Biology subjects

Dempster, E. L.

Publications and source records attributed to Dempster, E. L..

10 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↗

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↗

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↗

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↗

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↗

bioRxiv2021

Sites of active gene regulation in the prenatal frontal cortex and their role in neuropsychiatric disorders

Common genetic variation appears to largely influence risk for neuropsychiatric disorders through effects on gene regulation. It is therefore possible to shed light on the biology of these conditions by testing for enrichment of associated genetic variation within regulatory genomic regions operating in specific tissues or cell types. Here, we have used ATAC-Seq to map open chromatin (an index of active regulatory genomic regions) in bulk tissue, NeuN+ and NeuN- nuclei from the prenatal human frontal cortex, and tested enrichment of SNP heritability for 5 neuropsychiatric disorders (autism spectrum disorder, ADHD, bipolar disorder, major depressive disorder and schizophrenia) within these regions. We observed significant enrichment of SNP heritability for ADHD, major depressive disorder and schizophrenia within open chromatin regions mapped in bulk fetal frontal cortex, and for all 5 tested neuropsychiatric conditions when we restricted these sites to those overlapping histone modifications indicative of enhancers (H3K4me1) or promoters (H3K4me3) in fetal brain. SNP heritability for neuropsychiatric disorders was significantly enriched in open chromatin regions identified in fetal frontal cortex NeuN- as well as NeuN+ nuclei overlapping fetal brain H3K4me1 or H3K4me3 sites. We additionally demonstrate the utility of our mapped open chromatin regions for prioritizing potentially functional SNPs at genome-wide significant risk loci for neuropsychiatric disorders. Our data provide evidence for an early neurodevelopmental component to a range of neuropsychiatric conditions and highlight an important role for regulatory genomic regions active within both NeuN+ and NeuN- cells of the prenatal brain.

genetics↗

bioRxiv2020

Analysis of the transcriptome and DNA methylome in response to acute and recurrent low glucose in human primary astrocytes

Aims/hypothesisRecurrent hypoglycaemia (RH) is a major side-effect of intensive insulin therapy for people with diabetes. Changes in hypoglycaemia sensing by the brain contribute to the development of impaired counterregulatory responses to and awareness of hypoglycaemia. Little is known about the intrinsic changes in human astrocytes in response to acute and recurrent low glucose (RLG) exposure. MethodsHuman primary astrocytes (HPA) were exposed to zero, one, three or four bouts of low glucose (0.1 mmol/l) for three hours per day for four days to mimic RH. On the fourth day, DNA and RNA were collected. Differential gene expression and ontology analyses were performed using DESeq2 and GOseq respectively. DNA methylation was assessed using the Infinium MethylationEPIC BeadChip platform. Results24 differentially expressed genes (DEGs) were detected (after correction for multiple comparisons). One bout of low glucose exposure had the largest effect on gene expression. Pathway analyses revealed that endoplasmic-reticulum (ER) stress-related genes such as HSPA5, XBP1, and MANF, involved in the unfolded protein response (UPR), were all significantly increased following LG exposure, which was diminished following RLG. There was little correlation between differentially methylated positions and changes in gene expression yet the number of bouts of LG exposure produced distinct methylation signatures. Conclusions/interpretationThese data suggest that exposure of human astrocytes to transient LG triggers activation of genes involved in the UPR linked to endoplasmic reticulum (ER) stress. Following RLG, the activation of UPR related genes was diminished, suggesting attenuated ER stress. This may be mediated by metabolic adaptations to better preserve intracellular and/or ER ATP levels, but this requires further investigation.

neuroscience↗