Quantifying the proportion of different cell types in the human cortex using DNA methylation profiles
dc.contributor.author | Hannon, E | |
dc.contributor.author | Dempster, EL | |
dc.contributor.author | Davies, JP | |
dc.contributor.author | Chioza, B | |
dc.contributor.author | Blake, GET | |
dc.contributor.author | Burrage, J | |
dc.contributor.author | Policicchio, S | |
dc.contributor.author | Franklin, A | |
dc.contributor.author | Walker, EM | |
dc.contributor.author | Bamford, RA | |
dc.contributor.author | Schalkwyk, LC | |
dc.contributor.author | Mill, J | |
dc.date.accessioned | 2024-01-29T12:02:16Z | |
dc.date.issued | 2024-01-25 | |
dc.date.updated | 2024-01-29T09:10:01Z | |
dc.description.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. | en_GB |
dc.description.sponsorship | Engineering and Physical Sciences Research Council | en_GB |
dc.description.sponsorship | Medical Research Council | en_GB |
dc.description.sponsorship | Alzheimer's Research UK | en_GB |
dc.description.sponsorship | Medical Research Council | en_GB |
dc.identifier.citation | Vol. 22, No. 1, article 17 | en_GB |
dc.identifier.doi | https://doi.org/10.1186/s12915-024-01827-y | |
dc.identifier.grantnumber | EP/V052527/1 | en_GB |
dc.identifier.grantnumber | K013807 | en_GB |
dc.identifier.grantnumber | ARUK-PPG2018A-010 | en_GB |
dc.identifier.grantnumber | W004984 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/135189 | |
dc.identifier | ORCID: 0000-0001-6840-072X (Hannon, Eilis) | |
dc.identifier | ResearcherID: T-1349-2019 (Hannon, Eilis) | |
dc.language.iso | en | en_GB |
dc.publisher | BMC | en_GB |
dc.relation.url | https://github.com/ejh243/BrainFANS/tree/master/array/DNAm/preprocessing | en_GB |
dc.relation.url | https://doi.org/https://doi.org/10.5281/zenodo.10402167 | en_GB |
dc.relation.url | https://github.com/ejh243/BrainFANS/tree/master/array/DNAm/analysis/neuralCellComposition | en_GB |
dc.relation.url | https://github.com/ds420/CETYGO | en_GB |
dc.relation.url | https://doi.org/10.5281/zenodo.10418430 | en_GB |
dc.rights | © The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. | en_GB |
dc.subject | DNA methylation | en_GB |
dc.subject | Brain | en_GB |
dc.subject | Neurons | en_GB |
dc.subject | Glia | en_GB |
dc.subject | Cellular heterogeneity | en_GB |
dc.subject | Alzheimer’s disease | en_GB |
dc.title | Quantifying the proportion of different cell types in the human cortex using DNA methylation profiles | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2024-01-29T12:02:16Z | |
dc.identifier.issn | 1478-5854 | |
exeter.article-number | 17 | |
dc.description | This is the final version. Available from BMC via the DOI in this record. | en_GB |
dc.description | Availability of data and materials: All data generated or analysed during this study are included in this published article, its supplementary information files and publicly available repositories. Data generated for this project are available at NCBI Gene Express Omnibus (GEO) under accession number GSE234520 [64]. We also reanalysed data previously made available via GEO (via accession numbers GSE74193 [65], GSE59685 [66], GSE80970 [67], GSE88890 [68], GSE43414 [69]) and the synapse platform (syn7072866 [70], syn8263588 [71]). Code for the analyses presented here can be found on GitHub and Zenodo https://github.com/ejh243/BrainFANS/tree/master/array/DNAm/preprocessing (https://doi.org/https://doi.org/10.5281/zenodo.10402167). Specifically, code for the quality control of the DNAm data can be found at https://github.com/ejh243/BrainFANS/tree/master/array/DNAm/preprocessing and the code for the statistical analyses can be found at https://github.com/ejh243/BrainFANS/tree/master/array/DNAm/analysis/neuralCellComposition. Our new trained deconvolution models for brain are made available to the wider research community via our R package CETYGO available on GitHub (https://github.com/ds420/CETYGO; https://doi.org/10.5281/zenodo.10418430). | en_GB |
dc.identifier.eissn | 1741-7007 | |
dc.identifier.journal | BMC Biology | en_GB |
dc.relation.ispartof | BMC Biology, 22(1) | |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2024-01-11 | |
dcterms.dateSubmitted | 2023-07-05 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2024-01-25 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2024-01-29T09:10:03Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2024-01-29T12:02:21Z | |
refterms.panel | A | en_GB |
refterms.dateFirstOnline | 2024-01-25 |
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licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.