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LOcating non-unique matched tags (LONUT) to improve the detection of the enriched regions for ChIP-seq data.
PUBLISHED: 01-01-2013
One big limitation of computational tools for analyzing ChIP-seq data is that most of them ignore non-unique tags (NUTs) that match the human genome even though NUTs comprise up to 60% of all raw tags in ChIP-seq data. Effectively utilizing these NUTs would increase the sequencing depth and allow a more accurate detection of enriched binding sites, which in turn could lead to more precise and significant biological interpretations. In this study, we have developed a computational tool, LOcating Non-Unique matched Tags (LONUT), to improve the detection of enriched regions from ChIP-seq data. Our LONUT algorithm applies a linear and polynomial regression model to establish an empirical score (ES) formula by considering two influential factors, the distance of NUTs to peaks identified using uniquely matched tags (UMTs) and the enrichment score for those peaks resulting in each NUT being assigned to a unique location on the reference genome. The newly located tags from the set of NUTs are combined with the original UMTs to produce a final set of combined matched tags (CMTs). LONUT was tested on many different datasets representing three different characteristics of biological data types. The detected sites were validated using de novo motif discovery and ChIP-PCR. We demonstrate the specificity and accuracy of LONUT and show that our program not only improves the detection of binding sites for ChIP-seq, but also identifies additional binding sites.
Authors: Lara Rajeev, Eric G. Luning, Aindrila Mukhopadhyay.
Published: 07-21-2014
In vivo methods such as ChIP-chip are well-established techniques used to determine global gene targets for transcription factors. However, they are of limited use in exploring bacterial two component regulatory systems with uncharacterized activation conditions. Such systems regulate transcription only when activated in the presence of unique signals. Since these signals are often unknown, the in vitro microarray based method described in this video article can be used to determine gene targets and binding sites for response regulators. This DNA-affinity-purified-chip method may be used for any purified regulator in any organism with a sequenced genome. The protocol involves allowing the purified tagged protein to bind to sheared genomic DNA and then affinity purifying the protein-bound DNA, followed by fluorescent labeling of the DNA and hybridization to a custom tiling array. Preceding steps that may be used to optimize the assay for specific regulators are also described. The peaks generated by the array data analysis are used to predict binding site motifs, which are then experimentally validated. The motif predictions can be further used to determine gene targets of orthologous response regulators in closely related species. We demonstrate the applicability of this method by determining the gene targets and binding site motifs and thus predicting the function for a sigma54-dependent response regulator DVU3023 in the environmental bacterium Desulfovibrio vulgaris Hildenborough.
16 Related JoVE Articles!
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Generation of High Quality Chromatin Immunoprecipitation DNA Template for High-throughput Sequencing (ChIP-seq)
Authors: Sandra Deliard, Jianhua Zhao, Qianghua Xia, Struan F.A. Grant.
Institutions: Children's Hospital of Philadelphia Research Institute, University of Pennsylvania .
ChIP-sequencing (ChIP-seq) methods directly offer whole-genome coverage, where combining chromatin immunoprecipitation (ChIP) and massively parallel sequencing can be utilized to identify the repertoire of mammalian DNA sequences bound by transcription factors in vivo. "Next-generation" genome sequencing technologies provide 1-2 orders of magnitude increase in the amount of sequence that can be cost-effectively generated over older technologies thus allowing for ChIP-seq methods to directly provide whole-genome coverage for effective profiling of mammalian protein-DNA interactions. For successful ChIP-seq approaches, one must generate high quality ChIP DNA template to obtain the best sequencing outcomes. The description is based around experience with the protein product of the gene most strongly implicated in the pathogenesis of type 2 diabetes, namely the transcription factor transcription factor 7-like 2 (TCF7L2). This factor has also been implicated in various cancers. Outlined is how to generate high quality ChIP DNA template derived from the colorectal carcinoma cell line, HCT116, in order to build a high-resolution map through sequencing to determine the genes bound by TCF7L2, giving further insight in to its key role in the pathogenesis of complex traits.
Molecular Biology, Issue 74, Genetics, Biochemistry, Microbiology, Medicine, Proteins, DNA-Binding Proteins, Transcription Factors, Chromatin Immunoprecipitation, Genes, chromatin, immunoprecipitation, ChIP, DNA, PCR, sequencing, antibody, cross-link, cell culture, assay
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Massively Parallel Reporter Assays in Cultured Mammalian Cells
Authors: Alexandre Melnikov, Xiaolan Zhang, Peter Rogov, Li Wang, Tarjei S. Mikkelsen.
Institutions: Broad Institute.
The genetic reporter assay is a well-established and powerful tool for dissecting the relationship between DNA sequences and their gene regulatory activities. The potential throughput of this assay has, however, been limited by the need to individually clone and assay the activity of each sequence on interest using protein fluorescence or enzymatic activity as a proxy for regulatory activity. Advances in high-throughput DNA synthesis and sequencing technologies have recently made it possible to overcome these limitations by multiplexing the construction and interrogation of large libraries of reporter constructs. This protocol describes implementation of a Massively Parallel Reporter Assay (MPRA) that allows direct comparison of hundreds of thousands of putative regulatory sequences in a single cell culture dish.
Genetics, Issue 90, gene regulation, transcriptional regulation, sequence-activity mapping, reporter assay, library cloning, transfection, tag sequencing, mammalian cells
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Chromatin Isolation by RNA Purification (ChIRP)
Authors: Ci Chu, Jeffrey Quinn, Howard Y. Chang.
Institutions: Stanford University School of Medicine.
Long noncoding RNAs are key regulators of chromatin states for important biological processes such as dosage compensation, imprinting, and developmental gene expression 1,2,3,4,5,6,7. The recent discovery of thousands of lncRNAs in association with specific chromatin modification complexes, such as Polycomb Repressive Complex 2 (PRC2) that mediates histone H3 lysine 27 trimethylation (H3K27me3), suggests broad roles for numerous lncRNAs in managing chromatin states in a gene-specific fashion 8,9. While some lncRNAs are thought to work in cis on neighboring genes, other lncRNAs work in trans to regulate distantly located genes. For instance, Drosophila lncRNAs roX1 and roX2 bind numerous regions on the X chromosome of male cells, and are critical for dosage compensation 10,11. However, the exact locations of their binding sites are not known at high resolution. Similarly, human lncRNA HOTAIR can affect PRC2 occupancy on hundreds of genes genome-wide 3,12,13, but how specificity is achieved is unclear. LncRNAs can also serve as modular scaffolds to recruit the assembly of multiple protein complexes. The classic trans-acting RNA scaffold is the TERC RNA that serves as the template and scaffold for the telomerase complex 14; HOTAIR can also serve as a scaffold for PRC2 and a H3K4 demethylase complex 13. Prior studies mapping RNA occupancy at chromatin have revealed substantial insights 15,16, but only at a single gene locus at a time. The occupancy sites of most lncRNAs are not known, and the roles of lncRNAs in chromatin regulation have been mostly inferred from the indirect effects of lncRNA perturbation. Just as chromatin immunoprecipitation followed by microarray or deep sequencing (ChIP-chip or ChIP-seq, respectively) has greatly improved our understanding of protein-DNA interactions on a genomic scale, here we illustrate a recently published strategy to map long RNA occupancy genome-wide at high resolution 17. This method, Chromatin Isolation by RNA Purification (ChIRP) (Figure 1), is based on affinity capture of target lncRNA:chromatin complex by tiling antisense-oligos, which then generates a map of genomic binding sites at a resolution of several hundred bases with high sensitivity and low background. ChIRP is applicable to many lncRNAs because the design of affinity-probes is straightforward given the RNA sequence and requires no knowledge of the RNA's structure or functional domains.
Genetics, Issue 61, long noncoding RNA (lncRNA), genomics, chromatin binding, high-throughput sequencing, ChIRP
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Genome-wide Analysis using ChIP to Identify Isoform-specific Gene Targets
Authors: Michael L. Beshiri, Abul Islam, Dannielle C. DeWaal, William F. Richter, Jennifer Love, Nuria Lopez-Bigas, Elizaveta V. Benevolenskaya.
Institutions: University of Illinois Chicago - UIC, Universitat Pompeu Fabra, Whitehead Institute for Biomedical Research.
Recruitment of transcriptional and epigenetic factors to their targets is a key step in their regulation. Prominently featured in recruitment are the protein domains that bind to specific histone modifications. One such domain is the plant homeodomain (PHD), found in several chromatin-binding proteins. The epigenetic factor RBP2 has multiple PHD domains, however, they have different functions (Figure 4). In particular, the C-terminal PHD domain, found in a RBP2 oncogenic fusion in human leukemia, binds to trimethylated lysine 4 in histone H3 (H3K4me3)1. The transcript corresponding to the RBP2 isoform containing the C-terminal PHD accumulates during differentiation of promonocytic, lymphoma-derived, U937 cells into monocytes2. Consistent with both sets of data, genome-wide analysis showed that in differentiated U937 cells, the RBP2 protein gets localized to genomic regions highly enriched for H3K4me33. Localization of RBP2 to its targets correlates with a decrease in H3K4me3 due to RBP2 histone demethylase activity and a decrease in transcriptional activity. In contrast, two other PHDs of RBP2 are unable to bind H3K4me3. Notably, the C-terminal domain PHD of RBP2 is absent in the smaller RBP2 isoform4. It is conceivable that the small isoform of RBP2, which lacks interaction with H3K4me3, differs from the larger isoform in genomic location. The difference in genomic location of RBP2 isoforms may account for the observed diversity in RBP2 function. Specifically, RBP2 is a critical player in cellular differentiation mediated by the retinoblastoma protein (pRB). Consistent with these data, previous genome-wide analysis, without distinction between isoforms, identified two distinct groups of RBP2 target genes: 1) genes bound by RBP2 in a manner that is independent of differentiation; 2) genes bound by RBP2 in a differentiation-dependent manner. To identify differences in localization between the isoforms we performed genome-wide location analysis by ChIP-Seq. Using antibodies that detect both RBP2 isoforms we have located all RBP2 targets. Additionally we have antibodies that only bind large, and not small RBP2 isoform (Figure 4). After identifying the large isoform targets, one can then subtract them from all RBP2 targets to reveal the targets of small isoform. These data show the contribution of chromatin-interacting domain in protein recruitment to its binding sites in the genome.
Biochemistry, Issue 41, chromatin immunoprecipitation, ChIP-Seq, RBP2, JARID1A, KDM5A, isoform-specific recruitment
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Chromatin Immunoprecipitation (ChIP) using Drosophila tissue
Authors: Vuong Tran, Qiang Gan, Xin Chen.
Institutions: Johns Hopkins University.
Epigenetics remains a rapidly developing field that studies how the chromatin state contributes to differential gene expression in distinct cell types at different developmental stages. Epigenetic regulation contributes to a broad spectrum of biological processes, including cellular differentiation during embryonic development and homeostasis in adulthood. A critical strategy in epigenetic studies is to examine how various histone modifications and chromatin factors regulate gene expression. To address this, Chromatin Immunoprecipitation (ChIP) is used widely to obtain a snapshot of the association of particular factors with DNA in the cells of interest. ChIP technique commonly uses cultured cells as starting material, which can be obtained in abundance and homogeneity to generate reproducible data. However, there are several caveats: First, the environment to grow cells in Petri dish is different from that in vivo, thus may not reflect the endogenous chromatin state of cells in a living organism. Second, not all types of cells can be cultured ex vivo. There are only a limited number of cell lines, from which people can obtain enough material for ChIP assay. Here we describe a method to do ChIP experiment using Drosophila tissues. The starting material is dissected tissue from a living animal, thus can accurately reflect the endogenous chromatin state. The adaptability of this method with many different types of tissue will allow researchers to address a lot more biologically relevant questions regarding epigenetic regulation in vivo1, 2. Combining this method with high-throughput sequencing (ChIP-seq) will further allow researchers to obtain an epigenomic landscape.
Genetics, Issue 61, ChIP, Drosophila, testes, q-PCR, high throughput sequencing, epi-genetics
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From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
Authors: Wen-Ting Tsai, Ahmed Hassan, Purbasha Sarkar, Joaquin Correa, Zoltan Metlagel, Danielle M. Jorgens, Manfred Auer.
Institutions: Lawrence Berkeley National Laboratory, Lawrence Berkeley National Laboratory, Lawrence Berkeley National Laboratory.
Modern 3D electron microscopy approaches have recently allowed unprecedented insight into the 3D ultrastructural organization of cells and tissues, enabling the visualization of large macromolecular machines, such as adhesion complexes, as well as higher-order structures, such as the cytoskeleton and cellular organelles in their respective cell and tissue context. Given the inherent complexity of cellular volumes, it is essential to first extract the features of interest in order to allow visualization, quantification, and therefore comprehension of their 3D organization. Each data set is defined by distinct characteristics, e.g., signal-to-noise ratio, crispness (sharpness) of the data, heterogeneity of its features, crowdedness of features, presence or absence of characteristic shapes that allow for easy identification, and the percentage of the entire volume that a specific region of interest occupies. All these characteristics need to be considered when deciding on which approach to take for segmentation. The six different 3D ultrastructural data sets presented were obtained by three different imaging approaches: resin embedded stained electron tomography, focused ion beam- and serial block face- scanning electron microscopy (FIB-SEM, SBF-SEM) of mildly stained and heavily stained samples, respectively. For these data sets, four different segmentation approaches have been applied: (1) fully manual model building followed solely by visualization of the model, (2) manual tracing segmentation of the data followed by surface rendering, (3) semi-automated approaches followed by surface rendering, or (4) automated custom-designed segmentation algorithms followed by surface rendering and quantitative analysis. Depending on the combination of data set characteristics, it was found that typically one of these four categorical approaches outperforms the others, but depending on the exact sequence of criteria, more than one approach may be successful. Based on these data, we propose a triage scheme that categorizes both objective data set characteristics and subjective personal criteria for the analysis of the different data sets.
Bioengineering, Issue 90, 3D electron microscopy, feature extraction, segmentation, image analysis, reconstruction, manual tracing, thresholding
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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
Authors: James Smadbeck, Meghan B. Peterson, George A. Khoury, Martin S. Taylor, Christodoulos A. Floudas.
Institutions: Princeton University.
The aim of de novo protein design is to find the amino acid sequences that will fold into a desired 3-dimensional structure with improvements in specific properties, such as binding affinity, agonist or antagonist behavior, or stability, relative to the native sequence. Protein design lies at the center of current advances drug design and discovery. Not only does protein design provide predictions for potentially useful drug targets, but it also enhances our understanding of the protein folding process and protein-protein interactions. Experimental methods such as directed evolution have shown success in protein design. However, such methods are restricted by the limited sequence space that can be searched tractably. In contrast, computational design strategies allow for the screening of a much larger set of sequences covering a wide variety of properties and functionality. We have developed a range of computational de novo protein design methods capable of tackling several important areas of protein design. These include the design of monomeric proteins for increased stability and complexes for increased binding affinity. To disseminate these methods for broader use we present Protein WISDOM (, a tool that provides automated methods for a variety of protein design problems. Structural templates are submitted to initialize the design process. The first stage of design is an optimization sequence selection stage that aims at improving stability through minimization of potential energy in the sequence space. Selected sequences are then run through a fold specificity stage and a binding affinity stage. A rank-ordered list of the sequences for each step of the process, along with relevant designed structures, provides the user with a comprehensive quantitative assessment of the design. Here we provide the details of each design method, as well as several notable experimental successes attained through the use of the methods.
Genetics, Issue 77, Molecular Biology, Bioengineering, Biochemistry, Biomedical Engineering, Chemical Engineering, Computational Biology, Genomics, Proteomics, Protein, Protein Binding, Computational Biology, Drug Design, optimization (mathematics), Amino Acids, Peptides, and Proteins, De novo protein and peptide design, Drug design, In silico sequence selection, Optimization, Fold specificity, Binding affinity, sequencing
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Efficient Chromatin Immunoprecipitation using Limiting Amounts of Biomass
Authors: Dean Tantin, Warren P. Voth, Arvind Shakya.
Institutions: University of Utah School of Medicine.
Chromatin immunoprecipitation (ChIP) is a widely-used method for determining the interactions of different proteins with DNA in chromatin of living cells. Examples include sequence-specific DNA binding transcription factors, histones and their different modification states, enzymes such as RNA polymerases and ancillary factors, and DNA repair components. Despite its ubiquity, there is a lack of up-to-date, detailed methodologies for both bench preparation of material and for accurate analysis allowing quantitative metrics of interaction. Due to this lack of information, and also because, like any immunoprecipitation, conditions must be re-optimized for new sets of experimental conditions, the ChIP assay is susceptible to inaccurate or poorly quantitative results. Our protocol is ultimately derived from seminal work on transcription factor:DNA interactions1,2 , but incorporates a number of improvements to sensitivity and reproducibility for difficult-to-obtain cell types. The protocol has been used successfully3,4 , both using qPCR to quantify DNA enrichment, or using a semi-quantitative variant of the below protocol. This quantitative analysis of PCR-amplified material is performed computationally, and represents a limiting factor in the assay. Important controls and other considerations include the use of an isotype-matched antibody, as well as evaluation of a control region of genomic DNA, such as an intergenic region predicted not to be bound by the protein under study (or anticipated not to show changes under the experimental conditions). In addition, a standard curve of input material for every ChIP sample is used to derive absolute levels of enrichment in the experimental material. Use of standard curves helps to take into account differences between primer sets, regardless of how carefully they are designed, and also efficiency differences throughout the range of template concentrations for a single primer set. Our protocol is different from others that are available5-8 in that we extensively cover the later, analysis phase.
Molecular Biology, Issue 75, Genetics, Cellular Biology, Biomedical Engineering, Microbiology, Immunology, Biochemistry, Proteins, life sciences, animal models, chromatin immunoprecipitation, ChIP, chromatin, immunoprecipitation, gene regulation, T lymphocyte, transcription factor, chromatin modification, DNA, quantitative PCR, PCR, cells, isolation, animal model
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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
Authors: Haipeng Xing, Willey Liao, Yifan Mo, Michael Q. Zhang.
Institutions: Stony Brook University, Cold Spring Harbor Laboratory, University of Texas at Dallas.
ChIPseq is a widely used technique for investigating protein-DNA interactions. Read density profiles are generated by using next-sequencing of protein-bound DNA and aligning the short reads to a reference genome. Enriched regions are revealed as peaks, which often differ dramatically in shape, depending on the target protein1. For example, transcription factors often bind in a site- and sequence-specific manner and tend to produce punctate peaks, while histone modifications are more pervasive and are characterized by broad, diffuse islands of enrichment2. Reliably identifying these regions was the focus of our work. Algorithms for analyzing ChIPseq data have employed various methodologies, from heuristics3-5 to more rigorous statistical models, e.g. Hidden Markov Models (HMMs)6-8. We sought a solution that minimized the necessity for difficult-to-define, ad hoc parameters that often compromise resolution and lessen the intuitive usability of the tool. With respect to HMM-based methods, we aimed to curtail parameter estimation procedures and simple, finite state classifications that are often utilized. Additionally, conventional ChIPseq data analysis involves categorization of the expected read density profiles as either punctate or diffuse followed by subsequent application of the appropriate tool. We further aimed to replace the need for these two distinct models with a single, more versatile model, which can capably address the entire spectrum of data types. To meet these objectives, we first constructed a statistical framework that naturally modeled ChIPseq data structures using a cutting edge advance in HMMs9, which utilizes only explicit formulas-an innovation crucial to its performance advantages. More sophisticated then heuristic models, our HMM accommodates infinite hidden states through a Bayesian model. We applied it to identifying reasonable change points in read density, which further define segments of enrichment. Our analysis revealed how our Bayesian Change Point (BCP) algorithm had a reduced computational complexity-evidenced by an abridged run time and memory footprint. The BCP algorithm was successfully applied to both punctate peak and diffuse island identification with robust accuracy and limited user-defined parameters. This illustrated both its versatility and ease of use. Consequently, we believe it can be implemented readily across broad ranges of data types and end users in a manner that is easily compared and contrasted, making it a great tool for ChIPseq data analysis that can aid in collaboration and corroboration between research groups. Here, we demonstrate the application of BCP to existing transcription factor10,11 and epigenetic data12 to illustrate its usefulness.
Genetics, Issue 70, Bioinformatics, Genomics, Molecular Biology, Cellular Biology, Immunology, Chromatin immunoprecipitation, ChIP-Seq, histone modifications, segmentation, Bayesian, Hidden Markov Models, epigenetics
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Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
Authors: Helen H Won, Sasinya N Scott, A. Rose Brannon, Ronak H Shah, Michael F Berger.
Institutions: Memorial Sloan-Kettering Cancer Center, Memorial Sloan-Kettering Cancer Center.
Efforts to detect and investigate key oncogenic mutations have proven valuable to facilitate the appropriate treatment for cancer patients. The establishment of high-throughput, massively parallel "next-generation" sequencing has aided the discovery of many such mutations. To enhance the clinical and translational utility of this technology, platforms must be high-throughput, cost-effective, and compatible with formalin-fixed paraffin embedded (FFPE) tissue samples that may yield small amounts of degraded or damaged DNA. Here, we describe the preparation of barcoded and multiplexed DNA libraries followed by hybridization-based capture of targeted exons for the detection of cancer-associated mutations in fresh frozen and FFPE tumors by massively parallel sequencing. This method enables the identification of sequence mutations, copy number alterations, and select structural rearrangements involving all targeted genes. Targeted exon sequencing offers the benefits of high throughput, low cost, and deep sequence coverage, thus conferring high sensitivity for detecting low frequency mutations.
Molecular Biology, Issue 80, Molecular Diagnostic Techniques, High-Throughput Nucleotide Sequencing, Genetics, Neoplasms, Diagnosis, Massively parallel sequencing, targeted exon sequencing, hybridization capture, cancer, FFPE, DNA mutations
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RNA-seq Analysis of Transcriptomes in Thrombin-treated and Control Human Pulmonary Microvascular Endothelial Cells
Authors: Dilyara Cheranova, Margaret Gibson, Suman Chaudhary, Li Qin Zhang, Daniel P. Heruth, Dmitry N. Grigoryev, Shui Qing Ye.
Institutions: Children's Mercy Hospital and Clinics, School of Medicine, University of Missouri-Kansas City.
The characterization of gene expression in cells via measurement of mRNA levels is a useful tool in determining how the transcriptional machinery of the cell is affected by external signals (e.g. drug treatment), or how cells differ between a healthy state and a diseased state. With the advent and continuous refinement of next-generation DNA sequencing technology, RNA-sequencing (RNA-seq) has become an increasingly popular method of transcriptome analysis to catalog all species of transcripts, to determine the transcriptional structure of all expressed genes and to quantify the changing expression levels of the total set of transcripts in a given cell, tissue or organism1,2 . RNA-seq is gradually replacing DNA microarrays as a preferred method for transcriptome analysis because it has the advantages of profiling a complete transcriptome, providing a digital type datum (copy number of any transcript) and not relying on any known genomic sequence3. Here, we present a complete and detailed protocol to apply RNA-seq to profile transcriptomes in human pulmonary microvascular endothelial cells with or without thrombin treatment. This protocol is based on our recent published study entitled "RNA-seq Reveals Novel Transcriptome of Genes and Their Isoforms in Human Pulmonary Microvascular Endothelial Cells Treated with Thrombin,"4 in which we successfully performed the first complete transcriptome analysis of human pulmonary microvascular endothelial cells treated with thrombin using RNA-seq. It yielded unprecedented resources for further experimentation to gain insights into molecular mechanisms underlying thrombin-mediated endothelial dysfunction in the pathogenesis of inflammatory conditions, cancer, diabetes, and coronary heart disease, and provides potential new leads for therapeutic targets to those diseases. The descriptive text of this protocol is divided into four parts. The first part describes the treatment of human pulmonary microvascular endothelial cells with thrombin and RNA isolation, quality analysis and quantification. The second part describes library construction and sequencing. The third part describes the data analysis. The fourth part describes an RT-PCR validation assay. Representative results of several key steps are displayed. Useful tips or precautions to boost success in key steps are provided in the Discussion section. Although this protocol uses human pulmonary microvascular endothelial cells treated with thrombin, it can be generalized to profile transcriptomes in both mammalian and non-mammalian cells and in tissues treated with different stimuli or inhibitors, or to compare transcriptomes in cells or tissues between a healthy state and a disease state.
Genetics, Issue 72, Molecular Biology, Immunology, Medicine, Genomics, Proteins, RNA-seq, Next Generation DNA Sequencing, Transcriptome, Transcription, Thrombin, Endothelial cells, high-throughput, DNA, genomic DNA, RT-PCR, PCR
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Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis
Authors: Shan Zong, Shuyun Deng, Kenian Chen, Jia Qian Wu.
Institutions: The University of Texas Graduate School of Biomedical Sciences at Houston.
Hematopoietic stem cells (HSCs) are used clinically for transplantation treatment to rebuild a patient's hematopoietic system in many diseases such as leukemia and lymphoma. Elucidating the mechanisms controlling HSCs self-renewal and differentiation is important for application of HSCs for research and clinical uses. However, it is not possible to obtain large quantity of HSCs due to their inability to proliferate in vitro. To overcome this hurdle, we used a mouse bone marrow derived cell line, the EML (Erythroid, Myeloid, and Lymphocytic) cell line, as a model system for this study. RNA-sequencing (RNA-Seq) has been increasingly used to replace microarray for gene expression studies. We report here a detailed method of using RNA-Seq technology to investigate the potential key factors in regulation of EML cell self-renewal and differentiation. The protocol provided in this paper is divided into three parts. The first part explains how to culture EML cells and separate Lin-CD34+ and Lin-CD34- cells. The second part of the protocol offers detailed procedures for total RNA preparation and the subsequent library construction for high-throughput sequencing. The last part describes the method for RNA-Seq data analysis and explains how to use the data to identify differentially expressed transcription factors between Lin-CD34+ and Lin-CD34- cells. The most significantly differentially expressed transcription factors were identified to be the potential key regulators controlling EML cell self-renewal and differentiation. In the discussion section of this paper, we highlight the key steps for successful performance of this experiment. In summary, this paper offers a method of using RNA-Seq technology to identify potential regulators of self-renewal and differentiation in EML cells. The key factors identified are subjected to downstream functional analysis in vitro and in vivo.
Genetics, Issue 93, EML Cells, Self-renewal, Differentiation, Hematopoietic precursor cell, RNA-Sequencing, Data analysis
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The ChroP Approach Combines ChIP and Mass Spectrometry to Dissect Locus-specific Proteomic Landscapes of Chromatin
Authors: Monica Soldi, Tiziana Bonaldi.
Institutions: European Institute of Oncology.
Chromatin is a highly dynamic nucleoprotein complex made of DNA and proteins that controls various DNA-dependent processes. Chromatin structure and function at specific regions is regulated by the local enrichment of histone post-translational modifications (hPTMs) and variants, chromatin-binding proteins, including transcription factors, and DNA methylation. The proteomic characterization of chromatin composition at distinct functional regions has been so far hampered by the lack of efficient protocols to enrich such domains at the appropriate purity and amount for the subsequent in-depth analysis by Mass Spectrometry (MS). We describe here a newly designed chromatin proteomics strategy, named ChroP (Chromatin Proteomics), whereby a preparative chromatin immunoprecipitation is used to isolate distinct chromatin regions whose features, in terms of hPTMs, variants and co-associated non-histonic proteins, are analyzed by MS. We illustrate here the setting up of ChroP for the enrichment and analysis of transcriptionally silent heterochromatic regions, marked by the presence of tri-methylation of lysine 9 on histone H3. The results achieved demonstrate the potential of ChroP in thoroughly characterizing the heterochromatin proteome and prove it as a powerful analytical strategy for understanding how the distinct protein determinants of chromatin interact and synergize to establish locus-specific structural and functional configurations.
Biochemistry, Issue 86, chromatin, histone post-translational modifications (hPTMs), epigenetics, mass spectrometry, proteomics, SILAC, chromatin immunoprecipitation , histone variants, chromatome, hPTMs cross-talks
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Chromatin Interaction Analysis with Paired-End Tag Sequencing (ChIA-PET) for Mapping Chromatin Interactions and Understanding Transcription Regulation
Authors: Yufen Goh, Melissa J. Fullwood, Huay Mei Poh, Su Qin Peh, Chin Thing Ong, Jingyao Zhang, Xiaoan Ruan, Yijun Ruan.
Institutions: Agency for Science, Technology and Research, Singapore, A*STAR-Duke-NUS Neuroscience Research Partnership, Singapore, National University of Singapore, Singapore.
Genomes are organized into three-dimensional structures, adopting higher-order conformations inside the micron-sized nuclear spaces 7, 2, 12. Such architectures are not random and involve interactions between gene promoters and regulatory elements 13. The binding of transcription factors to specific regulatory sequences brings about a network of transcription regulation and coordination 1, 14. Chromatin Interaction Analysis by Paired-End Tag Sequencing (ChIA-PET) was developed to identify these higher-order chromatin structures 5,6. Cells are fixed and interacting loci are captured by covalent DNA-protein cross-links. To minimize non-specific noise and reduce complexity, as well as to increase the specificity of the chromatin interaction analysis, chromatin immunoprecipitation (ChIP) is used against specific protein factors to enrich chromatin fragments of interest before proximity ligation. Ligation involving half-linkers subsequently forms covalent links between pairs of DNA fragments tethered together within individual chromatin complexes. The flanking MmeI restriction enzyme sites in the half-linkers allow extraction of paired end tag-linker-tag constructs (PETs) upon MmeI digestion. As the half-linkers are biotinylated, these PET constructs are purified using streptavidin-magnetic beads. The purified PETs are ligated with next-generation sequencing adaptors and a catalog of interacting fragments is generated via next-generation sequencers such as the Illumina Genome Analyzer. Mapping and bioinformatics analysis is then performed to identify ChIP-enriched binding sites and ChIP-enriched chromatin interactions 8. We have produced a video to demonstrate critical aspects of the ChIA-PET protocol, especially the preparation of ChIP as the quality of ChIP plays a major role in the outcome of a ChIA-PET library. As the protocols are very long, only the critical steps are shown in the video.
Genetics, Issue 62, ChIP, ChIA-PET, Chromatin Interactions, Genomics, Next-Generation Sequencing
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Orthogonal Protein Purification Facilitated by a Small Bispecific Affinity Tag
Authors: Johan Nilvebrant, Tove Alm, Sophia Hober.
Institutions: Royal Institute of Technology.
Due to the high costs associated with purification of recombinant proteins the protocols need to be rationalized. For high-throughput efforts there is a demand for general methods that do not require target protein specific optimization1 . To achieve this, purification tags that genetically can be fused to the gene of interest are commonly used2 . The most widely used affinity handle is the hexa-histidine tag, which is suitable for purification under both native and denaturing conditions3 . The metabolic burden for producing the tag is low, but it does not provide as high specificity as competing affinity chromatography based strategies1,2. Here, a bispecific purification tag with two different binding sites on a 46 amino acid, small protein domain has been developed. The albumin-binding domain is derived from Streptococcal protein G and has a strong inherent affinity to human serum albumin (HSA). Eleven surface-exposed amino acids, not involved in albumin-binding4 , were genetically randomized to produce a combinatorial library. The protein library with the novel randomly arranged binding surface (Figure 1) was expressed on phage particles to facilitate selection of binders by phage display technology. Through several rounds of biopanning against a dimeric Z-domain derived from Staphylococcal protein A5, a small, bispecific molecule with affinity for both HSA and the novel target was identified6 . The novel protein domain, referred to as ABDz1, was evaluated as a purification tag for a selection of target proteins with different molecular weight, solubility and isoelectric point. Three target proteins were expressed in Escherishia coli with the novel tag fused to their N-termini and thereafter affinity purified. Initial purification on either a column with immobilized HSA or Z-domain resulted in relatively pure products. Two-step affinity purification with the bispecific tag resulted in substantial improvement of protein purity. Chromatographic media with the Z-domain immobilized, for example MabSelect SuRe, are readily available for purification of antibodies and HSA can easily be chemically coupled to media to provide the second matrix. This method is especially advantageous when there is a high demand on purity of the recovered target protein. The bifunctionality of the tag allows two different chromatographic steps to be used while the metabolic burden on the expression host is limited due to the small size of the tag. It provides a competitive alternative to so called combinatorial tagging where multiple tags are used in combination1,7.
Molecular Biology, Issue 59, Affinity chromatography, albumin-binding domain, human serum albumin, Z-domain
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Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
Authors: Viktor Martyanov, Robert H. Gross.
Institutions: Dartmouth College.
SCOPE is an ensemble motif finder that uses three component algorithms in parallel to identify potential regulatory motifs by over-representation and motif position preference1. Each component algorithm is optimized to find a different kind of motif. By taking the best of these three approaches, SCOPE performs better than any single algorithm, even in the presence of noisy data1. In this article, we utilize a web version of SCOPE2 to examine genes that are involved in telomere maintenance. SCOPE has been incorporated into at least two other motif finding programs3,4 and has been used in other studies5-8. The three algorithms that comprise SCOPE are BEAM9, which finds non-degenerate motifs (ACCGGT), PRISM10, which finds degenerate motifs (ASCGWT), and SPACER11, which finds longer bipartite motifs (ACCnnnnnnnnGGT). These three algorithms have been optimized to find their corresponding type of motif. Together, they allow SCOPE to perform extremely well. Once a gene set has been analyzed and candidate motifs identified, SCOPE can look for other genes that contain the motif which, when added to the original set, will improve the motif score. This can occur through over-representation or motif position preference. Working with partial gene sets that have biologically verified transcription factor binding sites, SCOPE was able to identify most of the rest of the genes also regulated by the given transcription factor. Output from SCOPE shows candidate motifs, their significance, and other information both as a table and as a graphical motif map. FAQs and video tutorials are available at the SCOPE web site which also includes a "Sample Search" button that allows the user to perform a trial run. Scope has a very friendly user interface that enables novice users to access the algorithm's full power without having to become an expert in the bioinformatics of motif finding. As input, SCOPE can take a list of genes, or FASTA sequences. These can be entered in browser text fields, or read from a file. The output from SCOPE contains a list of all identified motifs with their scores, number of occurrences, fraction of genes containing the motif, and the algorithm used to identify the motif. For each motif, result details include a consensus representation of the motif, a sequence logo, a position weight matrix, and a list of instances for every motif occurrence (with exact positions and "strand" indicated). Results are returned in a browser window and also optionally by email. Previous papers describe the SCOPE algorithms in detail1,2,9-11.
Genetics, Issue 51, gene regulation, computational biology, algorithm, promoter sequence motif
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