Spatial Transcriptomics-Aided Localization for Single-Cell Transcriptomics with STALocator

Abstract

Single-cell RNA-sequencing (scRNA-seq) techniques can measure gene expression at the single-cell resolution but lack spatial information. The spatial transcriptomics (ST) techniques simultaneously provide gene expression data and spatial information. However, the data quality on the spatial resolution or gene coverage is still much lower than the single-cell transcriptomics data. To this end, we develop a Spatial Transcriptomics-Aided Locator for single-cell transcriptomics (STALocator) to localize single cells to corresponding ST data. Applications on simulated data showed that STALocator performed better than other localization methods from different angles. When applied to human brain scRNA-seq data and dorsolateral prefrontal cortex 10x Visium data, STALocator could robustly reconstruct the laminar organization of layer-associated cell types. Applications on scRNA-seq data and Spatial Transcriptomics data of human squamous cell carcinoma illustrated that STALocator could robustly reconstruct the relative spatial relationship between tumor-specific keratinocytes, microenvironment-associated cell populations, and immune cells. Moreover, STALocator could enhance gene expression patterns for Slide-seqV2 data and predict genome-wide gene expression data for FISH data, leading to the identification of more spatially variable genes and more biologically relevant GO terms compared to raw data.