Subcellular spatially resolved gene neighborhood networks in single cells

Abstract

Mesenchymal stem cell (MSC)-based therapies have offered promising treatments against several disorders. However, the clinical efficacy and consistency remain underdeveloped. Single-cell and bulk molecular analyses have provided considerable heterogeneity of MSCs due to origin, expansion, and microenvironment. Image-based cellular omics methods elucidate ultimate variability in stem cell colonies, otherwise masked by bulk omics approaches. Here, we present a spatially resolved Gene Neighborhood Network (spaGNN) method to produce transcriptional density maps and analyze neighboring RNA distributions in single human MSCs and chondrocytes cultured on 2D collagen-coated substrates. This proposed strategy provides cell classification based on subcellular spatial features and gene neighborhood networks. Machine learning-based clustering of resultant data yields subcellular density classes of 20-plex biomarkers containing diverse transcript and protein features. The spaGNN reveals tissue-source-specific MSC transcription and spatial distribution characteristics. Multiplexed spaGNN analysis allows for rapid examination of spatially resolved subcellular features and activities in a broad range of cells used in pre-clinical and clinical research.