Single-cell Bisulfite Sequencing Research Articles

Analyzing single-cell bisulfite sequencing data with MethSCAn

Single-cell bisulfite sequencing (scBS) is a technique that enables the assessment of DNA methylation at single-base pair and single-cell resolution. The analysis of large datasets obtained from scBS requires preprocessing to reduce the data size, improve the signal-to-noise ratio and provide interpretability. Typically, this is achieved by dividing the genome into large tiles and averaging the methylation signals within each tile. Here we demonstrate that this coarse-graining approach can lead to signal dilution. We propose improved strategies to identify more informative regions for methylation quantification and a more accurate quantitation method than simple averaging. Our approach enables better discrimination of cell types and other features of interest and reduces the need for large numbers of cells. We also present an approach to detect differentially methylated regions between groups of cells and demonstrate its ability to identify biologically meaningful regions that are associated with genes involved in the core functions of specific cell types. Finally, we present the software tool MethSCAn for scBS data analysis (https://anders-biostat.github.io/MethSCAn).

Investigating the potential of single-cell DNA methylation data to detect allele-specific methylation and imprinting

Allele-specific methylation (ASM) is an epigenetic modification whereby one parental allele becomes methylated and the other unmethylated at a specific locus. ASM is most often driven by the presence of nearby heterozygous variants that influence methylation, but also occurs somatically in the context of genomic imprinting. In this study, we investigate ASM using publicly available single-cell reduced representation bisulfite sequencing (scRRBS) data on 608 B cells sampled from six healthy B cell samples and 1,230 cells from 11 chronic lymphocytic leukemia (CLL) samples. We developed a likelihood-based criterion to test whether a CpG exhibited ASM, based on the distributions of methylated and unmethylated reads both within and across cells. Applying our likelihood ratio test, 65,998 CpG sites exhibited ASM in healthy B cell samples according to a Bonferroni criterion (p<8.4×10-9), and 32,862 CpG sites exhibited ASM in CLL samples (p<8.5×10-9). We also called ASM at the sample level. To evaluate the accuracy of our method, we called heterozygous variants from the scRRBS data, which enabled variant-based calls of ASM within each cell. Comparing sample-level ASM calls to the variant-based measures of ASM, we observed a positive predictive value of 76%-100% across samples. We observed high concordance of ASM across samples and an overrepresentation of ASM in previously reported imprinted genes and genes with imprinting binding motifs. Our study demonstrates that single-cell bisulfite sequencing is a potentially powerful tool to investigate ASM, especially as studies expand to increase the number of samples and cells sequenced.

ScDMV: a zero-one inflated beta mixture model for DNA methylation variability with scBS-seq data.

The utilization of single-cell bisulfite sequencing (scBS-seq) methods allows for precise analysis of DNA methylation patterns at the individual cell level, enabling the identification of rare populations, revealing cell-specific epigenetic changes, and improving differential methylation analysis. Nonetheless, the presence of sparse data and an overabundance of zeros and ones, attributed to limited sequencing depth and coverage, frequently results in reduced precision accuracy during the process of differential methylation detection using scBS-seq. Consequently, there is a pressing demand for an innovative differential methylation analysis approach that effectively tackles these data characteristics and enhances recognition accuracy. We propose a novel beta mixture approach called scDMV for analyzing methylation differences in single-cell bisulfite sequencing data, which effectively handles excess zeros and ones and accommodates low-input sequencing. Our extensive simulation studies demonstrate that the scDMV approach outperforms several alternative methods in terms of sensitivity, precision, and controlling the false positive rate. Moreover, in real data applications, we observe that scDMV exhibits higher precision and sensitivity in identifying differentially methylated regions, even with low-input samples. In addition, scDMV reveals important information for GO enrichment analysis with single-cell whole-genome sequencing data that are often overlooked by other methods. The scDMV method, along with a comprehensive tutorial, can be accessed as an R package on the following GitHub repository: https://github.com/PLX-m/scDMV.

Joint single-cell profiling resolves 5mC and 5hmC and reveals their distinct gene regulatory effects.

Oxidative modification of 5-methylcytosine (5mC) by ten-eleven translocation (TET) DNA dioxygenases generates 5-hydroxymethylcytosine (5hmC), the most abundant form of oxidized 5mC. Existing single-cell bisulfite sequencing methods cannot resolve 5mC and 5hmC, leaving the cell-type-specific regulatory mechanisms of TET and 5hmC largely unknown. Here, we present joint single-nucleus (hydroxy)methylcytosine sequencing (Joint-snhmC-seq), a scalable and quantitative approach that simultaneously profiles 5hmC and true 5mC in single cells by harnessing differential deaminase activity of APOBEC3A toward 5mC and chemically protected 5hmC. Joint-snhmC-seq profiling of single nuclei from mouse brains reveals an unprecedented level of epigenetic heterogeneity of both 5hmC and true 5mC at single-cell resolution. We show that cell-type-specific profiles of 5hmC or true 5mC improve multimodal single-cell data integration, enable accurate identification of neuronal subtypes and uncover context-specific regulatory effects on cell-type-specific genes by TET enzymes.

Droplet-based bisulfite sequencing for high-throughput profiling of single-cell DNA methylomes

The genome-wide DNA methylation profile, or DNA methylome, is a critical component of the overall epigenomic landscape that modulates gene activities and cell fate. Single-cell DNA methylomic studies offer unprecedented resolution for detecting and profiling cell subsets based on methylomic features. However, existing single-cell methylomic technologies are based on use of tubes or well plates and these platforms are not easily scalable for handling a large number of single cells. Here we demonstrate a droplet-based microfluidic technology, Drop-BS, to construct single-cell bisulfite sequencing libraries for DNA methylome profiling. Drop-BS takes advantage of the ultrahigh throughput offered by droplet microfluidics to prepare bisulfite sequencing libraries of up to 10,000 single cells within 2 days. We apply the technology to profile mixed cell lines, mouse and human brain tissues to reveal cell type heterogeneity. Drop-BS offers a promising solution for single-cell methylomic studies requiring examination of a large cell population.

DNA Methylation Differences Between Zona Pellucida-Bound and Manually Selected Spermatozoa Are Associated With Autism Susceptibility.

Children conceived through intracytoplasmic sperm injection (ICSI) have been reported to have a higher risk of many abnormalities and disorders, including autism and intellectual disability, which may be due to bypassing of the natural sperm selection process during ICSI. Zona pellucida (ZP)-bound spermatozoa (ZPBS) have normal morphology and nuclear DNA. Using these spermatozoa for ICSI results in better outcomes compared with conventional ICSI. However, differences besides morphology that exist between sperm selected by ZP and by an embryologist and whether these differences affect the risk of autism in offspring after ICSI are unclear. To explore these questions, we compared genome-wide DNA methylation profiles between ZPBS and manually selected spermatozoa (MSS)using single-cell bisulfite sequencing. Global DNA methylation levels were significantly lower in ZPBS than in MSS. Using gene ontology (GO) analysis, genes overlapping differentially methylated regions (DMRs) were enriched in biological processes involving neurogenesis. Furthermore, we found that 47.8% of autism candidate genes were associated with DMRs, compared with 37.1% of matched background genes (P<0.001). This was mainly because of the high proportion of autism candidate genes with bivalent chromatin structure. In conclusion, bivalent chromatin structure results in large differences in the methylation of autism genes between MSS and ZPBS. ICSI using MSS, which increases the risk of methylation mutations compared with ZPBS, may lead to a higher risk of autism in offspring.

ScMethBank: a database for single-cell whole genome DNA methylation maps.

Single-cell bisulfite sequencing methods are widely used to assess epigenomic heterogeneity in cell states. Over the past few years, large amounts of data have been generated and facilitated deeper understanding of the epigenetic regulation of many key biological processes including early embryonic development, cell differentiation and tumor progression. It is an urgent need to build a functional resource platform with the massive amount of data. Here, we present scMethBank, the first open access and comprehensive database dedicated to the collection, integration, analysis and visualization of single-cell DNA methylation data and metadata. Current release of scMethBank includes processed single-cell bisulfite sequencing data and curated metadata of 8328 samples derived from 15 public single-cell datasets, involving two species (human and mouse), 29 cell types and two diseases. In summary, scMethBank aims to assist researchers who are interested in cell heterogeneity to explore and utilize whole genome methylation data at single-cell level by providing browse, search, visualization, download functions and user-friendly online tools. The database is accessible at: https://ngdc.cncb.ac.cn/methbank/scm/.

Single-cell DNA methylome analysis of circulating tumor cells.

ObjectivePrevious investigations of circulating tumor cells (CTCs) have mainly focused on their genomic or transcriptomic features, leaving their epigenetic landscape relatively uncharacterized. Here, we investigated the genome-wide DNA methylome of CTCs with a view to understanding the epigenetic regulatory mechanisms underlying cancer metastasis.MethodsWe evaluated single-cell DNA methylome and copy number alteration (CNA) in 196 single cells, including 107 CTCs collected from 17 cancer patients covering six different cancer types. Our single-cell bisulfite sequencing (scBS-seq) covered on average 11.78% of all CpG dinucleotides and accurately deduced the CNA patterns at 500 kb resolution.ResultsWe report distinct subclonal structures and different evolutionary histories of CTCs inferred from CNA and DNA methylation profiles. Furthermore, we demonstrate potential tumor origin classification based on the tissue-specific DNA methylation profiles of CTCs.ConclusionsOur work provides a comprehensive survey of genome-wide DNA methylome in single CTCs and reveals 5-methylcytosine (5-mC) heterogeneity in CTCs, addressing the potential epigenetic regulatory mechanisms underlying cancer metastasis and facilitating the future clinical application of CTCs.

Profiling DNA Methylation Genome-Wide in Single Cells.

Single-cell bisulfite sequencing (scBS-seq) enables profiling of DNA methylation at single-nucleotide resolution and across all genomic features. It can explore methylation differences between cells in mixed cell populations and profile methylation in very rare cell types, such as mammalian oocytes and cells from early embryos. Here, we outline the scBS-seq protocol in a 96-well plate format applicable to studies of moderate throughput.

DNA methylation establishment of CpG islands near maternally imprinted genes on chromosome 7 during mouse oocyte growth.

The genome methylation is globally erased in early fetal germ cells, and it is gradually re-established during gametogenesis. The expression of some imprinted genes is regulated by the methylation status of CpG islands, while the exact time of DNA methylation establishment near maternal imprinted genes during oocyte growth is not well known. Here, growing oocytes were divided into three groups based on follicle diameters including the S-group (60-100 μm), M-group (100-140 μm), and L-group (140-180 μm). The fully grown germinal vesicle (GV)-stage and metaphase II (M2)-stage mature oocytes were also collected. These oocytes were used for single-cell bisulfite sequencing to detect the methylation status of CpG islands near imprinted genes on chromosome 7. The results showed that the CpG islands near Ndn, Magel2, Mkrn3, Peg12, and Igf2 were completely unmethylated, but those of Peg3, Snrpn, and Kcnq1ot1 were hypermethylated in MII-stage oocytes. The methylation of CpG islands near different maternal imprinted genes occurred asynchronously, being completed in later-stage growing oocytes, fully grown GV oocytes, and mature MII-stage oocytes, respectively. These results show that CpG islands near some maternally imprinted genes are not necessarily methylated, and that the establishment of methylation of other maternally imprinted genes is completed at different stages of oocyte growth, providing a novel understanding of the establishment of maternally imprinted genes in oocytes.

Epigenetic evolution and lineage histories of chronic lymphocytic leukaemia.

Genetic and epigenetic intra-tumoral heterogeneity cooperate to shape the evolutionary course of cancer1. Chronic lymphocytic leukemia (CLL) is a highly informative model for cancer evolution as it undergoes substantial genetic diversification and evolution with therapy2,3. The CLL epigenome is also an important disease-defining feature4,5, and growing CLL populations diversify through stochastic DNA methylation (DNAme) changes – epimutations6. However, previous studies based on bulk DNAme sequencing could not answer whether epimutations affect CLL populations homogenously. To measure epimutation rate at single-cell resolution, we applied multiplexed single-cell reduced representation bisulfite sequencing (MscRRBS) to healthy donors B cell and CLL patient samples. We observed that the common clonal CLL origin results in consistently elevated epimutation rate, with low cell-to-cell epimutation rate variability. In contrast, variable epimutation rates across normal B cells reflect diverse evolutionary ages across the B cell differentiation trajectory, consistent with epimutations serving as a molecular clock. Heritable epimutation information allowed high-resolution lineage reconstruction with single-cell data, applicable directly to patient samples. CLL lineage tree shape revealed earlier branching and longer branch lengths than normal B cells, reflecting rapid drift after the initial malignant transformation and a greater proliferative history. MscRRBS integrated with single-cell transcriptomes and genotyping confirmed that genetic subclones map to distinct clades inferred solely based on epimutation information. Lastly, to examine potential lineage biases during therapy, we profiled serial samples during ibrutinib-associated lymphocytosis, and identified clades of cells preferentially expelled from the lymph node with therapy, marked by distinct transcriptional profiles. The single-cell integration of genetic, epigenetic and transcriptional information thus charts CLL’s lineage history and its evolution with therapy.

DNA methylation analysis and editing in single mammalian oocytes

Mammalian oocytes carry specific nongenetic information, including DNA methylation to the next generation, which is important for development and disease. However, evaluation and manipulation of specific methylation for both functional analysis and therapeutic purposes remains challenging. Here, we demonstrate evaluation of specific methylation in single oocytes from its sibling first polar body (PB1) and manipulation of specific methylation in single oocytes by microinjection-mediated dCas9-based targeted methylation editing. We optimized a single-cell bisulfite sequencing approach with high efficiency and demonstrate that the PB1 carries similar methylation profiles at specific regions to its sibling oocyte. By bisulfite sequencing of a single PB1, the methylation information regarding agouti viable yellow (Avy )-related coat color, as well as imprinting linked parthenogenetic development competency, in a single oocyte can be efficiently evaluated. Microinjection-based dCas9-Tet/Dnmt-mediated methylation editing allows targeted manipulation of specific methylation in single oocytes. By targeted methylation editing, we were able to reverse Avy -related coat color, generate full-term development of bimaternal mice, and correct familial Angelman syndrome in a mouse model. Our work will facilitate the investigation of specific methylation events in oocytes and provides a strategy for prevention and correction of maternally transmitted nongenetic disease or disorders.

Using local alignment to enhance single-cell bisulfite sequencing data efficiency.

Single-cell bisulfite sequencing (BS-seq) techniques have been developed for DNA methylation heterogeneity detection and studies with limited materials. However, the data deficiency such as low read mapping ratio is still a critical issue. We comprehensively characterize single-cell BS-seq data and reveal chimerical molecules to be the major source of alignment failures. These chimerical molecules are produced by recombination of genomic proximal sequences with microhomology regions (MR) after bisulfite conversion. In addition, we find DNA methylation within MR is highly variable, suggesting the necessity of removing these regions to accurately estimate DNA methylation levels. We further develop scBS-map to perform quality control and local alignment of bisulfite sequencing data, chimerical molecule determination and MR removal. Using scBS-map, we show remarkable increases in uniquely mapped reads, genomic coverage and number of CpG sites, and recover more functional elements with precise DNA methylation estimation. The scBS-map software is freely available at https://github.com/wupengomics/scBS-map. Supplementary data are available at Bioinformatics online.

BRIF-Seq: Bisulfite-Converted Randomly Integrated Fragments Sequencing at the Single-Cell Level

Single-cell bisulfite sequencing (scBS-seq) was developed to assess DNA methylation heterogeneity in human and mouse. However, the reads are under-represented in regions with high DNA methylation, because these regions are usually fragmented into long segments and are seldom sequenced on the Illumina platform. To reduce the read distribution bias and maximize the use of these long segments, we developed bisulfite-converted randomly integrated fragments sequencing (BRIF-seq), a method with high rates of read mapping and genome coverage. Single microspore of maize, which has a highly methylated and repetitive genome, was used to perform BRIF-seq. High coverage of the haploid genome was obtained to evaluate the methylation states of CG, CHG, and CHH (H = A, C, or T). Compared with scBS-seq, BRIF-seq produced reads that were distributed more evenly across the genome, including regions with high DNA methylation. Surprisingly, the methylation rates among the four microspores within one tetrad were similar, but differed significantly among tetrads, suggesting that non-simultaneous methylation reprogramming could occur among tetrads. Similar levels of heterogeneity, which often occur in low-copy regions, were detected in different genetic backgrounds. These results suggest that BRIF-seq can be applied for single-cell methylome analysis of any species with diverse genetic backgrounds.

HeteroMeth: A Database of Cell-to-cell Heterogeneity in DNA Methylation

DNA methylation is an important epigenetic mark that plays a vital role in gene expression and cell differentiation. The average DNA methylation level among a group of cells has been extensively documented. However, the cell-to-cell heterogeneity in DNA methylation, which reflects the differentiation of epigenetic status among cells, remains less investigated. Here we established a gold standard of the cell-to-cell heterogeneity in DNA methylation based on single-cell bisulfite sequencing (BS-seq) data. With that, we optimized a computational pipeline for estimating the heterogeneity in DNA methylation from bulk BS-seq data. We further built HeteroMeth, a database for searching, browsing, visualizing, and downloading the data for heterogeneity in DNA methylation for a total of 141 samples in humans, mice, Arabidopsis, and rice. Three genes are used as examples to illustrate the power of HeteroMeth in the identification of unique features in DNA methylation. The optimization of the computational strategy and the construction of the database in this study complement the recent experimental attempts on single-cell DNA methylomes and will facilitate the understanding of epigenetic mechanisms underlying cell differentiation and embryonic development. HeteroMeth is publicly available at http://qianlab.genetics.ac.cn/HeteroMeth.

High-Resolution Single-Cell DNA Methylation Measurements Reveal Epigenetically Distinct Hematopoietic Stem Cell Subpopulations.

SummaryIncreasing evidence of functional and transcriptional heterogeneity in phenotypically similar cells examined individually has prompted interest in obtaining parallel methylome data. We describe the development and application of such a protocol to index-sorted murine and human hematopoietic cells that are highly enriched in their content of functionally defined stem cells. Utilizing an optimized single-cell bisulfite sequencing protocol, we obtained quantitative DNA methylation measurements of up to 5.7 million CpGs in single hematopoietic cells. In parallel, we developed an analytical strategy (PDclust) to define single-cell DNA methylation states through pairwise comparisons of single-CpG methylation measurements. PDclust revealed that a single-cell epigenetic state can be described by a small (<1%) stochastically sampled fraction of CpGs and that these states are reflective of cell identity and state. Using relationships revealed by PDclust, we derive near complete methylomes for epigenetically distinct subpopulations of hematopoietic cells enriched for functional stem cell content.

Highly scalable generation of DNA methylation profiles in single cells.

We present a highly scalable assay for whole-genome methylation profiling of single cells. We use our approach, single-cell combinatorial indexing for methylation analysis (sci-MET), to produce 3,282 single-cell bisulfite sequencing libraries and achieve read alignment rates of 68 ± 8%. We apply sci-MET to discriminate the cellular identity of a mixture of three human cell lines and to identify excitatory and inhibitory neuronal populations from mouse cortical tissue.

Single-Cell Sequencing for Drug Discovery and Drug Development.

Next-generation sequencing (NGS), particularly single-cell sequencing, has revolutionized the scale and scope of genomic and biomedical research. Recent technological advances in NGS and singlecell studies have made the deep whole-genome (DNA-seq), whole epigenome and whole-transcriptome sequencing (RNA-seq) at single-cell level feasible. NGS at the single-cell level expands our view of genome, epigenome and transcriptome and allows the genome, epigenome and transcriptome of any organism to be explored without a priori assumptions and with unprecedented throughput. And it does so with single-nucleotide resolution. NGS is also a very powerful tool for drug discovery and drug development. In this review, we describe the current state of single-cell sequencing techniques, which can provide a new, more powerful and precise approach for analyzing effects of drugs on treated cells and tissues. Our review discusses single-cell whole genome/exome sequencing (scWGS/scWES), single-cell transcriptome sequencing (scRNA-seq), single-cell bisulfite sequencing (scBS), and multiple omics of single-cell sequencing. We also highlight the advantages and challenges of each of these approaches. Finally, we describe, elaborate and speculate the potential applications of single-cell sequencing for drug discovery and drug development.

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

DNA methylation (DNAme) is an important epigenetic mark in diverse species. Our current understanding of DNAme is based on measurements from bulk cell samples, which obscures intercellular differences and prevents analyses of rare cell types. Thus, the ability to measure DNAme in single cells has the potential to make important contributions to the understanding of several key biological processes, such as embryonic development, disease progression and aging. We have recently reported a method for generating genome-wide DNAme maps from single cells, using single-cell bisulfite sequencing (scBS-seq), allowing the quantitative measurement of DNAme at up to 50% of CpG dinucleotides throughout the mouse genome. Here we present a detailed protocol for scBS-seq that includes our most recent developments to optimize recovery of CpGs, mapping efficiency and success rate; reduce hands-on time; and increase sample throughput with the option of using an automated liquid handler. We provide step-by-step instructions for each stage of the method, comprising cell lysis and bisulfite (BS) conversion, preamplification and adaptor tagging, library amplification, sequencing and, lastly, alignment and methylation calling. An individual with relevant molecular biology expertise can complete library preparation within 3 d. Subsequent computational steps require 1-3 d for someone with bioinformatics expertise.

Profiling DNA methylome landscapes of mammalian cells with single-cell reduced-representation bisulfite sequencing.

The heterogeneity of DNA methylation within a population of cells necessitates DNA methylome profiling at single-cell resolution. Recently, we developed a single-cell reduced-representation bisulfite sequencing (scRRBS) technique in which we modified the original RRBS method by integrating all the experimental steps before PCR amplification into a single-tube reaction. These modifications enable scRRBS to provide digitized methylation information on ∼1 million CpG sites within an individual diploid mouse or human cell at single-base resolution. Compared with the single-cell bisulfite sequencing (scBS) technique, scRRBS covers fewer CpG sites, but it provides better coverage for CpG islands (CGIs), which are likely to be the most informative elements for DNA methylation. The entire procedure takes ∼3 weeks, and it requires strong molecular biology skills.