Aging increases cell-to-cell transcriptional variability upon immune stimulation
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Single-cell sequencing of mouse immune cells reveals how aging destabilizes a conserved transcriptional activation program.
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Single-cell resolution studies showed elevated cell-to-cell transcriptional variability in the activation program of CD4 T cells and in leukocytes from lungs of old mice.
“Only recently, studies performed at a single-cell resolution showed elevated cell-to-cell transcriptional variability in the activation program of CD4 T cells (16) and in leukocytes from lungs of old mice (17).”
From Aging promotes reorganization of the CD4 T cell landscape toward extreme regulatory and effector phenotypes (2019) · cites this paper as [16]
Genes up-regulated in young cells were associated with a naïve phenotype (e.g., Lef1, Ccr7, Dapl1).
“Differential expression analysis revealed that while genes up-regulated in young cells [ln(fold change) > 0.4, adjusted P < 10−3] were associated with a naïve phenotype [e.g., Lef1, Ccr7, and Dapl1 genes (16)]”
From Aging promotes reorganization of the CD4 T cell landscape toward extreme regulatory and effector phenotypes (2019) · cites this paper as [16]
Single cell RNA sequencing studies show greater cell-to-cell variability in gene expression in human T cells and pancreatic cells, as well as differences in cellular mutation rates with age.
“These observations are supported by a rapidly growing number of single cell RNA sequencing studies showing greater cell-to-cell variability in gene expression in human T cells and pancreatic cells, as well as differences in cellular mutation rates with age.47,48”
From Asynchronous, contagious and digital aging (2021) · cites this paper as [47]
The findings underline the sensitivity and power of single-cell transcriptomics to reveal differences in cell identities and changes within individual cell types under different treatments and conditions, including organismal aging.
“Our findings, in agreement with recent studies, underline the sensitivity and power of single-cell transcriptomics not only to reveal differences in cell identities, but also to reveal changes within individual cell types under different treatments and conditions 23,26,27,70 , including organismal aging 59,60 .”
From Single-cell transcriptomic profiling of the aging mouse brain (2019) · cites this paper as [26]
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- Authors
- Martinez-Jimenez, C. P., Eling, N., Chen, H.-C., Vallejos, C. A., Kolodziejczyk, A. A., Connor, F., Stojic, L., Rayner, T. F., Stubbington, M. J. T., Teichmann, S. A., de la Roche, M., Marioni, J. C., & Odom, D. T.
- Affiliations
- University of Cambridge, Cancer Research UK Cambridge Institute, Robinson Way, Cambridge CB2 0RE, UK.
- European Bioinformatics Institute (GB)
- University of Cambridge (GB)
- Corresponding author
- Eling, Nils
- Marioni, John C.
- Odom, Duncan T.
- ORCID
- Venue
- Science
- Published
- 2017-03-31
- Volume
- 355
- Issue
- 6332
- Pages
- 1433-1436
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC5405862
- OpenAlex
- W2602673171
- Cited by
- 339
- References
- 39
- Citations by year
- 2026: 27 · 2025: 35 · 2024: 36 · 2023: 44 · 2022: 37 · 2021: 41 · 2020: 33 · 2019: 36 · 2018: 32 · 2017: 18
- FWCI
- 13.29
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion)
- Apollo
- Funders
- European Molecular Biology Organization (award286825)
- Wellcome Trust (award286826, WT098051, award301140, WT107609)
- Cancer Research UK (award301139, 22231, award286828, 20412)
- European Molecular Biology Laboratory (award301142)
- European Research Council (award301138, 615584, award286829, 260507)
- Medical Research Council (award286827)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Life Sciences › Immunology and Microbiology › Immunology › T-cell and B-cell Immunology
- Keywords
- cellular aging, T cells, immune activation, transcriptomic heterogeneity, gene expression regulation, cross-species comparison, immunosenescence
- MeSH terms
- Aging, Animals, Immunologic Memory, Lymphocyte Activation, Male, Mice, Inbred C57BL, Receptors, Antigen, T-Cell, Genetic Variation, CD4-Positive T-Lymphocytes, Cellular Senescence, Sequence Analysis, RNA, Mice, Single-Cell Analysis, Transcriptome
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 ›
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