Spatial patterns of gene expression manifest at scales ranging from local to global.
However, current spatial transcriptomics methods either average local contexts or
are restricted to limited fields of view. Here, we introduce sci-Space, which retains
single-cell resolution while resolving spatial heterogeneity at larger scales.
Applying sci-Space to developing mouse embryos, we captured approximate
spatial coordinates and whole transcriptomes of about 120,000 nuclei. We
identify thousands of genes exhibiting anatomically patterned expression,
leverage spatial information to annotate cellular subtypes, show that cell types
vary substantially in their extent of spatial patterning, and reveal correlations
between pseudotime and the migratory patterns of differentiating neurons.
Looking forward, we anticipate that sci-Space will facilitate the construction
of spatially resolved single-cell atlases of mammalian development