Localizing gene expression
Single-cell RNA sequencing can provide information about cellular relationships based on shared transcriptomes, but most methods lose spatial information. Those methods that do retain spatial information can be limited to a specific set of genes and/or a small area. Srivatsan et al. introduce sci-Space, a spatial transcriptomic method that uses a grid of barcoded oligos on a slide that can be transferred to nuclei of an overlaid frozen tissue section to obtain both the spatial origin and the transcriptome of thousands of single cells per slide. The researchers used sci-Space to create a spatial atlas of mouse E14 sagittal sections, revealing spatially expressed genes across cell types. This application illustrates how sci-Space complements existing approaches in spatial genomics.
Science, abb9536, this issue p. 111
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.