Robust single-cell DNA methylome profiling with snmC-seq2

Luo, Chongyuan; Rivkin, Angeline; Zhou, Jingtian; Sandoval, Justin P.; Kurihara, Laurie; Lucero, Jacinta; Castanon, Rosa; Nery, Joseph R.; Pinto-Duarte, António; Bui, Brian; Fitzpatrick, Conor; O’Connor, Carolyn; Ruga, Seth; Eden, Marc E. Van; Da, Davis; Mash, Deborah C.; Behrens, M. Margarita; Ecker, Joseph R.

doi:10.1101/294355

Cited by 46 publications

(107 citation statements)

References 11 publications

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“…Regions of open chromatin and patterns of DNA methylation, including CG and non-CG methylation, are cell type-specific signatures of neuronal identity and can be assayed in single nuclei 16,18 . We applied single-nucleus methylC-Seq (snmC-Seq2 20,35 ) and open chromatin (snATAC-Seq 36 ) assays to nuclei isolated from the same MOp samples. Independent analyses of each epigenomic dataset identified n=42 cell types from 9,794 cells using snmC-Seq2, and n=33 types from 81,196 cells using snATAC-Seq ( Fig.…”

Section: Epigenetic Cell Types Of Mouse Mopmentioning

confidence: 99%

“…The main steps of this pipeline included: 1) Demultiplexing FASTQ files into single-cell files; 2) Reads level QC; 3) Mapping; 4) BAM file processing and QC; 5) final molecular profile generation. The details of the five steps for snmC-seq2 were described previously 69 . We mapped all the reads onto the mouse mm10 genome.…”

Section: Dna Methylation (Snmc-seq) Data Pre-processing (Salk)mentioning

confidence: 99%

“…Mapping and feature count pipeline for snmC-Seq2: We implemented a versatile mapping pipeline ( cemba-data.rtfd.io ) for all the single-cell methylome based technologies developed by our group 16,20,37 .…”

Section: Dna Methylation (Snmc-seq) Data Pre-processing (Salk)mentioning

confidence: 99%

“…By contrast, our full-length transcript sequencing using SMART-Seq v4 captured a greater number of genes per cell, but covered fewer cells (~6,500 per dataset). Single-nucleus DNA methylation data provided deep coverage of the epigenome per cell for a modest number of cells 16,20 (~10,000), whereas snATAC-Seq data scaled to over 100,000 cells but sampled fewer DNA fragments for individual cells 18 .Subsampling analysis of RNA-Seq datasets (Fig. 1e) shows that in general, scRNA-Seq (both SMART and 10x) detects more genes per cell than snRNA-Seq, and the 10x v3 platform performs substantially better than 10x v2.…”

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confidence: 99%

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An integrated transcriptomic and epigenomic atlas of mouse primary motor cortex cell types

Yao

Liu

Xie

et al. 2020

Preprint

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138

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Single cell transcriptomics has transformed the characterization of brain cell identity by providing quantitative molecular signatures for large, unbiased samples of brain cell populations. With the proliferation of taxonomies based on individual datasets, a major challenge is to integrate and validate results toward defining biologically meaningful cell types. We used a battery of single-cell transcriptome and epigenome measurements generated by the BRAIN Initiative Cell Census Network (BICCN) to comprehensively assess the molecular signatures of cell types in the mouse primary motor cortex (MOp). We further developed computational and statistical methods to integrate these multimodal data and quantitatively validate the reproducibility of the cell types. The reference atlas, based on more than 600,000 high quality single-cell or -nucleus samples assayed by six molecular modalities, is a comprehensive molecular account of the diverse neuronal and non-neuronal cell types in MOp.Collectively, our study indicates that the mouse primary motor cortex contains over 55 neuronal cell types that are highly replicable across analysis methods, sequencing technologies, and modalities. We find many concordant multimodal markers for each cell type, as well as thousands of genes and gene regulatory elements with discrepant transcriptomic and epigenomic signatures. These data highlight the complex molecular regulation of brain cell types and will directly enable design of reagents to target specific MOp cell types for functional analysis. IntroductionNeural circuits are characterized by extraordinary diversity of their cellular components 1,2 . Single-cell molecular assays, especially transcriptomic measurements by RNA-Seq, have accelerated the discovery and characterization of cell types across brain regions and in diverse species. Recent advances include single-cell transcriptome datasets with >10 5 individual cells, identifying hundreds of neuronal and non-neuronal cell types across the mouse nervous system 3-5 . As the number of profiled cells grows into the millions, a key question is whether these data will converge toward a comprehensive and coherent taxonomy of cell types with broad utility for organizing knowledge of brain cells and their function. Data from different modalities, including transcriptomic and epigenomic data, must be cross-referenced and integrated to establish robust and consistent cell type classifications.Although a comprehensive atlas should incorporate anatomical and physiological information, the high throughput of single cell sequencing assays makes integration of molecular data a particularly urgent challenge. A rigorous and reproducible consensus molecular atlas of brain cell types would drive progress across modalities, including obtaining functional information.Single cell sequencing technologies can measure multiple molecular signatures of cell identity. The core molecular identity of a cell is largely established during development and maintained by a combination of gene regulatory proteins...

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Section: Epigenetic Cell Types Of Mouse Mopmentioning

confidence: 99%

Section: Dna Methylation (Snmc-seq) Data Pre-processing (Salk)mentioning

confidence: 99%

Section: Dna Methylation (Snmc-seq) Data Pre-processing (Salk)mentioning

confidence: 99%

mentioning

confidence: 99%

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An integrated transcriptomic and epigenomic atlas of mouse primary motor cortex cell types

Yao

Liu

Xie

et al. 2020

Preprint

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138

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“…While alternative methods exist for generating methylomes that address this issue, namely single-cell BS-seq, these methods are currently not feasible for all organisms and tissues [22][23][24][25]. As a consequence, most currently available methylome data is not singlecell; analyses that can decode additional dimensions of information from this type of data are of high potential value in producing more insights from new and existing data.…”

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confidence: 99%

Processive and stochastic CHH methylation by plant DRM2 and CMT2 revealed by single-read methylome analysis

Harris

Zemach

2020

Preprint

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Cytosine methylome data is commonly generated through next-generation sequencing (NGS). Analyses of this data average methylation states of individual reads. We propose an alternate method of analysing single-read methylome data. Using this method, we identified patterns that relate to the mechanism of two plant non-CG methylating enzymes, DRM2 and CMT2: DRM2 has higher processivity than CMT2, and DRM2-methylated regions have higher variation among cells. Based on these patterns, we developed a classifier that predicts enzyme activity in different species and tissues. To facilitate further single-read analyses, we developed a genome browser optimised for visualising and analysing NGS data at single-read resolution.Background DNA methylation is a conserved epigenetic mechanism that regulates genome stability and expression in diverse eukaryotes [1][2][3][4]. This regulation is based on a dynamic addition or removal of a methyl group to/from the fifth carbon of a cytosine residue. DNA methylation appears in distinct genomic features, such as genes and transposable elements (TEs), and in different chromatin states, such as heterochromatin and euchromatin [2, 5-9]. In plants, DNA methylation occurs in three contexts: CG, CHG and CHH (where H is any base except G). These contexts are differentially regulated by four DNA methyltransferase (DNMT) families that share conserved methyl-transferase domain (MTD). METHYLTRANSFERASE1 (MET1) recognises hemi-methylated CG following DNA replication, and methylates the naked cytosine in the daughter strand [10,11]. CHROMOMETHYLASEs (CMTs), which are plant-specific DNMTs, bind histone H3 lysine 9 (H3K9me2) heterochromatin via a chromodomain (CD) to methylate non-CG contexts [12]. In flowering plants, CMT3 methylate mostly CHG sites, whereas CMT2 methylates mostly CHH sites [13,14]. The CHH methylation state is additionally regulated by plant DNMT3 orthologues or homologs, i.e. the DOMAINS REARRANGED METHYLASEs (DRMs) [15,16]. Similar to animal DNMT3, plant DNMT3 and DRMs function as de novo methylases, establishing methylation on unmethylated sites.

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Applications of single‐cell multi‐omics sequencing in deep understanding of brain diseases

Zhang¹,

Lü²,

Shen³

2022

Clinical and Translational Dis

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Single-cell sequencing (SCS) revolutionises our understanding of genomic, transcriptomic, proteomic and epigenomic landscapes of cells within the brain. • Single-cell sequencing technologies provide novel data sources available for exploration of brain development and brain-related diseases. • Applications of single-cell multi-omics sequencing facilitate brain intricacy deciphering as well as diagnosis, targeted therapy and prognosis of a wide range of brain diseases.

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Robust single-cell DNA methylome profiling with snmC-seq2

Cited by 46 publications

References 11 publications

An integrated transcriptomic and epigenomic atlas of mouse primary motor cortex cell types

An integrated transcriptomic and epigenomic atlas of mouse primary motor cortex cell types

Processive and stochastic CHH methylation by plant DRM2 and CMT2 revealed by single-read methylome analysis

Applications of single‐cell multi‐omics sequencing in deep understanding of brain diseases

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