Systematic mapping of chromatin state landscapes during mouse development

Du, Gorkin; Barozzi, Iros; Zhang, Y; Ay, Lee; B, Li; Zhao, Yuan; Wildberg, André; Ding, Bo; Zhang, B; Wang, M; Js, Strattan; Jm, Davidson; Qiu, Yefeng; Afzal, Muhammad; Ja, Akiyama; Plajzer-Frick, Ingrid; Cs, Pickle; Kato, Momoe; Th, Garvin; Qt, Pham; An, Harrington; Bj, Mannion; Ea, Lee; Fukuda-Yuzawa, Yoko; He, Yuan; Preißl, Sebastian; Chee, Sora; Ba, Williams; Trout, Diane; Amrhein, Henry; Yang, Hongbo; Cherry, J. Michael; Shen, Yin; Ecker,; Wang, W; De, Dickel; Visel, Axel; Pennacchio, Len A.; Ren, Bin

doi:10.1101/166652

Cited by 33 publications

(38 citation statements)

References 86 publications

(84 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…f , The predicted enhancer landscape of Bcl11b (top) and Tle4 (bottom). Each row is a correlated eDMR to the gene, columns from left to right are: (1) mCG rate and (2) ATAC FPKM in 161 subtypes; (3) bulk developing forebrain tissue mCG rate 6 and (4) H3K27ac FPKM 68 ; (5) adult frontal cortex H3K27ac 2 ; and (6) is a feDMR or not 6 . g , Partial correlation between mCG of enhancers and mCH of genes on separated loop anchors of DG (left) and CA1 (right) compared to random anchors with comparable distance (n=4,171, 4,036, 4,326, 5,133).…”

Section: Resultsmentioning

confidence: 99%

“…4f were gathered by: 1) mCG rate of each eDMR in 161 subtypes from this study, 2) snATAC subtype-level FPKM of each eDMR in the same subtype orders. The subtype snATAC profiles were merged from integration results as described in “STAGE II”, 3) mCG rate of each eDMR in forebrain tissue during ten developing time points from E10.5 to P0, data from He et al 6 , 4) H3K27ac FPKM of each eDMR in 7 developing time points from E11.5 to P0, data from Gorkin et al 68 , 5) H3K27ac FPKM of each eDMR in P56 frontal brain tissue, data from Lister et al 2 and 6) eDMR is overlapped with forebrain feDMR using “bedtools intersect”.…”

Section: Figure-specific Methodsmentioning

confidence: 99%

See 1 more Smart Citation

DNA Methylation Atlas of the Mouse Brain at Single-Cell Resolution

Liu

Zhou

Tian

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

SummaryMammalian brain cells are remarkably diverse in gene expression, anatomy, and function, yet the regulatory DNA landscape underlying this extensive heterogeneity is poorly understood. We carried out a comprehensive assessment of the epigenomes of mouse brain cell types by applying single nucleus DNA methylation sequencing to profile 110,294 nuclei from 45 regions of the mouse cortex, hippocampus, striatum, pallidum, and olfactory areas. We identified 161 cell clusters with distinct spatial locations and projection targets. We constructed taxonomies of these epigenetic types, annotated with signature genes, regulatory elements, and transcription factors. These features indicate the potential regulatory landscape supporting the assignment of putative cell types, and reveal repetitive usage of regulators in excitatory and inhibitory cells for determining subtypes. The DNA methylation landscape of excitatory neurons in the cortex and hippocampus varied continuously along spatial gradients. Using this deep dataset, an artificial neural network model was constructed that precisely predicts single neuron cell-type identity and brain area spatial location. Integration of high-resolution DNA methylomes with single-nucleus chromatin accessibility data allowed prediction of high-confidence enhancer-gene interactions for all identified cell types, which were subsequently validated by cell-type-specific chromatin conformation capture experiments. By combining multi-omic datasets (DNA methylation, chromatin contacts, and open chromatin) from single nuclei and annotating the regulatory genome of hundreds of cell types in the mouse brain, our DNA methylation atlas establishes the epigenetic basis for neuronal diversity and spatial organization throughout the mouse brain.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Figure-specific Methodsmentioning

confidence: 99%

DNA Methylation Atlas of the Mouse Brain at Single-Cell Resolution

Liu

Zhou

Tian

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…First, we use cluster-specific peaks from single cell ATACseq data to annotate the clusters. We collect previously determined enhancers in mouse tissues at 7 developmental stages from 11.5 days post conception until birth (10). Fig.…”

Section: Resultsmentioning

confidence: 99%

“…by alternating least squares (ALS) algorithm with 50 different initializations using a Monte Carlo type approach (16) and get the solutions 10 , 10 , 20 , 20 , which are used as initial solution in our optimization problem. We choose parameters 1 = ‖ − 10 to that in ref (17).…”

Section: Cluster-specific Featuresmentioning

confidence: 99%

Integrative analysis of single cell genomics data by coupled nonnegative matrix factorizations

Duren

Chen

Zamanighomi

et al. 2018

Preprint

View full text Add to dashboard Cite

When different types of functional genomics data are generated on single cells from different samples of cells from the same heterogeneous population, the clustering of cells in the different samples should be coupled. We formulate this "coupled clustering" problem as an optimization problem, and propose the method of coupled nonnegative matrix factorizations (coupled NMF) for its solution. The method is illustrated by the integrative analysis of single cell RNA-seq and single cell ATAC-seq data.Key words: coupled clustering, NMF, single cell genomic data Significance StatementsBiological samples are often heterogeneous mixtures of different types of cells. Suppose we have two single cell data sets, each providing information on a different cellular feature and generated on a different sample from this mixture. Then, the clustering of cells in the two samples should be coupled . CC-BY-NC-ND 4.0 International license It is made available under awas not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/312348 doi: bioRxiv preprint first posted online May. 2, 2018; as both clusterings are reflecting the underlying cell types in the same mixture. This "coupled clustering" problem is a new problem not covered by existing clustering methods. In this paper we develop an approach for its solution based the coupling of two nonnegative matrix factorizations. The method should be useful for integrative single cell genomics analysis tasks such as the joint analysis of single cell RNA-seq and single cell ATAC-seq data.

show abstract

“…Another cardiac GWAS example was rs17608766, the lead SNP for 325 six cardiac traits including aortic root size, blood pressure, pulse pressure, QRS 326 complex, QRS duration and systolic blood pressure. Our chromatin interaction maps 327 revealed that this SNP overlapped a D2 mesoderm specific enhancer that interacted 328 ~110 kb away with the promoter of WNT3 ( Fig. 5c), which is known to be involved in 329 cardiac mesoderm induction 51,52 and thus may explain the broad cardiac traits 330 potentially affected by this SNP.…”

mentioning

confidence: 96%

3D Chromatin Architecture Remodeling during Human Cardiomyocyte Differentiation Reveals A Role Of HERV-H In Demarcating Chromatin Domains

Zhang¹,

Preißl

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

29Restructuring of chromatin architecture is an essential process for establishing cell type-30 specific gene regulatory programs in eukaryotic cells including cardiomyocytes 1-3 . 31 Supporting its importance, recent studies have reported that a substantial number of 32 mutations discovered in congenital heart disease (CHD) patients reside in genes 33 encoding chromatin remodeling factors 4-6 ; yet, how chromatin structure reorganizes to 34 assemble gene regulatory networks crucial for controlling human cardiomyocyte 35 development remains to be elucidated. Here, through comprehensively analyzing high- 36 resolution genomic maps that detail the dynamic changes of chromatin architecture, 37 chromatin accessibility and modifications, and gene expression during human 38 pluripotent stem cell (PSC) cardiomyocyte differentiation, we reveal novel molecular 39 insights into how human PSC chromatin architecture is iteratively remodeled to build 40 gene regulatory networks directing cardiac lineage specification. Specifically, we 41 uncover a new class of human PSC-specific topologically associating domain (TAD) 42 that is created by the active transcription of the primate-specific endogenous 43 retrotransposon HERV-H. Silencing of specific HERV-Hs during the initial stages of 44 human PSC differentiation or by genome-editing results in the elimination of 45 corresponding TAD boundaries and reduced transcription of genes upstream of HERV-46Hs. Supporting their role in maintaining pluripotency, we discovered that deletion of 47 specific HERV-Hs leads to more efficient human PSC cardiomyocyte differentiation. 48Using chromatin interaction maps from these analyses, we also assigned potential 49 target genes to distal regulatory elements involved in cardiac differentiation. Genome-50 editing of enhancers harboring cardiac-disease risk loci associated with congenital and 51 adult heart diseases further confirmed that these loci regulate predicted target genes. 52 Our results highlight a novel role for HERV-Hs in establishing human-specific PSC 53 chromatin architecture, delineate the dynamic gene regulatory networks during 54 cardiomyocyte development and inform how non-coding genetic variants contribute to 55 congenital and adult heart diseases.56 3 MAIN TEXT 57 The three-dimensional organization of chromosomes enables long-range 58 communications between enhancers and promoters that are critical for building complex 59 gene regulatory networks in multicellular species 1,3 . In somatic cells, interphase 60 chromosomes occupy separate nuclear spaces known as chromosome territories 7 . 61 Each chromosome is folded into a dynamic but non-random hierarchical structure 62 characterized by stretches of transcriptionally active, megabase-long compartments that 63 are interspersed with stretches of transcriptionally inactive compartments 8 . These 64 compartments can be further partitioned into topologically associating domains (TADs), 65 which exhibit high levels of intra-domain interactions and relatively low levels of inter...

show abstract

Systematic mapping of chromatin state landscapes during mouse development

Cited by 33 publications

References 86 publications

DNA Methylation Atlas of the Mouse Brain at Single-Cell Resolution

DNA Methylation Atlas of the Mouse Brain at Single-Cell Resolution

Integrative analysis of single cell genomics data by coupled nonnegative matrix factorizations

3D Chromatin Architecture Remodeling during Human Cardiomyocyte Differentiation Reveals A Role Of HERV-H In Demarcating Chromatin Domains

Contact Info

Product

Resources

About