The DOI matches its Crossref record; 65 of the 66 references it cites were found in a registry or an index.
Single-Cell Chromatin Modification Profiling Reveals Increased Epigenetic Variations with Aging
Open accessAbstract
Post-translational modifications of histone proteins and exchanges of histone variants of chromatin are central to the regulation of nearly all DNA-templated biological processes. However, the degree and variability of chromatin modifications in specific human immune cells remain largely unknown. Here, we employ a highly multiplexed mass cytometry analysis to profile the global levels of a broad array of chromatin modifications in primary human immune cells at the single-cell level. Our data reveal markedly different cell-type- and hematopoietic-lineage-specific chromatin modification patterns. Differential analysis between younger and older adults shows that aging is associated with increased heterogeneity between individuals and elevated cell-to-cell variability in chromatin modifications. Analysis of a twin cohort unveils heritability of chromatin modifications and demonstrates that aging-related chromatin alterations are predominantly driven by non-heritable influences. Together, we present a powerful platform for chromatin and immunology research. Our discoveries highlight the profound impacts of aging on chromatin modifications.
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?
The cited source doesn’t support the claim as written.
In the citing paper
“Reduced levels of H3K122ac extend the lifespan of budding yeast (Sen et al., 2015) and levels of H3K27ac and H3K56ac decrease in many human immune cell types (Cheung et al., 2018; Dang et al., 2009).”
From Loss of epigenetic information as a cause of mammalian aging (2023) · cites this paper as Cheung et al., 2018
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredIn the citing paper
“In line with these findings, increased variation of chromatin modifications was recently observed in immune cells from elderly individuals (18).”
From Aging promotes reorganization of the CD4 T cell landscape toward extreme regulatory and effector phenotypes (2019) · cites this paper as [18]
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 · cited 1×
“We identified differentially expressed genes using the DESeq2 algorithm on the read count matrix (Love et al., 2014).”
Elements of Information Theory
Cover, T. M., & Thomas, J. A. (2001) · Wiley eBooks · doi:10.1002/0471200611 · Cited by 37,139
Regularization Paths for Generalized Linear Models via Coordinate Descent
Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927 · cited 1×
“Next, we used L-1 regularized logistic regression models (Friedman et al., 2010) to classify immune cells based on chromatin marks alone utilizing single-cell data from bio rep 1 as the training dataset.”
Chromatin modifications and their function
Kouzarides, T. (2007) · Cell · doi:10.1016/j.cell.2007.02.005 · Cited by 10,883
Adjusting batch effects in microarray expression data using empirical Bayes methods
Johnson, W. E., Li, C., & Rabinovic, A. (2007) · Biostatistics · doi:10.1093/biostatistics/kxj037 · Cited by 8,658 · cited 1×
“We combined the means from both panels (separately for standard and T cell focused panels) and integrated multiple batches by applying ComBat normalization (Johnson et al., 2007).”
High-Resolution Profiling of Histone Methylations in the Human Genome
Barski, A., Cuddapah, S., Cui, K., Roh, T.-Y., Schones, D. E., Wang, Z., Wei, G., Chepelev, I., & Zhao, K. (2007) · Cell · doi:10.1016/j.cell.2007.05.009 · Cited by 6,845 · cited 3×
“Together with the discoveries of higher euchromatin-associated H3K27me1 and H3K36me1 (Barski et al., 2007; Wagner and Carpenter, 2012) and lower heterochromatin-associated H3K27me3, H4K20me3, and H3K9me2 (Trojer and Reinberg, 2007) in memory T cells, our results suggest that chromatin in memory T…”
DNA double-stranded breaks induce histone H2AX phosphorylation on serine 139
Rogakou, E. P., Pilch, D. R., Orr, A. H., Ivanova, V. S., & Bonner, W. M. (1998) · Journal of Biological Chemistry · doi:10.1074/jbc.273.10.5858 · Cited by 5,438 · cited 2×
“Chromatin marks known to be present at low levels in unperturbed cells, such as DNA double-strand breakinduced g-H2AX (Rogakou et al., 1998), and citrullination marks (Cuthbert et al.”
The Polycomb complex PRC2 and its mark in life
Margueron, R., & Reinberg, D. (2011) · Nature · doi:10.1038/nature09784 · Cited by 3,347 · cited 1×
“Pivotal roles of PRCs in transcription silencing and chromatin compaction have been extensively described (Margueron and Reinberg, 2011).”
Nature, Nurture, or Chance: Stochastic Gene Expression and Its Consequences
Raj, A., & van Oudenaarden, A. (2008) · Cell · doi:10.1016/j.cell.2008.09.050 · Cited by 2,593
Single-cell chromatin accessibility reveals principles of regulatory variation
Buenrostro, J. D., Wu, B., Litzenburger, U. M., Ruff, D., Gonzales, M. L., Snyder, M. P., Chang, H. Y., & Greenleaf, W. J. (2015) · Nature · doi:10.1038/nature14590 · Cited by 2,308 · cited 2×
“While recent technologic breakthroughs have allowed investigations of chromatin dynamics using a small number of cells or even single cells (Buenrostro et al., 2015; Corces et al., 2016; Gomez et al., 2013; Rotem et al., 2015; Schwartzman and Tanay, 2015), a high-throughput method to measure the overall cellular level…”
Cited by
Sorted by citations
Systems biological assessment of immunity to mild versus severe COVID-19 infection in humans
Arunachalam, P. S., Wimmers, F., Mok, C. K. P., Perera, R. A. P. M., Scott, M., Hagan, T., Sigal, N., Feng, Y., Bristow, L., Tak-Yin Tsang, O., et al. (2020) · Science · doi:10.1126/science.abc6261 · Cited by 1,186
Self-citationDNA methylation aging clocks: challenges and recommendations
Bell, C. G., Lowe, R., Adams, P. D., Baccarelli, A. A., Beck, S., Bell, J. T., Christensen, B. C., Gladyshev, V. N., Heijmans, B. T., Horvath, S., et al. (2019) · Genome Biology · doi:10.1186/s13059-019-1824-y · Cited by 1,025
“Similarly, chromatin modification analyses in blood, while clearly indicating immune cell types, also identify an increase in cell-to-cell variability with age, or “epigenomic noise,” with particular increases in both H3K4me3 and H3K27me3 [177].”
Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)
Cossarizza, A., Chang, H., Radbruch, A., Acs, A., Adam, D., Adam‐Klages, S., Agace, W. W., Aghaeepour, N., Akdiş, M., Allez, M., et al. (2019) · European Journal of Immunology · doi:10.1002/eji.201970107 · Cited by 1,007
COVID-19 and the human innate immune system
Schultze, J. L., & Aschenbrenner, A. C. (2021) · Cell · doi:10.1016/j.cell.2021.02.029 · Cited by 714
“…suggested including DNA damage, increased epigenetic variations, release of mitochondrial DNA, age-related leakage of intestinal microbiota, persistent viral infections (e.g., with cytomegalovirus), or cellular immunosenescence (Cheung et al., 2018; Cunha et al., 2020; Franceschi et al., 2017).”
BackgroundEpigenetic regulation of aging: implications for interventions of aging and diseases
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
Metadata
- Authors
- Cheung, P., Vallania, F., Warsinske, H. C., Donato, M., Schaffert, S., Chang, S. E., Dvorak, M., Dekker, C. L., Davis, M. M., Utz, P. J., Khatri, P., & Kuo, A. J.
- Affiliations
- Stanford Medicine (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Utz, Paul J.
- Khatri, Purvesh
- Kuo, Alex J.
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2018-05
- Volume
- 173
- Issue
- 6
- Pages
- 1385-1397.e14
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC5984186
- OpenAlex
- W2801503232
- Cited by
- 323
- References
- 66
- Citations by year
- 2026: 21 · 2025: 27 · 2024: 31 · 2023: 40 · 2022: 40 · 2021: 47 · 2020: 56 · 2019: 35 · 2018: 25
- FWCI
- 14.85
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 395 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Cooperative Center for Human Immunology (U19AI057229)
- NIH (S10RR025518-01, S10RR027431-01, S10OD016318-01, 5U19AI109662-05, 5R01AI125197-02, 5U19AI057229-15, U19AI090019, 2T32AR050942-06A1, 5K12HL120001-02, 5T32AI07290-31, 5T32AI007290-29)
- Autoimmunity Center of Excellence (5U19AI110491-04, 5UM1A110498-04)
- NIAID/NIH
- Bill & Melinda Gates Foundation
- Donald E. and Delia B. Baxter Foundation
- Elizabeth F. Adler
- Henry Gustav Floren Trust
- NIH Clinical and Translational Science Award (UL1 RR025744)
- Novo Nordisk
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- chromatin modification, histone modifications, histone variants, aging, immune cells, single-cell profiling, mass cytometry, hematopoietic lineage, cellular heterogeneity, heritability, twin cohort, epigenetic variation
- MeSH terms
- Adolescent, Adult, Aged, Aging, Cell Separation, Chromatin, Diseases in Twins, Female, Flow Cytometry, Histones, Humans, Immune System, Leukocytes, Mononuclear, Male, Middle Aged, Monocytes, Protein Processing, Post-Translational, Registries, Immunophenotyping, Cell Lineage, Principal Component Analysis, Epigenesis, Genetic, Young Adult
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
