Subcellular spatially resolved gene neighborhood networks in single cells

Zhou Fang; Adam J Ford; Thomas Hu; Nicholas Zhang; Athanasios Mantalaris; Ahmet F Coskun

doi:10.1016/j.crmeth.2023.100476

Subcellular spatially resolved gene neighborhood networks in single cells

Cell Rep Methods. 2023 May 12;3(5):100476. doi: 10.1016/j.crmeth.2023.100476. eCollection 2023 May 22.

Authors

Zhou Fang^{1

2}, Adam J Ford¹, Thomas Hu^{1

3}, Nicholas Zhang^{1

4}, Athanasios Mantalaris¹, Ahmet F Coskun^{1

4

5}

Affiliations

¹ Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
² Machine Learning Graduate Program, Georgia Institute of Technology, Atlanta, GA, USA.
³ School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
⁴ Interdisciplinary Bioengineering Graduate Program, Georgia Institute of Technology, Atlanta, GA, USA.
⁵ Parker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA 30332, USA.

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.

Keywords: RNA proximity; RNA-RNA interactions; cell-type classification; gene neighborhood networks; spatial omics; subcellular transcriptomics.

MeSH terms

Fibroblasts
Gene Expression Profiling* / methods
Gene Regulatory Networks
In Situ Hybridization, Fluorescence / methods
Single-Cell Analysis* / methods

Abstract

MeSH terms

Grants and funding