Single-cell RNA-seq reveals changes in cell cycle and differentiation programs upon aging of hematopoietic stem cells
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Both intrinsic cell state changes and variations in the composition of stem cell populations have been implicated as contributors to aging. We used single-cell RNA-seq to dissect variability in hematopoietic stem cell (HSC) and hematopoietic progenitor cell populations from young and old mice from two strains. We found that cell cycle dominates the variability within each population and that there is a lower frequency of cells in the G1 phase among old compared with young long-term HSCs, suggesting that they traverse through G1 faster. Moreover, transcriptional changes in HSCs during aging are inversely related to those upon HSC differentiation, such that old short-term (ST) HSCs resemble young long-term (LT-HSCs), suggesting that they exist in a less differentiated state. Our results indicate both compositional changes and intrinsic, population-wide changes with age and are consistent with a model where a relationship between cell cycle progression and self-renewal versus differentiation of HSCs is affected by aging and may contribute to the functional decline of old HSCs.
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Our results are conceptually consistent with previous reports of large transcriptional changes with age in some cell identities and more subtle changes in others.
“Our results are conceptually consistent with previous reports of large transcriptional changes with age in some cell identities and more subtle changes in others (Kowalczyk et al. 2015; Keyes et al. 2016; Davie et al. 2018; Keyes and Fuchs 2018).”
From Murine single-cell RNA-seq reveals cell-identity- and tissue-specific trajectories of aging (2019) · cites this paper as Kowalczyk et al. 2015
Single-cell analysis provides a means of systematically detecting cellular subtypes that cannot be defined by a handful of markers or for which markers are not yet known.
“Whereas cell types were traditionally defined based on criteria such as morphology, physiology, and marker protein expression, single-cell analysis provides a means of systematically detecting cellular subtypes that cannot be defined by a handful of markers, or for which markers are not yet known102.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [102]
Age-associated gene expression changes have been identified at single-cell level in hematopoietic stem and progenitor cells, suggesting that for some tissues, cellular composition changes cannot fully explain age-associated epigenomic dysregulation.
“age-associated gene expression changes have recently been identified at the single-cell level in hematopoietic stem and progenitor cells (Kowalczyk et al., 2015), suggesting that at least for some tissues, changes in the cellular composition of a tissue with age cannot fully explain age-associated epigenomic dysregulation.”
From The Aging Epigenome (2016) · cites this paper as Kowalczyk et al., 2015
Single cell analyses in the hematopoietic system identified distinct aged and young transcriptional phenotypes and altered cell cycle kinetics.
“Single cell analyses in the hematopoietic system identified distinct aged and young transcriptional phenotypes (as in the different paths model), and altered cell cycle kinetics (as in the different rates model) (Kowalczyk et al., 2015), suggesting that both models are plausible in the context of myogenic activation.”
From Aging induces aberrant state transition kinetics in murine muscle stem cells (2020) · cites this paper as Kowalczyk et al., 2015
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Homeostatic mini-intestines through scaffold-guided organoid morphogenesis
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Single-cell transcriptomic profiling of the aging mouse brain
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Self-citationRevealing the vectors of cellular identity with single-cell genomics
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Self-citationAn atlas of the aging lung mapped by single cell transcriptomics and deep tissue proteomics
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Missing data and technical variability in single-cell RNA-sequencing experiments
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Molecular logic of cellular diversification in the mouse cerebral cortex
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Self-citationAging of hematopoietic stem cells
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Aging increases cell-to-cell transcriptional variability upon immune stimulation
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Somatic mutations and cell identity linked by Genotyping of Transcriptomes
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Overcoming confounding plate effects in differential expression analyses of single-cell RNA-seq data
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Self-citationMurine single-cell RNA-seq reveals cell-identity- and tissue-specific trajectories of aging
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“Cycling cells are similarly rare in young and old animals Previous reports highlight cell cycle activity changes with age in multiple cell populations (Chakkalakal et al. 2012; Kowalczyk et al. 2015; Nalapareddy et al. 2017).”
BackgroundMethodResultInfluentialThe chromatin accessibility signature of human immune aging stems from CD8+ T cells
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Heterogeneous Responses of Hematopoietic Stem Cells to Inflammatory Stimuli Are Altered with Age
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Self-citationEndocrine-Exocrine Signaling Drives Obesity-Associated Pancreatic Ductal Adenocarcinoma
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Single-cell RNA sequencing of human liver reveals hepatic stellate cell heterogeneity
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Multiomic spatial landscape of innate immune cells at human central nervous system borders
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Transcriptional Signatures of Aging
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Inferring relevant cell types for complex traits by using single-cell gene expression
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A comprehensive transcriptome signature of murine hematopoietic stem cell aging
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Impact of similarity metrics on single-cell RNA-seq data clustering
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Aging induces aberrant state transition kinetics in murine muscle stem cells
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Comparison of methods to detect differentially expressed genes between single-cell populations
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Missing Data and Technical Variability in Single-Cell RNA-Sequencing Experiments
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The Source and Dynamics of Adult Hematopoiesis: Insights from Lineage Tracing
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Induction of a colitogenic phenotype in Th1-like cells depends on interleukin-23 receptor signaling
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Molecular Logic of Cellular Diversification in the Mammalian Cerebral Cortex
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Self-citationComputational challenges of cell cycle analysis using single cell transcriptomics
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- Authors
- Kowalczyk, M. S., Tirosh, I., Heckl, D., Rao, T. N., Dixit, A., Haas, B. J., Schneider, R. K., Wagers, A. J., Ebert, B. L., & Regev, A.
- Affiliations
- Broad Institute (US)
- Brigham and Women's Hospital (US)
- Joslin Diabetes Center (US)
- ORCID
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2015-10-01
- Volume
- 25
- Issue
- 12
- Pages
- 1860-1872
- ISSN
- 1088-9051 (ISSN-L)
- 1549-5469
- PMC
- PMC4665007
- OpenAlex
- W2184331293
- Cited by
- 778
- References
- 75
- Citations by year
- 2026: 48 · 2025: 86 · 2024: 110 · 2023: 81 · 2022: 93 · 2021: 78 · 2020: 93 · 2019: 69 · 2018: 47 · 2017: 45 · 2016: 25 · 2015: 1 · 2013: 1
- Type
- posted content
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Genome Research (publishedVersion)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Funders
- European Hematology Association
- EMBO Long Term Fellowship
- HFSP Long Term Fellowship
- Rothschild Fellowship
- German Cancer Foundation
- Leukemia and Lymphoma Society
- National Institutes of Health (P01 CA066996)
- HHMI
- NIH CEGS (1P50HG006193-01)
- Klarman Family Foundation
- Howard Hughes Medical Institute
- Topics
- Health Sciences › Medicine › Hematology › Hematopoietic Stem Cell Transplantation
- 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
- Keywords
- hematopoietic stem cells, aging, cell cycle, G1 phase, hematopoietic progenitor cells, HSC differentiation, self-renewal, long-term HSCs, gene expression changes, stem cell heterogeneity, mouse strains
- MeSH terms
- Age Factors, Animals, Cell Cycle, Cell Differentiation, Female, Gene Expression Regulation, Hematopoietic Stem Cells, Models, Biological, Organ Specificity, Phenotype, Transcription, Genetic, Biomarkers, Cluster Analysis, Cellular Senescence, Sequence Analysis, RNA, Computational Biology, Gene Expression Profiling, Multipotent Stem Cells, Mice, Single-Cell Analysis, High-Throughput Nucleotide Sequencing, Transcriptome
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