Embryo-scale, single-cell spatial transcriptomics

TLDR

Spatial transcriptomics currently averages local contexts or is limited in field of view, despite gene expression patterns spanning local to global scales. The study introduces sci‑Space to provide single‑cell resolution across large spatial scales and aims to enable spatially resolved single‑cell atlases of mammalian development. The authors applied sci‑Space to developing mouse embryos, capturing spatial coordinates and whole transcriptomes for ~120,000 nuclei. The method uncovered thousands of anatomically patterned genes, enabled subtype annotation, revealed variable spatial patterning across cell types, and linked pseudotime to neuronal migration patterns.

Abstract

Spatial patterns of gene expression manifest at scales ranging from local (e.g., cell-cell interactions) to global (e.g., body axis patterning). 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.