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Zhang Z, Reynolds SR, Stolrow HG, Chen J, Christensen BC, Salas LA. Deciphering the role of immune cell composition in epigenetic age acceleration: Insights from cell-type deconvolution applied to human blood epigenetic clocks. Aging Cell 2024; 23:e14071. [PMID: 38146185 PMCID: PMC10928575 DOI: 10.1111/acel.14071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 12/04/2023] [Accepted: 12/05/2023] [Indexed: 12/27/2023] Open
Abstract
Aging is a significant risk factor for various human disorders, and DNA methylation clocks have emerged as powerful tools for estimating biological age and predicting health-related outcomes. Methylation data from blood DNA has been a focus of more recently developed DNA methylation clocks. However, the impact of immune cell composition on epigenetic age acceleration (EAA) remains unclear as only some clocks incorporate partial cell type composition information when analyzing EAA. We investigated associations of 12 immune cell types measured by cell-type deconvolution with EAA predicted by six widely-used DNA methylation clocks in data from >10,000 blood samples. We observed significant associations of immune cell composition with EAA for all six clocks tested. Across the clocks, nine or more of the 12 cell types tested exhibited significant associations with EAA. Higher memory lymphocyte subtype proportions were associated with increased EAA, and naïve lymphocyte subtypes were associated with decreased EAA. To demonstrate the potential confounding of EAA by immune cell composition, we applied EAA in rheumatoid arthritis. Our research maps immune cell type contributions to EAA in human blood and offers opportunities to adjust for immune cell composition in EAA studies to a significantly more granular level. Understanding associations of EAA with immune profiles has implications for the interpretation of epigenetic age and its relevance in aging and disease research. Our detailed map of immune cell type contributions serves as a resource for studies utilizing epigenetic clocks across diverse research fields, including aging-related diseases, precision medicine, and therapeutic interventions.
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Affiliation(s)
- Ze Zhang
- Department of EpidemiologyGeisel School of Medicine at DartmouthLebanonNew HampshireUSA
- Dartmouth Cancer CenterDartmouth‐Hitchcock Medical CenterLebanonNew HampshireUSA
- Quantitative Biomedical Sciences ProgramGuarini School of Graduate and Advanced StudiesHanoverNew HampshireUSA
| | - Samuel R. Reynolds
- Department of EpidemiologyGeisel School of Medicine at DartmouthLebanonNew HampshireUSA
| | - Hannah G. Stolrow
- Department of EpidemiologyGeisel School of Medicine at DartmouthLebanonNew HampshireUSA
- Dartmouth Cancer CenterDartmouth‐Hitchcock Medical CenterLebanonNew HampshireUSA
| | - Ji‐Qing Chen
- Department of EpidemiologyGeisel School of Medicine at DartmouthLebanonNew HampshireUSA
- Molecular and Cellular Biology ProgramGuarini School of Graduate and Advanced StudiesHanoverNew HampshireUSA
| | - Brock C. Christensen
- Department of EpidemiologyGeisel School of Medicine at DartmouthLebanonNew HampshireUSA
- Dartmouth Cancer CenterDartmouth‐Hitchcock Medical CenterLebanonNew HampshireUSA
- Quantitative Biomedical Sciences ProgramGuarini School of Graduate and Advanced StudiesHanoverNew HampshireUSA
- Molecular and Cellular Biology ProgramGuarini School of Graduate and Advanced StudiesHanoverNew HampshireUSA
| | - Lucas A. Salas
- Department of EpidemiologyGeisel School of Medicine at DartmouthLebanonNew HampshireUSA
- Dartmouth Cancer CenterDartmouth‐Hitchcock Medical CenterLebanonNew HampshireUSA
- Quantitative Biomedical Sciences ProgramGuarini School of Graduate and Advanced StudiesHanoverNew HampshireUSA
- Molecular and Cellular Biology ProgramGuarini School of Graduate and Advanced StudiesHanoverNew HampshireUSA
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Hannon E, Dempster EL, Davies JP, Chioza B, Blake GET, Burrage J, Policicchio S, Franklin A, Walker EM, Bamford RA, Schalkwyk LC, Mill J. Quantifying the proportion of different cell types in the human cortex using DNA methylation profiles. BMC Biol 2024; 22:17. [PMID: 38273288 PMCID: PMC10809680 DOI: 10.1186/s12915-024-01827-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 01/11/2024] [Indexed: 01/27/2024] Open
Abstract
BACKGROUND Due to interindividual variation in the cellular composition of the human cortex, it is essential that covariates that capture these differences are included in epigenome-wide association studies using bulk tissue. As experimentally derived cell counts are often unavailable, computational solutions have been adopted to estimate the proportion of different cell types using DNA methylation data. Here, we validate and profile the use of an expanded reference DNA methylation dataset incorporating two neuronal and three glial cell subtypes for quantifying the cellular composition of the human cortex. RESULTS We tested eight reference panels containing different combinations of neuronal- and glial cell types and characterised their performance in deconvoluting cell proportions from computationally reconstructed or empirically derived human cortex DNA methylation data. Our analyses demonstrate that while these novel brain deconvolution models produce accurate estimates of cellular proportions from profiles generated on postnatal human cortex samples, they are not appropriate for the use in prenatal cortex or cerebellum tissue samples. Applying our models to an extensive collection of empirical datasets, we show that glial cells are twice as abundant as neuronal cells in the human cortex and identify significant associations between increased Alzheimer's disease neuropathology and the proportion of specific cell types including a decrease in NeuNNeg/SOX10Neg nuclei and an increase of NeuNNeg/SOX10Pos nuclei. CONCLUSIONS Our novel deconvolution models produce accurate estimates for cell proportions in the human cortex. These models are available as a resource to the community enabling the control of cellular heterogeneity in epigenetic studies of brain disorders performed on bulk cortex tissue.
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Affiliation(s)
- Eilis Hannon
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Barrack Road, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, Devon, EX2 5DW, UK.
| | - Emma L Dempster
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Barrack Road, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, Devon, EX2 5DW, UK
| | - Jonathan P Davies
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Barrack Road, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, Devon, EX2 5DW, UK
| | - Barry Chioza
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Barrack Road, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, Devon, EX2 5DW, UK
| | - Georgina E T Blake
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Barrack Road, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, Devon, EX2 5DW, UK
| | - Joe Burrage
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Barrack Road, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, Devon, EX2 5DW, UK
| | - Stefania Policicchio
- Italian Institute of Technology, Center for Human Technologies (CHT), Genova, Italy
| | - Alice Franklin
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Barrack Road, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, Devon, EX2 5DW, UK
| | - Emma M Walker
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Barrack Road, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, Devon, EX2 5DW, UK
| | - Rosemary A Bamford
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Barrack Road, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, Devon, EX2 5DW, UK
| | - Leonard C Schalkwyk
- School of Life Sciences, University of Essex, Wivenhoe Park, Colchester, Essex, CO4 3SQ, UK
| | - Jonathan Mill
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Barrack Road, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, Devon, EX2 5DW, UK
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Cai M, Zhou J, McKennan C, Wang J. scMD facilitates cell type deconvolution using single-cell DNA methylation references. Commun Biol 2024; 7:1. [PMID: 38168620 PMCID: PMC10762261 DOI: 10.1038/s42003-023-05690-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 12/08/2023] [Indexed: 01/05/2024] Open
Abstract
The proliferation of single-cell RNA-sequencing data has led to the widespread use of cellular deconvolution, aiding the extraction of cell-type-specific information from extensive bulk data. However, those advances have been mostly limited to transcriptomic data. With recent developments in single-cell DNA methylation (scDNAm), there are emerging opportunities for deconvolving bulk DNAm data, particularly for solid tissues like brain that lack cell-type references. Due to technical limitations, current scDNAm sequences represent a small proportion of the whole genome for each single cell, and those detected regions differ across cells. This makes scDNAm data ultra-high dimensional and ultra-sparse. To deal with these challenges, we introduce scMD (single cell Methylation Deconvolution), a cellular deconvolution framework to reliably estimate cell type fractions from tissue-level DNAm data. To analyze large-scale complex scDNAm data, scMD employs a statistical approach to aggregate scDNAm data at the cell cluster level, identify cell-type marker DNAm sites, and create precise cell-type signature matrixes that surpass state-of-the-art sorted-cell or RNA-derived references. Through thorough benchmarking in several datasets, we demonstrate scMD's superior performance in estimating cellular fractions from bulk DNAm data. With scMD-estimated cellular fractions, we identify cell type fractions and cell type-specific differentially methylated cytosines associated with Alzheimer's disease.
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Affiliation(s)
- Manqi Cai
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jingtian Zhou
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California, San Diego, CA, USA
| | - Chris McKennan
- Department of Statistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jiebiao Wang
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA.
- Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA, USA.
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