Paul Datlinger

Single-Cell DNA Methylome Sequencing and Bioinformatic Inference of Epigenomic Cell-State Dynamics

Summary Methods for single-cell genome and transcriptome sequencing have contributed to our understanding of cellular heterogeneity, whereas methods for single-cell epigenomics are much less established. Here, we describe a whole-genome bisulfite sequencing (WGBS) assay that enables DNA methylation mapping in very small cell populations (μWGBS) and single cells (scWGBS). Our assay is optimized for profiling many samples at low coverage, and we describe a bioinformatic method that analyzes collections of single-cell methylomes to infer cell-state dynamics. Using these technological advances, we studied epigenomic cell-state dynamics in three in vitro models of cellular differentiation and pluripotency, where we observed characteristic patterns of epigenome remodeling and cell-to-cell heterogeneity. The described method enables single-cell analysis of DNA methylation in a broad range of biological systems, including embryonic development, stem cell differentiation, and cancer. It can also be used to establish composite methylomes that account for cell-to-cell heterogeneity in complex tissue samples.

Pooled CRISPR screening with single-cell transcriptome readout

CRISPR-based genetic screens are accelerating biological discovery, but current methods have inherent limitations. Widely used pooled screens are restricted to simple readouts including cell proliferation and sortable marker proteins. Arrayed screens allow for comprehensive molecular readouts such as transcriptome profiling, but at much lower throughput. Here we combine pooled CRISPR screening with single-cell RNA sequencing into a broadly applicable workflow, directly linking guide RNA expression to transcriptome responses in thousands of individual cells. Our method for CRISPR droplet sequencing (CROP-seq) enables pooled CRISPR screens with single-cell transcriptome resolution, which will facilitate high-throughput functional dissection of complex regulatory mechanisms and heterogeneous cell populations.

Epigenome mapping reveals distinct modes of gene regulation and widespread enhancer reprogramming by the oncogenic fusion protein EWS-FLI1.

Summary Transcription factor fusion proteins can transform cells by inducing global changes of the transcriptome, often creating a state of oncogene addiction. Here, we investigate the role of epigenetic mechanisms in this process, focusing on Ewing sarcoma cells that are dependent on the EWS-FLI1 fusion protein. We established reference epigenome maps comprising DNA methylation, seven histone marks, open chromatin states, and RNA levels, and we analyzed the epigenome dynamics upon downregulation of the driving oncogene. Reduced EWS-FLI1 expression led to widespread epigenetic changes in promoters, enhancers, and super-enhancers, and we identified histone H3K27 acetylation as the most strongly affected mark. Clustering of epigenetic promoter signatures defined classes of EWS-FLI1-regulated genes that responded differently to low-dose treatment with histone deacetylase inhibitors. Furthermore, we observed strong and opposing enrichment patterns for E2F and AP-1 among EWS-FLI1-correlated and anticorrelated genes. Our data describe extensive genome-wide rewiring of epigenetic cell states driven by an oncogenic fusion protein.

DNA methylation heterogeneity defines a disease spectrum in Ewing sarcoma

Developmental tumors in children and young adults carry few genetic alterations, yet they have diverse clinical presentation. Focusing on Ewing sarcoma, we sought to establish the prevalence and characteristics of epigenetic heterogeneity in genetically homogeneous cancers. We performed genome-scale DNA methylation sequencing for a large cohort of Ewing sarcoma tumors and analyzed epigenetic heterogeneity on three levels: between cancers, between tumors, and within tumors. We observed consistent DNA hypomethylation at enhancers regulated by the disease-defining EWS-FLI1 fusion protein, thus establishing epigenomic enhancer reprogramming as a ubiquitous and characteristic feature of Ewing sarcoma. DNA methylation differences between tumors identified a continuous disease spectrum underlying Ewing sarcoma, which reflected the strength of an EWS-FLI1 regulatory signature and a continuum between mesenchymal and stem cell signatures. There was substantial epigenetic heterogeneity within tumors, particularly in patients with metastatic disease. In summary, our study provides a comprehensive assessment of epigenetic heterogeneity in Ewing sarcoma and thereby highlights the importance of considering nongenetic aspects of tumor heterogeneity in the context of cancer biology and personalized medicine.

Next-generation sequencing identifies major DNA methylation changes during progression of Ph+ chronic myeloid leukemia

Little is known about the impact of DNA methylation on the evolution/progression of Ph+ chronic myeloid leukemia (CML). We investigated the methylome of CML patients in chronic phase (CP-CML), accelerated phase (AP-CML) and blast crisis (BC-CML) as well as in controls by reduced representation bisulfite sequencing. Although only ~600 differentially methylated CpG sites were identified in samples obtained from CP-CML patients compared with controls, ~6500 differentially methylated CpG sites were found in samples from BC-CML patients. In the majority of affected CpG sites, methylation was increased. In CP-CML patients who progressed to AP-CML/BC-CML, we identified up to 897 genes that were methylated at the time of progression but not at the time of diagnosis. Using RNA-sequencing, we observed downregulated expression of many of these genes in BC-CML compared with CP-CML samples. Several of them are well-known tumor-suppressor genes or regulators of cell proliferation, and gene re-expression was observed by the use of epigenetic active drugs. Together, our results demonstrate that CpG site methylation clearly increases during CML progression and that it may provide a useful basis for revealing new targets of therapy in advanced CML.

Differential DNA Methylation Analysis without a Reference Genome.

Summary Genome-wide DNA methylation mapping uncovers epigenetic changes associated with animal development, environmental adaptation, and species evolution. To address the lack of high-throughput methods for DNA methylation analysis in non-model organisms, we developed an integrated approach for studying DNA methylation differences independent of a reference genome. Experimentally, our method relies on an optimized 96-well protocol for reduced representation bisulfite sequencing (RRBS), which we have validated in nine species (human, mouse, rat, cow, dog, chicken, carp, sea bass, and zebrafish). Bioinformatically, we developed the RefFreeDMA software to deduce ad hoc genomes directly from RRBS reads and to pinpoint differentially methylated regions between samples or groups of individuals (http://RefFreeDMA.computational-epigenetics.org). The identified regions are interpreted using motif enrichment analysis and/or cross-mapping to annotated genomes. We validated our method by reference-free analysis of cell-type-specific DNA methylation in the blood of human, cow, and carp. In summary, we present a cost-effective method for epigenome analysis in ecology and evolution, which enables epigenome-wide association studies in natural populations and species without a reference genome.

Imprinted expression in cystic embryoid bodies shows an embryonic and not an extra-embryonic pattern.

A large subset of mammalian imprinted genes show extra-embryonic lineage (EXEL) specific imprinted expression that is restricted to placental trophectoderm lineages and to visceral yolk sac endoderm (ysE). Isolated ysE provides a homogenous in vivo model of a mid-gestation extra-embryonic tissue to examine the mechanism of EXEL-specific imprinted gene silencing, but an in vitro model of ysE to facilitate more rapid and cost-effective experiments is not available. Reports indicate that ES cells differentiated into cystic embryoid bodies (EBs) contain ysE, so here we investigate if cystic EBs model ysE imprinted expression. The imprinted expression pattern of cystic EBs is shown to resemble fetal liver and not ysE. To investigate the reason for this we characterized the methylome and transcriptome of cystic EBs in comparison to fetal liver and ysE, by whole genome bisulphite sequencing and RNA-seq. Cystic EBs show a fetal liver pattern of global hypermethylation and low expression of repeats, while ysE shows global hypomethylation and high expression of IAPEz retroviral repeats, as reported for placenta. Transcriptome analysis confirmed that cystic EBs are more similar to fetal liver than ysE and express markers of early embryonic endoderm. Genome-wide analysis shows that ysE shares epigenetic and repeat expression features with placenta. Contrary to previous reports, we show that cystic EBs do not contain ysE, but are more similar to the embryonic endoderm of fetal liver. This explains why cystic EBs reproduce the imprinted expression seen in the embryo but not that seen in the ysE.

THE DIAGENODE EPIGENETICS CUSTOM SERVICE: Complete workflows for genome-scale DNA methylation and histone marks analysis

Epigenetics is crucial for the regulation of gene expression and has broad relevance in biological processes like development, disease and response to the environment. For more than 10 years Diagenode has been developing innovative tools to study epigenetic marks such as post-translational modifications of histones and DNA methylation. We are now utilizing our expertise by offering custom services. Our services include full workflows for ChIP-sequencing as well as reduced representation bisulfite sequencing (RRBS) with our new optimized “Premium RRBSTM technology. In addition, we also offer bioinformatic analysis of your results, both standard and customized. The Diagenode Epigenetics Custom Services helps you to complete your epigenetics workflow from your starting biological material to your final results. INTRODUCTION Diagenode offers a wide range of products for every step of your epigenetic analysis. Our chromatin immunoprecipitation (ChIP) solutions are powerful tools to study the association of protein to DNA for analysis of epigenetics modifications, chromatin remodeling and regulation of gene expression by transcription factors. DNA methylation can be studied using reduced representation bisulfite sequencing (RRBS). The Diagenode Premium RRBSTM technology provides high coverage of up to 4 million CpGs for human, distributed mainly in CpG islands and promoter regions, as well as in other genomic elements such as enhancers, CpG island shores and non-coding RNAs. Diagenode offers ChIP-seq and RRBS as custom services. We process your samples through a complete workflow leading to high-quality analyzed data. 1 Workflow 1 Workflow The complete workflow for ChIP-seq contains 6 steps (Figure 1) and results can be provided within 8-10 weeks for a standard project. The service is flexible as researchers may choose either to order the full process or only some specific steps Figure 1. ChIP-seq workflow. Description of the complete procedure for ChIP-sequencing Diagenode uses its chromatin shearing and ChIP optimization expertise to find the protocol that is most suited to your model. Moreover, we include several quality control steps to validate the quality of the libraries before Illumina® sequencing. Figure 2. ChIP optimization and quality control. For every new service, several conditions are tested and the efficiency of (a) chromatin shearing, (b) ChIP Immunoprecipitation and (c) library preparation are thoroughly checked. The RRBS protocol includes the use of methylated and unmethylated spike-in controls and the multiplexing of the samples. A precise volume is selected during quantitative PCR for each sample prior to pooling to ensure a balanced representation of each sample in the pool. Figure 5. Diagenode Premium RRBSTM workflow. Chromatin Preparation Chromatin Shearing Chromatin IP DNA Purification NGS Library Preparation Sequencing and Bioinformatics Analysis 1 2 3 4 5 6 5’ 3’ 3’ 5’ C GGC CGG C C GGC CGG C CH3 Ch3 CGG AGCC CCGA GGC CH3 TCGG AGCC CCGA GGCT CH3 TCGG AGUU UUGA GGUT CH3 TCGG AGUU UUGA GGUT CH3 16 pools with 6 samples each 96 samples

Abstract PR13: DNA methylation mapping and computational modeling in a large Ewing sarcoma cohort identifies principles of tumor heterogeneity and their impact on clinical phenotypes

Ewing sarcoma is an excellent model for studying the role of epigenetic deregulation and tumor heterogeneity, given its low mutation rates and the well-defined oncogenic driver. We have recently shown that the fusion oncogene EWS-FLI1 induces widespread epigenetic rewiring in proximal and distal enhancers (Tomazou et al. Cell Reports 2015). In the current study, we validate the clinical relevance of our results in a large cohort of primary tumors, and we explore the prevalence, characteristics, and clinical impact of epigenetic tumor heterogeneity in Ewing sarcoma. We used reduced representation bisulfite sequencing (RRBS) to generate genome-wide profiles of DNA methylation in 141 Ewing sarcoma primary tumors, 17 Ewing sarcoma cell lines, and 32 primary mesenchymal stem cell (MSC) samples. Deep sequencing resulted in DNA methylation measurements for an average of 3.5 million unique CpGs per sample with excellent data quality (>98% bisulfite conversion efficiency). In addition, for three primary tumors we generated comprehensive reference epigenome maps using whole genome bisulfite sequencing (WGBS) and ChIP-seq for seven histone marks (H3K4me3, H3K4me1, H3K27me3, H3K27ac, H3K56ac, H3K36me3, and H3K9me3). We show that DNA methylation data can be used to infer enhancer activity differences among tumors, allowing us to exploit our large primary tumor dataset to systematically compare the regulation of EWS-FLI1 correlated and anticorrelated enhancers. We also identified Ewing-specific DNA methylation patterns. For example, Ewing sarcoma samples consistently show higher DNA methylation than MSCs at AP-1 binding sites, but lower DNA methylation at EWS-FLI1 binding sites. To explore epigenetic heterogeneity within individual tumors, we developed a bioinformatic algorithm that quantifies DNA methylation disorder. Using individual reads containing multiple DNA methylation measurements from single cells, we assign scores at single-nucleotide resolution. This method uses a probabilistic model to account for overall methylation rate and expected disorder levels. By evaluating the likelihood of the data in a model that assumes that the DNA methylation status of a CpG is independent of the methylation status of a nearby CpG, we identify extremely heterogeneous as well as highly epigenetically conserved genomic elements. These different region types show distinct patterns of enrichment for regulatory modes and transcription factor binding. We also compared the observed DNA methylation disorder in 141 Ewing tumors to those observed in 17 Ewing sarcoma cell lines, 32 primary MSC samples, and several hundred additional tumor and normal samples that are unrelated to Ewing sarcoma. This analysis identified Ewing-specific patterns of DNA methylation heterogeneity and stratifies patients based on epigenetic heterogeneity. Our dataset constitutes the largest available resource of genome-scale DNA methylation maps in a solid pediatric tumor. It strongly confirms the relevance of enhancer reprogramming and tumor heterogeneity in Ewing sarcoma, and it constitutes a starting point to develop DNA methylation biomarkers for prognosis and patient stratification. This study is supported by the Austrian National Bank (OeNB project #15714) and the Kapsch group (https://www.kapsch.net/). This abstract is also presented as Poster A24. Citation Format: Nathan C. Sheffield, Franck Tirode, Sandrine Grossetete-Lalami, Paul Datlinger, Andreas Schonegger, Johanna Hadler, Diana Walder, Ingeborg M. Ambros, Ana Teresa Amaral, Enrique de Alava, Katharina Schallmoser, Dirk Strunk, Beate Rinner, Bernadette Liegl-Atzwanger, Berthold Huppertz, Andreas Leithner, Uta Dirksen, Peter Ambros, Olivier Delattre, Heinrich Kovar, Christoph Bock, Eleni M. Tomazou. DNA methylation mapping and computational modeling in a large Ewing sarcoma cohort identifies principles of tumor heterogeneity and their impact on clinical phenotypes. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Pediatric Cancer Research: From Mechanisms and Models to Treatment and Survivorship; 2015 Nov 9-12; Fort Lauderdale, FL. Philadelphia (PA): AACR; Cancer Res 2016;76(5 Suppl):Abstract nr PR13.