3D exploration of genomes: a standardized Hi-C data analysis

The biological information of the organisms is stored in the DNA, which folds up into elaborate physical structures inside the cell nucleus. The packing of the genetic material is not only useful to allow spatial compactness, but it assumes also a functional relevance. In such a way, the understanding that nuclear organization plays an important role in the epigenetic regulation poses considerable challenges. During the past fifteen years, several techniques have been developed to explore the architecture of chromatin within the nucleus, such as Chromosome Conformation Capture (3C) and derived 3C protocols (4C, 5C) or Fluorescence In-Situ Hybridization (FISH). However, a genome-wide analysis was only possible after 2009, when the Hi-C protocol was introduced, which first allowed for a comprehensive mapping of genome interactions. In order to process Hi-C data, several software are needed to perform each step of the analysis, from the preprocessing to the visualization of the data. Moreover, a normalization procedure is required to remove biases, introduced by the experimental protocol itself or related to genome features. To address these needs we developed HiCtool, a standardized bioinformatic pipeline that handles efficiently the Hi-C analysis, from the preprocessing and the normalization of the data to the visualization of heatmaps. HiCtool contains the first pipeline for the data preprocessing and also a section for the topological domains analysis, to allow further investigation about genomes conformations. By using HiCtool, we successfully run several Hi-C datasets of different cell lines and conditions of human and mouse, with the aim of creating the biggest library of standardized processed data ever. We collected all these datasets on GITAR (Genome Interaction Tools and Resources), a framework we built to work on and manage genomic interaction data. GITAR contains either a standardized library to process Hi-C data (HiCtool) and the collection of datasets we processed. In such a way, we provide users a powerful and easy tool, both for analysis and epigenetic comparative studies on different species or conditions

PHGDH expression increases with progression of Alzheimer's disease pathology and symptoms.

Chen et al. reveal an increase of phosphoglycerate dehydrogenase (PHGDH) mRNA and protein levels in two mouse models and four human cohorts in Alzheimer's disease brains compared to age- and sex-matched control brains. The increase of PHGDH expression in human brain correlates with symptomatic development and disease pathology

Rainbow-Seq: Combining Cell Lineage Tracing with Single-Cell RNA Sequencing in Preimplantation Embryos

Summary: We developed the Rainbow-seq technology to trace cell division history and reveal single-cell transcriptomes. With distinct fluorescent protein genes as lineage markers, Rainbow-seq enables each single-cell RNA sequencing (RNA-seq) experiment to simultaneously decode the lineage marker genes and read single-cell transcriptomes. We triggered lineage tracking in each blastomere at the 2-cell stage, observed microscopically inequivalent contributions of the progeny to the two embryonic poles at the blastocyst stage, and analyzed every single cell at either 4- or 8-cell stage with deep paired-end sequencing of full-length transcripts. Although lineage difference was not marked unequivocally at a single-gene level, it became clear when the transcriptome was analyzed as a whole. Moreover, several groups of novel transcript isoforms with embedded repeat sequences exhibited lineage difference, suggesting a possible link between DNA demethylation and cell fate decision. Rainbow-seq bridged a critical gap between division history and single-cell RNA-seq assays. : Preimplantation, Cell fate, Single cell, Lineage tracing, Transposon Subject Areas: Preimplantation, Cell fate, Single cell, Lineage tracing, Transposo

Stress-induced RNA–chromatin interactions promote endothelial dysfunction

Global interaction of chromatin-associated RNAs and DNA can be identified in situ. Here the authors report the genome-wide increase of interchromosomal RNA-DNA interactions and demonstrate the importance of such RNA-DNA contacts exemplified by LINC00607 RNA and SERPINE1 gene’s super enhancer in dysfunctional endothelial cell models