Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography

TLDR

Spatial transcriptomics is rapidly expanding, yet many assays produce mixed‑cell data rather than single‑cell resolution, making them attractive for complex tissues that require comprehensive expression profiles. The authors present a model‑based probabilistic method that uses single‑cell data to deconvolve cell mixtures in spatial transcriptomics. The method employs single‑cell reference data to probabilistically infer the spatial arrangement of cell types within tissue mixtures. Applying the method to mouse brain and developmental heart data from multiple platforms, the inferred cell‑type maps align with expected anatomical organization.

Abstract

Abstract The field of spatial transcriptomics is rapidly expanding, and with it the repertoire of available technologies. However, several of the transcriptome-wide spatial assays do not operate on a single cell level, but rather produce data comprised of contributions from a – potentially heterogeneous – mixture of cells. Still, these techniques are attractive to use when examining complex tissue specimens with diverse cell populations, where complete expression profiles are required to properly capture their richness. Motivated by an interest to put gene expression into context and delineate the spatial arrangement of cell types within a tissue, we here present a model-based probabilistic method that uses single cell data to deconvolve the cell mixtures in spatial data. To illustrate the capacity of our method, we use data from different experimental platforms and spatially map cell types from the mouse brain and developmental heart, which arrange as expected.