Subcellular spatially resolved gene neighborhood networks in single cells

• Type of Material: Article
• 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
• Language: English
• Date: 2023-05-12
• Publisher: Elsevier
• Publication Version: Final Published Version
• Copyright Statement: © 2023 The Authors
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.crmeth.2023.100476
• Title of Journal or Parent Work: Cell Reports Methods
• Volume: 3
• Issue: 5
• Keywords: RNA-RNA interactions; gene neighborhood networks; cell-type classification; RNA proximity; spatial omics; subcellular transcriptomics
• Research Categories: Engineering, Biomedical

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.