Uncovering uncharacterized binding of transcription factors from ATAC-seq footprinting data.

Transcription factors (TFs) are crucial epigenetic regulators, which enable cells to dynamically adjust gene expression in response to environmental signals. Computational procedures like digital genomic footprinting on chromatin accessibility assays such as ATACseq can be used to identify bound TFs in a genome-wide scale. This method utilizes short regions of low accessibility signals due to steric hindrance of DNA bound proteins, called footprints (FPs), which are combined with motif databases for TF identification. However, while over 1600 TFs have been described in the human genome, only ~ 700 of these have a known binding motif. Thus, a substantial number of FPs without overlap to a known DNA motif are normally discarded from FP analysis. In addition, the FP method is restricted to organisms with a substantial number of known TF motifs. Here we present DENIS (DE Novo motIf diScovery), a framework to generate and systematically investigate the potential of de novo TF motif discovery from FPs. DENIS includes functionality (1) to isolate FPs without binding motifs, (2) to perform de novo motif generation and (3) to characterize novel motifs. Here, we show that the framework rediscovers artificially removed TF motifs, quantifies de novo motif usage during an early embryonic development example dataset, and is able to analyze and uncover TF activity in organisms lacking canonical motifs. The latter task is exemplified by an investigation of a scATAC-seq dataset in zebrafish which covers different cell types during hematopoiesis.

Incorporating network diffusion and peak location information for better single-cell ATAC-seq data analysis.

Single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) data provided new insights into the understanding of epigenetic heterogeneity and transcriptional regulation. With the increasing abundance of dataset resources, there is an urgent need to extract more useful information through high-quality data analysis methods specifically designed for scATAC-seq. However, analyzing scATAC-seq data poses challenges due to its near binarization, high sparsity and ultra-high dimensionality properties. Here, we proposed a novel network diffusion-based computational method to comprehensively analyze scATAC-seq data, named Single-Cell ATAC-seq Analysis via Network Refinement with Peaks Location Information (SCARP). SCARP formulates the Network Refinement diffusion method under the graph theory framework to aggregate information from different network orders, effectively compensating for missing signals in the scATAC-seq data. By incorporating distance information between adjacent peaks on the genome, SCARP also contributes to depicting the co-accessibility of peaks. These two innovations empower SCARP to obtain lower-dimensional representations for both cells and peaks more effectively. We have demonstrated through sufficient experiments that SCARP facilitated superior analyses of scATAC-seq data. Specifically, SCARP exhibited outstanding cell clustering performance, enabling better elucidation of cell heterogeneity and the discovery of new biologically significant cell subpopulations. Additionally, SCARP was also instrumental in portraying co-accessibility relationships of accessible regions and providing new insight into transcriptional regulation. Consequently, SCARP identified genes that were involved in key Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to diseases and predicted reliable cis-regulatory interactions. To sum up, our studies suggested that SCARP is a promising tool to comprehensively analyze the scATAC-seq data.

Benchmarking algorithms for joint integration of unpaired and paired single-cell RNA-seq and ATAC-seq data.

BACKGROUND: Single-cell RNA-sequencing (scRNA-seq) measures gene expression in single cells, while single-nucleus ATAC-sequencing (snATAC-seq) quantifies chromatin accessibility in single nuclei. These two data types provide complementary information for deciphering cell types and states. However, when analyzed individually, they sometimes produce conflicting results regarding cell type/state assignment. The power is compromised since the two modalities reflect the same underlying biology. Recently, it has become possible to measure both gene expression and chromatin accessibility from the same nucleus. Such paired data enable the direct modeling of the relationships between the two modalities. Given the availability of the vast amount of single-modality data, it is desirable to integrate the paired and unpaired single-modality datasets to gain a comprehensive view of the cellular complexity. RESULTS: We benchmark nine existing single-cell multi-omic data integration methods. Specifically, we evaluate to what extent the multiome data provide additional guidance for analyzing the existing single-modality data, and whether these methods uncover peak-gene associations from single-modality data. Our results indicate that multiome data are helpful for annotating single-modality data. However, we emphasize that the availability of an adequate number of nuclei in the multiome dataset is crucial for achieving accurate cell type annotation. Insufficient representation of nuclei may compromise the reliability of the annotations. Additionally, when generating a multiome dataset, the number of cells is more important than sequencing depth for cell type annotation. CONCLUSIONS: Seurat v4 is the best currently available platform for integrating scRNA-seq, snATAC-seq, and multiome data even in the presence of complex batch effects.

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.

scBridge embraces cell heterogeneity in single-cell RNA-seq and ATAC-seq data integration.

Single-cell multi-omics data integration aims to reduce the omics difference while keeping the cell type difference. However, it is daunting to model and distinguish the two differences due to cell heterogeneity. Namely, even cells of the same omics and type would have various features, making the two differences less significant. In this work, we reveal that instead of being an interference, cell heterogeneity could be exploited to improve data integration. Specifically, we observe that the omics difference varies in cells, and cells with smaller omics differences are easier to be integrated. Hence, unlike most existing works that homogeneously treat and integrate all cells, we propose a multi-omics data integration method (dubbed scBridge) that integrates cells in a heterogeneous manner. In brief, scBridge iterates between i) identifying reliable scATAC-seq cells that have smaller omics differences, and ii) integrating reliable scATAC-seq cells with scRNA-seq data to narrow the omics gap, thus benefiting the integration for the rest cells. Extensive experiments on seven multi-omics datasets demonstrate the superiority of scBridge compared with six representative baselines.

AtacAnnoR: a reference-based annotation tool for single cell ATAC-seq data.

Here, we present AtacAnnoR, a two-round annotation method for scATAC-seq data using well-annotated scRNA-seq data as reference. We evaluate AtacAnnoR's performance against six competing methods on 11 benchmark datasets. Our results show that AtacAnnoR achieves the highest mean accuracy and the highest mean balanced accuracy and performs particularly well when unpaired scRNA-seq data are used as the reference. Furthermore, AtacAnnoR implements a 'Combine and Discard' strategy to further improve annotation accuracy when annotations of multiple references are available. AtacAnnoR has been implemented in an R package and can be directly integrated into currently popular scATAC-seq analysis pipelines.

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.

Cellcano: supervised cell type identification for single cell ATAC-seq data.

Computational cell type identification is a fundamental step in single-cell omics data analysis. Supervised celltyping methods have gained increasing popularity in single-cell RNA-seq data because of the superior performance and the availability of high-quality reference datasets. Recent technological advances in profiling chromatin accessibility at single-cell resolution (scATAC-seq) have brought new insights to the understanding of epigenetic heterogeneity. With continuous accumulation of scATAC-seq datasets, supervised celltyping method specifically designed for scATAC-seq is in urgent need. Here we develop Cellcano, a computational method based on a two-round supervised learning algorithm to identify cell types from scATAC-seq data. The method alleviates the distributional shift between reference and target data and improves the prediction performance. After systematically benchmarking Cellcano on 50 well-designed celltyping tasks from various datasets, we show that Cellcano is accurate, robust, and computationally efficient. Cellcano is well-documented and freely available at https://marvinquiet.github.io/Cellcano/ .

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.

A Unified Deep Learning Framework for Single-Cell ATAC-Seq Analysis Based on ProdDep Transformer Encoder.

Recent advances in single-cell sequencing assays for the transposase-accessibility chromatin (scATAC-seq) technique have provided cell-specific chromatin accessibility landscapes of cis-regulatory elements, providing deeper insights into cellular states and dynamics. However, few research efforts have been dedicated to modeling the relationship between regulatory grammars and single-cell chromatin accessibility and incorporating different analysis scenarios of scATAC-seq data into the general framework. To this end, we propose a unified deep learning framework based on the ProdDep Transformer Encoder, dubbed PROTRAIT, for scATAC-seq data analysis. Specifically motivated by the deep language model, PROTRAIT leverages the ProdDep Transformer Encoder to capture the syntax of transcription factor (TF)-DNA binding motifs from scATAC-seq peaks for predicting single-cell chromatin accessibility and learning single-cell embedding. Based on cell embedding, PROTRAIT annotates cell types using the Louvain algorithm. Furthermore, according to the identified likely noises of raw scATAC-seq data, PROTRAIT denoises these values based on predated chromatin accessibility. In addition, PROTRAIT employs differential accessibility analysis to infer TF activity at single-cell and single-nucleotide resolution. Extensive experiments based on the Buenrostro2018 dataset validate the effeteness of PROTRAIT for chromatin accessibility prediction, cell type annotation, and scATAC-seq data denoising, therein outperforming current approaches in terms of different evaluation metrics. Besides, we confirm the consistency between the inferred TF activity and the literature review. We also demonstrate the scalability of PROTRAIT to analyze datasets containing over one million cells.

peaksat: an R package for ChIP-seq peak saturation analysis.

BACKGROUND: Epigenomic profiling assays such as ChIP-seq have been widely used to map the genome-wide enrichment profiles of chromatin-associated proteins and posttranslational histone modifications. Sequencing depth is a key parameter in experimental design and quality control. However, due to variable sequencing depth requirements across experimental conditions, it can be challenging to determine optimal sequencing depth, particularly for projects involving multiple targets or cell types. RESULTS: We developed the peaksat R package to provide target read depth estimates for epigenomic experiments based on the analysis of peak saturation curves. We applied peaksat to establish the distinctive read depth requirements for ChIP-seq studies of histone modifications in different cell lines. Using peaksat, we were able to estimate the target read depth required per library to obtain high-quality peak calls for downstream analysis. In addition, peaksat was applied to other sequence-enrichment methods including CUT&RUN and ATAC-seq. CONCLUSION: peaksat addresses a need for researchers to make informed decisions about whether their sequencing data has been generated to an adequate depth and subsequently sufficient meaningful peaks, and failing that, how many more reads would be required per library. peaksat is applicable to other sequence-based methods that include calling peaks in their analysis.

Chromatin-bound protein colocalization analysis using bedGraph2Cluster and PanChIP.

Computational pipelines for chromatin immunoprecipitation sequencing analysis can neglect colocalization events that occur in a mere subset of the genome. Here, we detail a streamlined approach for assessing colocalization of chromatin-bound proteins using the bedGraph2Cluster and PanChIP algorithms. Using histone modifications as an example, bedGraph2Cluster performs clustering analysis on chromatin binding patterns of target proteins. PanChIP then compares these clusters with a reference library of chromatin binding patterns and measures the overlap in peaks, capturing the heterogeneity in chromatin binding and colocalization patterns. For complete details on the use and execution of this protocol, please refer to Sanidas et al. (2022).1.

Profiling the Epigenetic Landscape of the Tumor Microenvironment Using Chromatin Immunoprecipitation Sequencing.

Cancer cells within a tumor exhibit phenotypic plasticity that allows adaptation and survival in hostile tumor microenvironments. Reprogramming of epigenetic landscapes can support tumor progression within a specific microenvironment by influencing chromatin accessibility and modulating cell identity. The profiling of epigenetic landscapes within various tumor cell populations has significantly improved our understanding of tumor progression and plasticity. This protocol describes an integrated approach using chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) optimized to profile genome-wide post-translational modifications of histone tails in tumors. Essential tools amenable to ChIP-seq to isolate tumor cell populations of interest from the tumor microenvironment are also presented to provide a comprehensive approach to perform heterogeneous epigenetic landscape profiling of the tumor microenvironment.

Spatial profiling of chromatin accessibility in mouse and human tissues.

Cellular function in tissue is dependent on the local environment, requiring new methods for spatial mapping of biomolecules and cells in the tissue context1. The emergence of spatial transcriptomics has enabled genome-scale gene expression mapping2-5, but the ability to capture spatial epigenetic information of tissue at the cellular level and genome scale is lacking. Here we describe a method for spatially resolved chromatin accessibility profiling of tissue sections using next-generation sequencing (spatial-ATAC-seq) by combining in situ Tn5 transposition chemistry6 and microfluidic deterministic barcoding5. Profiling mouse embryos using spatial-ATAC-seq delineated tissue-region-specific epigenetic landscapes and identified gene regulators involved in the development of the central nervous system. Mapping the accessible genome in the mouse and human brain revealed the intricate arealization of brain regions. Applying spatial-ATAC-seq to tonsil tissue resolved the spatially distinct organization of immune cell types and states in lymphoid follicles and extrafollicular zones. This technology progresses spatial biology by enabling spatially resolved chromatin accessibility profiling to improve our understanding of cell identity, cell state and cell fate decision in relation to epigenetic underpinnings in development and disease.

SpyChIP identifies cell type-specific transcription factor occupancy from complex tissues.

Chromatin immunoprecipitation (ChIP) is an important technique for characterizing protein-DNA binding in vivo. One drawback of ChIP-based techniques is the lack of cell type-specificity when profiling complex tissues. To overcome this limitation, we developed SpyChIP to identify cell type-specific transcription factor (TF) binding sites in native physiological contexts without tissue dissociation or nuclei sorting. SpyChIP takes advantage of a specific covalent isopeptide bond that rapidly forms between the 15-amino acid SpyTag and the 17-kDa protein SpyCatcher. In SpyChIP, the target TF is fused with SpyTag by genome engineering, and an epitope tagged SpyCatcher is expressed in cell populations of interest, where it covalently binds to SpyTag-TF. Cell type-specific ChIP is obtained by immunoprecipitating chromatin prepared from whole tissues using antibodies directed against the epitope-tagged SpyCatcher. Using SpyChIP, we identified the genome-wide binding profiles of the Hox protein Ultrabithorax (Ubx) in two distinct cell types of the Drosophila haltere imaginal disc. Our results revealed extensive region-specific Ubx-DNA binding events, highlighting the significance of cell type-specific ChIP and the limitations of whole-tissue ChIP approaches. Analysis of Ubx::SpyChIP results provided insights into the relationship between chromatin accessibility and Ubx-DNA binding, as well as different mechanisms Ubx employs to regulate its downstream cis-regulatory modules. In addition to SpyChIP, we suggest that SpyTag-SpyCatcher technology, as well as other protein pairs that form covalent isopeptide bonds, will facilitate many additional in vivo applications that were previously impractical.

RFX transcription factors control a miR-150/PDAP1 axis that restrains the proliferation of human T cells.

Within the immune system, microRNAs (miRNAs) exert key regulatory functions. However, what are the mRNA targets regulated by miRNAs and how miRNAs are transcriptionally regulated themselves remain for the most part unknown. We found that in primary human memory T helper lymphocytes, miR-150 was the most abundantly expressed miRNA, and its expression decreased drastically upon activation, suggesting regulatory roles. Constitutive MIR150 gene expression required the RFX family of transcription factors, and its activation-induced down-regulation was linked to their reduced expression. By performing miRNA pull-down and sequencing experiments, we identified PDGFA-associated protein 1 (PDAP1) as one main target of miR-150 in human T lymphocytes. PDAP1 acted as an RNA-binding protein (RBP), and its CRISPR/Cas-9-mediated deletion revealed that it prominently contributed to the regulation of T-cell proliferation. Overall, using an integrated approach involving quantitative analysis, unbiased genomics, and genome editing, we identified RFX factors, miR-150, and the PDAP1 RBP as the components of a regulatory axis that restrains proliferation of primary human T lymphocytes.

An RNA Polymerase III General Transcription Factor Engages in Cell Type-Specific Chromatin Looping.

Transcription factors (TFs) bind DNA in a sequence-specific manner and are generally cell type-specific factors and/or developmental master regulators. In contrast, general TFs (GTFs) are part of very large protein complexes and serve for RNA polymerases' recruitment to promoter sequences, generally in a cell type-independent manner. Whereas, several TFs have been proven to serve as anchors for the 3D genome organization, the role of GTFs in genome architecture have not been carefully explored. Here, we used ChIP-seq and Hi-C data to depict the role of TFIIIC, one of the RNA polymerase III GTFs, in 3D genome organization. We find that TFIIIC genome occupancy mainly occurs at specific regions, which largely correspond to Alu elements; other characteristic classes of repetitive elements (REs) such as MIR, FLAM-C and ALR/alpha are also found depending on the cell's developmental origin. The analysis also shows that TFIIIC-enriched regions are involved in cell type-specific DNA looping, which does not depend on colocalization with the master architectural protein CTCF. This work extends previous knowledge on the role of TFIIIC as a bona fide genome organizer whose action participates in cell type-dependent 3D genome looping via binding to REs.

The chromatin accessibility landscape in the dental pulp of mouse molars and incisors.

The dental pulp is a promising source of progenitor cells for regenerative medicine. The natural function of dental pulp is to produce odontoblasts to generate reparative dentin. Stem cells within the pulp tissue originate from the migrating neural crest cells and possess mesenchymal stem cell properties with the ability to differentiate into multiple lineages. To elucidate the transcriptional control mechanisms underlying cell fate determination, we compared the transcriptome and chromatin accessibility in primary dental pulp tissue derived from 5-6-day-old mice. Using RNA sequencing and assay for transposase-accessible chromatin using sequencing (ATAC-seq), we correlated gene expression with chromatin accessibility. We found that the majority of ATAC-seq peaks were concentrated at genes associated with development and cell differentiation. Most of these genes were highly expressed in the mouse dental pulp. Surprisingly, we uncovered a group of genes encoding master transcription factors that were not expressed in the dental pulp but retained open chromatin states. Within this group, we identified key developmental genes important for specification of the neural crest, adipocyte, neural, myoblast, osteoblast and hepatocyte lineages. Collectively, our results uncover a complex relationship between gene expression and the chromatin accessibility landscape in the mouse dental pulp.

The RNA helicases DDX5 and DDX17 facilitate neural differentiation of human pluripotent stem cells NTERA2.

AIMS: Understanding human neurogenesis is critical toward regenerative medicine for neurodegeneration. However, little is known how neural differentiation is regulated by DEAD box-containing RNA helicases, which comprise a diverse class of RNA remodeling enzymes. MATERIALS AND METHODS: ChIP-seq was utilized to identify binding sites of DDX5 and DDX17 in both human pluripotent stem cell (hPSC) line NTERA2 and their retinoic acid-induced neural derivatives. RNA-seq was used to elucidate genes differentially expressed upon depletion of DDX5 and DDX17. Neurosphere assay, flow cytometry, and immunofluorescence staining were performed to test the effect of depletion of the two RNA helicases in neural differentiation. KEY FINDINGS: We show here that expression of DDX5 and DDX17 is abundant throughout neural differentiation of NTERA2, and is mostly localized within the nucleus. The two RNA helicases occupy chromatin genome-wide at regions associated with neurogenesis-related genes in both hPSCs and their neural derivatives. Further, both DDX5 and DDX17 are mutually required for controlling transcriptional expression of these genes, but are not important for maintenance of stem cell state of hPSCs. In contrast, they facilitate early neural differentiation of hPSCs, generation of neurospheres from the stem cells, and transcriptional expression of key neurogenic transcription factors such as SOX1 and PAX6 during neural differentiation. Importantly, DDX5 and DDX17 are critical for differentiation of hPSCs toward NESTIN- and TUBB3-positive cells, which represent neural progenitors and mature neurons, respectively. SIGNIFICANCE: Collectively, our findings suggest the role of DDX5 and DDX17 in transcriptional regulation of genes involved in neurogenesis, and hence in neural differentiation of hPSCs.

Kidney cell type-specific changes in the chromatin and transcriptome landscapes following epithelial Hdac1 and Hdac2 knockdown.

Recent studies have identified at least 20 different kidney cell types based upon chromatin structure and gene expression. Histone deacetylases (HDACs) are epigenetic transcriptional repressors via deacetylation of histone lysines resulting in inaccessible chromatin. We reported that kidney epithelial HDAC1 and HDAC2 activity is critical for maintaining a healthy kidney and preventing fluid-electrolyte abnormalities. However, to what extent does Hdac1/Hdac2 knockdown affect chromatin structure and subsequent transcript expression in the kidney? To answer this question, we used single nucleus assay for transposase-accessible chromatin-sequencing (snATAC-seq) and snRNA-seq to profile kidney nuclei from male and female, control, and littermate kidney epithelial Hdac1/Hdac2 knockdown mice. Hdac1/Hdac2 knockdown resulted in significant changes in the chromatin structure predominantly within the promoter region of gene loci involved in fluid-electrolyte balance such as the aquaporins, with both increased and decreased accessibility captured. Moreover, Hdac1/Hdac2 knockdown resulted different gene loci being accessible with a corresponding increased transcript number in the kidney, but among all mice only 24%-30% of chromatin accessibility agreed with transcript expression (e.g., open chromatin and increased transcript). To conclude, although chromatin structure does affect transcription, ∼70% of the differentially expressed genes cannot be explained by changes in chromatin accessibility and HDAC1/HDAC2 had a minimal effect on these global patterns. Yet, the genes that are targets of HDAC1 and HDAC2 are critically important for maintaining kidney function.