Faculty Opinions recommendation of Cell-type-specific transcriptome and histone modification dynamics during cellular reprogramming in the Arabidopsis stomatal lineage.

AbstractBackgroundThe identification of enhancer is a challenging task. Various types of epigenetic information including histone modification have been utilized in the construction of enhancer prediction models based on a diverse panel of machine learning models. However, DNA methylation profiles generated from the whole genome bisulfate sequencing (WGBS) have not been fully explored for their potential in enhancer prediction despite the fact that low methylated regions (LMRs) have been implied to be distal active regulatory regions.MethodIn this work we propose a prediction framework, LMethyR-SVM, using LMRs identified from cell-type-specific WGBS DNA methylation profiles based on an unlabeled-negative learning framework. In LMethyR-SVM, the set of cell-type-specific LMRs is further divided into three sets: reliable positive, like positive, and likely negative, according to their resemblance to a small set of experimentally validated enhancers in the VISTA database based on an estimated non-parametric density distribution. Then, the prediction model is trained by solving a weighted support vector machine.ResultsWe demonstrate the performance of LMethyR-SVM by using the WGBS DNA methylation profiles derived from the H1 human embryonic stem cell type (H1) and the fetal lung fibroblast cell type (IMR90). The predicted enhancers are highly conserved with a reasonable validation rate based on a set of commonly used positive markers including transcription factors, p300 binding and DNase-I hypersensitive sites. In addition, we show evidence that the large fraction of LMethyR-SVM predicted enhancers are not predicted by ChromHMM in H1 cell type and they are more enriched for the FANTOM5 enhancers.ConclusionOur work suggests that low methylated regions detected from the WGBS data are useful as complementary resources to histone modification marks in developing models for the prediction of cell type-specific enhancers.

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Histone modification profiles are predictive for tissue/cell-type specific expression of both protein-coding and microRNA genes

BMC Bioinformatics ◽

10.1186/1471-2105-12-155 ◽

2011 ◽

Vol 12 (1) ◽

Cited By ~ 24

Author(s):

Zhihua Zhang ◽

Michael Q Zhang

Keyword(s):

Histone Modification ◽

Tissue Cell ◽

Cell Type ◽

Specific Expression ◽

Protein Coding ◽

Cell Type Specific Expression ◽

Cell Type Specific ◽

Microrna Genes

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Cell-type–specific transcriptome and histone modification dynamics during cellular reprogramming in the Arabidopsis stomatal lineage

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1911400116 ◽

2019 ◽

Vol 116 (43) ◽

pp. 21914-21924 ◽

Cited By ~ 9

Author(s):

Laura R. Lee ◽

Diego L. Wengier ◽

Dominique C. Bergmann

Keyword(s):

Gene Expression ◽

Single Cell ◽

Histone Modifications ◽

Developmental Plasticity ◽

Plant Cells ◽

Cell Types ◽

Cellular Reprogramming ◽

Cell Type ◽

Chip Sequencing ◽

Cell Type Specific

Plant cells maintain remarkable developmental plasticity, allowing them to clonally reproduce and to repair tissues following wounding; yet plant cells normally stably maintain consistent identities. Although this capacity was recognized long ago, our mechanistic understanding of the establishment, maintenance, and erasure of cellular identities in plants remains limited. Here, we develop a cell-type–specific reprogramming system that can be probed at the genome-wide scale for alterations in gene expression and histone modifications. We show that relationships among H3K27me3, H3K4me3, and gene expression in single cell types mirror trends from complex tissue, and that H3K27me3 dynamics regulate guard cell identity. Further, upon initiation of reprogramming, guard cells induce H3K27me3-mediated repression of a regulator of wound-induced callus formation, suggesting that cells in intact tissues may have mechanisms to sense and resist inappropriate dedifferentiation. The matched ChIP-sequencing (seq) and RNA-seq datasets created for this analysis also serve as a resource enabling inquiries into the dynamic and global-scale distribution of histone modifications in single cell types in plants.

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Taiji-reprogram: a framework to uncover cell-type specific regulators and predict cellular reprogramming cocktails

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab100 ◽

2021 ◽

Vol 3 (4) ◽

Author(s):

Jun Wang ◽

Cong Liu ◽

Yue Chen ◽

Wei Wang

Keyword(s):

Cell Types ◽

Cellular Reprogramming ◽

Genetic Networks ◽

Disease Models ◽

Cell Type ◽

Cell Type Specificity ◽

Cell Conversion ◽

Direct Cell ◽

Cell Type Specific ◽

Cell Specialization

Abstract Cellular reprogramming is a promising technology to develop disease models and cell-based therapies. Identification of the key regulators defining the cell type specificity is pivotal to devising reprogramming cocktails for successful cell conversion but remains a great challenge. Here, we present a systems biology approach called Taiji-reprogram to efficiently uncover transcription factor (TF) combinations for conversion between 154 diverse cell types or tissues. This method integrates the transcriptomic and epigenomic data to construct cell-type specific genetic networks and assess the global importance of TFs in the network. Comparative analysis across cell types revealed TFs that are specifically important in a particular cell type and often tightly associated with cell-type specific functions. A systematic search of TFs with differential importance in the source and target cell types uncovered TF combinations for desired cell conversion. We have shown that Taiji-reprogram outperformed the existing methods to better recover the TFs in the experimentally validated reprogramming cocktails. This work not only provides a comprehensive catalog of TFs defining cell specialization but also suggests TF combinations for direct cell conversion.

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