空间转录组数据通过镶嵌假说揭示纯细胞类型

Yiliu Wang, Christof Koch, Uygar Sümbül
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引用次数: 0

摘要

神经元在解剖、分子和生理特性方面表现出显著的多样性。虽然神经元子集的刻板印象是神经科学的支柱,但在高维特征空间(如单细胞 RNA-seq 数据所定义的空间)中进行聚类往往没有定论,细胞似乎占据连续而非离散的区域。在视网膜这一分层结构中,同一离散类型的神经元避免在空间上相互靠近。这一原则与特征空间中的聚类无关,一直是视网膜细胞类型的黄金标准,但其在大脑皮层中的适用性却鲜有探索。在这里,我们通过开发空间转录组数据的统计点过程分析框架,为这种镶嵌假说提供了证据。我们证明了许多兴奋性和抑制性神经元类型都存在空间回避现象。当细胞类型合并时,空间回避现象就会消失,这有可能为评估假定细胞类型的纯度提供一个金标准指标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spatial transcriptomic data reveals pure cell types via the mosaic hypothesis
Neurons display remarkable diversity in their anatomical, molecular, and physiological properties. Although observed stereotypy in subsets of neurons is a pillar of neuroscience, clustering in high-dimensional feature spaces, such as those defined by single cell RNA-seq data, is often inconclusive and cells seemingly occupy continuous, rather than discrete, regions. In the retina, a layered structure, neurons of the same discrete type avoid spatial proximity with each other. While this principle, which is independent of clustering in feature space, has been a gold standard for retinal cell types, its applicability to the cortex has been only sparsely explored. Here, we provide evidence for such a mosaic hypothesis by developing a statistical point process analysis framework for spatial transcriptomic data. We demonstrate spatial avoidance across many excitatory and inhibitory neuronal types. Spatial avoidance disappears when cell types are merged, potentially offering a gold standard metric for evaluating the purity of putative cell types.
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