A5010305144- Single-cell and spatial transcriptomics
- Epigenetics and DNA Methylation
- Genomics and Chromatin Dynamics
- Immune cells in cancer
- Cancer Genomics and Diagnostics
- Liver physiology and pathology
- Pluripotent Stem Cells Research
- RNA modifications and cancer
- Organ Transplantation Techniques and Outcomes
- Genetic Syndromes and Imprinting
- Glioma Diagnosis and Treatment
- Gene Regulatory Network Analysis
University of Edinburgh
2016-2024
Centre for Inflammation Research
2023-2024
The Queen's Medical Research Institute
2023
Institute of Genetics and Cancer
2020-2021
MRC Centre for Regenerative Medicine
2021
Edinburgh Cancer Research
2021
Abstract Parallel single-cell sequencing protocols represent powerful methods for investigating regulatory relationships, including epigenome-transcriptome interactions. Here, we report a method parallel chromatin accessibility, DNA methylation and transcriptome profiling. scNMT-seq (single-cell nucleosome, transcription sequencing) uses GpC methyltransferase to label open followed by bisulfite RNA sequencing. We validate applying it differentiating mouse embryonic stem cells, finding links...
Glioblastoma multiforme (GBM) is an aggressive brain tumor for which current immunotherapy approaches have been unsuccessful. Here, we explore the mechanisms underlying immune evasion in GBM. By serially transplanting GBM stem cells (GSCs) into immunocompetent hosts, uncover acquired capability of GSCs to escape clearance by establishing enhanced immunosuppressive microenvironment. Mechanistically, this not elicited via genetic selection subclones, but through epigenetic immunoediting...
Abstract The liver has a unique ability to regenerate 1,2 ; however, in the setting of acute failure (ALF), this regenerative capacity is often overwhelmed, leaving emergency transplantation as only curative option 3–5 . Here, advance understanding human regeneration, we use paired single-nucleus RNA sequencing combined with spatial profiling healthy and ALF explant livers generate single-cell, pan-lineage atlas regeneration. We uncover novel ANXA2 + migratory hepatocyte subpopulation, which...
Measurements of single-cell methylation are revolutionizing our understanding epigenetic control gene expression, yet the intrinsic data sparsity limits scope for quantitative analysis such data. Here, we introduce Melissa (MEthyLation Inference Single cell Analysis), a Bayesian hierarchical method to cluster cells based on local patterns, discovering patterns variability between cells. The clustering also acts as an effective regularization imputation unassayed CpG sites, enabling transfer...
DNA methylation is an intensely studied epigenetic mark, yet its functional role incompletely understood. Attempts to quantitatively associate average gene expression yield poor correlations outside of the well-understood methylation-switch at CpG islands.Here, we use probabilistic machine learning extract higher order features associated with profile across a defined region. These quantitate precisely notions shape profile, capturing spatial in genomic regions. Using these promoter-proximal...
Abstract The liver has a unique ability to regenerate 1,2 , however in the setting of acute failure (ALF) this regenerative capacity is often overwhelmed and emergency transplantation only curative option 3-5 . To advance our understanding human regeneration inform design pro-regenerative therapies, we use paired single-nuclei RNA sequencing (snRNA-seq) combined with spatial profiling healthy ALF explant livers generate first single-cell, pan-lineage atlas regeneration. We uncover novel...
High-throughput single-cell measurements of DNA methylomes can quantify methylation heterogeneity and uncover its role in gene regulation. However, technical limitations sparse coverage preclude this task. scMET is a hierarchical Bayesian model which overcomes sparsity, sharing information across cells genomic features to robustly genuine biological heterogeneity. identify highly variable that drive epigenetic heterogeneity, perform differential variability analyses. We illustrate how...
Abstract Parallel single-cell sequencing protocols represent powerful methods for investigating regulatory relationships, including epigenome-transcriptome interactions. Here, we report a novel method parallel chromatin accessibility, DNA methylation and transcriptome profiling. scNMT-seq (single-cell nucleosome, transcription sequencing) uses GpC methyltransferase to label open followed by bisulfite RNA sequencing. We validate applying it differentiating mouse embryonic stem cells, finding...
High-throughput measurements of DNA methylation are increasingly becoming a mainstay biomedical investigations. While the status individual cytosines can sometimes be informative, several recent papers have shown that functional role is better captured by quantitative analysis spatial variation across genomic region.Here, we present BPRMeth, Bioconductor package quantifies profiles generalized linear model regression. The original implementation has been enhanced in two important ways:...
Abstract Formation of the three primary germ layers during gastrulation is an essential step in establishment vertebrate body plan. Recent studies employing single cell RNA-sequencing have identified major transcriptional changes associated with layer specification. Global epigenetic reprogramming accompanies these changes, but role epigenome regulating early fate choice remains unresolved, and coordination between different unclear. Here we describe first triple-omics map chromatin...
Abstract Measurements of DNA methylation at the single cell level are promising to revolutionise our understanding epigenetic control gene expression. Yet, intrinsic limitations technology result in very sparse coverage CpG sites (around 5% 20% coverage), effectively limiting analysis repertoire a semi-quantitative level. Here we introduce Melissa (MEthyLation Inference for Single Analysis), Bayesian hierarchical method quantify spatially-varying profiles across genomic regions from...
Abstract High throughput measurements of DNA methylomes at single-cell resolution are a promising resource to quantify the heterogeneity methylation and uncover its role in gene regulation. However, limitations technology result sparse CpG coverage, effectively posing challenges robustly genuine heterogeneity. Here we tackle these issues by introducing scMET, hierarchical Bayesian model which overcomes data sparsity sharing information across cells genomic features, resulting robust...