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Caloric Restriction Reprograms the Single-Cell Transcriptional Landscape of Rattus Norvegicus Aging
Open accessAbstract
Aging causes a functional decline in tissues throughout the body that may be delayed by caloric restriction (CR). However, the cellular profiles and signatures of aging, as well as those ameliorated by CR, remain unclear. Here, we built comprehensive single-cell and single-nucleus transcriptomic atlases across various rat tissues undergoing aging and CR. CR attenuated aging-related changes in cell type composition, gene expression, and core transcriptional regulatory networks. Immune cells were increased during aging, and CR favorably reversed the aging-disturbed immune ecosystem. Computational prediction revealed that the abnormal cell-cell communication patterns observed during aging, including the excessive proinflammatory ligand-receptor interplay, were reversed by CR. Our work provides multi-tissue single-cell transcriptional landscapes associated with aging and CR in a mammal, enhances our understanding of the robustness of CR as a geroprotective intervention, and uncovers how metabolic intervention can act upon the immune system to modify the process of aging.
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“However, CR has diverse effects across multiple biological systems42,43.”
From Effect of long-term caloric restriction on DNA methylation measures of biological aging in healthy adults from the CALERIE trial (2023) · cites this paper as [42]
In the citing paper
“Notably, multiple tissues exhibited an increase in the numbers and percentages of pro-inflammatory immune cells as a function of age, a conclusion independently drawn in single cell transcriptomic surveys of 7 tissues in aging rats and 3 tissues in aging mice.50,51”
From Asynchronous, contagious and digital aging (2021) · cites this paper as [50]
In the citing paper
“Aging is associated with changes in the population of immune cells infiltrates,51 and caloric restriction can reverse those trends.50”
From Asynchronous, contagious and digital aging (2021) · cites this paper as [50]
In the citing paper
“Dietary restriction can generally ‘reprogramme’ both the transcriptome and the proteome in ageing rats and mice210,211.”
From Ageing and rejuvenation of tissue stem cells and their niches (2023) · cites this paper as [210]
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Citations
Cites
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888
featureCounts: an efficient general purpose program for assigning sequence reads to genomic features
Liao, Y., Smyth, G. K., & Shi, W. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt656 · Cited by 27,219
HISAT: a fast spliced aligner with low memory requirements
Kim, D., Langmead, B., & Salzberg, S. L. (2015) · Nature Methods · doi:10.1038/nmeth.3317 · Cited by 24,112
López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M., & Kroemer, G. (2013) · Cell · doi:10.1016/j.cell.2013.05.039 · Cited by 14,982
Metascape provides a biologist-oriented resource for the analysis of systems-level datasets
Zhou, Y., Zhou, B., Pache, L., Chang, M., Khodabakhshi, A. H., Tanaseichuk, O., Benner, C., & Chanda, S. K. (2019) · Nature Communications · doi:10.1038/s41467-019-09234-6 · Cited by 14,611
ggplot2: Elegant Graphics for Data Analysis
Ginestet, C. E. (2011) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2010.00676_9.x · Cited by 14,511
ggplot2: Elegant Graphics for Data Analysis
Wickham, H. (2009) · doi:10.1007/978-0-387-98141-3 · Cited by 14,447
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110
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Li, X., Li, C., Zhang, W., Wang, Y., Qian, P., & Huang, H. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01502-8 · Cited by 1,443
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Kang, W., Liu, H., Hu, Q., Wang, L., Liu, J., Zheng, Z., Zhang, W., Ren, J., Zhu, F., & Liu, G. (2022) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-022-01211-8 · Cited by 732
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Cohen, P., & Kajimura, S. (2021) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-021-00350-0 · Cited by 572
Cai, Y., Song, W., Li, J., Jing, Y., Liang, C., Zhang, L., Zhang, X., Zhang, W., Liu, B., An, Y., et al. (2022) · Science China Life Sciences · doi:10.1007/s11427-022-2161-3 · Cited by 450
Self-citationA Single-Cell Transcriptomic Atlas of Human Skin Aging
Zou, Z., Long, X., Zhao, Q., Zheng, Y., Song, M., Ma, S., Jing, Y., Wang, S., He, Y., Esteban, C. R., et al. (2021) · Developmental Cell · doi:10.1016/j.devcel.2020.11.002 · Cited by 402
Self-citationAgeing and rejuvenation of tissue stem cells and their niches
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Zhu, X., Chen, Z., Shen, W., Huang, G., Sedivy, J. M., Wang, H., & Ju, Z. (2021) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-021-00646-9 · Cited by 425
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Mogilenko, D. A., Shchukina, I., & Artyomov, M. N. (2021) · Nature reviews. Immunology · doi:10.1038/s41577-021-00646-4 · Cited by 396
Bao, H., Cao, J., Chen, M., Min, C., Chen, W., Chen, X., Chen, Y., Chen, Y., Chen, Y., Chen, Z., et al. (2023) · Science China Life Sciences · doi:10.1007/s11427-023-2305-0 · Cited by 402
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Robbins, P. D., Jurk, D., Khosla, S., Kirkland, J. L., LeBrasseur, N. K., Miller, J. D., Passos, J. F., Pignolo, R. J., Tchkonia, T., & Niedernhofer, L. J. (2021) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-050120-105018 · Cited by 360
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Liu, G., Bào, Y., Qu, J. T., Zhang, W., Zhāng, T., Kang, W., Yang, F., Ji, Q., Jiang, X., Ma, Y., et al. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa894 · Cited by 333
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Zheng, Y., Liu, X., Le, W., Xie, L., Li, H., Wen, W., Wang, S., Ma, S., Huang, Z., Ye, J., et al. (2020) · Protein & Cell · doi:10.1007/s13238-020-00762-2 · Cited by 270
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Nunn, A. V. W., Guy, G. W., Brysch, W., Botchway, S. W., Frasch, W. D., Calabrese, E. J., & Bell, J. D. (2020) · Immunity & Ageing · doi:10.1186/s12979-020-00204-x · Cited by 60
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Fazeli, P. K., Zhang, Y., O’Keefe, J., Pesaresi, T., Lun, M., Lawney, B. P., & Steinhauser, M. L. (2020) · Molecular Metabolism · doi:10.1016/j.molmet.2020.101082 · Cited by 56
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Huang, D., Zuo, Y., Zhang, C., Sun, G., Jing, Y., Lei, J., Ma, S., Sun, S., Lu, H., Zhang, X., et al. (2022) · Protein & Cell · doi:10.1093/procel/pwac057 · Cited by 56
Self-citation(2023) · doi:10.1016/j.celrep.2023.112593 · Cited by 53
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Rogers, R. S., Wang, H., Durham, T., Stefely, J. A., Owiti, N. A., Markhard, A. L., Sandler, L., To, T., & Mootha, V. K. (2023) · PLoS Biology · doi:10.1371/journal.pbio.3002117 · Cited by 52
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Yu, W., Yu, Y., Sun, S., Lu, C., Zhai, J., Lei, Y., Bai, F., Wang, R., & Chen, J. (2024) · Nutrients · doi:10.3390/nu16223830 · Cited by 53
Self-citationYan, Z., Yang, J. H., Wei, W.-T., Zhou, M., Mo, D.-X., Wan, X., Ma, R., Wu, M.-M., Huang, J.-H., Liu, Y.-J., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-48261-w · Cited by 50
Xie, W., Ke, Y., You, Q., Li, J., Chen, L., Li, D., Fang, J., Chen, X., Zhou, Y., Chen, L., et al. (2022) · Arteriosclerosis, Thrombosis, and Vascular Biology · doi:10.1161/atvbaha.121.316883 · Cited by 46
Abedini, A., Sánchez-Navaro, A., Wu, J., Klötzer, K. A., Ma, Z., Poudel, B., Doke, T., Balzer, M. S., Frederick, J., Černecká, H., et al. (2023) · Journal of Clinical Investigation · doi:10.1172/jci157165 · Cited by 45
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Nomura, S. (2020) · Journal of Human Genetics · doi:10.1038/s10038-020-00844-3 · Cited by 47
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Cao, P., Yang, W., Wang, P., Li, X., & Nashun, B. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.664843 · Cited by 45
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Lu, R. J., Wang, E. K., & Benayoun, B. A. (2021) · Briefings in Functional Genomics · doi:10.1093/bfgp/elab009 · Cited by 43
Pini, M., Czibik, G., Sawaki, D., Mezdari, Z., Braud, L., Delmont, T., Mercedes, R., Martel, C., Buron, N., Marcelin, G., et al. (2021) · Aging Cell · doi:10.1111/acel.13421 · Cited by 43
Yu, T., Guo, J., Zhu, S., Zhang, X., Zhu, Z. Z., Cheng, S., & Cong, X. (2020) · Journal of Functional Foods · doi:10.1016/j.jff.2020.104277 · Cited by 42
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Drapela, S., Ilter, D., & Gomes, A. P. (2022) · Molecular Oncology · doi:10.1002/1878-0261.13261 · Cited by 41
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Wu, G., Dai, Y., Yan, Y., Zheng, X., Zhang, H., Li, H., & Chen, W. (2022) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2022.174959 · Cited by 39
(2023) · doi:10.1093/lifemedi/lnad022 · Cited by 38
Self-citation
Metadata
- Authors
- Ma, S., Sun, S., Geng, L., Song, M., Wang, W., Ye, Y., Ji, Q., Zou, Z., Wang, S., He, X., Li, W., Esteban, C. R., Long, X., Guo, G., Chan, P., Zhou, Q., Belmonte, J. C. I., Zhang, W., Qu, J., & Liu, G.-H.
- Affiliations
- Chinese Academy of Sciences (CN)
- Capital Medical University (CN)
- Peking Union Medical College Hospital (CN)
- Zhejiang University (CN)
- Corresponding author
- Zhang, Weiqi
- Qu, Jing
- Liu, Guang-Hui
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2020-03
- Volume
- 180
- Issue
- 5
- Pages
- 984-1001.e22
- ISSN
- 0092-8674 (ISSN-L)
- OpenAlex
- W3007560401
- Cited by
- 349
- References
- 88
- Citations by year
- 2026: 35 · 2025: 60 · 2024: 51 · 2023: 64 · 2022: 53 · 2021: 51 · 2020: 35
- FWCI
- 16.93
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China Stem Cell and Translational Research
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Health Sciences › Medicine › Geriatrics and Gerontology › Sirtuins and Resveratrol in Medicine
- Keywords
- caloric restriction, aging, single-cell transcriptomics, single-nucleus transcriptomics, Rattus norvegicus, cell type composition, immune cells, pro-inflammatory signaling, intercellular communication, transcriptional regulatory networks, geroprotective interventions, tissue aging
- MeSH terms
- Aging, Animals, Gene Expression Regulation, Humans, Immune System, Caloric Restriction, Rats, Gene Regulatory Networks, Single-Cell Analysis, Transcriptome, Cellular Reprogramming
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