Simultaneous profiling of transcriptome and DNA methylome from a single cell.

BackgroundSingle-cell transcriptome and single-cell methylome technologies have become powerful tools to study RNA and DNA methylation profiles of single cells at a genome-wide scale. A major challenge has been to understand the direct correlation of DNA methylation and gene expression within single-cells. Due to large cell-to-cell variability and the lack of direct measurements of transcriptome and methylome of the same cell, the association is still unclear.ResultsHere, we describe a novel method (scMT-seq) that simultaneously profiles both DNA methylome and transcriptome from the same cell. In sensory neurons, we consistently identify transcriptome and methylome heterogeneity among single cells but the majority of the expression variance is not explained by proximal promoter methylation, with the exception of genes that do not contain CpG islands. By contrast, gene body methylation is positively associated with gene expression for only those genes that contain a CpG island promoter. Furthermore, using single nucleotide polymorphism patterns from our hybrid mouse model, we also find positive correlation of allelic gene body methylation with allelic expression.ConclusionsOur method can be used to detect transcriptome, methylome, and single nucleotide polymorphism information within single cells to dissect the mechanisms of epigenetic gene regulation

DNA methylation-based classification of central nervous system tumours.

Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging-with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology

Noninvasive approaches to detect methylation-based markers to monitor gliomas

In this review, we summarize the current approaches used to detect glioma tissue-derived DNA methylation markers in liquid biopsy specimens with the aim to diagnose, prognosticate and potentially track treatment response and evolution of patients with gliomas

Exploring the neuroblastoma DNA methylome: from biology to biomarker

Neuroblastoma (NB), a childhood tumor arising from immature sympathetic nervous system cells, is a heterogeneous disease with prognosis ranging from excellent long-term survival to high-risk with fatal outcome. In order to determine the most appropriate treatment modality, patients are stratified into risk groups at the time of diagnosis, based on combinations of clinical and biological parameters, namely age of the patient, tumor stage, histology, grade of differentiation, MYCN oncogene amplification, chromosome 11q aberration and DNA ploidy. However, use of this risk classification system has shown that accurate assessment of NB prognosis remains difficult and that additional prognostic markers are warranted. Therefore, we aimed to identify prognostic tumor DNA methylation biomarkers for NB. To find new biomarkers, we profiled the primary tumor DNA methylome using methyl-CpG-binding domain (MBD) sequencing, i.e. massively parallel sequencing of methylation-enriched DNA fractions, captured using the high affinity of MBD to bind methylated cytosines. As proof of principle, we applied this technology to 8 NB cell lines, and in combination with mRNA expression studies, this led to a first selection of 43 candidate biomarkers. Next, methylation-specific PCR (MSP) assays were designed, to allow candidate-specific methylation analysis in a primary tumor cohort of 89 samples. As such, we identified new prognostic DNA methylation biomarkers, and delineated the technological aspects and data analysis pipeline to set up a more extended biomarker study. In this follow-up study, the DNA methylome of 102 primary tumors, selected for risk classification and survival, was characterized by MBD sequencing. Differential methylation analyses between the prognostic patient groups put forward 78 top-ranking biomarker candidates, which were subsequently tested on two independent cohorts of 132 and 177 samples, adopting the high-throughput MSP pipeline of our pilot study. Multiple individual MSP assays were prognostically validated and through the implementation of a newly developed statistical framework, a robust 58-marker methylation signature predicting overall and event-free survival was established. This study represents the largest DNA methylation (biomarker) study in NB so far. The MBD sequencing data were shared with the research community through the format of a data descriptor. As such, these data are fully available to others, ensuring its reusability for other research purposes. To illustrate how these data can be applied to gain new insights into the NB pathology, we characterized the DNA methylome of stage 4S NB, a special type of NB found in infants with widespread metastases at diagnosis that paradoxically is associated with an excellent outcome due to its remarkable capacity to undergo spontaneous regression. More specifically, we compared promoter methylation levels between stage 4S, stage 1/2 (localized disease with favorable prognosis) and stage 4 (metastatic disease with dismal prognosis) tumors, and showed that specific chromosomal locations are enriched in stage 4S differentially methylated promoters and that specific subtelomeric promoters are hypermethylated in stage 4S. Furthermore, genes involved in important oncogenic pathways, in neural crest development and differentiation, and in epigenetic processes are differentially methylated and expressed in stage 4S. In conclusion, by exploring the DNA methylome of NB, we have not only demonstrated that DNA methylation patterns are intimately related to NB biology, but also found additional clinically relevant prognostic biomarkers

EpiGe: A machine-learning strategy for rapid classification of medulloblastoma using PCR-based methyl-genotyping

Molecular classification of medulloblastoma is critical for the treatment of this brain tumor. Array-based DNA methylation profiling has emerged as a powerful approach for brain tumor classification. However, this technology is currently not widely available. We present a machine-learning decision support system (DSS) that enables the classification of the principal molecular groups—WNT, SHH, and non-WNT/non-SHH—directly from quantitative PCR (qPCR) data. We propose a framework where the developed DSS appears as a user-friendly web-application—EpiGe-App—that enables automated interpretation of qPCR methylation data and subsequent molecular group prediction. The basis of our classification strategy is a previously validated six-cytosine signature with subgroup-specific methylation profiles. This reduced set of markers enabled us to develop a methyl-genotyping assay capable of determining the methylation status of cytosines using qPCR instruments. This study provides a comprehensive approach for rapid classification of clinically relevant medulloblastoma groups, using readily accessible equipment and an easy-to-use web-application.The study was supported by Associations of Parents and Families of Children with Cancer and by funding of the Spanish Ministry of for Science, Innovation and University (grant PI20/00519; PI CL) and the Foundation La Marató TV3 (grant 201921-30; PI CL). We acknowledge the multidisciplinary team who helped in the molecular analyses and care of patients, and the BioBank Hospital Sant Joan de Déu of the Spanish BioBank Network for sample procurement. We also acknowledge Marta Fortuny for communication strategy advice and Eduard Puig for legal assistance and data protection regulations. Authors acknowledge the SJD Fundraising Team.Peer ReviewedArticle signat per 23 autors/es: Soledad Gómez-González, Joshua Llano, Marta Garcia, Alicia Garrido-Garcia, Mariona Suñol, Isadora Lemos, Sara Perez-Jaume, Noelia Salvador, Nagore Gene-Olaciregui, Raquel Arnau Galán, Vicente Santa-María, Marta Perez-Somarriba, Alicia Castañeda, José Hinojosa, Ursula Winter, Francisco Barbosa Moreira, Fabiana Lubieniecki, Valeria Vazquez, Jaume Mora, Ofelia Cruz, Andrés Morales La Madrid, Alexandre Perera, Cinzia Lavarino.Postprint (published version

Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is the most common type of leukemia. The Cancer Genome Atlas Research Network has demonstrated the increasing genomic complexity of acute myeloid leukemia (AML). In addition, the network has facilitated our understanding of the molecular events leading to this deadly form of malignancy for which the prognosis has not improved over past decades. AML is a highly heterogeneous disease, and cytogenetics and molecular analysis of the various chromosome aberrations including deletions, duplications, aneuploidy, balanced reciprocal translocations and fusion of transcription factor genes and tyrosine kinases has led to better understanding and identification of subgroups of AML with different prognoses. Furthermore, molecular classification based on mRNA expression profiling has facilitated identification of novel subclasses and defined high-, poor-risk AML based on specific molecular signatures. However, despite increased understanding of AML genetics, the outcome for AML patients whose number is likely to rise as the population ages, has not changed significantly. Until it does, further investigation of the genomic complexity of the disease and advances in drug development are needed. In this review, leading AML clinicians and research investigators provide an up-to-date understanding of the molecular biology of the disease addressing advances in diagnosis, classification, prognostication and therapeutic strategies that may have significant promise and impact on overall patient survival

Genome-wide methylome analysis using MethylCap-seq uncovers 4 hypermethylated markers with high sensitivity for both adeno- and squamous-cell cervical carcinoma

Background: Cytology-based screening methods for cervical adenocarcinoma (ADC) and to a lesser extent squamous-cell carcinoma (SCC) suffer from low sensitivity. DNA hypermethylation analysis in cervical scrapings may improve detection of SCC, but few methylation markers have been described for ADC. We aimed to identify novel methylation markers for the early detection of both ADC and SCC. Results: Genome-wide methylation profiling for 20 normal cervices, 6 ADC and 6 SCC using MethylCap-seq yielded 53 candidate regions hypermethylated in both ADC and SCC. Verification and independent validation of the 15 most significant regions revealed 5 markers with differential methylation between 17 normals and 13 cancers. Quantitative methylation-specific PCR on cervical cancer scrapings resulted in detection rates ranging between 80% and 92% while between 94% and 99% of control scrapings tested negative. Four markers (SLC6A5, SOX1, SOX14 and TBX20) detected ADC and SCC with similar sensitivity. In scrapings from women referred with an abnormal smear (n = 229), CIN3+ sensitivity was between 36% and 71%, while between 71% and 93% of adenocarcinoma in situ (AdCIS) were detected; and CIN0/1 specificity was between 88% and 98%. Compared to hrHPV, the combination SOX1/SOX14 showed a similar CIN3+ sensitivity (80% vs. 75%, respectively, P>0.2), while specificity improved (42% vs. 84%, respectively, P < 10(-5)). Conclusion: SOX1 and SOX14 are methylation biomarkers applicable for screening of all cervical cancer types

Doctor of Philosophy

dissertationPost-transcriptional RNA modifications provide new structural and functional features to modified RNA molecules. Extensive research in the past has resulted in isolation of over 100 distinct nucleotide modifications from different organisms and in different RNA species. These modified nucleotides are distributed within the entire transcriptome comprising the cellular epitranscriptome. The ultimate goal of the research in the field is to address what the specific functions of specific modifications are, and also the impact of each on cellular physiology. However, the first question to be addressed is how these > 100 modified nucleotides are distributed within the transcriptome. RNA modification profiling using conventional techniques has provided a great body of knowledge about the distribution of many modifications in RNAs. However, these findings remained limited mostly to tRNAs and rRNAs, the two most abundant and also highly modified RNA species in different organisms. This is partly because of the lower sensitivity of applied classical technologies. Here in this dissertation, in Chapter 2, we are reporting an optimized new RNA bisulfite protocol suitable for high-throughput RNA cytosine methylation profiling. We present the results of application of this technique for 5-methyl-cytosine (m5C) profiling in mouse embryonic fibroblasts (MEFs) RNAs, isolated from wt and dnmt2-/- mice to explore the target specificity of DNA methyltransferase 2 (DNMT2) enzyme. In Chapter 3, we present a substantially novel technique: Aza-IP, for enrichment and identification of the direct targets of RNA cytosine methyltransferases (m5C-RMTs) as well as iv determination of the exact modified bases in the same experiment. We provide the results of the Aza-IP technique for two human m5C-RMTs; DNMT2 and NSUN2, representing their known and novel RNA targets/modified bases. In Chapter 4 we discuss how similar technologies to both of the RNA bisulfite sequencing and Aza-IP techniques as well as other methodologies can be applied and extended for transcriptome-wide profiling of RNA modifications other than m5C. In Chapter 5 we present the future directions of the work focused on cataloguing the direct targets of all human m5C-RMTs in human cultured cells in mouse and fish model systems, to elucidate the functions of cytosine methylation in RNA molecules