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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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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. |
format | Online Article Text |
id | pubmed-10261906 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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