Spatial transcriptomic data reveals pure cell types via the mosaic hypothesis

Abstract

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.