The DOI matches its Crossref record; 76 of the 76 references it cites were found in a registry or an index.
Heterogeneity in old fibroblasts is linked to variability in reprogramming and wound healing
Open accessAbstract
Age-associated chronic inflammation (inflammageing) is a central hallmark of ageing 1, but its influence on specific cells remains largely unknown. Fibroblasts are present in most tissues and contribute to wound healing 2,3. They are also the most widely used cell type for reprogramming to induced pluripotent stem (iPS) cells, a process that has implications for regenerative medicine and rejuvenation strategies 4. Here we show that fibroblast cultures from old mice secrete inflammatory cytokines and exhibit increased variability in the efficiency of iPS cell reprogramming between mice. Variability between individuals is emerging as a feature of old age 5-8, but the underlying mechanisms remain unknown. To identify drivers of this variability, we performed multi-omics profiling of fibroblast cultures from young and old mice that have different reprogramming efficiencies. This approach revealed that fibroblast cultures from old mice contain 'activated fibroblasts' that secrete inflammatory cytokines, and that the proportion of activated fibroblasts in a culture correlates with the reprogramming efficiency of that culture. Experiments in which conditioned medium was swapped between cultures showed that extrinsic factors secreted by activated fibroblasts underlie part of the variability between mice in reprogramming efficiency, and we have identified inflammatory cytokines, including TNF, as key contributors. Notably, old mice also exhibited variability in wound healing rate in vivo. Single-cell RNA-sequencing analysis identified distinct subpopulations of fibroblasts with different cytokine expression and signalling in the wounds of old mice with slow versus fast healing rates. Hence, a shift in fibroblast composition, and the ratio of inflammatory cytokines that they secrete, may drive the variability between mice in reprogramming in vitro and influence wound healing rate in vivo. This variability may reflect distinct stochastic ageing trajectories between individuals, and could help in developing personalized strategies to improve iPS cell generation and wound healing in elderly individuals.
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“Lowering extracellular pH combined with an increase in lactic acid promotes myofibroblast differentiation via activation of TGF-β1 and lactate itself may serve as an additional energy source promoting a synthetic phenotype in mesenchymal cells44–46.”
From Fibrosis: from mechanisms to medicines (2020) · cites this paper as [46]
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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
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r25 · Cited by 21,849
Model-based Analysis of ChIP-Seq (MACS)
Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., Myers, R. M., Brown, M., Li, W., et al. (2008) · Genome Biology · doi:10.1186/gb-2008-9-9-r137 · Cited by 18,386
Comprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453
Heinz, S., Benner, C., Spann, N., Bertolino, E., Lin, Y. C., Laslo, P., Cheng, J. X., Murre, C., Singh, H., & Glass, C. K. (2010) · Molecular Cell · doi:10.1016/j.molcel.2010.05.004 · Cited by 13,382
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
Gurtner, G. C., Werner, S., Barrandon, Y., & Longaker, M. T. (2008) · Nature · doi:10.1038/nature07039 · Cited by 6,542
Self-citationA bivalent chromatin structure marks key developmental genes in embryonic stem cells
Bernstein, B. E., Mikkelsen, T. S., Xie, X., Kamal, M., Huebert, D. J., Cuff, J., Fry, B., Meissner, A., Wernig, M., Plath, K., et al. (2006) · Cell · doi:10.1016/j.cell.2006.02.041 · Cited by 5,361
Self-citationSmith, C. A., Want, E. J., O’Maille, G., Abagyan, R. A., & Siuzdak, G. E. (2006) · Analytical Chemistry · doi:10.1021/ac051437y · Cited by 5,296
Chronic Inflammation (Inflammaging) and Its Potential Contribution to Age-Associated Diseases
Franceschi, C., & Campisi, J. (2014) · The Journals of Gerontology Series A: Biological Sciences and Medical Sciences · doi:10.1093/gerona/glu057 · Cited by 3,891
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Metadata
- Authors
- Mahmoudi, S., Mancini, E., Xu, L., Moore, A., Jahanbani, F., Hebestreit, K., Srinivasan, R., Li, X., Devarajan, K., Prélot, L., Ang, C. E., Shibuya, Y., Benayoun, B. A., Chang, A. L. S., Wernig, M., Wysocka, J., Longaker, M. T., Snyder, M. P., & Brunet, A.
- Affiliations
- Stanford University (US)
- Stanford Medicine (US)
- Institute for Stem Cell Biology and Regenerative Medicine (US)
- University of Southern California (US)
- Corresponding author
- Brunet, Anne
- ORCID
- Venue
- Nature
- Published
- 2019-10-23
- Volume
- 574
- Issue
- 7779
- Pages
- 553-558
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC7253295
- OpenAlex
- W2981890739
- Cited by
- 285
- References
- 69
- Citations by year
- 2026: 15 · 2025: 48 · 2024: 43 · 2023: 56 · 2022: 36 · 2021: 53 · 2020: 34
- FWCI
- 21.47
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Institutes of Health (1s10od025212-01)
- National Institute on Aging (K99AG049934, R00AG049934, P01AG036695)
- National Institute of General Medical Sciences (R01GM116892, P01GM099130)
- National Institute of Diabetes and Digestive and Kidney Diseases (P30DK116074)
- National Center for Research Resources (S10RR025518)
- Topics
- Health Sciences › Medicine › Surgery › Wound Healing and Treatments
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Pluripotent Stem Cells Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Keywords
- fibroblast heterogeneity, inflammaging, induced pluripotent stem cells, wound healing, aged mice, inflammatory cytokines, TNF-α, fibroblast activation, interindividual variability, fibroblast subpopulations, conditioned medium
- MeSH terms
- Aging, Animals, Cell Line, Fibroblasts, Humans, Jews, Male, Mice, Inbred C57BL, Stochastic Processes, Time Factors, Wound Healing, Signal Transduction, Cytokines, Cellular Senescence, Culture Media, Conditioned, Sequence Analysis, RNA, Inflammation Mediators, Mice, Induced Pluripotent Stem Cells, Single-Cell Analysis, Cellular Reprogramming
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