Subcellular spatially resolved gene neighborhood networks in single cells

• Authors: Zhou Fang, Georgia Institute of Technology and Emory University; Adam J Ford, Georgia Institute of Technology and Emory University; Thomas Hu, Georgia Institute of Technology and Emory University, Atlanta; Nicholas Zhang, Georgia Institute of Technology and Emory University; Athanasios Mantalaris, Georgia Institute of Technology and Emory University; Ahmet F Coskun, Georgia Institute of Technology and Emory University
• Journal Title: Cell Reports Methods
• Volume: Volume 3, Number 5
• Publisher: Elsevier | 2023-05-12
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1016/j.crmeth.2023.100476
• Subjects: Engineering, Biomedical
• Keywords: spatial omics; subcellular transcriptomics; gene neighborhood networks; RNA proximity; RNA-RNA interactions; cell-type classification

Abstract

Image-based spatial omics methods such as fluorescence in situ hybridization (FISH) generate molecular profiles of single cells at single-molecule resolution. Current spatial transcriptomics methods focus on the distribution of single genes. However, the spatial proximity of RNA transcripts can play an important role in cellular function. We demonstrate a spatially resolved gene neighborhood network (spaGNN) pipeline for the analysis of subcellular gene proximity relationships. In spaGNN, machine-learning-based clustering of subcellular spatial transcriptomics data yields subcellular density classes of multiplexed transcript features. The nearest-neighbor analysis produces heterogeneous gene proximity maps in distinct subcellular regions. We illustrate the cell-type-distinguishing capability of spaGNN using multiplexed error-robust FISH data of fibroblast and U2-OS cells and sequential FISH data of mesenchymal stem cells (MSCs), revealing tissue-source-specific MSC transcriptomics and spatial distribution characteristics. Overall, the spaGNN approach expands the spatial features that can be used for cell-type classification tasks.