Biology subjects

Hannon, E.

Publications and source records attributed to Hannon, E..

At least 19 recordsLinked to original sources

bioRxiv2026

Cell-type-resolved DNA methylation profiling in the cortex reveals female-specific X chromosome differences in autism

Sex differences are a prominent feature of many neurodevelopmental conditions, particularly autism for which approximately three males are diagnosed for every female. Although epigenetic dysregulation has been implicated in autism, the extent to which autism-associated epigenetic differences vary by sex and neural cell type remains poorly understood. Using fluorescence-activated nuclei sorting (FANS), we isolated neuron-enriched (NeuN+), oligodendrocyte-enriched (SOX10+), microglia-enriched (IRF8+) and astrocyte-enriched (NeuN-/SOX10-/IRF8-) nuclei from post-mortem prefrontal cortex tissue from 24 autistic (70.8% male) and 23 non-autistic (69.6% male) donors, and performed cell-type-resolved epigenome-wide association studies (EWASs) of DNA methylation. We identified cell-type-specific autism-associated differentially methylated positions (DMPs), with microglia harbouring the largest number of associations. Modelling the interaction between sex and diagnosis revealed a striking female-specific signature concentrated on the X chromosome in microglia. At the majority of these sex-by-autism DMPs, autistic females showed lower DNA methylation compared to control females consistent with an altered X chromosome inactivation (XCI) profile. Across the X chromosome, autism-associated DNA methylation differences were significantly larger in females than males and were greater at sites annotated to genes normally subject to XCI than at genes that escape XCI. Together, these data further support the importance of microglia in autism and suggest that altered X chromosome dosage regulation may contribute to sex-dependent molecular mechanisms in autism.

genomics↗

bioRxiv2026

Schizophrenia-associated DNA methylation differences in the cortex are neuron-specific

Schizophrenia is a complex neuropsychiatric disorder in which genetic risk is thought to converge on cell type-specific regulatory mechanisms in the brain. We performed a cell type-resolved epigenome-wide association study (EWAS) of schizophrenia using fluorescence-activated nuclei sorting (FANS) to isolate neuron-enriched (NeuN+), oligodendrocyte-enriched (SOX10+) and other glial-enriched (NeuN-/SOX10-) nuclei populations alongside total prefrontal cortex nuclei fractions from 216 donors (104 schizophrenia cases and 112 controls). We identified 16 differentially methylated positions (DMPs) in neuron-enriched nuclei at experiment-wide significance and more than 400 additional neuronal DMPs at a discovery threshold. In contrast, no significant associations were identified in oligodendrocyte-enriched, glial-enriched or total nuclei fractions, demonstrating that schizophrenia-associated cortical methylomic variation is highly neuron-specific and largely masked in bulk tissue analyses. Neuronal DMPs exhibited a significant bias towards hypomethylation in schizophrenia and were enriched at loci implicated by genetic studies, including CACNA1C, CACNA1G and TRIO. Pathway analyses implicated genes involved in neurodevelopment, cell adhesion, synapse organisation, neurotransmission and synaptic plasticity. Schizophrenia-associated DNA methylation signatures identified in prefrontal cortex neurons showed correlated effects in neuronal nuclei isolated from the hippocampus and striatum, indicating partial conservation of disease-associated epigenetic alterations across brain regions. Together, these findings provide strong evidence for widespread neuron-specific epigenetic dysregulation in schizophrenia and highlight the importance of cell type-resolved approaches for elucidating the molecular mechanisms underlying psychiatric disease.

genomics↗

bioRxiv2025

Optimised fluorescence-activated nuclei sorting for epigenomic analysis of cortical cell types

Increased understanding of the functional complexity of the genome has led to growing recognition of the role of non-sequence-based regulatory variation in disorders of the human central nervous system. Most genomic analyses of the brain are limited by the use of bulk tissue, which comprises a heterogeneous mix of different neural cell types with distinct epigenetic profiles, thereby limiting the ability to attribute regulatory changes to specific cell populations. Given the limited availability of human post-mortem tissue resources and the importance of integrating multi-omic data from the same samples, there is a critical need for methods that enable parallel, cell-type-resolved genomic profiling. We present optimised protocols using fluorescence-activated nuclei sorting (FANS) to isolate nuclei from different human and mouse brain cell types for downstream multi-omic analysis. Our approach enables the robust purification of neuronal, oligodendrocyte, microglial and other glial-origin nuclei from both adult and fetal brain tissue. We demonstrate that FANS-isolated nuclei are compatible with a wide range of genomic assays, including profiling of DNA modifications, histone modifications, chromatin accessibility, and gene expression. This protocol maximises the utility of limited post-mortem tissue resources and provides a unified workflow for comprehensive, cell-type-specific interrogation of molecular mechanisms involved in the brain.

genomics↗

bioRxiv2025

Methylomic signatures of tau and amyloid-beta in transgenic mouse models of Alzheimer's disease neuropathology

Alzheimers disease (AD) is characterized by progressive neurodegeneration driven by tau and amyloid-{beta} (A{beta}) pathology. Emerging evidence implicates a role for epigenetic modifications, particularly altered DNA methylation (DNAm), in AD pathogenesis. However, few studies have comprehensively investigated DNAm in experimental models. Here, we profile DNAm dynamics in two widely used transgenic mouse models of tau (rTg4510) and A{beta} (J20) neuropathology, focusing on variation in the entorhinal cortex and hippocampus. Using reduced representation bisulfite sequencing (RRBS) and methylation arrays across multiple disease stages, we identified widespread DNAm alterations associated with genotype and neuropathological burden. In rTg4510 mice, tau accumulation was linked to extensive DNAm remodeling at genes involved in neuronal plasticity and apoptosis (including Dcaf5, Creb3l4, and As3mt). J20 mice exhibited more modest changes annotated to immune-related genes, notably at Grk2, Ncam2, and Prmt8. Of note, tau-associated DNAm changes were more consistent across brain areas than those associated with A{beta} pathology. Comparison with DNAm data from human studies revealed that a subset of DNAm sites mirrored those observed in the human AD cortex, including hypermethylation at Ank1 and Prdm16. These findings provide evidence for pathology-associated epigenetic alterations in AD, highlight shared and distinct DNAm signatures of tau and A{beta}, and offer insight into molecular mechanisms that may precede overt neurodegeneration. Our work underscores the utility of epigenomic profiling in transgenic models and provides a foundation for identifying novel targets for early intervention in AD.

neuroscience↗

bioRxiv2025

Cell-type-specific DNA methylation dynamics in the prenatal and postnatal human cortex

The human cortex undergoes extensive epigenetic remodelling during development, although the precise temporal and cell-type-specific dynamics of DNA methylation remain incompletely understood. In this study, we profiled genome-wide DNA methylation across human cortex tissue from donors aged 6 post-conception weeks (pcw) to 108 years of age. We observed widespread, developmentally regulated, changes in DNA methylation, with pronounced shifts occurring during early- and mid-gestation that were distinct from age-associated modifications in the postnatal cortex. Using fluorescence-activated nuclei sorting (FANS), we optimized a protocol for the isolation of SATB2-positive neuronal nuclei, enabling the identification of cell-type-specific DNA methylation trajectories in developing neuronal and non-neuronal populations. Developmentally dynamic DNA methylation sites were significantly enriched near genes implicated in autism and schizophrenia, supporting a role for epigenetic dysregulation in neurodevelopmental disorders. Our findings underscore the prenatal period as a critical window of epigenomic plasticity in the central nervous system with important implications for understanding the genetic basis of neurodevelopmental phenotypes.

genomics↗

bioRxiv2024

Identification of SENP7 and UTF1/VENTX as new loci influencing clusteredprotocadherin methylation across blood and brain using a genome-wide associationstudy

The epigenetic regulation of clustered protocadherin (cPCDH) genes is tightly linked to their function as specific cell surface barcodes for neural self-nonself discrimination. Differential cPCDH DNA methylation in both blood and brain has been implicated in diverse human traits and diseases ranging from BMI, aging, cancer and brain disorders. However, the unique regulation of cPCDH methylation remains poorly understood. Therefore, we performed a genome-wide association study to evaluate the association of >7 million genetic variants with DNA methylation at 607 cPCDH CpGs measured in whole blood of 3777 individuals and validated findings in prefrontal cortex samples obtained from 523 brain donors. We observed concordant cPCDH methylation patterns in blood and prefrontal cortex, which switched between hypo-, intermediate and hypermethylation over short distances with the former overlapping with the promoter regions of each cPCDH member. Through methylation quantitative trait locus (meQTL) analysis in trans, we first confirmed the broad effect of the candidate gene SMCHD1 on cPCDH methylation in blood which validated in prefrontal cortex. Through a genome-wide analysis, we next identified the SENP7 and UTF1/VENTX loci to have widespread, subcluster-specific effects on cPCDH methylation in blood which was validated in prefrontal cortex. While SENP7 can indirectly affect DNA methylation through the deSUMOylation of the chromatin repressor KAP1, UTF1 and VENTX are two genes involved in embryonic development not previously implicated in epigenetic regulation. Our findings shed new light on the processes involved in cPCDH methylation that may underlie associations with disease.

genetics↗

bioRxiv2024

Guidance for the design and analysis of cell-type specific epigenetic epidemiology studies.

Recent studies on the role of epigenetics in disease have focused on DNA methylation profiled in bulk tissues limiting the detection of the cell-type affected by disease related changes. Advances in isolating homogeneous populations of cells now make it possible to identify DNA methylation differences associated with disease in specific cell-types. Critically, these datasets will require a bespoke analytical framework that can characterise whether the difference affects multiple or is specific to a particular cell-type. We take advantage of a large set of DNA methylation profiles (n = 751) obtained from five different purified cell populations isolated from human prefrontal cortex samples and evaluate the effects on study design, data preprocessing and statistical analysis for cell-specific studies, particularly for scenarios where multiple cell types are included. We describe novel quality control metrics that confirm successful isolation of purified cell populations, which when included in standard preprocessing pipelines provide confidence in the dataset. Our power calculations show substantial gains in detecting differentially methylated positions for some purified cell populations compared to bulk tissue analyses, countering concerns regarding the feasibility of generating large enough sample sizes for informative epidemiological studies. In a simulation study, we evaluated different regression models finding that this choice impacts on the robustness of the results. These findings informed our proposed two-stage framework for association analyses. Overall, our results provide guidance for cell-specific EWAS, establishing standards for study design and analysis, while showcasing the potential of cell-specific DNA methylation analyses to reveal links between epigenetic dysregulation and disease.

bioinformatics↗

bioRxiv2024

Imputed DNA methylation outperforms measured loci associations with smoking and chronological age

Multi-locus signatures of blood-based DNA methylation are well-established biomarkers for lifestyle and health outcomes. Here, we focus on two CpGs that are strongly associated with age and smoking behaviour. Imputing these loci via epigenome-wide CpGs results in stronger associations with outcomes in external datasets compared to directly measured CpGs. If extended epigenome-wide, CpG imputation could augment historic arrays and recently-released, inexpensive but lower-content arrays, thereby yielding better-powered association studies.

genetics↗

bioRxiv2024

Leveraging epigenetic signatures to determine the cell-type of origin from long read sequencing data

DNA methylation differs across tissue- and cell-types with important implications for the analysis of disease-associated differences in tissues such as blood. To uncover the biological processes affected by epigenetic dysregulation, it is essential for epigenetic studies to generate data from the appropriate cell-types. Here we propose a framework to do this computationally from long-read sequencing data, bypassing the need to isolate subtypes of cells experimentally. Using reference data for six common blood cell-types, we evaluate the potential of this approach for attributing reads to specific cells using sequencing data generated from whole blood. Our analyses show that cell-type can be accurately classified using small regions of the genome comparable in size to those generated by long-read sequencing platforms, although the accuracy of classification varies across different regions of the genome and between cell-types. We found that for approximately one third of the genome it is possible to accurately discriminate reads originating from lymphocytes and myeloid cells with the prediction of more specialised subtypes of blood cell-types also encouraging. Our approach provides an alternative computational method for generating cell-specific DNA methylation profiles for epigenetic epidemiology, accelerating our ability to reveal critical insights of the role of the epigenome in health and disease.

bioinformatics↗

bioRxiv2024

An atlas of expressed transcripts in the prenatal and postnatal human cortex

Alternative splicing is a post-transcriptional mechanism that increases the diversity of expressed transcripts and plays an important role in regulating gene expression in the developing central nervous system. We used long-read transcriptome sequencing to characterise the structure and abundance of full-length transcripts in the human cortex from donors aged 6 weeks post-conception to 83 years old. We identified thousands of novel transcripts, with dramatic differences in the diversity of expressed transcripts between prenatal and postnatal cortex. A large proportion of these previously uncharacterised transcripts have high coding potential, with corresponding peptides detected in proteomic data. Novel putative coding sequences are highly conserved and overlap de novo mutations in genes linked with neurodevelopmental disorders in individuals with relevant clinical phenotypes. Our findings underscore the potential of novel coding sequences to harbor clinically relevant variants, offering new insights into the genetic architecture of human disease. Our cortical transcript annotations are available as a resource to the research community via an online database.

genomics↗

bioRxiv2023

Developmentally dynamic changes in DNA methylation in the human pancreas

Development of the human pancreas requires the precise temporal control of gene expression via epigenetic mechanisms and the binding of key transcription factors. We quantified genome-wide patterns of DNA methylation in human fetal pancreatic samples from donors aged 6 to 21 post-conception weeks. We found dramatic changes in DNA methylation across pancreas development, with >21% of sites characterized as developmental differentially methylated positions (dDMPs) including many annotated to genes associated with monogenic diabetes. An analysis of DNA methylation in postnatal pancreas tissue showed that the dramatic temporal changes in DNA methylation occurring in the developing pancreas are largely limited to the prenatal period. Significant differences in DNA methylation were observed between males and females at a number of autosomal sites, with a small proportion of sites showing sex-specific DNA methylation trajectories across pancreas development. Pancreas dDMPs were not distributed equally across the genome, and were depleted in regulatory domains characterized by open chromatin and the binding of known pancreatic development transcription factors. Finally, we compared our pancreas dDMPs to previous findings from the human brain, identifying evidence for tissue-specific developmental changes in DNA methylation. To our knowledge, this represents the most extensive exploration of DNA methylation patterns during human fetal pancreas development, confirming the prenatal period as a time of major epigenomic plasticity.

genomics↗

bioRxiv2023

Long-read transcript sequencing identifies differential isoform expression in the entorhinal cortex in a transgenic model of tau pathology

Increasing evidence suggests that alternative splicing plays an important role in Alzheimers disease (AD), a devastating neurodegenerative disorder involving the intracellular aggregation of hyperphosphorylated tau. We used long-read cDNA sequencing to profile transcript diversity in the entorhinal cortex of wild-type (WT) and transgenic (TG) mice harboring a mutant form of human tau. Whole transcriptome profiling showed that previously reported gene-level expression differences between WT and TG mice reflect changes in the abundance of specific transcripts. Ultradeep targeted long-read cDNA sequencing of genes implicated in AD revealed hundreds of novel isoforms and identified specific transcripts associated with the development of tau pathology. Our results highlight the importance of differential transcript usage, even in the absence of gene-level expression alterations, as a mechanism underpinning gene regulation in the development of neuropathology. Our transcript annotations and a novel informatics pipeline for the analysis of long-read transcript sequencing data are provided as a resource to the community.

neuroscience↗

bioRxiv2023

Quantifying the proportion of different cell types in the human cortex using DNA methylation profiles

BackgroundDue to inter-individual 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. ResultsWe tested eight reference panels containing different combinations of neuronal- and glial-cell types and characterized their performance in deconvoluting cell proportions from computationally reconstructed or empirically-derived human cortex DNA methylation data. Our analyses demonstrate that 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 Alzheimers disease neuropathology and the proportion of specific cell types including a decrease in NeuNNeg/SOX10Neg nuclei and an increase of NeuNNeg/SOX10Pos nuclei. ConclusionsOur 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.

genomics↗

bioRxiv2023

Genetic impacts on DNA methylation help elucidate regulatory genomic processes

Pinpointing genetic impacts on DNA methylation can improve our understanding of pathways that underlie gene regulation and disease risk. We report heritability and methylation quantitative trait locus (meQTL) analysis at 724,499 CpGs profiled with the Illumina Infinium MethylationEPIC array in 2,358 blood samples from three UK cohorts, with replication. Methylation levels at 34.2% of CpGs were affected by SNPs, and 98% of effects were cis-acting or within 1 Mbp of the tested CpG. Our results are consistent with meQTL analyses based on the former Illumina Infinium HumanMethylation450 array. Both meQTL SNPs and CpGs with meQTLs were overrepresented in enhancers, which have improved coverage on this platform compared to previous approaches. Co-localisation analyses across genetic effects on DNA methylation and 56 human traits identified 1,520 co-localisations across 1,325 unique CpGs and 34 phenotypes, including in disease-relevant genes, such ICOSLG (inflammatory bowel disease), and USP1 and DOCK7 (total cholesterol levels). Enrichment analysis of meQTLs and integration with expression QTLs gave insights into mechanisms underlying cis-meQTLs, for example through disruption of transcription factor binding sites for CTCF and SMC3, and trans-meQTLs, for example through regulating the expression of ACD and SENP7 which can modulate DNA methylation at distal sites. Our findings improve the characterisation of the mechanisms underlying DNA methylation variability and are informative for prioritisation of GWAS variants for functional follow-ups. A results database and viewer are available online.

genomics↗

bioRxiv2023

Assessing the burden of rare DNA methylation outliers in schizophrenia

Along with case-control group differences in DNA methylation (DNAm) identified in epigenomewide association studies (EWAS), multiple rare DNAm outliers may exist in subsets of cases, underlying the etiological heterogeneity of some disorders. This creates an impetus for novel approaches focused on detecting rare/private outliers in the individual methylomes. Here, we present a novel, data-driven method - Outlier Methylation Analysis (OMA) - which through optimization detects genomic regions with strongly deviating DNAm levels, which we call outlier methylation regions (OMRs). Focusing on schizophrenia (SCZ) - a neuropsychiatric disorder with a heterogeneous etiology - we applied the OMA method in two independent, publicly available SCZ case-control samples with DNAm array information. We found SCZ cases had an increased burden of OMRs compared to controls (IRR=1.22, p=1.8x10-8), and case OMRs were enriched in regions relevant to cellular differentiation and development (i.e. polycomb repressed elements in the Gm12878 differentiated cell line, p=1.9x10-5, and poised promoters in the H1hesc stem cell line, p=5.4x10-4). Furthermore, SCZ cases were ~2.5-fold enriched (p=1.1x10-3) for OMRs overlapping genesets associated with developmental processes. The OMR burden was reduced in clozapine-treated, compared to untreated, SCZ cases (IRR=0.88, p=9.5x10-3), and also associated with increased chronological age (IRR=1.01, p= 2.7x10-16). Our findings demonstrate an elevated burden of OMRs in SCZ, implying methylomic dysregulation in SCZ which could correspond to the etiological heterogeneity among cases. These results remain to be causally examined and replicated in other cohorts and tissues. For this, and applications in other traits, we offer the OMA method to the scientific community.

genetics↗

bioRxiv2022

Uncertainty quantification of reference based cellular deconvolution algorithms

The majority of epigenetic epidemiology studies to date have generated genome-wide profiles from bulk tissues (e.g. whole blood) however these are vulnerable to confounding from variation in cellular composition. Proxies for cellular composition can be mathematically derived from the bulk tissue profiles using a deconvolution algorithm however, there is no method to assess the validity of these estimates for a dataset where the true cellular proportions are unknown. In this study, we describe, validate and characterise a sample level accuracy metric for derived cellular heterogeneity variables. The CETYGO score captures the deviation between a samples DNAm profile and its expected profile given the estimated cellular proportions and cell type reference profiles.We demonstrate that the CETYGO score consistently distinguishes inaccurate and incomplete deconvolutions when applied to reconstructed whole blood profiles. By applying our novel metric to > 6,300 empirical whole blood profiles, we find that estimating accurate cellular composition is influenced by both technical and biological variation. In particular, we show that when using the standard reference panel for whole blood, less accurate estimates are generated for females, neonates, older individuals and smokers. Our results highlight the utility of a metric to assess the accuracy of cellular deconvolution, and describe how it can enhance studies of DNA methylation that are reliant on statistical proxies for cellular heterogeneity. To facilitate incorporating our methodology into existing pipelines, we have made it freely available as an R package (https://github.com/ds420/CETYGO).

bioinformatics↗

bioRxiv2022

A comparison of feature selection methodologies and learning algorithms in the development of a DNA methylation-based telomere length estimator

The field of epigenomics holds great promise in understanding and treating disease with advances in machine learning (ML) and artificial intelligence being vitally important in this pursuit. Increasingly, research now utilises DNA methylation measures at cytosine-guanine dinucleotides (CpG) to detect disease and estimate biological traits such as aging. Given the high dimensionality of DNA methylation data, feature-selection techniques are commonly employed to reduce dimensionality and identify the most important subset of features. In this study, we test and compare a range of feature-selection methods and ML algorithms in the development of a novel DNA methylation-based telomere length (TL) estimator. We found that principal component analysis in advance of elastic net regression led to the overall best performing estimator when evaluated using a nested cross-validation analysis and two independent test cohorts. In contrast, the baseline model of elastic net regression with no prior feature reduction stage performed worst - suggesting a prior feature-selection stage may have important utility. The variance in performance across tested approaches shows that estimators are sensitive to data set heterogeneity and the development of an optimal DNA methylation-based estimator should benefit from the robust methodological approach used in this study. Additionally, we observed that different DNA methylation-based TL estimators, which have few common CpGs, are associated with many of the same biological entities. Moreover, our methodology which utilises a range of feature-selection approaches and ML algorithms could be applied to other biological markers and disease phenotypes, to examine their relationship with DNA methylation and predictive value.

bioinformatics↗

bioRxiv2022

DNA methylation signatures of Alzheimer's disease neuropathology in the cortex are primarily driven by variation in non-neuronal cell-types

Alzheimers disease (AD) is a chronic neurodegenerative disease characterized by the progressive accumulation of amyloid-beta and neurofibrillary tangles of tau in the neocortex. Utilizing extensive neuropathology data from the Brains for Dementia Research (BDR) cohort we performed the most systematic epigenome-wide association study (EWAS) of multiple measures of AD neuropathology yet undertaken, profiling DNA methylation in two cortical regions from 631 donors. We meta-analyzed our results with those from previous studies of DNA methylation in AD cortex (total n = 2,013 donors), identifying 334 cortical differentially methylated positions (DMPs) associated with AD pathology including methylomic variation at novel loci not previously implicated in dementia. We subsequently characterized DNA methylation in purified nuclei populations - enriched for neurons, oligodendrocytes and microglia - exploring the extent to which cortex AD-associated DMPs reflect differences manifest in specific cell populations. We find that the majority of DMPs identified in bulk cortex tissue actually reflect DNA methylation differences occurring in non-neuronal cells, with dramatically increased effect sizes observed in microglia-enriched nuclei populations. Our study highlights the power of utilizing multiple measures of neuropathology to identify epigenetic signatures of AD and the importance of characterizing disease-associated variation in purified neural cell-types.

genetics↗