scholarly journals scSPLAT, a scalable plate-based protocol for single cell WGBS library preparation

2021 ◽  
Author(s):  
Amanda Raine ◽  
Anders Lundmark ◽  
Alva Annett ◽  
Ann-Christin Wiman ◽  
Marco Cavalli ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Single Cell ◽  
Single Cells ◽  
K562 Cells ◽  
Epigenetic Mark ◽  
Library Preparation ◽  
New Development ◽  
Liver Nuclei ◽  
Pooling Strategy ◽  
Methyl Cytosine

DNA methylation is a central epigenetic mark that has diverse roles in gene regulation, development, and maintenance of genome integrity. 5 methyl cytosine (5mC) can be interrogated at base resolution in single cells by using bisulfite sequencing (scWGBS). Several different scWGBS strategies have been described in recent years to study DNA methylation in single cells. However, there remain limitations with respect to cost-efficiency and yield. Herein, we present a new development in the field of scWGBS library preparation; single cell Splinted Ligation Adapter Tagging (scSPLAT). scSPLAT employs a pooling strategy to facilitate sample preparation at a higher scale and throughput than previously possible. We demonstrate the accuracy and robustness of the method by generating data from 225 single K562 cells and from 309 single liver nuclei and compare scSPLAT against other scWGBS methods.

Comparative analysis of sequencing technologies platforms for single-cell transcriptomics

2018 ◽  
Author(s):  
Kedar Nath Natarajan ◽  
Zhichao Miao ◽  
Miaomiao Jiang ◽  
Xiaoyun Huang ◽  
Hongpo Zhou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Cells ◽  
K562 Cells ◽  
Library Preparation ◽  
Rna Seq ◽  
Illumina Hiseq ◽  
Technical Variability ◽  
Sequencing Technologies ◽  
Sequencing Platforms

AbstractAll single-cell RNA-seq protocols and technologies require library preparation prior to sequencing on a platform such as Illumina. Here, we present the first report to utilize the BGISEQ-500 platform for scRNA-seq, and compare the sensitivity and accuracy to Illumina sequencing. We generate a scRNA-seq resource of 468 unique single-cells and 1,297 matched single cDNA samples, performing SMARTer and Smart-seq2 protocols on mESCs and K562 cells with RNA spike-ins. We sequence these libraries on both BGISEQ-500 and Illumina HiSeq platforms using single- and paired-end reads. The two platforms have comparable sensitivity and accuracy in terms of quantification of gene expression, and low technical variability. Our study provides a standardised scRNA-seq resource to benchmark new scRNA-seq library preparation protocols and sequencing platforms.

scholarly journals Genome-scale oscillations in DNA methylation during exit from pluripotency

2018 ◽  
Author(s):  
Steffen Rulands ◽  
Heather J Lee ◽  
Stephen J Clark ◽  
Christof Angermueller ◽  
Sébastien A Smallwood ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Single Cell ◽  
Epigenetic Mark ◽  
List Type ◽  
Dna Hypomethylation ◽  
Cell Fate Decisions ◽  
Oscillatory Dynamics ◽  
Genome Scale

SummaryPluripotency is accompanied by the erasure of parental epigenetic memory with naïve pluripotent cells exhibiting global DNA hypomethylation both in vitro and in vivo. Exit from pluripotency and priming for differentiation into somatic lineages is associated with genome-wide de novo DNA methylation. We show that during this phase, coexpression of enzymes required for DNA methylation turnover, DNMT3s and TETs, promotes cell-to-cell variability in this epigenetic mark. Using a combination of single-cell sequencing and quantitative biophysical modelling, we show that this variability is associated with coherent, genome-scale, oscillations in DNA methylation with an amplitude dependent on CpG density. Analysis of parallel single-cell transcriptional and epigenetic profiling provides evidence for oscillatory dynamics both in vitro and in vivo. These observations provide fresh insights into the emergence of epigenetic heterogeneity during early embryo development, indicating that dynamic changes in DNA methylation might influence early cell fate decisions.HighlightsCo-expression of DNMT3s and TETs drive genome-scale oscillations of DNA methylationOscillation amplitude is greatest at a CpG density characteristic of enhancersCell synchronisation reveals oscillation period and link with primary transcriptsMultiomic single-cell profiling provides evidence for oscillatory dynamics in vivo

scholarly journals Single-cell multi-omic profiling of chromatin conformation and DNA methylation

2019 ◽  
Author(s):  
Dong-Sung Lee ◽  
Chongyuan Luo ◽  
Jingtian Zhou ◽  
Sahaana Chandran ◽  
Angeline Rivkin ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Single Cell ◽  
Genome Organization ◽  
Single Cells ◽  
Cell Types ◽  
Cell Type ◽  
Cell Level ◽  
3D Genome ◽  
Heterogeneous Samples ◽  
Single Data

Abstract The ability to profile epigenomic features in single cells is facilitating the study of the variation in transcription regulation at the single cell level. Single cell methods have also facilitated the generation of cell-type resolved transcriptomic and epigenetic profiles of lineages derived from complex heterogeneous samples. However, integrating different epigenetic features remain challenging, as many current methods profile a single data type at at time. Furthermore, some epigenetic features, such as 3D genome organization, are intrinsically variable between single cells of the same lineage, so it remains unclear how well these methods may resolve cell-types from complex mixtures. Here we describe a method for profiling 3D genome organization and DNA methylation in single cells. This protocol accompanies Lee et al. (Nature Methods 2019) after peer review to aid potential users in applying the method to their own samples.

scholarly journals Experimental and Bioinformatic Approaches to Studying DNA Methylation in Cancer

Cancers ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 349
Author(s):  
Angelika Merkel ◽  
Manel Esteller
Keyword(s):  
Dna Methylation ◽  
Cancer Research ◽  
Data Processing ◽  
Computational Analysis ◽  
Single Cells ◽  
Cellular Heterogeneity ◽  
Primary Data ◽  
Cell Populations ◽  
Epigenetic Mark ◽  
Bioinformatic Approaches

DNA methylation is an essential epigenetic mark. Alterations of normal DNA methylation are a defining feature of cancer. Here, we review experimental and bioinformatic approaches to showcase the breadth and depth of information that this epigenetic mark provides for cancer research. First, we describe classical approaches for interrogating bulk DNA from cell populations as well as more recently developed approaches for single cells and multi-Omics. Second, we focus on the computational analysis from primary data processing to the identification of unique methylation signatures. Additionally, we discuss challenges such as sparse data and cellular heterogeneity.

scholarly journals BROCKMAN: Deciphering variance in epigenomic regulators byk-mer factorization

2017 ◽  
Author(s):  
Carl G. de Boer ◽  
Aviv Regev
Keyword(s):  
Single Cell ◽  
Dna Sequences ◽  
Single Cells ◽  
K562 Cells ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Variable Component ◽  
Variable Expression ◽  
Diverse Interactions ◽  
Word Frequencies

AbstractBackgroundVariation in chromatin organization across single cells can help shed important light on the mechanisms controlling gene expression, but scale, noise, and sparsity pose significant challenges for interpretation of single cell chromatin data. Here, we develop BROCKMAN (Brockman Representation Of Chromatin byK-mers in Mark-Associated Nucleotides), an approach to infer variation in transcription factor (TF) activity across samples through unsupervised analysis of the variation in DNA sequences associated with an epigenomic mark.ResultsBROCKMAN represents each sample as a vector of epigenomic-mark-associated DNA word frequencies, and decomposes the resulting matrix to find hidden structure in the data, followed by unsupervised grouping of samples and identification of the TFs that distinguish groups. Applied to single cell ATAC-seq, BROCKMAN readily distinguished cell types, treatments, batch effects, experimental artifacts, and cycling cells. We show that each variable component in thek-mer landscape reflects a set of co-varying TFs, which are often known to physically interact. For example, in K562 cells, AP-1 TFs were central determinant of variability in chromatin accessibility through their variable expression levels and diverse interactions with other TFs. We provide a theoretical basis for why cooperative TF binding – and any associated epigenomic mark – is inherently more variable than non-cooperative binding.ConclusionsBROCKMAN and related approaches will help gain a mechanistic understanding of thetransdeterminants of chromatin variability between cells, treatments, and individuals.

scholarly journals scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells

2017 ◽  
Author(s):  
Stephen J. Clark ◽  
Ricard Argelaguet ◽  
Chantriolnt-Andreas Kapourani ◽  
Thomas M. Stubbs ◽  
Heather J. Lee ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Single Cell ◽  
Single Cells ◽  
Embryonic Stem ◽  
Transcriptome Profiling ◽  
Chromatin Accessibility ◽  
Open Chromatin ◽  
Dynamic Coupling ◽  
Single Cell Sequencing ◽  
Molecular Layers

AbstractParallel single-cell sequencing protocols represent powerful methods for investigating regulatory relationships, including epigenome-transcriptome interactions. Here, we report a novel 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.

Genome-wide base-resolution mapping of DNA methylation in single cells using single-cell bisulfite sequencing (scBS-seq)

2017 ◽  
Vol 12 (3) ◽  
pp. 534-547 ◽  
Author(s):  
Stephen J Clark ◽  
Sébastien A Smallwood ◽  
Heather J Lee ◽  
Felix Krueger ◽  
Wolf Reik ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Single Cell ◽  
Bisulfite Sequencing ◽  
Single Cells ◽  
Genome Wide ◽  
Resolution Mapping

scholarly journals Single-Cell DNA-Methylation Analysis Reveals Epigenetic Chimerism in Preimplantation Embryos

Science ◽  
2013 ◽  
Vol 341 (6150) ◽  
pp. 1110-1112 ◽  
Author(s):  
Chanchao Lorthongpanich ◽  
Lih Feng Cheow ◽  
Sathish Balu ◽  
Stephen R. Quake ◽  
Barbara B. Knowles ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Early Stage ◽  
Single Cells ◽  
Preimplantation Embryos ◽  
Epigenetic Mark ◽  
Epigenetic Alterations ◽  
Multiple Loci ◽  
Mammalian Embryos ◽  
Genomic Reprogramming ◽  
Dna Methylation Analysis

Epigenetic alterations are increasingly recognized as causes of human cancers and disease. These aberrations are likely to arise during genomic reprogramming in mammalian preimplantation embryos, when their epigenomes are most vulnerable. However, this process is only partially understood because of the experimental inaccessibility of early-stage embryos. Here, we introduce a methodologic advance, probing single cells for various DNA-methylation errors at multiple loci, to reveal failed maintenance of epigenetic mark results in chimeric mice, which display unpredictable phenotypes leading to developmental arrest. Yet we show that mouse pronuclear transfer can be used to ameliorate such reprogramming defects. This study not only details the epigenetic reprogramming dynamics in early mammalian embryos but also suggests diagnostic and potential future therapeutic applications.

scholarly journals Combined single-cell profiling of expression and DNA methylation reveals splicing regulation and heterogeneity

2018 ◽  
Author(s):  
Stephanie M. Linker ◽  
Lara Urban ◽  
Stephen Clark ◽  
Mariya Chhatriwala ◽  
Shradha Amatya ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Alternative Splicing ◽  
Single Cell ◽  
Single Cells ◽  
Exon Skipping ◽  
Sequence Composition ◽  
Local Sequence ◽  
Single Cell Profiling ◽  
Induced Pluripotent ◽  
Cell Variation

AbstractBackgroundAlternative splicing is a key regulatory mechanism in eukaryotic cells and increases the effective number of functionally distinct gene products. Using bulk RNA sequencing, splicing variation has been studied across human tissues and in genetically diverse populations. This has identified disease-relevant splicing events, as well as associations between splicing and genomic variations, including sequence composition and conservation. However, variability in splicing between single cells from the same tissue or cell type and its determinants remain poorly understood.ResultsWe applied parallel DNA methylation and transcriptome sequencing to differentiating human induced pluripotent stem cells to characterize splicing variation (exon skipping) and its determinants. Our results shows that variation in single-cell splicing can be accurately predicted based on local sequence composition and genomic features. We observe moderate but consistent contributions from local DNA methylation profiles to splicing variation across cells. A combined model that is built based on sequence as well as DNA methylation information accurately predicts different splicing modes of individual cassette exons (AUC=0.85). These categories include the conventional inclusion and exclusion patterns, but also more subtle modes of cell-to-cell variation in splicing. Finally, we identified and characterized associations between DNA methylation and splicing changes during cell differentiation.ConclusionsOur study yields new insights into alternative splicing at the single-cell level and reveals a previously underappreciated link between DNA methylation variation and splicing.

scholarly journals Accurate prediction of single-cell DNA methylation states using deep learning

2016 ◽  
Author(s):  
Christof Angermueller ◽  
Heather J. Lee ◽  
Wolf Reik ◽  
Oliver Stegle
Keyword(s):  
Dna Methylation ◽  
Single Cell ◽  
Deep Neural Networks ◽  
Single Cells ◽  
Cell Types ◽  
Computational Approach ◽  
Methylation Data ◽  
Sequence Composition ◽  
Technological Advances ◽  
Genome Wide

AbstractRecent technological advances have enabled assaying DNA methylation at single-cell resolution. Current protocols are limited by incomplete CpG coverage and hence methods to predict missing methylation states are critical to enable genome-wide analyses. Here, we report DeepCpG, a computational approach based on deep neural networks to predict DNA methylation states from DNA sequence and incomplete methylation profiles in single cells. We evaluated DeepCpG on single-cell methylation data from five cell types generated using alternative sequencing protocols, finding that DeepCpG yields substantially more accurate predictions than previous methods. Additionally, we show that the parameters of our model can be interpreted, thereby providing insights into the effect of sequence composition on methylation variability.

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