Single‐Cell Simultaneous Metabolome and Transcriptome Profiling Revealing Metabolite‐Gene Correlation Network
Abstract Metabolic studies at the single cell level can directly define the cellular phenotype closest to physiological or disease states. However, the current single cell metabolome (SCM) study using mass spectroscopy has difficulty giving a complete view of the metabolic activity in the cell, and...
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| Language: | English |
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Wiley
2025-01-01
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| Series: | Advanced Science |
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| Online Access: | https://doi.org/10.1002/advs.202411276 |
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| author | Xiying Mao Dandan Xia Miao Xu Yan Gao Le Tong Chen Lu Weiqi Li Runmin Xie Qinghuai Liu Dechen Jiang Songtao Yuan |
| author_facet | Xiying Mao Dandan Xia Miao Xu Yan Gao Le Tong Chen Lu Weiqi Li Runmin Xie Qinghuai Liu Dechen Jiang Songtao Yuan |
| author_sort | Xiying Mao |
| collection | DOAJ |
| description | Abstract Metabolic studies at the single cell level can directly define the cellular phenotype closest to physiological or disease states. However, the current single cell metabolome (SCM) study using mass spectroscopy has difficulty giving a complete view of the metabolic activity in the cell, and the prediction of the metabolism‐phenotype relationship is limited by the potential inconsistency between transcriptomic and metabolic levels. Here, the single‐cell simultaneous metabolome and transcriptome profiling method (scMeT‐seq) is developed at one single cell, based on sub‐picoliter sampling from the cell for the initial metabolome profiling followed by single cell transcriptome sequencing. This design not only provides sufficient cytoplasm for SCM but also nicely keeps the cellular viability for the accurate transcriptomic analysis in the same cell. Integrative analysis of scMeT‐seq reveals both dynamical and cell state‐specific associations between metabolome and transcriptome in the macrophages with defined metabolic perturbations. Moreover, metabolite signatures are mapped to the single‐cell trajectory and gene correlation network of macrophage transition, which allows the unsupervised functional interpretation of metabolome. Thus, the established scMeT‐seq should lead to a new perspective in metabolic research by transforming metabolomics from a metabolite snapshot to a functional approach. |
| format | Article |
| id | doaj-art-9103a876d9844c7585bf1cda688ab869 |
| institution | Kabale University |
| issn | 2198-3844 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
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| series | Advanced Science |
| spelling | doaj-art-9103a876d9844c7585bf1cda688ab8692025-01-29T09:50:19ZengWileyAdvanced Science2198-38442025-01-01124n/an/a10.1002/advs.202411276Single‐Cell Simultaneous Metabolome and Transcriptome Profiling Revealing Metabolite‐Gene Correlation NetworkXiying Mao0Dandan Xia1Miao Xu2Yan Gao3Le Tong4Chen Lu5Weiqi Li6Runmin Xie7Qinghuai Liu8Dechen Jiang9Songtao Yuan10Department of Ophthalmology The First Affiliated Hospital of Nanjing Medical University Nanjing 210029 P. R. ChinaThe State Key Lab of Analytical Chemistry for Life Science Chemistry and Biomedicine Innovation Center (ChemBIC) School of Chemistry and Chemical Engineering Nanjing University Nanjing 210093 P. R. ChinaDepartment of Ophthalmology The First Affiliated Hospital of Nanjing Medical University Nanjing 210029 P. R. ChinaDepartment of Ophthalmology The First Affiliated Hospital of Nanjing Medical University Nanjing 210029 P. R. ChinaThe State Key Lab of Analytical Chemistry for Life Science Chemistry and Biomedicine Innovation Center (ChemBIC) School of Chemistry and Chemical Engineering Nanjing University Nanjing 210093 P. R. ChinaDepartment of Ophthalmology The First Affiliated Hospital of Nanjing Medical University Nanjing 210029 P. R. ChinaDepartment of Ophthalmology The First Affiliated Hospital of Nanjing Medical University Nanjing 210029 P. R. ChinaDepartment of Ophthalmology The First Affiliated Hospital of Nanjing Medical University Nanjing 210029 P. R. ChinaDepartment of Ophthalmology The First Affiliated Hospital of Nanjing Medical University Nanjing 210029 P. R. ChinaThe State Key Lab of Analytical Chemistry for Life Science Chemistry and Biomedicine Innovation Center (ChemBIC) School of Chemistry and Chemical Engineering Nanjing University Nanjing 210093 P. R. ChinaDepartment of Ophthalmology The First Affiliated Hospital of Nanjing Medical University Nanjing 210029 P. R. ChinaAbstract Metabolic studies at the single cell level can directly define the cellular phenotype closest to physiological or disease states. However, the current single cell metabolome (SCM) study using mass spectroscopy has difficulty giving a complete view of the metabolic activity in the cell, and the prediction of the metabolism‐phenotype relationship is limited by the potential inconsistency between transcriptomic and metabolic levels. Here, the single‐cell simultaneous metabolome and transcriptome profiling method (scMeT‐seq) is developed at one single cell, based on sub‐picoliter sampling from the cell for the initial metabolome profiling followed by single cell transcriptome sequencing. This design not only provides sufficient cytoplasm for SCM but also nicely keeps the cellular viability for the accurate transcriptomic analysis in the same cell. Integrative analysis of scMeT‐seq reveals both dynamical and cell state‐specific associations between metabolome and transcriptome in the macrophages with defined metabolic perturbations. Moreover, metabolite signatures are mapped to the single‐cell trajectory and gene correlation network of macrophage transition, which allows the unsupervised functional interpretation of metabolome. Thus, the established scMeT‐seq should lead to a new perspective in metabolic research by transforming metabolomics from a metabolite snapshot to a functional approach.https://doi.org/10.1002/advs.202411276functional metabolomicsmass spectroscopymulti‐omicsRNA sequencingsingle‐cell |
| spellingShingle | Xiying Mao Dandan Xia Miao Xu Yan Gao Le Tong Chen Lu Weiqi Li Runmin Xie Qinghuai Liu Dechen Jiang Songtao Yuan Single‐Cell Simultaneous Metabolome and Transcriptome Profiling Revealing Metabolite‐Gene Correlation Network Advanced Science functional metabolomics mass spectroscopy multi‐omics RNA sequencing single‐cell |
| title | Single‐Cell Simultaneous Metabolome and Transcriptome Profiling Revealing Metabolite‐Gene Correlation Network |
| title_full | Single‐Cell Simultaneous Metabolome and Transcriptome Profiling Revealing Metabolite‐Gene Correlation Network |
| title_fullStr | Single‐Cell Simultaneous Metabolome and Transcriptome Profiling Revealing Metabolite‐Gene Correlation Network |
| title_full_unstemmed | Single‐Cell Simultaneous Metabolome and Transcriptome Profiling Revealing Metabolite‐Gene Correlation Network |
| title_short | Single‐Cell Simultaneous Metabolome and Transcriptome Profiling Revealing Metabolite‐Gene Correlation Network |
| title_sort | single cell simultaneous metabolome and transcriptome profiling revealing metabolite gene correlation network |
| topic | functional metabolomics mass spectroscopy multi‐omics RNA sequencing single‐cell |
| url | https://doi.org/10.1002/advs.202411276 |
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