The DOI matches its Crossref record; 87 of the 88 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
Flagged first, then by citations
1 of the 88 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Items 301–348 of 348
Gut microbiota and kidney aging: insights into current research
Saadat, Y. R., Barzegari, A., Saadatian, Z., Montazersaheb, S., & Vahed, S. Z. (2025) · Nutrition & Metabolism · doi:10.1186/s12986-025-01032-w · Cited by 1
Effects of a prolonged diet regimen on autophagic function in rat islets with aging
Gu, Z., Miao, X., Cui, J., Yang, F., Ma, L.-C., Li, C., Sun, B., & Yan, S. (2021) · Experimental Gerontology · doi:10.1016/j.exger.2021.111659 · Cited by 2
Su, X. (2020) · Science Translational Medicine · doi:10.1126/scitranslmed.abb2771 · Cited by 1
Green, A. P., Klimm, F., Marshall, A. S., Leetmaa, R., Aryaman, J., Gómez-Durán, A., Chinnery, P. F., & Jones, N. S. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.04.547509 · Cited by 1
Zhang, R., Jadhav, D. A., Krämer, B., Gonzalez‐Vicente, A., & the Kidney Precision Medicine Project (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.20.572656 · Cited by 1
Sarver, D. C., Saqib, M., Chen, F., & Wong, G. W. (2024) · eLife · doi:10.7554/elife.96926.2 · Cited by 1
Sarver, D. C., Saqib, M., Chen, F., & Wong, G. W. (2024) · eLife · doi:10.7554/elife.96926.3 · Cited by 1
Vascular aging and cardiovascular diseases
Zhao, Y., Wu, A., LiYong, L. I. A. O., Liu, M., XueTing, G. O. N. G., & Tian, X. (2021) · Scientia Sinica Vitae · doi:10.1360/ssv-2020-0344
グレリン－神経ペプチドY空腹系と人参養栄湯 ―健康長寿に向けて―
乾明夫, 由基, 小., 榊, 弥., 川元, 美., 秀伍, 藏., & 宇都, 奈. (2020) · 自律神経 · doi:10.32272/ans.57.3_165
Caloric restriction reverses aging: one cell at a time
Simon, L. M. (2021) · The Journal of Cardiovascular Aging · doi:10.20517/jca.2021.12
Orchestration of alternative splicing regulates bone marrow mesenchymal stem cells fate during agingPreprint
Xiao, Y., Cai, G., Xu, F., Guo, Q., Huang, Y., Su, T., Li, C., Luo, X., Zheng, Y., & Yang, M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.27.493685
The 6th international cell senescence association conference
Kawamoto, S., Matsumoto, T., Takasugi, M., & Hara, E. (2022) · Genes to Cells · doi:10.1111/gtc.12970
Tong, R., Li, R., Zhao, Y., Luo, L., Zhong, J., Li, Z., Lin, W., Chen, Y., Shi, J., Gao, Y., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.11.498621 · Cited by 1
Nadler, A., Emery, R., & Shah, N. C. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-824315-2.00954-4
Progressive Senescence Programs Induce Intrinsic Vulnerability to Aging-related Breast CancerPreprint
Bai, H., Liu, X., Lin, M., Meng, Y., Li, N., Clarke, M. F., & Cai, S. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.08.539818
Schneider, A. L., Saraiva-Agostinho, N., & Barbosa‐Morais, N. L. (2023) · eLife · doi:10.7554/elife.88623.1
Exploring the parabiosis model’s evolution, applications and effects
Li, H. (2023) · Theoretical and Natural Science · doi:10.54254/2753-8818/8/20240390
BIG to CNCB: An Exploratory Journey from Genomics to Bioinformation
Yang, Y., Xue, Y., Wu, Z., & Yang, H. (2024) · 中国科学院院刊：英文版 · doi:10.1051/bcas/2024007
Single-nucleus multiple-organ chromatin accessibility mapping in the ratPreprint
Li, R., Yuan, Y., Duan, S., Deng, Q., Ma, W., Liu, C., Gao, P., Li, L., & Liu, C. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.11.622900
Kong, B. S., L’Yi, S., Ramirez-Hernandez, E. G., Hong, S., Weidenspointner, R., Song, S., Caserta, C., Cho, Y. M., & Kory, N. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.09.647991
Lysergic Acid Diethylamide extends lifespan in Caenorhabditis elegansPreprint
Carrilho, B. D. S., Duarte-Silva, A. T., Martins, I., Moura, A., Vilas, M., Ventura, M. A. V. D. C., Moll, F., Aguilaniu, H., Domith, I., & Rehen, S. K. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.16.659936
Wu, J.-J., Zhu, S., Tang, Y.-F., Gu, F., Liu, J., & Sun, H. (2023) · Figshare · doi:10.6084/m9.figshare.22615288
Tuyéras, R., Morcuende, Á., Llinares, C., Segerstolpe, Å., Kellis, M., Femenía, T., & Agudelo, L. Z. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.29.702626
Shao, C., Peng, D., Zhao, Y., Shu, Y., Zhuang, S., Huang, Q., & Song, B. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.08.704722
Wei, W.-T., Yan, Z., Wu, H., Zhou, M., Mo, D.-X., Wan, X., Ma, R., Wu, M.-M., Huang, J.-H., Liu, Y.-J., et al. (2026) · Science China Life Sciences · doi:10.1007/s11427-025-3108-3
Kimura, S., Sekiya, S., Yamashiro, S., Kikuchi, T., Haga, M., & Shimizu, T. (2026) · EMBO Reports · doi:10.1038/s44319-026-00757-w · Cited by 1
Sun, C., Fu, M., Liu, J., Zheng, K., Chen, S., Deng, L., Zhuang, Y., Wang, K., Ren, R., & Liang, C. (2026) · Contemporary Clinical Trials Communications · doi:10.1016/j.conctc.2026.101632
The role of an mRNA-binding protein YB-1 in formation of stress granules and translation
Budkina, K. (2021) · theses.fr (ABES) · doi:10.70675/0c546666z45a7z423bz9dc1z1b5b0c8558fc
Rejuvenating stem cells to reverse aging
Ranwa, S., Yadav, S., Nurboo, S., & Nazir, A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-33159-6.00019-9
Immunosenescence in Idiopathic Pulmonary Fibrosis
Xiao, S., Li, Z., Zhang, H., Cai, W., Yu, S., & Song, X. (2026) · Journal of Inflammation Research · doi:10.2147/jir.s586167
Caloric restriction rejuvenates aged adult stem cells: From mechanisms to interventions
Chai, Y., Liu, X., Bi, J., Zeng, J., Yao, M., Shi, F., & Xu, S. (2026) · Ageing Research Reviews · doi:10.1016/j.arr.2026.103204
Effect of Fisetin on the Expression of Some Age-Related Genes
Oruç, K. D., Mutlu, A. G., & Kıyak, A. (2026) · Biology Bulletin · doi:10.1134/s1062359025602496
Large-scale single-cell mapping of concealed mitochondrial ageingPreprint
Jethwani, H., Sukandar, K. K., Green, A. P., Insalata, F., Golder, Z. J., Schon, K. R., Asquith, B., Chinnery, P. F., & Jones, N. S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.14.738425
Caloric restriction suppresses colorectal tumors and melanoma through CD8 T cells in aged mice
Horino, T., Horiguchi, H., Torigoe, D., Yumoto, S., Kadomatsu, T., Harada, K., Sato, M., Moroishi, T., Iwatsuki, M., & Oike, Y. (2026) · Cell Reports · doi:10.1016/j.celrep.2026.117844
Ayubi, N., Wibawa, J. C., Kurnaz, M., & Komaini, A. (2026) · Physiology International · doi:10.1556/2060.2026.00863
Han, Y., Shon, J., & Park, Y. J. (2026) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2026.2728313
ANGPTL8 induces the aging of hematopoietic stem/progenitor cells
Chen, M., Zhang, M., Mei, H., Bai, X., Wang, X., Tang, Y., Jiang, H., Zhang, L., Zhou, C., & Yuan, Y. (2026) · Biogerontology · doi:10.1007/s10522-026-10517-x
Age-related cardiac dysfunction is not associated with shorter telomere length in laboratory rats
Strandberg, S. T., Opstad, T. B., Romaine, A., Espe, E., Sadredini, M., Løken, K. G., Palmero, S., Gravning, J. A., Sjaastad, I., Lunde, I. G., et al. (2026) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2026.1790767
Sexual reproduction impacts vertebrate organ agingPreprint
Chen, J., Tsenter, A., Costa, E. K., Yu, D., Lin, X., Chen, K. L., Bot, V. A., Housh, M. J., Heraud, L.-P., Perry, S. V., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.10.08.757363
In Vivo Transcriptomics-Based Framework for Systematic Indication ExpansionPreprint
Palmer, Z., Koirala, S., Talukdar, N., Zhang, X., Garver, H. S., Mahlmeister, M., Huang, L., Joseph, D., Leshchiner, D. R., Shankar, R., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.10.12.758524
Zhang, J., Su, W., Duan, J., Sun, Y., & Liu, B. (2026) · The Innovation Life · doi:10.59717/j.xinn-life.2026.100262
Dreck, M., Grabis, E., Schneider, L. A., Lind, M., Heinrichs, P., Christmann, T., Neidert, N. N., Brose, T. Z., Kreuzaler, S., Pankratz, F., et al. (2026) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2026.1910122
Epigenetics in development, disease, and therapy: from mechanisms to clinical interventions
Ren, Y., Yang, L., Fei, B., Guo, R., Huang, J., Xu, Z., Wang, R., Zhang, P., Cao, D., Chen, P., et al. (2026) · Science China Life Sciences · doi:10.1007/s11427-025-3356-1
Self-citationDynamic force sensing by bone delays aging and extends lifespan
Zhang, Z., Cai, Z., Liu, S., Chu, Y., Jiang, Z., Zhang, W., Pan, Y., Lin, Y., Wei, X., Chen, J., et al. (2026) · Cell Metabolism · doi:10.1016/j.cmet.2026.09.020
Self-citationTang, Y., Lu, P., Chen, H., & Lin, D. (2026) · Metabolism and Diseases · doi:10.1016/j.metdis.2026.100039
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
