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

Farlik, Matthias; Sheffield, Nathan C.; Nuzzo, Angelo; Datlinger, Paul; Schönegger, Andreas; Klughammer, Johanna; Bock, Christoph

doi:10.1016/j.celrep.2015.02.001

Cited by 379 publications

(356 citation statements)

References 39 publications

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“…This concern relates to changes in either DNA methylation or mRNA levels. Implementation of innovative single-cell assays covering genome, epigenome, and transcriptional analysis [84,85] may refine detection of cell type-specific changes. Of further note, subtle changes in AD-associated CpGs could also influence cell type-specific alternative RNA splicing [86] that is thought to represent a primary link between genetic variation and disease [87].…”

Section: Discussionmentioning

confidence: 99%

Driver or Passenger: Epigenomes in Alzheimer’s Disease

Hoffmann¹,

Sportelli²,

Ziller³

et al. 2017

Epigenomes

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Alzheimer's disease (AD) is a fatal neurodegenerative disease which is on the rise worldwide. Despite a wealth of information, genetic factors contributing to the emergence of AD still remain incompletely understood. Sporadic AD is polygenetic in nature and is associated with various environmental risks. Epigenetic mechanisms are well-recognized in the mediation of gene environment interactions, and analysis of epigenetic changes at the genome scale can offer new insights into the relationship between brain epigenomes and AD. In fact, recent epigenome-wide association studies (EWAS) indicate that changes in DNA methylation are an early event preceding clinical manifestation and are tightly associated with AD neuropathology. Further, candidate genes from EWAS interact with those from genome-wide association studies (GWAS) that can undergo epigenetic changes in their upstream gene regulatory elements. Functionally, AD-associated DNA methylation changes partially influence transcription of candidate genes involved in pathways relevant to AD. The timing of epigenomic changes in AD together with the genes affected indicate a critical role, however, further mechanistic insight is required to corroborate this hypothesis. In this respect, recent advances in neuronal reprogramming of patient-derived cells combined with new genome-editing techniques offer unprecedented opportunities to dissect the functional and mechanistic role of epigenomic changes in AD.

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Section: Discussionmentioning

confidence: 99%

Driver or Passenger: Epigenomes in Alzheimer’s Disease

Hoffmann¹,

Sportelli²,

Ziller³

et al. 2017

Epigenomes

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show abstract

“…DNA methylation potentially captures this complexity at a single cell level [48]. However, such single-cell approaches are unlikely to be practical or economic on large numbers of clinical or population-based cohort samples.…”

Section: What Is a Cell Type Anyway?mentioning

confidence: 99%

Is Cellular Heterogeneity Merely a Confounder to be Removed from Epigenome-Wide Association Studies?

et al. 2017

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Excitement about DNA methylation biomarkers has been tempered by a growing appreciation of the complex causal relations with cell fate. Intersample differences in DNA methylation can be partitioned into those that are independent of cellular heterogeneity and those that are caused by differential mixtures of cell types. Generally, the field has assumed that the former are more likely to be causative of disease. The latter has been considered a likely consequence of disease and a confounder to be removed. We argue that the conceptual separation of these signals is artificial and not necessarily informative about causation. DNA methylation is a very sensitive measure of cell fate mix and therefore reveals much about underlying disease etiology including aspects of causation. DNA methylation marks integrate genetic & environmental influences & hence have potential as prognostic & stratification biomarkers & also as read-outs of intervention efficacyLoci-specific DNA 5-methylcytosine levels at CpG sites are easily measured at scale in biological samples. They vary across individuals and this variation has been robustly associated with a range of disease phenotypes and environmental exposures [1][2][3][4][5][6]. As interindividual DNA methylation is specified by the interaction of both genotypic and environmental influences [7,8], it may be a more powerful prognostic biomarker than either genotypic or lifestyle factors alone [9]. DNA methylation is dynamic throughout the lifecourse and is potentially modifiable by therapeutic interventions. This raises the possibility of sensitive real-time biomarkers of disease status to track intervention efficacy [10].Locus-specific observations were first to demonstrate the role of early life environmental influence on interindividual variation in methylation. For instance, postnatal maternal care in rats or childhood trauma in humans, affects DNA methylation levels at the NR3C1 gene in blood and the hippocampus; methylation levels at this locus then predict adult psychopathology [11][12][13]. Observations such as this have inspired many epigenome wide association studies (EWAS) to determine the epigenetic consequences of early life influences. Although epigenetic programming was originally envisioned to pertain to very early life factors (i.e., fetal programming, see for example [14]), it could be generalized to account for environmental influences throughout the lifecourse. Some of the best evidence for the later life effect of the environment on DNA methylation is from Fasanelli and colleagues, who showed (by EWAS) that smoking reversibly caused hypomethylation of the AHRR and FR2L3 genes in adults and that

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“…scBS used a post-bisulfite adaptor tagging (PBAT) protocol, avoiding damage of the two ligated adapters during the bisulfite treatment and improved PCR amplification. Farlik and colleagues (59) further extended the scBS method, named scWGBS, with direct library construction after bisulfite conversion, low-coverage sequencing (capturing 1.54 million CpGs with 4.6 million reads on average), and a supervised bioinformatics strategy using in silico merging of the data from multiple single cells to increase the overall coverage.…”

Section: Single-cell Epigenomic Sequencingmentioning

confidence: 99%

Single-Cell Sequencing for Precise Cancer Research: Progress and Prospects

et al. 2016

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Advances in genomic technology have enabled the faithful detection and measurement of mutations and the gene expression profile of cancer cells at the single-cell level. Recently, several single-cell sequencing methods have been developed that permit the comprehensive and precise analysis of the cancer-cell genome, transcriptome, and epigenome. The use of these methods to analyze cancer cells has led to a series of unanticipated discoveries, such as the high heterogeneity and stochastic changes in cancer-cell populations, the new driver mutations and the complicated clonal evolution mechanisms, and the novel identification of biomarkers of variant tumors. These methods and the knowledge gained from their utilization could potentially improve the early detection and monitoring of rare cancer cells, such as circulating tumor cells and disseminated tumor cells, and promote the development of personalized and highly precise cancer therapy. Here, we discuss the current methods for single cancer-cell sequencing, with a strong focus on those practically used or potentially valuable in cancer research, including single-cell isolation, whole genome and transcriptome amplification, epigenome profiling, multi-dimensional sequencing, and next-generation sequencing and analysis. We also examine the current applications, challenges, and prospects of single cancer-cell sequencing.

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

Cited by 379 publications

References 39 publications

Driver or Passenger: Epigenomes in Alzheimer’s Disease

Driver or Passenger: Epigenomes in Alzheimer’s Disease

Is Cellular Heterogeneity Merely a Confounder to be Removed from Epigenome-Wide Association Studies?

Single-Cell Sequencing for Precise Cancer Research: Progress and Prospects

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