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

Extracellular RNA in a single droplet of human serum reflects physiologic and disease states.

Extracellular RNAs (exRNAs) are present in human serum. It remains unclear to what extent these circulating exRNAs may reflect human physiologic and disease states. Here, we developed SILVER-seq (Small Input Liquid Volume Extracellular RNA Sequencing) to efficiently sequence both integral and fragmented exRNAs from a small droplet (5 μL to 7 μL) of liquid biopsy. We calibrated SILVER-seq in reference to other RNA sequencing methods based on milliliters of input serum and quantified droplet-to-droplet and donor-to-donor variations. We carried out SILVER-seq on more than 150 serum droplets from male and female donors ranging from 18 y to 48 y of age. SILVER-seq detected exRNAs from more than a quarter of the human genes, including small RNAs and fragments of mRNAs and long noncoding RNAs (lncRNAs). The detected exRNAs included those derived from genes with tissue (e.g., brain)-specific expression. The exRNA expression levels separated the male and female samples and were correlated with chronological age. Noncancer and breast cancer donors exhibited pronounced differences, whereas donors with or without cancer recurrence exhibited moderate differences in exRNA expression patterns. Even without using differentially expressed exRNAs as features, nearly all cancer and noncancer samples and a large portion of the recurrence and nonrecurrence samples could be correctly classified by exRNA expression values. These data suggest the potential of using exRNAs in a single droplet of serum for liquid biopsy-based diagnostics

Genome-wide Analysis of the Interplay Between Chromatin-associated Rna and 3d Genome Organization in Human Cells

The interphase genome is dynamically organized in the nucleus and decorated with chromatin-associated RNA (caRNA). It remains unclear whether the genome architecture modulates the spatial distribution of caRNA and vice versa. Here, we generate a resource of genome-wide RNA-DNA and DNA-DNA contact maps in human cells. These maps reveal the chromosomal domains demarcated by locally transcribed RNA, hereafter termed RNA-defined chromosomal domains. Further, the spreading of caRNA is constrained by the boundaries of topologically associating domains (TADs), demonstrating the role of the 3D genome structure in modulating the spatial distribution of RNA. Conversely, stopping transcription or acute depletion of RNA induces thousands of chromatin loops genome-wide. Activation or suppression of the transcription of specific genes suppresses or creates chromatin loops straddling these genes. Deletion of a specific caRNA-producing genomic sequence promotes chromatin loops that straddle the interchromosomal target sequences of this caRNA. These data suggest a feedback loop where the 3D genome modulates the spatial distribution of RNA, which in turn affects the dynamic 3D genome organization

Spatial and Temporal Organization of the Genome: Current State and Future Aims of the 4D Nucleome Project

The four-dimensional nucleome (4DN) consortium studies the architecture of the genome and the nucleus in space and time. We summarize progress by the consortium and highlight the development of technologies for (1) mapping genome folding and identifying roles of nuclear components and bodies, proteins, and RNA, (2) characterizing nuclear organization with time or single-cell resolution, and (3) imaging of nuclear organization. With these tools, the consortium has provided over 2,000 public datasets. Integrative computational models based on these data are starting to reveal connections between genome structure and function. We then present a forward-looking perspective and outline current aims to (1) delineate dynamics of nuclear architecture at different timescales, from minutes to weeks as cells differentiate, in populations and in single cells, (2) characterize cis-determinants and trans-modulators of genome organization, (3) test functional consequences of changes in cis- and trans-regulators, and (4) develop predictive models of genome structure and function

GITAR: An Open Source Tool for Analysis and Visualization of Hi-C Data

Interactions between chromatin segments play a large role in functional genomic assays and developments in genomic interaction detection methods have shown interacting topological domains within the genome. Among these methods, Hi-C plays a key role. Here, we present the Genome Interaction Tools and Resources (GITAR), a software to perform a comprehensive Hi-C data analysis, including data preprocessing, normalization, and visualization, as well as analysis of topologically-associated domains (TADs). GITAR is composed of two main modules: (1) HiCtool, a Python library to process and visualize Hi-C data, including TAD analysis; and (2) processed data library, a large collection of human and mouse datasets processed using HiCtool. HiCtool leads the user step-by-step through a pipeline, which goes from the raw Hi-C data to the computation, visualization, and optimized storage of intra-chromosomal contact matrices and TAD coordinates. A large collection of standardized processed data allows the users to compare different datasets in a consistent way, while saving time to obtain data for visualization or additional analyses. More importantly, GITAR enables users without any programming or bioinformatic expertise to work with Hi-C data. GITAR is publicly available at http://genomegitar.org as an open-source software. Keywords: Chromatin interaction, Pipeline, Hi-C data normalization, Topologically-associated domain, Processed Hi-C data librar

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