High-throughput single-cell measurements of DNA methylomes can quantify methylation heterogeneity and uncover its role in gene regulation. However, technical limitations and sparse coverage can preclude this task. scMET is a hierarchical Bayesian model which overcomes sparsity, sharing information across cells and genomic features to robustly quantify genuine biological heterogeneity. scMET can identify highly variable features that drive epigenetic heterogeneity, and perform differential methylation and variability analyses. We illustrate how scMET facilitates the characterization of epigenetically distinct cell populations and how it enables the formulation of novel hypotheses on the epigenetic regulation of gene expression. scMET is available at https://github.com/andreaskapou/scMET.
@article{Kapourani2021,author={Kapourani, Chantriolnt-Andreas and Argelaguet, Ricard and Sanguinetti, Guido and Vallejos, Catalina A.},doi={10.1186/s13059-021-02329-8},issn={1474760X},journal={Genome Biology},keywords={DNA methylation,Epigenetic heterogeneity,Hierarchical Bayes,Single-cell},number={1},pages={1--21},pmid={33879195},publisher={Genome Biology},title={{scMET: Bayesian modeling of DNA methylation heterogeneity at single-cell resolution}},url={https://doi.org/10.1186/s13059-021-02329-8},volume={22},year={2021}}
Cell
Glioblastomas acquire myeloid-affiliated transcriptional programs via epigenetic immunoediting to elicit immune evasion
Gangoso, Ester, Southgate, Benjamin, Bradley, Leanne, Rus, Stefanie, Galvez-Cancino, Felipe, McGivern, Niamh, Güç, Esra,
Kapourani, Chantriolnt-Andreas, Byron, Adam, Ferguson, Kirsty M., Alfazema, Neza, Morrison, Gillian, Grant, Vivien, Blin, Carla, Sou, Ieng Fong, Marques-Torrejon, Maria Angeles, Conde, Lucia, Parrinello, Simona, Herrero, Javier, Beck, Stephan, Brandner, Sebastian, Brennan, Paul M., Bertone, Paul, Pollard, Jeffrey W., Quezada, Sergio A., Sproul, Duncan, Frame, Margaret C., Serrels, Alan, and Pollard, Steven M.
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, we uncover an acquired capability of GSCs to escape immune clearance by establishing an enhanced immunosuppressive tumor microenvironment. Mechanistically, this is not elicited via genetic selection of tumor subclones, but through an epigenetic immunoediting process wherein stable transcriptional and epigenetic changes in GSCs are enforced following immune attack. These changes launch a myeloid-affiliated transcriptional program, which leads to increased recruitment of tumor-associated macrophages. Furthermore, we identify similar epigenetic and transcriptional signatures in human mesenchymal subtype GSCs. We conclude that epigenetic immunoediting may drive an acquired immune evasion program in the most aggressive mesenchymal GBM subtype by reshaping the tumor immune microenvironment.
@article{Gangoso2021,author={Gangoso, Ester and Southgate, Benjamin and Bradley, Leanne and Rus, Stefanie and Galvez-Cancino, Felipe and McGivern, Niamh and G{\"{u}}{\c{c}}, Esra and Kapourani, Chantriolnt-Andreas and Byron, Adam and Ferguson, Kirsty M. and Alfazema, Neza and Morrison, Gillian and Grant, Vivien and Blin, Carla and Sou, Ieng Fong and Marques-Torrejon, Maria Angeles and Conde, Lucia and Parrinello, Simona and Herrero, Javier and Beck, Stephan and Brandner, Sebastian and Brennan, Paul M. and Bertone, Paul and Pollard, Jeffrey W. and Quezada, Sergio A. and Sproul, Duncan and Frame, Margaret C. and Serrels, Alan and Pollard, Steven M.},doi={10.1016/j.cell.2021.03.023},issn={10974172},journal={Cell},keywords={DNA methylation,chemokine,epigenetics,glioblastoma,immune evasion,immunoediting,interferon signaling,macrophage,neural stem cell,syngeneic},number={9},pages={2454--2470.e26},pmid={33857425},title={{Glioblastomas acquire myeloid-affiliated transcriptional programs via epigenetic immunoediting to elicit immune evasion}},url={https://doi.org/10.1016/j.cell.2021.03.023},volume={184},year={2021}}
2019
GBIO
Melissa: Bayesian clustering and imputation of single-cell methylomes
Kapourani, Chantriolnt-Andreas, and Sanguinetti, Guido
Measurements of single-cell methylation are revolutionizing our understanding of epigenetic control of gene expression, yet the intrinsic data sparsity limits the scope for quantitative analysis of such data. Here, we introduce Melissa (MEthyLation Inference for Single cell Analysis), a Bayesian hierarchical method to cluster cells based on local methylation patterns, discovering patterns of epigenetic variability between cells. The clustering also acts as an effective regularization for data imputation on unassayed CpG sites, enabling transfer of information between individual cells. We show both on simulated and real data sets that Melissa provides accurate and biologically meaningful clusterings and state-of-the-art imputation performance.
@article{kapourani2019melissa,author={Kapourani, Chantriolnt-Andreas and Sanguinetti, Guido},journal={Genome biology},number={1},pages={61},publisher={BioMed Central},title={{Melissa: Bayesian clustering and imputation of single-cell methylomes}},url={https://doi.org/10.1186/s13059-019-1665-8},volume={20},year={2019}}
Nature
Multi-omics profiling of mouse gastrulation at single-cell resolution
Argelaguet, Ricard, Clark, Stephen J, Mohammed, Hisham, Stapel, L Carine, Krueger, Christel,
Kapourani, Chantriolnt-Andreas, Imaz-Rosshandler, Ivan, Lohoff, Tim, Xiang, Yunlong, Hanna, Courtney W, and Others,
Formation of the three primary germ layers during gastrulation is an essential step in the establishment of the vertebrate body plan and is associated with major transcriptional changes. Global epigenetic reprogramming accompanies these changes, but the role of the epigenome in regulating early cell-fate choice remains unresolved, and the coordination between different molecular layers is unclear. Here we describe a single-cell multi-omics map of chromatin accessibility, DNA methylation and RNA expression during the onset of gastrulation in mouse embryos. The initial exit from pluripotency coincides with the establishment of a global repressive epigenetic landscape, followed by the emergence of lineage-specific epigenetic patterns during gastrulation. Notably, cells committed to mesoderm and endoderm undergo widespread coordinated epigenetic rearrangements at enhancer marks, driven by ten-eleven translocation (TET)-mediated demethylation and a concomitant increase of accessibility. By contrast, the methylation and accessibility landscape of ectodermal cells is already established in the early epiblast. Hence, regulatory elements associated with each germ layer are either epigenetically primed or remodelled before cell-fate decisions, providing the molecular framework for a hierarchical emergence of the primary germ layers.
@article{argelaguet2019multi,author={Argelaguet, Ricard and Clark, Stephen J and Mohammed, Hisham and Stapel, L Carine and Krueger, Christel and Kapourani, Chantriolnt-Andreas and Imaz-Rosshandler, Ivan and Lohoff, Tim and Xiang, Yunlong and Hanna, Courtney W and Others},journal={Nature},number={7787},pages={487--491},publisher={Nature Publishing Group},title={{Multi-omics profiling of mouse gastrulation at single-cell resolution}},url={https://doi.org/10.1038/s41586-019-1825-8},volume={576},year={2019}}
PhD Thesis
Kapourani, C.-A. (2019). Spatial statistical modelling of epigenomic variability [The University of Edinburgh]. http://hdl.handle.net/1842/35647
Each cell in our body carries the same genetic information encoded in the DNA, yet the human organism contains hundreds of cell types which differ substantially in physiology and functionality. This variability stems from the existence of regulatory mechanisms that control gene expression, and hence phenotype. The field of epigenetics studies how changes in biochemical factors, other than the DNA sequence itself, might affect gene regulation. The advent of high throughput sequencing platforms has enabled the profiling of different epigenetic marks on a genome-wide scale; however, bespoke computational methods are required to interpret these high-dimensional data and investigate the coupling between the epigenome and transcriptome. This thesis contributes to the development of statistical models to capture spatial correlations of epigenetic marks, with the main focus being DNA methylation. To this end, we developed BPRMeth (Bayesian Probit Regression for Methylation), a probabilistic model for extracting higher order methylation features that precisely quantify the spatial variability of bulk DNA methylation patterns. Using such features, we constructed an accurate machine learning predictor of gene expression from DNA methylation and identified prototypical methylation profiles that explain most of the variability across promoter regions. The BPRMeth model, and its algorithmic implementation, were subsequently substantially extended both to accommodate different data types, and to improve the scalability of the algorithm. Bulk experiments have paved the way for mapping the epigenetic landscape, nonetheless, they fall short of explaining the epigenetic heterogeneity and quantifying its dynamics, which inherently occur at the single cell level. Single cell bisulfite sequencing protocols have been recently developed, however, due to intrinsic limitations of the technology they result in extremely sparse coverage of CpG sites, effectively limiting the analysis repertoire to a semi-quantitative level. To overcome these difficulties we developed Melissa (MEthyLation Inference for Single cell Analysis), a Bayesian hierarchical model that leverages local correlations between neighbouring CpGs and similarity between individual cells to jointly impute missing methylation states, and cluster cells based on their genome-wide methylation profiles. A recent experimental innovation enables the parallel profiling of DNA methylation, transcription and chromatin accessibility (scNMT-seq), making it possible to link transcriptional and epigenetic heterogeneity at the single cell resolution. For the scNMT-seq study, we applied the extended BPRMeth model to quantify cell-to-cell chromatin accessibility heterogeneity around promoter regions and subsequently link it to transcript abundance. This revealed that genes with conserved accessibility profiles are associated with higher average expression levels. In summary, this thesis proposes statistical methods to model and interpret epigenomic data generated from high throughput sequencing experiments. Due to their statistical power and flexibility we anticipate that these methods will be applicable to future sequencing technologies and become widespread tools in the high throughput bioinformatics workbench for performing biomedical data analysis.
@thesis{kapourani2019spatial,author={Kapourani, Chantriolnt-Andreas},publisher={The University of Edinburgh},school={The University of Edinburgh},title={{Spatial statistical modelling of epigenomic variability}},url={http://hdl.handle.net/1842/35647},year={2019}}
2018
NatComms
scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells
Clark, Stephen J, Argelaguet, Ricard,
Kapourani, Chantriolnt-Andreas, Stubbs, Thomas M., Lee, Heather J., Alda-Catalinas, Celia, Krueger, Felix, Sanguinetti, Guido, Kelsey, Gavin, Marioni, John C., Stegle, Oliver, and Reik, Wolf
Parallel single-cell sequencing protocols represent powerful methods for investigating regulatory relationships, including epigenome-transcriptome interactions. Here, we report a single-cell method for parallel chromatin accessibility, DNA methylation and transcriptome profiling. scNMT-seq (single-cell nucleosome, methylation and transcription sequencing) uses a GpC methyltransferase to label open chromatin followed by bisulfite and RNA sequencing. We validate scNMT-seq by applying it to differentiating mouse embryonic stem cells, finding links between all three molecular layers and revealing dynamic coupling between epigenomic layers during differentiation.
@article{clark2018scnmt,author={Clark, Stephen J and Argelaguet, Ricard and Kapourani, Chantriolnt-Andreas and Stubbs, Thomas M. and Lee, Heather J. and Alda-Catalinas, Celia and Krueger, Felix and Sanguinetti, Guido and Kelsey, Gavin and Marioni, John C. and Stegle, Oliver and Reik, Wolf},journal={Nature communications},number={1},pages={781},publisher={Nature Publishing Group},title={{scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells}},url={https://doi.org/10.1038/s41467-018-03149-4},volume={9},year={2018}}
Bioinformatics
BPRMeth: A flexible Bioconductor package for modelling methylation profiles
Kapourani, Chantriolnt-Andreas, and Sanguinetti, Guido
Motivation: High-throughput measurements of DNA methylation are increasingly becoming a mainstay of biomedical investigations. While the methylation status of individual cytosines can sometimes be informative, several recent papers have shown that the functional role of DNA methylation is better captured by a quantitative analysis of the spatial variation of methylation across a genomic region. Results: Here, we present BPRMeth, a Bioconductor package that quantifies methylation profiles by generalized linear model regression. The original implementation has been enhanced in two important ways: we introduced a fast, variational inference approach that enables the quantification of Bayesian posterior confidence measures on the model, and we adapted the method to use several observation models, making it suitable for a diverse range of platforms including single-cell analyses and methylation arrays.
@article{Kapourani2018b,author={Kapourani, Chantriolnt-Andreas and Sanguinetti, Guido},doi={10.1093/bioinformatics/bty129},issn={14602059},journal={Bioinformatics},number={14},pages={2485--2486},pmid={29522078},title={{BPRMeth: A flexible Bioconductor package for modelling methylation profiles}},url={https://doi.org/10.1093/bioinformatics/bty129},volume={34},year={2018}}
2016
Bioinformatics
Higher order methylation features for clustering and prediction in epigenomic studies
Kapourani, Chantriolnt-Andreas, and Sanguinetti, Guido
Motivation: DNA methylation is an intensely studied epigenetic mark, yet its functional role is incompletely understood. Attempts to quantitatively associate average DNA methylation to gene expression yield poor correlations outside of the well-understood methylation-switch at CpG islands. Results: Here we use probabilistic machine learning to extract higher order features associated with the methylation profile across a defined region. These features quantitate precisely notions of shape of a methylation profile, capturing spatial correlations in DNA methylation across genomic regions. Using these higher order features across promoter-proximal regions, we are able to construct a powerful machine learning predictor of gene expression, significantly improving upon the predictive power of average DNA methylation levels. Furthermore, we can use higher order features to cluster promoter-proximal regions, showing that five major patterns of methylation occur at promoters across different cell lines, and we provide evidence that methylation beyond CpG islands may be related to regulation of gene expression. Our results support previous reports of a functional role of spatial correlations in methylation patterns, and provide a mean to quantitate such features for downstream analyses. Availability: https://github.com/andreaskapou/BPRMeth
@article{Kapourani2016,archiveprefix={arXiv},author={Kapourani, Chantriolnt-Andreas and Sanguinetti, Guido},doi={10.1093/bioinformatics/btw432},eprint={1603.08386},issn={14602059},journal={Bioinformatics},number={17},pages={i405--i412},pmid={27587656},title={{Higher order methylation features for clustering and prediction in epigenomic studies}},url={https:/doi.org/10.1093/bioinformatics/btw432},volume={32},year={2016}}
2014
EBPISM
Extending the Social Network Interaction Model to Facilitate Collaboration through Service Provision
Social network technology has been established as a prominent way of communication between members of an organization or enterprise. This paper presents an approach extending the typical social network interaction model to promote participant collaboration through service provision within an organization, towards the Enterprise 2.0 vision. The proposed interaction model between enterprise network participants incorporates their actual roles in the organization and enables the definition of custom relation types implementing custom policies and rules. It supports a complex mechanism for refined content propagation according to participant relations and/or roles. Moreover, the collaboration of participants to provide services and complete specific business tasks through Social Business Process Management is facilitated by enabling the execution of specific activities in each participant profile according to his/her actual role. To explore the potential of the proposed interaction model towards Enterprise 2.0, two prototype social networks, developed to serve different communities and needs, are discussed as case studies.
@inproceedings{hatzi2014extending,author={Hatzi, Ourania and Meletakis, Giannis and Katsivelis, Panagiotis and Kapourani, Chantriolnt-Andreas and Nikolaidou, Mara and Anagnostopoulos, Dimosthenis},booktitle={In Enterprise, Business-Process and Information Systems Modeling},organization={Springer Berlin Heidelberg},pages={pp----94},title={{Extending the Social Network Interaction Model to Facilitate Collaboration through Service Provision}},url={https://doi.org/10.1007/978-3-662-43745-2{\_}7},year={2014}}