Single-cell sequencing technologies, such as single-cell RNA sequencing (scRNA-seq) and single-cell methylation (scMET) profiling, have transformed our understanding of cellular heterogeneity by revealing gene expression and regulation at unprecedented resolution. However, these methods lack spatial context necessary for understanding tissue organization and cellular interactions. Spatial transcriptomics adds spatial information but is often limited in the number of genes or methylation sites profiled. Integrating spatial and single-cell genomic data can bridge this gap, offering multi-modal insights into complex tissues.

In this study, we integrated single-cell genomic data using the ScaleBio scRNA kit, ScaleBio Methylation kit, and Vizgen MERSCOPE® platform on adjacent brain slices from three regions in one mouse. Samples were profiled using Vizgen’s MERSCOPE workflow with a 500-gene pan-neuron panel and ScaleBio’s kits for transcriptome-wide RNA and methylation profiling. Integration of data from both platforms showed high-quality, spatially resolved information on gene expression and methylation states.

Our analysis revealed distinct spatial localization of scRNA clusters, high correlation between gene expression in Vizgen and scRNA data, and alignment with the Allen Brain Atlas for imputed spatial gene locations. Additionally, scMET data provided insights into epigenomic states, mapping methylation patterns onto spatial transcriptome data.

This study demonstrates that integrating spatial gene expression, scRNA-seq, and scMET data enables comprehensive spatial genomic analysis, revealing gene expression and regulatory landscapes within complex neuronal tissues.