Obtaining a catalog of cell types is a fundamental building block for understanding the brain. The ideal classification of cell-types is based on the profile of molecules expressed by a cell, in particular, the profile of genes expressed. One strategy is, therefore, to obtain as many single-cell transcriptomes as possible and isolate clusters of neurons with similar gene expression profiles. In this study, we explored an alternative strategy. We explored whether cell-types can be algorithmically derived by combining protein tissue stains with transcript expression profiles. We developed an algorithm that aims to distribute cell-types in the different layers of somatosensory cortex of the developing rat constrained by the tissue- and cellular level data. We found that the spatial distribution of major inhibitory cell types can be approximated using the available data. The result is a depth-wise atlas of inhibitory cell-types of the rat somatosensory cortex. In principle, any data that constrains what can occur in a particular part of the brain can also strongly constrain the derivation of cell-types. This draft inhibitory cell-type mapping is therefore dynamic and can iteratively converge towards the ground truth as further data is integrated.

中文翻译：

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体感皮质中抑制性亚型的位置定位图。

获取细胞类型的目录是了解大脑的基本要素。细胞类型的理想分类是基于细胞表达的分子的特征，尤其是表达的基因的特征。因此，一种策略是获得尽可能多的单细胞转录组，并分离具有相似基因表达谱的神经元簇。在这项研究中，我们探索了一种替代策略。我们探讨了是否可以通过将蛋白质组织染色剂与转录表达谱相结合来从算法上推导细胞类型。我们开发了一种算法，该算法旨在在受组织和细胞水平数据约束的发育中大鼠体感皮层的不同层中分布细胞类型。我们发现主要抑制细胞类型的空间分布可以使用可用的数据进行近似。结果是大鼠体感皮层抑制细胞类型的深度图谱。原则上，任何限制大脑特定部位可能发生的数据都可以强烈限制细胞类型的推导。因此，该抑制细胞类型作图法是动态的，并且可以在集成更多数据时迭代地收敛到地面实况。