Recovering single-cell expression profiles from spatial transcriptomics with scResolve

Many popular spatial transcriptomics techniques lack single-cell resolution. Instead, these methods measure the collective gene expression for each location from a mixture of cells, potentially containing multiple cell types. Here, we developed scResolve, a method for recovering single-cell expression profiles from spatial transcriptomics measurements at multi-cellular resolution. scResolve accurately restores expression profiles of individual cells at their locations, which is unattainable with cell type deconvolution. Applications of scResolve on human breast cancer data and human lung disease data demonstrate that scResolve enables cell-type-specific differential gene expression analysis between different tissue contexts and accurate identification of rare cell populations. The spatially resolved cellular-level expression profiles obtained through scResolve facilitate more flexible and precise spatial analysis that complements raw multi-cellular level analysis. [Display omitted] •Enhances the resolution of multi-cellular spatial transcriptomics data•Recovers single-cell expression profiles of individual cells at their locations•Enables cell-type-specific differential expression analysis across tissue locations•Application to idiopathic pulmonary fibrosis sections identifies senescent cells Spatial transcriptomics maps gene expression in tissue sections, revealing cell organization within their natural context. However, many techniques lack single-cell resolution, capturing collective gene expression from mixed-cell populations. We developed scResolve, a method to recover expression profiles of individual cells at their locations from spatial transcriptomics data at multi-cellular resolution. Chen et al. introduce scResolve, a method that increases the resolution of multi-cellular spatial transcriptomics data to the single-cell level. Unlike methods providing cell type proportions, scResolve reconstructs individual cell expression profiles, enabling high-resolution analysis. Using scResolve, the authors identify rare cell populations in fibrosis lung tissues.