Rediscovering pluripotency: from teratocarcinomas to embryonic stem cells

Barbaric, Ivana; Harrison, N.

doi:10.1387/ijdb.123503ib

Cited by 11 publications

(10 citation statements)

References 68 publications

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“…Using a complete strand of origin information acquired by Holo-Seq total RNA pipeline, we identified 301 (66 known, 235 unannotated) antisense transcripts and analyzed their abundance in ten individual mESCs (Additional file 3 : Table S5; Additional file 1 : Figure S10). For example, the antisense transcript from Zmynd8, at an Oct4-occupied enhancer, has been shown to be an important regulator of mESCs self-renewal [ 25 ] (Fig. 3a ; Additional file 1 : Figure S10 b).…”

Section: Resultsmentioning

confidence: 99%

Holo-Seq: single-cell sequencing of holo-transcriptome

et al. 2018

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Current single-cell RNA-seq approaches are hindered by preamplification bias, loss of strand of origin information, and the inability to observe small-RNA and mRNA dual transcriptomes. Here, we introduce a single-cell holo-transcriptome sequencing (Holo-Seq) that overcomes all three hurdles. Holo-Seq has the same quantitative accuracy and uniform coverage with a complete strand of origin information as bulk RNA-seq. Most importantly, Holo-Seq can simultaneously observe small RNAs and mRNAs in a single cell. Furthermore, we acquire small RNA and mRNA dual transcriptomes of 32 human hepatocellular carcinoma single cells, which display the genome-wide super-enhancer activity and hepatic neoplasm kinetics of these cells.Electronic supplementary materialThe online version of this article (10.1186/s13059-018-1553-7) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Holo-Seq: single-cell sequencing of holo-transcriptome

et al. 2018

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“…However, further GO term analysis showed that top-ranked genes (690 genes ahead of Utf1) are mostly related to fundamental processes ( S11 Fig ), so it was not easy to confirm novel regulators of mouse ES cell self-renewal and pluripotency directly. Through the Kolmogorov–Smirnov test [ 35 ], we found that 10 genes shortlisted by SICER and 44 genes shortlisted by MUSIC showed no significant enrichment at the top of the gene prioritization list of the CRISPR-sgRNA screen (SICER: P<0.703, MUSIC: P<0.293, S12 Fig ), but 46 genes of the 100 genes shortlisted by CLUES were significantly enriched at the top of the gene prioritization list of the CRISPR-sgRNA screen (P<0.002), and seven genes among these had either unknown or not fully confirmed roles in mouse ES cells [ 20 , 21 ] ( Fig 5D ). This result encouraged us to knock out these genes individually in mouse ES cells to confirm their functions.…”

Section: Resultsmentioning

confidence: 99%

“…To further validate the value of CLUES, we prioritized broad enrichment signals of Nanog, Oct4, H3K4me3 and H3K27me3 with CLUES in mouse ES cell and identified novel regulators of mouse ES cell self-renewal and pluripotency with the help of a genome-wide CRISPR/Cas9 negative selection genetic screen. We successfully identified Fam60a, Zmynd8, and Abt1, which are either novel or have unconfirmed roles in mouse ES cells [ 20 , 21 ] as novel regulators of mouse ES cell self-renewal and pluripotency.…”

Section: Introductionmentioning

confidence: 99%

Clustering-local-unique-enriched-signals (CLUES) promotes identification of novel regulators of ES cell self-renewal and pluripotency

Yang

Pan

et al. 2018

PLoS ONE

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BackgroundKey regulators of developmental processes can be prioritized through integrated analysis of ChIP-Seq data of master transcriptional factors (TFs) such as Nanog and Oct4, active histone modifications (HMs) such as H3K4me3 and H3K27ac, and repressive HMs such as H3K27me3. Recent studies show that broad enrichment signals such as super-enhancers and broad H3K4me3 enrichment signals play more dominant roles than short enrichment signals of the master TFs and H3K4me3 in epigenetic regulatory mechanism. Besides the broad enrichment signals, up to ten thousands of short enrichment signals of these TFs and HMs exist in genome. Prioritization of these broad enrichment signals from ChIP-Seq data is a prerequisite for such integrated analysis.ResultsHere, we present a method named Clustering-Local-Unique-Enriched-Signals (CLUES), which uses an adaptive-size-windows strategy to identify enriched regions (ERs) and cluster them into broad enrichment signals. Tested on 62 ENCODE ChIP-Seq datasets of Ctcf and Nrsf, CLUES performs equally well as MACS2 regarding prioritization of ERs with the TF’s motif. Tested on 165 ENCODE ChIP-Seq datasets of H3K4me3, H3K27me3, and H3K36me3, CLUES performs better than existing algorithms on prioritizing broad enrichment signals implicating cell functions influenced by epigenetic regulatory mechanism in cells. Most importantly, CLUES helps to confirm several novel regulators of mouse ES cell self-renewal and pluripotency through integrated analysis of prioritized broad enrichment signals of H3K4me3, H3K27me3, Nanog and Oct4 with the support of a CRISPR/Cas9 negative selection genetic screen.ConclusionsCLUES holds promise for prioritizing broad enrichment signals from ChIP-Seq data. The download site for CLUES is https://github.com/Wuchao1984/CLUESv1.

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“…ZMYND8 encodes a zinc finger protein with a complex role in maintaining pluripotency. Although only expressed at low levels, either up- or downregulation of ZMYND8 can induce differentiation in ES cells [33]. JARID2 is a component of chromatin modification complex PRC2 in embryonic stem cells and is required for multilineage differentiation.…”

Section: Resultsmentioning

confidence: 99%

DeGNServer: Deciphering Genome-Scale Gene Networks through High Performance Reverse Engineering Analysis

Wei

Zhao

2013

BioMed Research International

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Analysis of genome-scale gene networks (GNs) using large-scale gene expression data provides unprecedented opportunities to uncover gene interactions and regulatory networks involved in various biological processes and developmental programs, leading to accelerated discovery of novel knowledge of various biological processes, pathways and systems. The widely used context likelihood of relatedness (CLR) method based on the mutual information (MI) for scoring the similarity of gene pairs is one of the accurate methods currently available for inferring GNs. However, the MI-based reverse engineering method can achieve satisfactory performance only when sample size exceeds one hundred. This in turn limits their applications for GN construction from expression data set with small sample size. We developed a high performance web server, DeGNServer, to reverse engineering and decipher genome-scale networks. It extended the CLR method by integration of different correlation methods that are suitable for analyzing data sets ranging from moderate to large scale such as expression profiles with tens to hundreds of microarray hybridizations, and implemented all analysis algorithms using parallel computing techniques to infer gene-gene association at extraordinary speed. In addition, we integrated the SNBuilder and GeNa algorithms for subnetwork extraction and functional module discovery. DeGNServer is publicly and freely available online.

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Rediscovering pluripotency: from teratocarcinomas to embryonic stem cells

Cited by 11 publications

References 68 publications

Holo-Seq: single-cell sequencing of holo-transcriptome

Holo-Seq: single-cell sequencing of holo-transcriptome

Clustering-local-unique-enriched-signals (CLUES) promotes identification of novel regulators of ES cell self-renewal and pluripotency

DeGNServer: Deciphering Genome-Scale Gene Networks through High Performance Reverse Engineering Analysis

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