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Identification of the metabolic state of surviving cardiomyocytes in the human infarcted heart by spatial single-cell transcriptomics
The metabolic status of surviving cardiomyocytes (CM) in the myocardial tissues of patients who sustained myocardial infarction (MI) is largely unknown. Spatial single-cell RNA-sequencing (scRNA-seq) is a novel tool that enables the unbiased analysis of RNA signatures within intact tissues. We emplo...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10180026/ https://www.ncbi.nlm.nih.gov/pubmed/37187809 http://dx.doi.org/10.1097/CP9.0000000000000038 |
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author | Shen, Yan Kim, Il-man Weintraub, Neal L. Tang, Yaoliang |
author_facet | Shen, Yan Kim, Il-man Weintraub, Neal L. Tang, Yaoliang |
author_sort | Shen, Yan |
collection | PubMed |
description | The metabolic status of surviving cardiomyocytes (CM) in the myocardial tissues of patients who sustained myocardial infarction (MI) is largely unknown. Spatial single-cell RNA-sequencing (scRNA-seq) is a novel tool that enables the unbiased analysis of RNA signatures within intact tissues. We employed this tool to assess the metabolic profiles of surviving CM in the myocardial tissues of patients post-MI. METHODS: A spatial scRNA-seq dataset was used to compare the genetic profiles of CM from patients with MI and control patients; we analyzed the metabolic adaptations of surviving CM within the ischemic niche. A standard pipeline in Seurat was used for data analysis, including normalization, feature selection, and identification of highly variable genes using principal component analysis (PCA). Harmony was used to remove batch effects and integrate the CM samples based on annotations. Uniform manifold approximation and projection (UMAP) was used for dimensional reduction. The Seurat “FindMarkers” function was used to identify differentially expressed genes (DEGs), which were analyzed by the Gene Ontology (GO) enrichment pathway. Finally, the scMetabolism R tool pipeline with parameters method = VISION (Vision is a flexible system that utilizes a high-throughput pipeline and an interactive web-based report to annotate and explore scRNA-seq datasets in a dynamic manner) and metabolism.type = Kyoto Encyclopedia of Genes and Genomes (KEGG) was used to quantify the metabolic activity of each CM. RESULTS: Analysis of spatial scRNA-seq data showed fewer surviving CM in infarcted hearts than in control hearts. GO analysis revealed repressed pathways in oxidative phosphorylation, cardiac cell development, and activated pathways in response to stimuli and macromolecular metabolic processes. Metabolic analysis showed downregulated energy and amino acid pathways and increased purine, pyrimidine, and one-carbon pool by folate pathways in surviving CM. CONCLUSIONS: Surviving CM within the infarcted myocardium exhibited metabolic adaptations, as evidenced by the downregulation of most pathways linked to oxidative phosphorylation, glucose, fatty acid, and amino acid metabolism. In contrast, pathways linked to purine and pyrimidine metabolism, fatty acid biosynthesis, and one-carbon metabolism were upregulated in surviving CM. These novel findings have implications for the development of effective strategies to improve the survival of hibernating CM within the infarcted heart. |
format | Online Article Text |
id | pubmed-10180026 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-101800262023-05-13 Identification of the metabolic state of surviving cardiomyocytes in the human infarcted heart by spatial single-cell transcriptomics Shen, Yan Kim, Il-man Weintraub, Neal L. Tang, Yaoliang Cardiol Plus Original Articles The metabolic status of surviving cardiomyocytes (CM) in the myocardial tissues of patients who sustained myocardial infarction (MI) is largely unknown. Spatial single-cell RNA-sequencing (scRNA-seq) is a novel tool that enables the unbiased analysis of RNA signatures within intact tissues. We employed this tool to assess the metabolic profiles of surviving CM in the myocardial tissues of patients post-MI. METHODS: A spatial scRNA-seq dataset was used to compare the genetic profiles of CM from patients with MI and control patients; we analyzed the metabolic adaptations of surviving CM within the ischemic niche. A standard pipeline in Seurat was used for data analysis, including normalization, feature selection, and identification of highly variable genes using principal component analysis (PCA). Harmony was used to remove batch effects and integrate the CM samples based on annotations. Uniform manifold approximation and projection (UMAP) was used for dimensional reduction. The Seurat “FindMarkers” function was used to identify differentially expressed genes (DEGs), which were analyzed by the Gene Ontology (GO) enrichment pathway. Finally, the scMetabolism R tool pipeline with parameters method = VISION (Vision is a flexible system that utilizes a high-throughput pipeline and an interactive web-based report to annotate and explore scRNA-seq datasets in a dynamic manner) and metabolism.type = Kyoto Encyclopedia of Genes and Genomes (KEGG) was used to quantify the metabolic activity of each CM. RESULTS: Analysis of spatial scRNA-seq data showed fewer surviving CM in infarcted hearts than in control hearts. GO analysis revealed repressed pathways in oxidative phosphorylation, cardiac cell development, and activated pathways in response to stimuli and macromolecular metabolic processes. Metabolic analysis showed downregulated energy and amino acid pathways and increased purine, pyrimidine, and one-carbon pool by folate pathways in surviving CM. CONCLUSIONS: Surviving CM within the infarcted myocardium exhibited metabolic adaptations, as evidenced by the downregulation of most pathways linked to oxidative phosphorylation, glucose, fatty acid, and amino acid metabolism. In contrast, pathways linked to purine and pyrimidine metabolism, fatty acid biosynthesis, and one-carbon metabolism were upregulated in surviving CM. These novel findings have implications for the development of effective strategies to improve the survival of hibernating CM within the infarcted heart. Lippincott Williams & Wilkins 2023-04-04 2023 /pmc/articles/PMC10180026/ /pubmed/37187809 http://dx.doi.org/10.1097/CP9.0000000000000038 Text en Copyright © 2023 China Heart House. https://creativecommons.org/licenses/by-sa/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License (https://creativecommons.org/licenses/by-sa/4.0/) , which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms. |
spellingShingle | Original Articles Shen, Yan Kim, Il-man Weintraub, Neal L. Tang, Yaoliang Identification of the metabolic state of surviving cardiomyocytes in the human infarcted heart by spatial single-cell transcriptomics |
title | Identification of the metabolic state of surviving cardiomyocytes in the human infarcted heart by spatial single-cell transcriptomics |
title_full | Identification of the metabolic state of surviving cardiomyocytes in the human infarcted heart by spatial single-cell transcriptomics |
title_fullStr | Identification of the metabolic state of surviving cardiomyocytes in the human infarcted heart by spatial single-cell transcriptomics |
title_full_unstemmed | Identification of the metabolic state of surviving cardiomyocytes in the human infarcted heart by spatial single-cell transcriptomics |
title_short | Identification of the metabolic state of surviving cardiomyocytes in the human infarcted heart by spatial single-cell transcriptomics |
title_sort | identification of the metabolic state of surviving cardiomyocytes in the human infarcted heart by spatial single-cell transcriptomics |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10180026/ https://www.ncbi.nlm.nih.gov/pubmed/37187809 http://dx.doi.org/10.1097/CP9.0000000000000038 |
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