Past attempts to do so using anatomical, physiological or molecular features of cortical cells have not resulted in a unified taxonomy of neuronal or glial cell types, partly due to limited data. Single-cell transcriptomics is enabling, for the first time, systematic high-throughput measurements of cortical cells and generation of datasets that hold the promise of being complete, accurate and permanent. Statistical analyses of these data reveal clusters that often correspond to cell types previously defined by morphological or physiological criteria and that appear conserved across cortical areas and species. To capitalize on these new methods, we propose the adoption of a transcriptome-based taxonomy of cell types for mammalian neocortex. This classification should be hierarchical and use a standardized nomenclature. It should be based on a probabilistic definition of a cell type and incorporate data from different approaches, developmental stages and species. A community-based classification and data aggregation model, such as a knowledge graph, could provide a common foundation for the study of cortical circuits. This community-based classification, nomenclature and data aggregation could serve as an example for cell type atlases in other parts of the body.
为了理解皮质回路的功能，有必要对其细胞多样性进行编目。过去，人们试图通过皮质细胞的解剖学、生理学或分子特征来实现这一目标，但并没有形成神经元或神经胶质细胞类型的统一分类法，部分原因是数据有限。单细胞转录组学首次能够对皮质细胞进行系统的高通量测量，并生成有望完整、准确和永久的数据集。对这些数据的统计分析揭示了经常与以前通过形态学或生理学标准定义的细胞类型相对应的簇，并且这些细胞类型似乎在皮质区域和物种中是保守的。为了充分利用这些新方法，我们建议采用基于转录组的哺乳动物新皮质细胞类型分类法。这种分类应该是分层的，并使用标准化的命名法。它应该基于细胞类型的概率定义，并整合来自不同方法、发育阶段和物种的数据。基于社区的分类和数据聚合模型，如知识图谱，可以为皮质回路的研究提供一个共同的基础。这种基于社区的分类、命名法和数据聚合可以作为身体其他部位细胞类型图谱的范例。
