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Subcellular spatially resolved gene neighborhood networks in single cells

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...

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Detalles Bibliográficos
Autores principales: Fang, Zhou, Ford, Adam J., Hu, Thomas, Zhang, Nicholas, Mantalaris, Athanasios, Coskun, Ahmet F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10261906/
https://www.ncbi.nlm.nih.gov/pubmed/37323566
http://dx.doi.org/10.1016/j.crmeth.2023.100476
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author Fang, Zhou
Ford, Adam J.
Hu, Thomas
Zhang, Nicholas
Mantalaris, Athanasios
Coskun, Ahmet F.
author_facet Fang, Zhou
Ford, Adam J.
Hu, Thomas
Zhang, Nicholas
Mantalaris, Athanasios
Coskun, Ahmet F.
author_sort Fang, Zhou
collection PubMed
description 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.
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spelling pubmed-102619062023-06-15 Subcellular spatially resolved gene neighborhood networks in single cells Fang, Zhou Ford, Adam J. Hu, Thomas Zhang, Nicholas Mantalaris, Athanasios Coskun, Ahmet F. Cell Rep Methods Article 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. Elsevier 2023-05-12 /pmc/articles/PMC10261906/ /pubmed/37323566 http://dx.doi.org/10.1016/j.crmeth.2023.100476 Text en © 2023 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Fang, Zhou
Ford, Adam J.
Hu, Thomas
Zhang, Nicholas
Mantalaris, Athanasios
Coskun, Ahmet F.
Subcellular spatially resolved gene neighborhood networks in single cells
title Subcellular spatially resolved gene neighborhood networks in single cells
title_full Subcellular spatially resolved gene neighborhood networks in single cells
title_fullStr Subcellular spatially resolved gene neighborhood networks in single cells
title_full_unstemmed Subcellular spatially resolved gene neighborhood networks in single cells
title_short Subcellular spatially resolved gene neighborhood networks in single cells
title_sort subcellular spatially resolved gene neighborhood networks in single cells
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10261906/
https://www.ncbi.nlm.nih.gov/pubmed/37323566
http://dx.doi.org/10.1016/j.crmeth.2023.100476
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