Chromatin Immunoprecipitation of Retroviral Genomes with Antibodies Recognizing Modified Histones and Specific Viral Proteins.

Mammalian cells have developed and optimized defense mechanisms to prevent or hamper viral infection. The early transcriptional silencing of incoming viral DNAs is one such antiviral strategy and seems to be of fundamental importance, since most cell types silence unintegrated retroviral DNAs. In this chapter, a method for chromatin immunoprecipitation of unintegrated DNA is described. This technique allows investigators to examine histone and co-factor interactions with unintegrated viral DNAs as well as to analyze histone modifications in general or in a kinetic fashion at various time points during viral infection.

BIOMAPP::CHIP: large-scale motif analysis.

BACKGROUND: Discovery biological motifs plays a fundamental role in understanding regulatory mechanisms. Computationally, they can be efficiently represented as kmers, making the counting of these elements a critical aspect for ensuring not only the accuracy but also the efficiency of the analytical process. This is particularly useful in scenarios involving large data volumes, such as those generated by the ChIP-seq protocol. Against this backdrop, we introduce BIOMAPP::CHIP, a tool specifically designed to optimize the discovery of biological motifs in large data volumes. RESULTS: We conducted a comprehensive set of comparative tests with state-of-the-art algorithms. Our analyses revealed that BIOMAPP::CHIP outperforms existing approaches in various metrics, excelling both in terms of performance and accuracy. The tests demonstrated a higher detection rate of significant motifs and also greater agility in the execution of the algorithm. Furthermore, the SMT component played a vital role in the system's efficiency, proving to be both agile and accurate in kmer counting, which in turn improved the overall efficacy of our tool. CONCLUSION: BIOMAPP::CHIP represent real advancements in the discovery of biological motifs, particularly in large data volume scenarios, offering a relevant alternative for the analysis of ChIP-seq data and have the potential to boost future research in the field. This software can be found at the following address: (https://github.com/jadermcg/biomapp-chip).

MMCT-Loop: a mix model-based pipeline for calling targeted 3D chromatin loops.

Protein-specific Chromatin Conformation Capture (3C)-based technologies have become essential for identifying distal genomic interactions with critical roles in gene regulation. The standard techniques include Chromatin Interaction Analysis by Paired-End Tag (ChIA-PET), in situ Hi-C followed by chromatin immunoprecipitation (HiChIP) also known as PLAC-seq. To identify chromatin interactions from these data, a variety of computational methods have emerged. Although these state-of-art methods address many issues with loop calling, only few methods can fit different data types simultaneously, and the accuracy as well as the efficiency these approaches remains limited. Here we have generated a pipeline, MMCT-Loop, which ensures the accurate identification of strong loops as well as dynamic or weak loops through a mixed model. MMCT-Loop outperforms existing methods in accuracy, and the detected loops show higher activation functionality. To highlight the utility of MMCT-Loop, we applied it to conformational data derived from neural stem cell (NSCs) and uncovered several previously unidentified regulatory regions for key master regulators of stem cell identity. MMCT-Loop is an accurate and efficient loop caller for targeted conformation capture data, which supports raw data or pre-processed valid pairs as input, the output interactions are formatted and easily uploaded to a genome browser for visualization.

Hi-Tag: a simple and efficient method for identifying protein-mediated long-range chromatin interactions with low cell numbers.

Protein-mediated chromatin interactions can be revealed by coupling proximity-based ligation with chromatin immunoprecipitation. However, these techniques require complex experimental procedures and millions of cells per experiment, which limits their widespread application in life science research. Here, we develop a novel method, Hi-Tag, that identifies high-resolution, long-range chromatin interactions through transposase tagmentation and chromatin proximity ligation (with a phosphorothioate-modified linker). Hi-Tag can be implemented using as few as 100,000 cells, involving simple experimental procedures that can be completed within 1.5 days. Meanwhile, Hi-Tag is capable of using its own data to identify the binding sites of specific proteins, based on which, it can acquire accurate interaction information. Our results suggest that Hi-Tag has great potential for advancing chromatin interaction studies, particularly in the context of limited cell availability.

Combinatorial single-cell profiling of major chromatin types with MAbID.

Gene expression programs result from the collective activity of numerous regulatory factors. Studying their cooperative mode of action is imperative to understand gene regulation, but simultaneously measuring these factors within one sample has been challenging. Here we introduce Multiplexing Antibodies by barcode Identification (MAbID), a method for combinatorial genomic profiling of histone modifications and chromatin-binding proteins. MAbID employs antibody-DNA conjugates to integrate barcodes at the genomic location of the epitope, enabling combined incubation of multiple antibodies to reveal the distributions of many epigenetic markers simultaneously. We used MAbID to profile major chromatin types and multiplexed measurements without loss of individual data quality. Moreover, we obtained joint measurements of six epitopes in single cells of mouse bone marrow and during mouse in vitro differentiation, capturing associated changes in multifactorial chromatin states. Thus, MAbID holds the potential to gain unique insights into the interplay between gene regulatory mechanisms, especially for low-input samples and in single cells.

CATCH-UP: A High-Throughput Upstream-Pipeline for Bulk ATAC-Seq and ChIP-Seq Data.

Assay for transposase-accessible chromatin (ATAC) and chromatin immunoprecipitation (ChIP), coupled with next-generation sequencing (NGS), have revolutionized the study of gene regulation. A lack of standardization in the analysis of the highly dimensional datasets generated by these techniques has made reproducibility difficult to achieve, leading to discrepancies in the published, processed data. Part of this problem is due to the diverse range of bioinformatic tools available for the analysis of these types of data. Secondly, a number of different bioinformatic tools are required sequentially to convert raw data into a fully processed and interpretable output, and these tools require varying levels of computational skills. Furthermore, there are many options for quality control that are not uniformly employed during data processing. We address these issues with a complete assay for transposase-accessible chromatin sequencing (ATAC-seq) and chromatin immunoprecipitation sequencing (ChIP-seq) upstream pipeline (CATCH-UP), an easy-to-use, Python-based pipeline for the analysis of bulk ChIP-seq and ATAC-seq datasets from raw fastq files to visualizable bigwig tracks and peaks calls. This pipeline is simple to install and run, requiring minimal computational knowledge. The pipeline is modular, scalable, and parallelizable on various computing infrastructures, allowing for easy reporting of methodology to enable reproducible analysis of novel or published datasets.

Validating a re-implementation of an algorithm to integrate transcriptome and ChIP-seq data.

Transcription factor binding to a gene regulatory region induces or represses its expression. Binding and expression target analysis (BETA) integrates the binding and gene expression data to predict this function. First, the regulatory potential of the factor is modeled based on the distance of its binding sites from the transcription start sites in a decay function. Then the differential expression statistics from an experiment where this factor was perturbed represent the binding effect. The rank product of the two values is employed to order in importance. This algorithm was originally implemented in Python. We reimplemented the algorithm in R to take advantage of existing data structures and other tools for downstream analyses. Here, we attempted to replicate the findings in the original BETA paper. We applied the new implementation to the same datasets using default and varying inputs and cutoffs. We successfully replicated the original results. Moreover, we showed that the method was appropriately influenced by varying the input and was robust to choices of cutoffs in statistical testing.

Analyzing histone ChIP-seq data with a bin-based probability of being signal.

Histone ChIP-seq is one of the primary methods for charting the cellular epigenomic landscape, the components of which play a critical regulatory role in gene expression. Analyzing the activity of regulatory elements across datasets and cell types can be challenging due to shifting peak positions and normalization artifacts resulting from, for example, differing read depths, ChIP efficiencies, and target sizes. Moreover, broad regions of enrichment seen in repressive histone marks often evade detection by commonly used peak callers. Here, we present a simple and versatile method for identifying enriched regions in ChIP-seq data that relies on estimating a gamma distribution fit to non-overlapping 5kB genomic bins to establish a global background. We use this distribution to assign a probability of being signal (PBS) between zero and one to each 5 kB bin. This approach, while lower in resolution than typical peak-calling methods, provides a straightforward way to identify enriched regions and compare enrichments among multiple datasets, by transforming the data to values that are universally normalized and can be readily visualized and integrated with downstream analysis methods. We demonstrate applications of PBS for both broad and narrow histone marks, and provide several illustrations of biological insights which can be gleaned by integrating PBS scores with downstream data types.

Exploring the Genomic Landscape: An In-depth ChIP-seq Analysis Protocol for Uncovering Protein-DNA Interactions.

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is a widely employed technique for investigating protein-DNA interactions. However, the absence of a standardized and clear workflow necessitates researchers to independently assemble methodologies from diverse resources. This lack of uniformity hampers reproducibility and makes version control a complex endeavor, thereby limiting the accessibility of ChIP-seq analyses to individuals with extensive training in bioinformatics. In light of these challenges, we have developed an executable protocol that addresses these limitations. Our protocol encompasses all aspects of ChIP-seq analysis, ranging from quality control of raw reads to peak calling and downstream functional analyses. We have implemented two distinct approaches for peak calling, providing researchers with flexibility to choose the most suitable method for their specific experimental needs. This protocol will contribute to the scientific community by providing a standardized and clear resource that will enhance the reproducibility and accessibility of ChIP-seq analyses. © 2023 Wiley Periodicals LLC. Basic Protocol: ChIP-seq analysis workflow Alternative Protocol: Call differentially enriched peaks by using MACS3.

Protocol to capture transcription factor-mediated 3D chromatin interactions using affinity tag-based BL-HiChIP.

Pioneer transcription factors (TFs) can directly establish higher-order chromatin interactions to instruct gene transcription. Here, we present a protocol for capturing TF-mediated 3D chromatin interactions using affinity tag-based bridge linker (BL)-Hi-chromatin immunoprecipitation (HiChIP). We describe steps for constructing FLAG-tagged TF, performing BL-HiChIP, and preparing the library. We then detail procedures for sequencing, data analysis, and quality control. This protocol has potential applications in 3D chromatin analysis centered on any specific TF in any type of cells without the need of optimal antibodies. For complete details on the use and execution of this protocol, please refer to Ren et al. (2022).1.

An optimized protocol for chromatin immunoprecipitation from murine inguinal white adipose tissue.

Chromatin immunoprecipitation (ChIP) protocols have been used to reveal protein-DNA interactions of various cell types and tissues; however, optimization is required for each specific type of sample. Here, we present a ChIP protocol from murine inguinal white adipose tissue. We describe steps for tissue harvesting, crosslinking, chromatin extraction, shearing, immunoprecipitation, and purification. We then detail procedures for analysis including library preparation, sequencing, and qRT-PCR validation. For complete details on the use and execution of this protocol, please refer to Antonia Katsouda et al. (2022).1.

Chromatin Immunoprecipitation (ChIP) of Heat Shock Protein 90 (Hsp90).

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is a widely used technique for genome-wide mapping of protein-DNA interactions and epigenetic marks in vivo. Recent studies have suggested an important role of heat shock protein 90 (Hsp90) in chromatin. This molecular chaperone assists other proteins to acquire their mature and functional conformation and helps in the assembly of many complexes. In this chapter, we provide specific details on how to perform Hsp90 ChIP-seq from Drosophila Schneider (S2) cells. Briefly, cells are simultaneously lyzed and reversibly cross-linked to stabilize protein-DNA interactions. Chromatin is prepared from isolated nuclei and sheared by sonication. Hsp90-bound loci are immunoprecipitated and the corresponding DNA fragments are purified and sequenced. The described approach revealed that Hsp90 binds close to the transcriptional start site of around one-third of all Drosophila coding genes and characterized the role of the chaperone at chromatin.

Broadly Applicable Control Approaches Improve Accuracy of ChIP-Seq Data.

Chromatin ImmunoPrecipitation (ChIP) is a widely used method for the analysis of protein-DNA interactions in vivo; however, ChIP has pitfalls, particularly false-positive signal enrichment that permeates the data. We have developed a new approach to control for non-specific enrichment in ChIP that involves the expression of a non-genome-binding protein targeted in the IP alongside the experimental target protein due to the sharing of epitope tags. ChIP of the protein provides a "sensor" for non-specific enrichment that can be used for the normalization of the experimental data, thereby correcting for non-specific signals and improving data quality as validated against known binding sites for several proteins that we tested, including Fkh1, Orc1, Mcm4, and Sir2. We also tested a DNA-binding mutant approach and showed that, when feasible, ChIP of a site-specific DNA-binding mutant of the target protein is likely an ideal control. These methods vastly improve our ChIP-seq results in S. cerevisiae and should be applicable in other systems.

Chromatin Immunoprecipitation Assay in Mouse Retinal Tissue.

Chromatin immunoprecipitation (ChIP) is one of the most widely used methods for investigating interactions between proteins and DNA sequences. ChIP plays an important role in the transcriptional regulation study, which can locate the target genes of transcription factors and cofactors or monitor the sequence-specific genomic regions of histone modification. To analyze the interaction between transcription factors and several candidate genes, ChIP coupled with quantitative PCR (ChIP-PCR) assay is a basic tool. With the development of next-generation sequencing technology, ChIP-coupled sequencing (ChIP-seq) can provide the protein-DNA interaction information in a genome-wide dimension, which helps a lot in identifying new target genes. This chapter describes a protocol for performing ChIP-seq of transcription factors from retinal tissues.

Characterizing the targets of transcription regulators by aggregating ChIP-seq and perturbation expression data sets.

Mapping the gene targets of chromatin-associated transcription regulators (TRs) is a major goal of genomics research. ChIP-seq of TRs and experiments that perturb a TR and measure the differential abundance of gene transcripts are a primary means by which direct relationships are tested on a genomic scale. It has been reported that there is a poor overlap in the evidence across gene regulation strategies, emphasizing the need for integrating results from multiple experiments. Although research consortia interested in gene regulation have produced a valuable trove of high-quality data, there is an even greater volume of TR-specific data throughout the literature. In this study, we show a workflow for the identification, uniform processing, and aggregation of ChIP-seq and TR perturbation experiments for the ultimate purpose of ranking human and mouse TR-target interactions. Focusing on an initial set of eight regulators (ASCL1, HES1, MECP2, MEF2C, NEUROD1, PAX6, RUNX1, and TCF4), we identified 497 experiments suitable for analysis. We used this corpus to examine data concordance, to identify systematic patterns of the two data types, and to identify putative orthologous interactions between human and mouse. We build upon commonly used strategies to forward a procedure for aggregating and combining these two genomic methodologies, assessing these rankings against independent literature-curated evidence. Beyond a framework extensible to other TRs, our work also provides empirically ranked TR-target listings, as well as transparent experiment-level gene summaries for community use.

Chromatin Immunoprecipitation (ChIP) to Study the Transcriptional Regulatory Network that Controls Iron Homeostasis in Arabidopsis thaliana.

In plants, gene expression is orchestrated by thousands of transcription factors (TFs). For instance, a large set of bHLH TFs are involved in the regulation of iron homeostasis in Arabidopsis thaliana. The identification of the direct target genes of TFs through uncovering the interaction between the TFs and cis-regulatory elements has become an essential step toward a comprehensive understanding of the iron homeostasis transcriptional regulatory network in Arabidopsis. Chromatin immunoprecipitation (ChIP) followed by qRT-PCR (ChIP-qPCR), sequencing (ChIP-seq), or chip hybridization (ChIP-chip) is a robust tool to investigate protein-DNA interactions in plants in a physiological context. The procedure generally includes six steps: DNA-protein crosslink, isolation of nuclei, shearing of chromatin, immunoprecipitation, DNA purification, and qRT-PCR analyses. In this protocol, we describe guidelines, experimental setup, and conditions for ChIP experiment in Arabidopsis. This protocol focuses on seedlings grown in control and iron deficiency conditions, but can readily be adapted for use with other Arabidopsis tissues or samples. In addition, the protocol could also be applied to perform ChIP-chip or ChIP-seq experiments.

Chromatin Preparation and Chromatin Immunoprecipitation from Drosophila Heads.

Chromatin immunoprecipitation (ChIP) is a widely used method to map protein-DNA interactions in vivo. Formaldehyde cross-linked chromatin is fragmented, and the protein of interest is immunoprecipitated using a specific antibody. The co-immunoprecipitated DNA is then purified and analyzed by quantitative PCR (ChIP-qPCR) or next-generation sequencing (ChIP-seq). Therefore, from the amount of DNA recovered, it can be inferred the localization and abundance of the target protein at specific loci or throughout the entire genome. This protocol describes how to perform ChIP from Drosophila adult fly heads.

Chromatin Immunoprecipitation in the Cnidarian Model System Exaiptasia diaphana.

Histone post-translational modifications (PTMs) and other epigenetic modifications regulate the chromatin accessibility of genes to the transcriptional machinery, thus affecting an organism's capacity to respond to environmental stimuli. Chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq) has been widely utilized to identify and map protein-DNA interactions in the fields of epigenetics and gene regulation. However, the field of cnidarian epigenetics is hampered by a lack of applicable protocols, partly due to the unique features of model organisms such as the symbiotic sea anemone Exaiptasia diaphana, whose high water content and mucus amounts obstruct molecular methods. Here, a specialized ChIP procedure is presented, which facilitates the investigation of protein-DNA interactions in E. diaphana gene regulation. The cross-linking and chromatin extraction steps were optimized for efficient immunoprecipitation and then validated by performing ChIP using an antibody against the histone mark H3K4me3. Subsequently, the specificity and effectiveness of the ChIP assay were confirmed by measuring the relative occupancy of H3K4me3 around several constitutively activated gene loci using quantitative PCR and by next-generation sequencing for genome-wide scale analysis. This optimized ChIP protocol for the symbiotic sea anemone E. diaphana facilitates the investigation of the protein-DNA interactions involved in organismal responses to environmental changes that affect symbiotic cnidarians, such as corals.

Titration-based normalization of antibody amount improves consistency of ChIP-seq experiments.

Chromatin immunoprecipitation (ChIP) is an antibody-based approach that is frequently utilized in chromatin biology and epigenetics. The challenge in experimental variability by unpredictable nature of usable input amounts from samples and undefined antibody titer in ChIP reaction still remains to be addressed. Here, we introduce a simple and quick method to quantify chromatin inputs and demonstrate its utility for normalizing antibody amounts to the optimal titer in individual ChIP reactions. For a proof of concept, we utilized ChIP-seq validated antibodies against the key enhancer mark, acetylation of histone H3 on lysine 27 (H3K27ac), in the experiments. The results indicate that the titration-based normalization of antibody amounts improves assay outcomes including the consistency among samples both within and across experiments for a broad range of input amounts.

Statistical Analysis in ChIP-seq-Related Applications.

Chromatin immunoprecipitation sequencing (ChIP-seq) has been widely performed to identify protein binding information along the genome. The sequencing protocol is quite flexible and mature to measure different types of protein binding as long as sequencing parameters are properly tailored to accommodate protein features. Two distinct types of protein binding are point-source-like binding by transcription factors and diffused-distribution binding by histone modifications. Consequently, statistical approaches have been proposed to address ChIP-seq-related questions according to different protein features. In this chapter, we briefly summarize statistical principles, approaches, and tools that are widely implemented in modeling ChIP-seq data, from raw data quality control to final result reporting. We discuss the key solutions in addressing eight routine questions in ChIP-seq applications. We also include discussion on approaches fitting unique data features in different ChIP-seq types. We hope this chapter will serve as a brief guide, especially for ChIP-seq beginners, to provide them with a high-level overview to understand and design processing plans for their ChIP-seq experiments.