Chromatin accessibility profiling methods

Cellular zinc status alters chromatin accessibility and binding of p53 to DNA

Zn2+ is an essential metal required by approximately 850 human transcription factors. How these proteins acquire their essential Zn2+ cofactor and whether they are sensitive to changes in the labile Zn2+ pool in cells remain open questions. Using ATAC-seq to profile regions of accessible chromatin coupled with transcription factor enrichment analysis, we examined how increases and decreases in the labile zinc pool affect chromatin accessibility and transcription factor enrichment. We found 685 transcription factor motifs were differentially enriched, corresponding to 507 unique transcription factors. The pattern of perturbation and the types of transcription factors were notably different at promoters versus intergenic regions, with zinc-finger transcription factors strongly enriched in intergenic regions in elevated Zn2+ To test whether ATAC-seq and transcription factor enrichment analysis predictions correlate with changes in transcription factor binding, we used ChIP-qPCR to profile six p53 binding sites. We found that for five of the six targets, p53 binding correlates with the local accessibility determined by ATAC-seq. These results demonstrate that changes in labile zinc alter chromatin accessibility and transcription factor binding to DNA.

Interpretable deep residual network uncovers nucleosome positioning and associated features

Nucleosomes represent elementary building units of eukaryotic chromosomes and consist of DNA wrapped around a histone octamer flanked by linker DNA segments. Nucleosomes are central in epigenetic pathways and their genomic positioning is associated with regulation of gene expression, DNA replication, DNA methylation and DNA repair, among other functions. Building on prior discoveries that DNA sequences noticeably affect nucleosome positioning, our objective is to identify nucleosome positions and related features across entire genome. Here, we introduce an interpretable framework based on the concepts of deep residual networks (NuPoSe). Trained on high-coverage human experimental MNase-seq data, NuPoSe is able to learn sequence and structural patterns associated with nucleosome organization in human genome. NuPoSe can be also applied to unseen data from different organisms and cell types. Our findings point to 43 informative features, most of them constitute tri-nucleotides, di-nucleotides and one tetra-nucleotide. Most features are significantly associated with the nucleosomal structural characteristics, namely, periodicity of nucleosomal DNA and its location with respect to a histone octamer. Importantly, we show that features derived from the 27 bp linker DNA flanking nucleosomes contribute up to 10% to the quality of the prediction model. This, along with the comprehensive training sets, deep-learning architecture, and feature selection method, may contribute to the NuPoSe's 80-89% classification accuracy on different independent datasets.

Epigenetic Modifications of Hormonal Signaling Pathways in Plant Drought Response and Tolerance for Sustainable Food Security

Drought significantly challenges global food security, necessitating a comprehensive understanding of plant molecular responses for effective mitigation strategies. Epigenetic modifications, such as DNA methylation and histone modifications, are key in regulating genes and hormones essential for drought response. While microRNAs (miRNAs) primarily regulate gene expression post-transcriptionally, they can also interact with epigenetic pathways as potential effectors that influence chromatin remodeling. Although the role of miRNAs in epigenetic memory is still being explored, understanding their contribution to drought response requires examining these indirect effects on epigenetic modifications. A key aspect of this exploration is epigenetic memory in drought-adapted plants, offering insights into the transgenerational inheritance of adaptive traits. Understanding the mechanisms that govern the maintenance and erasure of these epigenetic imprints provides nuanced insights into how plants balance stability and flexibility in their epigenomes. A major focus is on the dynamic interaction between hormonal pathways-such as those for abscisic acid (ABA), ethylene, jasmonates, and salicylic acid (SA)-and epigenetic mechanisms. This interplay is crucial for fine-tuning gene expression during drought stress, leading to physiological and morphological adaptations that enhance plant drought resilience. This review also highlights the transformative potential of advanced technologies, such as bisulfite sequencing and CRISPR-Cas9, in providing comprehensive insights into plant responses to water deficit conditions. These technologies pave the way for developing drought-tolerant crops, which is vital for sustainable agriculture.

How Histone Acetyltransferases Shape Plant Photomorphogenesis and UV Response

Higher plants have developed complex mechanisms to adapt to fluctuating environmental conditions with light playing a vital role in photosynthesis and influencing various developmental processes, including photomorphogenesis. Exposure to ultraviolet (UV) radiation can cause cellular damage, necessitating effective DNA repair mechanisms. Histone acetyltransferases (HATs) play a crucial role in regulating chromatin structure and gene expression, thereby contributing to the repair mechanisms. HATs facilitate chromatin relaxation, enabling transcriptional activation necessary for plant development and stress responses. The intricate relationship between HATs, light signaling pathways and chromatin dynamics has been increasingly understood, providing valuable insights into plant adaptability. This review explores the role of HATs in plant photomorphogenesis, chromatin remodeling and gene regulation, highlighting the importance of chromatin modifications in plant responses to light and various stressors. It emphasizes the need for further research on individual HAT family members and their interactions with other epigenetic factors. Advanced genomic approaches and genome-editing technologies offer promising avenues for enhancing crop resilience and productivity through targeted manipulation of HAT activities. Understanding these mechanisms is essential for developing strategies to improve plant growth and stress tolerance, contributing to sustainable agriculture in the face of a changing climate.

Spatially resolved epigenome sequencing via Tn5 transposition and deterministic DNA barcoding in tissue

Spatial epigenetic mapping of tissues enables the study of gene regulation programs and cellular functions with the dependency on their local tissue environment. Here we outline a complete procedure for two spatial epigenomic profiling methods: spatially resolved genome-wide profiling of histone modifications using in situ cleavage under targets and tagmentation (CUT&Tag) chemistry (spatial-CUT&Tag) and transposase-accessible chromatin sequencing (spatial-ATAC-sequencing) for chromatin accessibility. Both assays utilize in-tissue Tn5 transposition to recognize genomic DNA loci followed by microfluidic deterministic barcoding to incorporate spatial address codes. Furthermore, these two methods do not necessitate prior knowledge of the transcription or epigenetic markers for a given tissue or cell type but permit genome-wide unbiased profiling pixel-by-pixel at the 10 μm pixel size level and single-base resolution. To support the widespread adaptation of these methods, details are provided in five general steps: (1) sample preparation; (2) Tn5 transposition in spatial-ATAC-sequencing or antibody-controlled pA-Tn5 tagmentation in CUT&Tag; (3) library preparation; (4) next-generation sequencing; and (5) data analysis using our customed pipelines available at: https://github.com/dyxmvp/Spatial_ATAC-seq and https://github.com/dyxmvp/spatial-CUT-Tag . The whole procedure can be completed on four samples in 2-3 days. Familiarity with basic molecular biology and bioinformatics skills with access to a high-performance computing environment are required. A rudimentary understanding of pathology and specimen sectioning, as well as deterministic barcoding in tissue-specific skills (e.g., design of a multiparameter barcode panel and creation of microfluidic devices), are also advantageous. In this protocol, we mainly focus on spatial profiling of tissue region-specific epigenetic landscapes in mouse embryos and mouse brains using spatial-ATAC-sequencing and spatial-CUT&Tag, but these methods can be used for other species with no need for species-specific probe design.

Simultaneous profiling of chromatin accessibility and DNA methylation in complete plant genomes using long-read sequencing

Epigenetic regulations, including chromatin accessibility, nucleosome positioning and DNA methylation intricately shape genome function. However, current chromatin profiling techniques relying on short-read sequencing technologies fail to characterise highly repetitive genomic regions and cannot detect multiple chromatin features simultaneously. Here, we performed Simultaneous Accessibility and DNA Methylation Sequencing (SAM-seq) of purified plant nuclei. Thanks to the use of long-read nanopore sequencing, SAM-seq enables high-resolution profiling of m6A-tagged chromatin accessibility together with endogenous cytosine methylation in plants. Analysis of naked genomic DNA revealed significant sequence preference biases of m6A-MTases, controllable through a normalisation step. By applying SAM-seq to Arabidopsis and maize nuclei we obtained fine-grained accessibility and DNA methylation landscapes genome-wide. We uncovered crosstalk between chromatin accessibility and DNA methylation within nucleosomes of genes, TEs, and centromeric repeats. SAM-seq also detects DNA footprints over cis-regulatory regions. Furthermore, using the single-molecule information provided by SAM-seq we identified extensive cellular heterogeneity at chromatin domains with antagonistic chromatin marks, suggesting that bivalency reflects cell-specific regulations. SAM-seq is a powerful approach to simultaneously study multiple epigenetic features over unique and repetitive sequences, opening new opportunities for the investigation of epigenetic mechanisms.

Tackling neurodegeneration in vitro with omics: a path towards new targets and drugs

Drug discovery is a generally inefficient and capital-intensive process. For neurodegenerative diseases (NDDs), the development of novel therapeutics is particularly urgent considering the long list of late-stage drug candidate failures. Although our knowledge on the pathogenic mechanisms driving neurodegeneration is growing, additional efforts are required to achieve a better and ultimately complete understanding of the pathophysiological underpinnings of NDDs. Beyond the etiology of NDDs being heterogeneous and multifactorial, this process is further complicated by the fact that current experimental models only partially recapitulate the major phenotypes observed in humans. In such a scenario, multi-omic approaches have the potential to accelerate the identification of new or repurposed drugs against a multitude of the underlying mechanisms driving NDDs. One major advantage for the implementation of multi-omic approaches in the drug discovery process is that these overarching tools are able to disentangle disease states and model perturbations through the comprehensive characterization of distinct molecular layers (i.e., genome, transcriptome, proteome) up to a single-cell resolution. Because of recent advances increasing their affordability and scalability, the use of omics technologies to drive drug discovery is nascent, but rapidly expanding in the neuroscience field. Combined with increasingly advanced in vitro models, which particularly benefited from the introduction of human iPSCs, multi-omics are shaping a new paradigm in drug discovery for NDDs, from disease characterization to therapeutics prediction and experimental screening. In this review, we discuss examples, main advantages and open challenges in the use of multi-omic approaches for the in vitro discovery of targets and therapies against NDDs.

Functional analysis of cell plasticity using single-cell technologies

Metazoan organisms are heterocellular systems composed of hundreds of different cell types, which arise from an isogenic genome through differentiation. Cellular 'plasticity' further enables cells to alter their fate in response to exogenous cues and is involved in a variety of processes, such as wound healing, infection, and cancer. Recent advances in cellular model systems, high-dimensional single-cell technologies, and lineage tracing have sparked a renaissance in plasticity research. Here, we discuss the definition of cell plasticity, evaluate state-of-the-art model systems and techniques to study cell-fate dynamics, and explore the application of single-cell technologies to obtain functional insights into cell plasticity in healthy and diseased tissues. The integration of advanced biomimetic model systems, single-cell technologies, and high-throughput perturbation studies is enabling a new era of research into non-genetic plasticity in metazoan systems.

Chromatin accessibility mediated transcriptome changes contribute to flavor substance alterations and jasmonic acid hyperaccumulation during oolong tea withering process

During the oolong tea withering process, abiotic stresses induce significant changes in the content of various flavor substances and jasmonic acid (JA). However, the changes in chromatin accessibility during withering and their potential impact remain poorly understood. By integrating ATAC-seq, RNA-seq, metabolite, and hormone assays, we characterized the withering treatment-induced changes in chromatin accessibility, gene expression levels, important metabolite contents, and JA and JA-ILE contents. Additionally, we analyzed the effects of chromatin accessibility alterations on gene expression changes, content changes of important flavor substances, and JA hyperaccumulation. Our analysis identified a total of 3451 open- and 13 426 close-differentially accessible chromatin regions (DACRs) under withering treatment. Our findings indicate that close-DACRs-mediated down-regulated differentially expressed genes (DEGs) resulted in the reduced accumulation of multiple catechins during withering, whereas open-DACRs-mediated up-regulated DEGs contributed to the increased accumulation of important terpenoids, JA, JA-ILE and short-chain C5/C6 volatiles. We further highlighted important DACRs-mediated DEGs associated with the synthesis of catechins, terpenoids, JA and JA and short-chain C5/C6 volatiles and confirmed the broad effect of close-DACRs on catechin synthesis involving almost all enzymes in the pathway during withering. Importantly, we identified a novel MYB transcription factor (CsMYB83) regulating catechin synthesis and verified the binding of CsMYB83 in the promoter-DACRs regions of key catechin synthesis genes using DAP-seq. Overall, our results not only revealed a landscape of chromatin alters-mediated transcription, flavor substance and hormone changes under oolong tea withering, but also provided target genes for flavor improvement breeding in tea plant.

Epigenomic Characterization of Lymphoid Neoplasms

Lymphoid neoplasms represent a heterogeneous group of disease entities and subtypes with markedly different molecular and clinical features. Beyond genetic alterations, lymphoid tumors also show widespread epigenomic changes. These severely affect the levels and distribution of DNA methylation, histone modifications, chromatin accessibility, and three-dimensional genome interactions. DNA methylation stands out as a tracer of cell identity and memory, as B cell neoplasms show epigenetic imprints of their cellular origin and proliferative history, which can be quantified by an epigenetic mitotic clock. Chromatin-associated marks are informative to uncover altered regulatory regions and transcription factor networks contributing to the development of distinct lymphoid tumors. Tumor-intrinsic epigenetic and genetic aberrations cooperate and interact with microenvironmental cells to shape the transcriptome at different phases of lymphoma evolution, and intraclonal heterogeneity can now be characterized by single-cell profiling. Finally, epigenetics offers multiple clinical applications, including powerful diagnostic and prognostic biomarkers as well as therapeutic targets.

3D genome structural variations play important roles in regulating seed oil content of Brassica napus

Dissecting the complex regulatory mechanism of seed oil content (SOC) is one of the main research goals in Brassica napus. Increasing evidence suggests that genome architecture is linked to multiple biological functions. However, the effect of genome architecture on SOC regulation remains unclear. Here, we used high-throughput chromatin conformation capture to characterize differences in the three-dimensional (3D) landscape of genome architecture of seeds from two B. napus lines, N53-2 (with high SOC) and Ken-C8 (with low SOC). Bioinformatics analysis demonstrated that differentially accessible regions and differentially expressed genes between N53-2 and Ken-C8 were preferentially enriched in regions with quantitative trait loci (QTLs)/associated genomic regions (AGRs) for SOC. A multi-omics analysis demonstrated that expression of SOC-related genes was tightly correlated with genome structural variations in QTLs/AGRs of B. napus. The candidate gene BnaA09g48250D, which showed structural variation in a QTL/AGR on chrA09, was identified by fine-mapping of a KN double-haploid population derived from hybridization of N53-2 and Ken-C8. Overexpression and knockout of BnaA09g48250D led to significant increases and decreases in SOC, respectively, in the transgenic lines. Taken together, our results reveal the 3D genome architecture of B. napus seeds and the roles of genome structural variations in SOC regulation, enriching our understanding of the molecular mechanisms of SOC regulation from the perspective of spatial chromatin structure.

Dose rate dependent reduction in chromatin accessibility at transcriptional start sites long time after exposure to gamma radiation

Ionizing radiation (IR) impact cellular and molecular processes that require chromatin remodelling relevant for cellular integrity. However, the cellular implications of ionizing radiation (IR) delivered per time unit (dose rate) are still debated. This study investigates whether the dose rate is relevant for inflicting changes to the epigenome, represented by chromatin accessibility, or whether it is the total dose that is decisive. CBA/CaOlaHsd mice were whole-body exposed to either chronic low dose rate (2.5 mGy/h for 54 d) or the higher dose rates (10 mGy/h for 14 d and 100 mGy/h for 30 h) of gamma radiation (⁶⁰Co, total dose: 3 Gy). Chromatin accessibility was analysed in liver tissue samples using Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-Seq), both one day after and over three months post-radiation (>100 d). The results show that the dose rate contributes to radiation-induced epigenomic changes in the liver at both sampling timepoints. Interestingly, chronic low dose rate exposure to a high total dose (3 Gy) did not inflict long-term changes to the epigenome. In contrast to the acute high dose rate given to the same total dose, reduced accessibility at transcriptional start sites (TSS) was identified in genes relevant for the DNA damage response and transcriptional activity. Our findings link dose rate to essential biological mechanisms that could be relevant for understanding long-term changes after ionizing radiation exposure. However, future studies are needed to comprehend the biological consequence of these findings.

Spatial -omics technologies: the new enterprise in 3D breast cancer models

The fields of tissue bioengineering, -omics, and spatial biology are advancing rapidly, each offering the opportunity for a paradigm shift in breast cancer research. However, to date, collaboration between these fields has not reached its full potential. In this review, we describe the most recently generated 3D breast cancer models regarding the biomaterials and technological platforms employed. Additionally, their biological evaluation is reported, highlighting their advantages and limitations. Specifically, we focus on the most up-to-date -omics and spatial biology techniques, which can generate a deeper understanding of the biological relevance of bioengineered 3D breast cancer in vitro models, thus paving the way towards truly clinically relevant microphysiological systems, improved drug development success rates, and personalised medicine approaches.

Cellular zinc status alters chromatin accessibility and binding of transcription factor p53 to genomic sites

Zinc (Zn2+) is an essential metal required by approximately 2500 proteins. Nearly half of these proteins act on DNA, including > 850 human transcription factors, polymerases, DNA damage response factors, and proteins involved in chromatin architecture. How these proteins acquire their essential Zn2+ cofactor and whether they are sensitive to changes in the labile Zn2+ pool in cells remain open questions. Here, we examine how changes in the labile Zn2+ pool affect chromatin accessibility and transcription factor binding to DNA. We observed both increases and decreases in accessibility in different chromatin regions via ATAC-seq upon treating MCF10A cells with elevated Zn2+ or the Zn2+-specific chelator tris(2-pyridylmethyl)amine (TPA). Transcription factor enrichment analysis was used to correlate changes in chromatin accessibility with transcription factor motifs, revealing 477 transcription factor motifs that were differentially enriched upon Zn2+ perturbation. 186 of these transcription factor motifs were enriched in Zn2+ and depleted in TPA, and the majority correspond to Zn2+ finger transcription factors. We selected TP53 as a candidate to examine how changes in motif enrichment correlate with changes in transcription factor occupancy by ChIP-qPCR. Using publicly available ChIP-seq and nascent transcription datasets, we narrowed the 50,000+ ATAC-seq peaks to 2164 TP53 targets and subsequently selected 6 high-probability TP53 binding sites for testing. ChIP-qPCR revealed that for 5 of the 6 targets, TP53 binding correlates with the local accessibility determined by ATAC-seq. These results demonstrate that changes in labile zinc directly alter chromatin accessibility and transcription factor binding to DNA.

ATACAmp: a tool for detecting ecDNA/HSRs from bulk and single-cell ATAC-seq data

BACKGROUND

High oncogene expression in cancer cells is a major cause of rapid tumor progression and drug resistance. Recent cancer genome research has shown that oncogenes as well as regulatory elements can be amplified in the form of extrachromosomal DNA (ecDNA) or subsequently integrated into chromosomes as homogeneously staining regions (HSRs). These genome-level variants lead to the overexpression of the corresponding oncogenes, resulting in poor prognosis. Most existing detection methods identify ecDNA using whole genome sequencing (WGS) data. However, these techniques usually detect many false positive regions owing to chromosomal DNA interference.

RESULTS

In the present study, an algorithm called "ATACAmp" that can identify ecDNA/HSRs in tumor genomes using ATAC-seq data has been described. High chromatin accessibility, one of the characteristics of ecDNA, makes ATAC-seq naturally enriched in ecDNA and reduces chromosomal DNA interference. The algorithm was validated using ATAC-seq data from cell lines that have been experimentally determined to contain ecDNA regions. ATACAmp accurately identified the majority of validated ecDNA regions. AmpliconArchitect, the widely used ecDNA detecting tool, was used to detect ecDNA regions based on the WGS data of the same cell lines. Additionally, the Circle-finder software, another tool that utilizes ATAC-seq data, was assessed. The results showed that ATACAmp exhibited higher accuracy than AmpliconArchitect and Circle-finder. Moreover, ATACAmp supported the analysis of single-cell ATAC-seq data, which linked ecDNA to specific cells.

CONCLUSIONS

ATACAmp, written in Python, is freely available on GitHub under the MIT license: https://github.com/chsmiss/ATAC-amp . Using ATAC-seq data, ATACAmp offers a novel analytical approach that is distinct from the conventional use of WGS data. Thus, this method has the potential to reduce the cost and technical complexity associated ecDNA analysis.

cisDynet: An integrated platform for modeling gene-regulatory dynamics and networks

Chromatin accessibility sequencing has been widely used for uncovering genetic regulatory mechanisms and inferring gene regulatory networks. However, effectively integrating large-scale chromatin accessibility datasets has posed a significant challenge. This is due to the lack of a comprehensive end-to-end solution, as many existing tools primarily emphasize data preprocessing and overlook downstream analyses. To bridge this gap, we have introduced cisDynet, a holistic solution that combines streamlined data preprocessing using Snakemake and R functions with advanced downstream analysis capabilities. cisDynet excels in conventional data analyses, encompassing peak statistics, peak annotation, differential analysis, motif enrichment analysis, and more. Additionally, it allows to perform sophisticated data exploration, such as tissue-specific peak identification, time course data modeling, integration of RNA-seq data to establish peak-to-gene associations, constructing regulatory networks, and conducting enrichment analysis of genome-wide association study (GWAS) variants. As a proof of concept, we applied cisDynet to reanalyze comprehensive ATAC-seq datasets across various tissues from the Encyclopedia of DNA Elements (ENCODE) project. The analysis successfully delineated tissue-specific open chromatin regions (OCRs), established connections between OCRs and target genes, and effectively linked these discoveries with 1861 GWAS variants. Furthermore, cisDynet was instrumental in dissecting the time course open chromatin data of mouse embryonic development, revealing the dynamic behavior of OCRs over developmental stages and identifying key transcription factors governing differentiation trajectories. In summary, cisDynet offers researchers a user-friendly solution that minimizes the need for extensive coding, ensures the reproducibility of results, and greatly simplifies the exploration of epigenomic data.

Chromatin accessibility profiling of targeted cell populations with laser capture microdissection coupled to ATAC-seq

Spatially resolved omics technologies reveal context-dependent cellular regulatory networks in tissues of interest. Beyond transcriptome analysis, information on epigenetic traits and chromatin accessibility can provide further insights on gene regulation in health and disease. Nevertheless, compared to the enormous advancements in spatial transcriptomics technologies, the field of spatial epigenomics is much younger and still underexplored. In this study, we report laser capture microdissection coupled to ATAC-seq (LCM-ATAC-seq) applied to fresh frozen samples for the spatial characterization of chromatin accessibility. We first demonstrate the efficient use of LCM coupled to in situ tagmentation and evaluate its performance as a function of cell number, microdissected areas, and tissue type. Further, we demonstrate its use for the targeted chromatin accessibility analysis of discrete contiguous or scattered cell populations in tissues via single-nuclei capture based on immunostaining for specific cellular markers.

Chromatin accessibility in the Drosophila embryo is determined by transcription factor pioneering and enhancer activation

Chromatin accessibility is integral to the process by which transcription factors (TFs) read out cis-regulatory DNA sequences, but it is difficult to differentiate between TFs that drive accessibility and those that do not. Deep learning models that learn complex sequence rules provide an unprecedented opportunity to dissect this problem. Using zygotic genome activation in Drosophila as a model, we analyzed high-resolution TF binding and chromatin accessibility data with interpretable deep learning and performed genetic validation experiments. We identify a hierarchical relationship between the pioneer TF Zelda and the TFs involved in axis patterning. Zelda consistently pioneers chromatin accessibility proportional to motif affinity, whereas patterning TFs augment chromatin accessibility in sequence contexts where they mediate enhancer activation. We conclude that chromatin accessibility occurs in two tiers: one through pioneering, which makes enhancers accessible but not necessarily active, and the second when the correct combination of TFs leads to enhancer activation.

Inferring chromatin accessibility during murine hematopoiesis through phylogenetic analysis

OBJECTIVE

Diversification of cell types and changes in epigenetic states during cell differentiation processes are important for understanding development. Recently, phylogenetic analysis using DNA methylation and histone modification information has been shown useful for inferring these processes. The purpose of this study was to examine whether chromatin accessibility data can help infer these processes in murine hematopoiesis.

RESULTS

Chromatin accessibility data could partially infer the hematopoietic differentiation hierarchy. Furthermore, based on the ancestral state estimation of internal nodes, the open/closed chromatin states of differentiating progenitor cells could be predicted with a specificity of 0.86-0.99 and sensitivity of 0.29-0.72. These results suggest that the phylogenetic analysis of chromatin accessibility could offer important information on cell differentiation, particularly for organisms from which progenitor cells are difficult to obtain.

Beyond assembly: the increasing flexibility of single-molecule sequencing technology

The maturation of high-throughput short-read sequencing technology over the past two decades has shaped the way genomes are studied. Recently, single-molecule, long-read sequencing has emerged as an essential tool in deciphering genome structure and function, including filling gaps in the human reference genome, measuring the epigenome and characterizing splicing variants in the transcriptome. With recent technological developments, these single-molecule technologies have moved beyond genome assembly and are being used in a variety of ways, including to selectively sequence specific loci with long reads, measure chromatin state and protein-DNA binding in order to investigate the dynamics of gene regulation, and rapidly determine copy number variation. These increasingly flexible uses of single-molecule technologies highlight a young and fast-moving part of the field that is leading to a more accessible era of nucleic acid sequencing.

3D organization of regulatory elements for transcriptional regulation in Arabidopsis

BACKGROUND

Although spatial organization of compartments and topologically associating domains at large scale is relatively well studied, the spatial organization of regulatory elements at fine scale is poorly understood in plants.

RESULTS

Here we perform high-resolution chromatin interaction analysis using paired-end tag sequencing approach. We map chromatin interactions tethered with RNA polymerase II and associated with heterochromatic, transcriptionally active, and Polycomb-repressive histone modifications in Arabidopsis. Analysis of the regulatory repertoire shows that distal active cis-regulatory elements are linked to their target genes through long-range chromatin interactions with increased expression of the target genes, while poised cis-regulatory elements are linked to their target genes through long-range chromatin interactions with depressed expression of the target genes. Furthermore, we demonstrate that transcription factor MYC2 is critical for chromatin spatial organization, and propose that MYC2 occupancy and MYC2-mediated chromatin interactions coordinately facilitate transcription within the framework of 3D chromatin architecture. Analysis of functionally related gene-defined chromatin connectivity networks reveals that genes implicated in flowering-time control are functionally compartmentalized into separate subdomains via their spatial activity in the leaf or shoot apical meristem, linking active mark- or Polycomb-repressive mark-associated chromatin conformation to coordinated gene expression.

CONCLUSION

The results reveal that the regulation of gene transcription in Arabidopsis is not only by linear juxtaposition, but also by long-range chromatin interactions. Our study uncovers the fine scale genome organization of Arabidopsis and the potential roles of such organization in orchestrating transcription and development.

Characterization of an eye field-like state during optic vesicle organoid development

Specification of the eye field (EF) within the neural plate marks the earliest detectable stage of eye development. Experimental evidence, primarily from non-mammalian model systems, indicates that the stable formation of this group of cells requires the activation of a set of key transcription factors. This crucial event is challenging to probe in mammals and, quantitatively, little is known regarding the regulation of the transition of cells to this ocular fate. Using optic vesicle organoids to model the onset of the EF, we generate time-course transcriptomic data allowing us to identify dynamic gene expression programmes that characterize this cellular-state transition. Integrating this with chromatin accessibility data suggests a direct role of canonical EF transcription factors in regulating these gene expression changes, and highlights candidate cis-regulatory elements through which these transcription factors act. Finally, we begin to test a subset of these candidate enhancer elements, within the organoid system, by perturbing the underlying DNA sequence and measuring transcriptomic changes during EF activation.

True-to-scale DNA-density maps correlate with major accessibility differences between active and inactive chromatin

Chromatin compaction differences may have a strong impact on accessibility of individual macromolecules and macromolecular assemblies to their DNA target sites. Estimates based on fluorescence microscopy with conventional resolution, however, suggest only modest compaction differences (∼2-10×) between the active nuclear compartment (ANC) and inactive nuclear compartment (INC). Here, we present maps of nuclear landscapes with true-to-scale DNA densities, ranging from <5 to >300 Mbp/μm3. Maps are generated from individual human and mouse cell nuclei with single-molecule localization microscopy at ∼20 nm lateral and ∼100 nm axial optical resolution and are supplemented by electron spectroscopic imaging. Microinjection of fluorescent nanobeads with sizes corresponding to macromolecular assemblies for transcription into nuclei of living cells demonstrates their localization and movements within the ANC and exclusion from the INC.

3D genome organization and epigenetic regulation in autoimmune diseases

Three-dimensional (3D) genomics is an emerging field of research that investigates the relationship between gene regulatory function and the spatial structure of chromatin. Chromatin folding can be studied using chromosome conformation capture (3C) technology and 3C-based derivative sequencing technologies, including chromosome conformation capture-on-chip (4C), chromosome conformation capture carbon copy (5C), and high-throughput chromosome conformation capture (Hi-C), which allow scientists to capture 3D conformations from a single site to the entire genome. A comprehensive analysis of the relationships between various regulatory components and gene function also requires the integration of multi-omics data such as genomics, transcriptomics, and epigenomics. 3D genome folding is involved in immune cell differentiation, activation, and dysfunction and participates in a wide range of diseases, including autoimmune diseases. We describe hierarchical 3D chromatin organization in this review and conclude with characteristics of C-techniques and multi-omics applications of the 3D genome. In addition, we describe the relationship between 3D genome structure and the differentiation and maturation of immune cells and address how changes in chromosome folding contribute to autoimmune diseases.

OCRFinder: a noise-tolerance machine learning method for accurately estimating open chromatin regions

Open chromatin regions are the genomic regions associated with basic cellular physiological activities, while chromatin accessibility is reported to affect gene expressions and functions. A basic computational problem is to efficiently estimate open chromatin regions, which could facilitate both genomic and epigenetic studies. Currently, ATAC-seq and cfDNA-seq (plasma cell-free DNA sequencing) are two popular strategies to detect OCRs. As cfDNA-seq can obtain more biomarkers in one round of sequencing, it is considered more effective and convenient. However, in processing cfDNA-seq data, due to the dynamically variable chromatin accessibility, it is quite difficult to obtain the training data with pure OCRs or non-OCRs, and leads to a noise problem for either feature-based approaches or learning-based approaches. In this paper, we propose a learning-based OCR estimation approach with a noise-tolerance design. The proposed approach, named OCRFinder, incorporates the ideas of ensemble learning framework and semi-supervised strategy to avoid potential overfitting of noisy labels, which are the false positives on OCRs and non-OCRs. Compared to different noise control strategies and state-of-the-art approaches, OCRFinder achieved higher accuracies and sensitivities in the experiments. In addition, OCRFinder also has an excellent performance in ATAC-seq or DNase-seq comparison experiments.

Base-resolution UV footprinting by sequencing reveals distinctive damage signatures for DNA-binding proteins

Decades ago, it was shown that proteins binding to DNA can quantitatively alter the formation of DNA damage by UV light. This established the principle of UV footprinting for non-intrusive study of protein-DNA contacts in living cells, albeit at limited scale and precision. Here, we perform deep base-resolution quantification of the principal UV damage lesion, the cyclobutane pyrimidine dimer (CPD), at select human promoter regions using targeted CPD sequencing. Several transcription factors exhibited distinctive and repeatable damage signatures indicative of site occupancy, involving strong (up to 17-fold) position-specific elevations and reductions in CPD formation frequency relative to naked DNA. Positive damage modulation at some ETS transcription factor binding sites coincided at base level with melanoma somatic mutation hotspots. Our work provides proof of concept for the study of protein-DNA interactions at individual loci using light and sequencing, and reveals widespread and potent modulation of UV damage in regulatory regions.

Dam Assisted Fluorescent Tagging of Chromatin Accessibility (DAFCA) for Optical Genome Mapping in Nanochannel Arrays

Proteins and enzymes in the cell nucleus require physical access to their DNA target sites in order to perform genomic tasks such as gene activation and transcription. Hence, chromatin accessibility is a central regulator of gene expression, and its genomic profile holds essential information on the cell type and state. We utilized the E. coli Dam methyltransferase in combination with a fluorescent cofactor analogue to generate fluorescent tags in accessible DNA regions within the cell nucleus. The accessible portions of the genome are then detected by single-molecule optical genome mapping in nanochannel arrays. This method allowed us to characterize long-range structural variations and their associated chromatin structure. We show the ability to create whole-genome, allele-specific chromatin accessibility maps composed of long DNA molecules extended in silicon nanochannels.

Profiling the epigenetic landscape of the antigen receptor repertoire: the missing epi-immunogenomics data

High-resolution sequencing methods that capture the epigenetic landscape within the T cell receptor (TCR) gene loci are pivotal for a fundamental understanding of the epigenetic regulatory mechanisms of the TCR repertoire. In our opinion, filling the gaps in our understanding of the epigenetic mechanisms regulating the TCR repertoire will benefit the development of strategies that can modulate the TCR repertoire composition by leveraging the dynamic nature of epigenetic modifications.

PlantCADB: A Comprehensive Plant Chromatin Accessibility Database

Chromatin accessibility landscapes are essential for detecting regulatory elements, illustrating the corresponding regulatory networks, and, ultimately, understanding the molecular basis underlying key biological processes. With the advancement of sequencing technologies, a large volume of chromatin accessibility data has been accumulated and integrated for humans and other mammals. These data have greatly advanced the study of disease pathogenesis, cancer survival prognosis, and tissue development. To advance the understanding of molecular mechanisms regulating plant key traits and biological processes, we developed a comprehensive plant chromatin accessibility database (PlantCADB) from 649 samples of 37 species. These samples are abiotic stress-related (such as heat, cold, drought, and salt; 159 samples), development-related (232 samples), and/or tissue-specific (376 samples). Overall, 18,339,426 accessible chromatin regions (ACRs) were compiled. These ACRs were annotated with genomic information, associated genes, transcription factor footprint, motif, and single-nucleotide polymorphisms (SNPs). Additionally, PlantCADB provides various tools to visualize ACRs and corresponding annotations. It thus forms an integrated, annotated, and analyzed plant-related chromatin accessibility resource, which can aid in better understanding genetic regulatory networks underlying development, important traits, stress adaptations, and evolution.PlantCADB is freely available at https://bioinfor.nefu.edu.cn/PlantCADB/.

Key transcription factors influence the epigenetic landscape to regulate retinal cell differentiation

How the diverse neural cell types emerge from multipotent neural progenitor cells during central nervous system development remains poorly understood. Recent scRNA-seq studies have delineated the developmental trajectories of individual neural cell types in many neural systems including the neural retina. Further understanding of the formation of neural cell diversity requires knowledge about how the epigenetic landscape shifts along individual cell lineages and how key transcription factors regulate these changes. In this study, we dissect the changes in the epigenetic landscape during early retinal cell differentiation by scATAC-seq and identify globally the enhancers, enriched motifs, and potential interacting transcription factors underlying the cell state/type specific gene expression in individual lineages. Using CUT&Tag, we further identify the enhancers bound directly by four key transcription factors, Otx2, Atoh7, Pou4f2 and Isl1, including those dependent on Atoh7, and uncover the sequential and combinatorial interactions of these factors with the epigenetic landscape to control gene expression along individual retinal cell lineages such as retinal ganglion cells (RGCs). Our results reveal a general paradigm in which transcription factors collaborate and compete to regulate the emergence of distinct retinal cell types such as RGCs from multipotent retinal progenitor cells (RPCs).

One genome, multiple phenotypes: decoding the evolution and mechanisms of environmentally induced developmental plasticity in insects

Plasticity in developmental processes gives rise to remarkable environmentally induced phenotypes. Some of the most striking and well-studied examples of developmental plasticity are seen in insects. For example, beetle horn size responds to nutritional state, butterfly eyespots are enlarged in response to temperature and humidity, and environmental cues also give rise to the queen and worker castes of eusocial insects. These phenotypes arise from essentially identical genomes in response to an environmental cue during development. Developmental plasticity is taxonomically widespread, affects individual fitness, and may act as a rapid-response mechanism allowing individuals to adapt to changing environments. Despite the importance and prevalence of developmental plasticity, there remains scant mechanistic understanding of how it works or evolves. In this review, we use key examples to discuss what is known about developmental plasticity in insects and identify fundamental gaps in the current knowledge. We highlight the importance of working towards a fully integrated understanding of developmental plasticity in a diverse range of species. Furthermore, we advocate for the use of comparative studies in an evo-devo framework to address how developmental plasticity works and how it evolves.

Simultaneous Single-Cell Profiling of the Transcriptome and Accessible Chromatin Using SHARE-seq

The ability to analyze the transcriptomic and epigenomic states of individual single cells has in recent years transformed our ability to measure and understand biological processes. Recent advancements have focused on increasing sensitivity and throughput to provide richer and deeper biological insights at the cellular level. The next frontier is the development of multiomic methods capable of analyzing multiple features from the same cell, such as the simultaneous measurement of the transcriptome and the chromatin accessibility of candidate regulatory elements. In this chapter, we discuss and describe SHARE-seq (Simultaneous high-throughput ATAC, and RNA expression with sequencing) for carrying out simultaneous chromatin accessibility and transcriptome measurements in single cells, together with the experimental and analytical considerations for achieving optimal results.

Profiling human pathogenic repeat expansion regions by synergistic and multi-level impacts on molecular connections

Whilst DNA repeat expansions cause numerous heritable human disorders, their origins and underlying pathological mechanisms are often unclear. We collated a dataset comprising 224 human repeat expansions encompassing 203 different genes, and performed a systematic analysis with respect to key topological features at the DNA, RNA and protein levels. Comparison with controls without known pathogenicity and genomic regions lacking repeats, allowed the construction of the first tool to discriminate repeat regions harboring pathogenic repeat expansions (DPREx). At the DNA level, pathogenic repeat expansions exhibited stronger signals for DNA regulatory factors (e.g. H3K4me3, transcription factor-binding sites) in exons, promoters, 5'UTRs and 5'genes but were not significantly different from controls in introns, 3'UTRs and 3'genes. Additionally, pathogenic repeat expansions were also found to be enriched in non-B DNA structures. At the RNA level, pathogenic repeat expansions were characterized by lower free energy for forming RNA secondary structure and were closer to splice sites in introns, exons, promoters and 5'genes than controls. At the protein level, pathogenic repeat expansions exhibited a preference to form coil rather than other types of secondary structure, and tended to encode surface-located protein domains. Guided by these features, DPREx ( http://biomed.nscc-gz.cn/zhaolab/geneprediction/# ) achieved an Area Under the Curve (AUC) value of 0.88 in a test on an independent dataset. Pathogenic repeat expansions are thus located such that they exert a synergistic influence on the gene expression pathway involving inter-molecular connections at the DNA, RNA and protein levels.

scGREAT: Graph-based regulatory element analysis tool for single-cell multi-omics data

Motivation

With the development in single-cell multi-omics sequencing technology and data integration algorithms, we have entered the single-cell multi-omics era. Current multi-omics analysis algorithms failed to systematically dissect the heterogeneity within the datasets when inferring cis-regulatory events. Thus, there is a need for cis-regulatory element inferring algorithms that considers the cellular heterogeneity.

Results

Here, we propose scGREAT, a single-cell multi-omics regulatory state analysis Python package with a rapid graph-based correlation measurement L. The graph-based correlation method assigns each cell a local L index, pinpointing specific cell groups of certain regulatory states. Such single-cell resolved regulatory state information enables the heterogeneity analysis equipped in the package. Applying scGREAT to the 10X Multiome PBMC dataset, we demonstrated how it could help subcluster cell types, infer regulation-based pseudo-time trajectory, discover feature modules, and find cluster-specific regulatory gene-peak pairs. Besides, we showed that global L index, which is the average of all local L values, is a better replacement for Pearson's r in ruling out confounding regulatory relationships that are not of research interests.

Availability

https://github.com/ChaozhongLiu/scGREAT.

Systematically characterizing the roles of E3-ligase family members in inflammatory responses with massively parallel Perturb-seq

E3 ligases regulate key processes, but many of their roles remain unknown. Using Perturb-seq, we interrogated the function of 1,130 E3 ligases, partners and substrates in the inflammatory response in primary dendritic cells (DCs). Dozens impacted the balance of DC1, DC2, migratory DC and macrophage states and a gradient of DC maturation. Family members grouped into co-functional modules that were enriched for physical interactions and impacted specific programs through substrate transcription factors. E3s and their adaptors co-regulated the same processes, but partnered with different substrate recognition adaptors to impact distinct aspects of the DC life cycle. Genetic interactions were more prevalent within than between modules, and a deep learning model, comβVAE, predicts the outcome of new combinations by leveraging modularity. The E3 regulatory network was associated with heritable variation and aberrant gene expression in immune cells in human inflammatory diseases. Our study provides a general approach to dissect gene function.

Computational Analysis of Maize Enhancer Regulatory Elements Using ATAC-STARR-seq

The blueprints to development, response to the environment, and cellular function are largely the manifestation of distinct gene expression programs controlled by the spatiotemporal activity of cis-regulatory elements. Although biochemical methods for identifying accessible chromatin - a hallmark of active cis-regulatory elements - have been developed, approaches capable of measuring and quantifying cis-regulatory activity are only beginning to be realized. Massively Parallel Reporter Assays coupled to chromatin accessibility profiling present a high-throughput solution for testing the transcription-activating capacity of millions of putatively regulatory DNA sequences in parallel. However, clear computational pipelines for analyzing these high-throughput sequencing-based reporter assays are lacking. In this protocol, I layout and rationalize a computational framework for the processing and analysis of Assay for Transposase Accessible Chromatin profiling followed by Self-Transcribed Active Regulatory Region sequencing (ATAC-STARR-seq) data from a recent study in Zea mays. The approach described herein can be adapted to other sequencing-based reporter assays and is largely agnostic to the model organism with the appropriate input substitutions.

Adaptive optical two-photon fluorescence microscopy probes cellular organization of ocular lenses in vivo

The mammalian ocular lens is an avascular multicellular organ that grows continuously throughout life. Traditionally, its cellular organization is investigated using dissected lenses, which eliminates in vivo environmental and structural support. Here, we demonstrated that two-photon fluorescence microscopy (2PFM) can visualize lens cells in vivo. To maintain subcellular resolution at depth, we employed adaptive optics (AO) to correct aberrations due to ocular and lens tissues, which led to substantial signal and resolution improvements. Imaging lens cells up to 980 μm deep, we observed novel cellular organizations including suture-associated voids, enlarged vacuoles, and large cavities, contrary to the conventional view of a highly ordered organization. We tracked these features longitudinally over weeks and observed the incorporation of new cells during growth. Taken together, non-invasive longitudinal in vivo imaging of lens morphology using AO 2PFM will allow us to directly observe the development or alterations of lens cellular organization in living animals.

Epigenetic remodeling of the immune landscape in cancer: therapeutic hurdles and opportunities

The tumor immune microenvironment represents a sophisticated ecosystem where various immune cell subtypes communicate with cancer cells and stromal cells. The dynamic cellular composition and functional characteristics of the immune landscape along the trajectory of cancer development greatly impact the therapeutic efficacy and clinical outcome in patients receiving systemic antitumor therapy. Mounting evidence has suggested that epigenetic mechanisms are the underpinning of many aspects of antitumor immunity and facilitate immune state transitions during differentiation, activation, inhibition, or dysfunction. Thus, targeting epigenetic modifiers to remodel the immune microenvironment holds great potential as an integral part of anticancer regimens. In this review, we summarize the epigenetic profiles and key epigenetic modifiers in individual immune cell types that define the functional coordinates of tumor permissive and non-permissive immune landscapes. We discuss the immunomodulatory roles of current and prospective epigenetic therapeutic agents, which may open new opportunities in enhancing cancer immunotherapy or overcoming existing therapeutic challenges in the management of cancer.

Artificial intelligence-based multi-omics analysis fuels cancer precision medicine

With biotechnological advancements, innovative omics technologies are constantly emerging that have enabled researchers to access multi-layer information from the genome, epigenome, transcriptome, proteome, metabolome, and more. A wealth of omics technologies, including bulk and single-cell omics approaches, have empowered to characterize different molecular layers at unprecedented scale and resolution, providing a holistic view of tumor behavior. Multi-omics analysis allows systematic interrogation of various molecular information at each biological layer while posing tricky challenges regarding how to extract valuable insights from the exponentially increasing amount of multi-omics data. Therefore, efficient algorithms are needed to reduce the dimensionality of the data while simultaneously dissecting the mysteries behind the complex biological processes of cancer. Artificial intelligence has demonstrated the ability to analyze complementary multi-modal data streams within the oncology realm. The coincident development of multi-omics technologies and artificial intelligence algorithms has fuelled the development of cancer precision medicine. Here, we present state-of-the-art omics technologies and outline a roadmap of multi-omics integration analysis using an artificial intelligence strategy. The advances made using artificial intelligence-based multi-omics approaches are described, especially concerning early cancer screening, diagnosis, response assessment, and prognosis prediction. Finally, we discuss the challenges faced in multi-omics analysis, along with tentative future trends in this field. With the increasing application of artificial intelligence in multi-omics analysis, we anticipate a shifting paradigm in precision medicine becoming driven by artificial intelligence-based multi-omics technologies.

Single-Cell Joint Profiling of Open Chromatin and Transcriptome by Paired-Seq

Simultaneous detection of chromatin accessibility and transcription from the same cells promises to greatly facilitate the dissection of cell-type-specific gene regulatory programs in complex tissues. Paired-seq enables joint analysis of open chromatin and nuclear transcriptome from up to a million cells in parallel. It achieves ultra-high-throughput single-cell multiomics with the use of a combinatorial barcoding strategy involving sequential ligation of multiplexed DNA barcodes to chromatin DNA fragments and reverse transcription products, followed by high-throughput DNA sequencing of the resulting DNA libraries and deconvolution of single-cell multiomic maps based on cell-specific barcodes.

GAGAM v1.2: An Improvement on Peak Labeling and Genomic Annotated Gene Activity Matrix Construction

Single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) is rapidly becoming a powerful technology for assessing the epigenetic landscape of thousands of cells. However, the sparsity of the resulting data poses significant challenges to their interpretability and informativeness. Different computational methods are available, proposing ways to generate significant features from accessibility data and process them to obtain meaningful results. Foremost among them is the peak calling, which interprets the raw scATAC-seq data generating the peaks as features. However, scATAC-seq data are not trivially comparable with single-cell RNA sequencing (scRNA-seq) data, an increasingly pressing challenge since the necessity of multimodal experiments integration. For this reason, this study wants to improve the concept of the Gene Activity Matrix (GAM), which links the accessibility data to the genes, by proposing an improved version of the Genomic-Annotated Gene Activity Matrix (GAGAM) concept. Specifically, this paper presents GAGAM v1.2, a new and better version of GAGAM v1.0. GAGAM aims to label the peaks and link them to the genes through functional annotation of the whole genome. Using genes as features in scATAC-seq datasets makes different datasets comparable and allows linking gene accessibility and expression. This link is crucial for gene regulation understanding and fundamental for the increasing impact of multi-omics data. Results confirm that our method performs better than the previous GAMs and shows a preliminary comparison with scRNA-seq data.

Single-cell transcriptomics of the goldfish retina reveals genetic divergence in the asymmetrically evolved subgenomes after allotetraploidization

The recent whole-genome duplication (WGD) in goldfish (Carassius auratus) approximately 14 million years ago makes it a valuable model for studying gene evolution during the early stages after WGD. We analyzed the transcriptome of the goldfish retina at the level of single-cell (scRNA-seq) and open chromatin regions (scATAC-seq). We identified a group of genes that have undergone dosage selection, accounting for 5% of the total 11,444 ohnolog pairs. We also identified 306 putative sub/neo-functionalized ohnolog pairs that are likely to be under cell-type-specific genetic variation at single-cell resolution. Diversification in the expression patterns of several ohnolog pairs was observed in the retinal cell subpopulations. The single-cell level transcriptome analysis in this study uncovered the early stages of evolution in retinal cell of goldfish after WGD. Our results provide clues for understanding the relationship between the early stages of gene evolution after WGD and the evolution of diverse vertebrate retinal functions.

Benchmarking automated cell type annotation tools for single-cell ATAC-seq data

As single-cell chromatin accessibility profiling methods advance, scATAC-seq has become ever more important in the study of candidate regulatory genomic regions and their roles underlying developmental, evolutionary, and disease processes. At the same time, cell type annotation is critical in understanding the cellular composition of complex tissues and identifying potential novel cell types. However, most existing methods that can perform automated cell type annotation are designed to transfer labels from an annotated scRNA-seq data set to another scRNA-seq data set, and it is not clear whether these methods are adaptable to annotate scATAC-seq data. Several methods have been recently proposed for label transfer from scRNA-seq data to scATAC-seq data, but there is a lack of benchmarking study on the performance of these methods. Here, we evaluated the performance of five scATAC-seq annotation methods on both their classification accuracy and scalability using publicly available single-cell datasets from mouse and human tissues including brain, lung, kidney, PBMC, and BMMC. Using the BMMC data as basis, we further investigated the performance of these methods across different data sizes, mislabeling rates, sequencing depths and the number of cell types unique to scATAC-seq. Bridge integration, which is the only method that requires additional multimodal data and does not need gene activity calculation, was overall the best method and robust to changes in data size, mislabeling rate and sequencing depth. Conos was the most time and memory efficient method but performed the worst in terms of prediction accuracy. scJoint tended to assign cells to similar cell types and performed relatively poorly for complex datasets with deep annotations but performed better for datasets only with major label annotations. The performance of scGCN and Seurat v3 was moderate, but scGCN was the most time-consuming method and had the most similar performance to random classifiers for cell types unique to scATAC-seq.

Cis-Regulation by NACs: A Promising Frontier in Wheat Crop Improvement

Crop traits are controlled by multiple genes; however, the complex spatio-temporal transcriptional behavior of genes cannot be fully understood without comprehending the role of transcription factors (TFs) and the underlying mechanisms of the binding interactions of their cis-regulatory elements. NAC belongs to one of the largest families of plant-specific TFs and has been associated with the regulation of many traits. This review provides insight into the cis-regulation of genes by wheat NACs (TaNACs) for the improvement in yield-related traits, including phytohormonal homeostasis, leaf senescence, seed traits improvement, root modulation, and biotic and abiotic stresses in wheat and other cereals. We also discussed the current potential, knowledge gaps, and prospects of TaNACs.

Exploitation of epigenetic variation of crop wild relatives for crop improvement and agrobiodiversity preservation

Crop wild relatives (CWRs) are recognized as the best potential source of traits for crop improvement. However, successful crop improvement using CWR relies on identifying variation in genes controlling desired traits in plant germplasms and subsequently incorporating them into cultivars. Epigenetic diversity may provide an additional layer of variation within CWR and can contribute novel epialleles for key traits for crop improvement. There is emerging evidence that epigenetic variants of functional and/or agronomic importance exist in CWR gene pools. This provides a rationale for the conservation of epigenotypes of interest, thus contributing to agrobiodiversity preservation through conservation and (epi)genetic monitoring. Concepts and techniques of classical and modern breeding should consider integrating recent progress in epigenetics, initially by identifying their association with phenotypic variations and then by assessing their heritability and stability in subsequent generations. New tools available for epigenomic analysis offer the opportunity to capture epigenetic variation and integrate it into advanced (epi)breeding programmes. Advances in -omics have provided new insights into the sources and inheritance of epigenetic variation and enabled the efficient introduction of epi-traits from CWR into crops using epigenetic molecular markers, such as epiQTLs.

Multi-Omics Approaches and Resources for Systems-Level Gene Function Prediction in the Plant Kingdom

In higher plants, the complexity of a system and the components within and among species are rapidly dissected by omics technologies. Multi-omics datasets are integrated to infer and enable a comprehensive understanding of the life processes of organisms of interest. Further, growing open-source datasets coupled with the emergence of high-performance computing and development of computational tools for biological sciences have assisted in silico functional prediction of unknown genes, proteins and metabolites, otherwise known as uncharacterized. The systems biology approach includes data collection and filtration, system modelling, experimentation and the establishment of new hypotheses for experimental validation. Informatics technologies add meaningful sense to the output generated by complex bioinformatics algorithms, which are now freely available in a user-friendly graphical user interface. These resources accentuate gene function prediction at a relatively minimal cost and effort. Herein, we present a comprehensive view of relevant approaches available for system-level gene function prediction in the plant kingdom. Together, the most recent applications and sought-after principles for gene mining are discussed to benefit the plant research community. A realistic tabulation of plant genomic resources is included for a less laborious and accurate candidate gene discovery in basic plant research and improvement strategies.

Epigenetic tumor heterogeneity in the era of single-cell profiling with nanopore sequencing

Nanopore sequencing has brought the technology to the next generation in the science of sequencing. This is achieved through research advancing on: pore efficiency, creating mechanisms to control DNA translocation, enhancing signal-to-noise ratio, and expanding to long-read ranges. Heterogeneity regarding epigenetics would be broad as mutations in the epigenome are sensitive to cause new challenges in cancer research. Epigenetic enzymes which catalyze DNA methylation and histone modification are dysregulated in cancer cells and cause numerous heterogeneous clones to evolve. Detection of this heterogeneity in these clones plays an indispensable role in the treatment of various cancer types. With single-cell profiling, the nanopore sequencing technology could provide a simple sequence at long reads and is expected to be used soon at the bedside or doctor's office. Here, we review the advancements of nanopore sequencing and its use in the detection of epigenetic heterogeneity in cancer.

A combinatorial indexing strategy for low-cost epigenomic profiling of plant single cells

Understanding how cis-regulatory elements facilitate gene expression is a key question in biology. Recent advances in single-cell genomics have led to the discovery of cell-specific chromatin landscapes that underlie transcription programs in animal models. However, the high equipment and reagent costs of commercial systems limit their applications for many laboratories. In this study, we developed a combinatorial index and dual PCR barcode strategy to profile the Arabidopsis thaliana root single-cell epigenome without any specialized equipment. We generated chromatin accessibility profiles for 13 576 root nuclei with an average of 12 784 unique Tn5 integrations per cell. Integration of the single-cell assay for transposase-accessible chromatin sequencing and RNA sequencing data sets enabled the identification of 24 cell clusters with unique transcription, chromatin, and cis-regulatory signatures. Comparison with single-cell data generated using the commercial microfluidic platform from 10X Genomics revealed that this low-cost combinatorial index method is capable of unbiased identification of cell-type-specific chromatin accessibility. We anticipate that, by removing cost, instrumentation, and other technical obstacles, this method will be a valuable tool for routine investigation of single-cell epigenomes and provide new insights into plant growth and development and plant interactions with the environment.

Multi-Cell-Type Openness-Weighted Association Studies for Trait-Associated Genomic Segments Prioritization

Openness-weighted association study (OWAS) is a method that leverages the in silico prediction of chromatin accessibility to prioritize genome-wide association studies (GWAS) signals, and can provide novel insights into the roles of non-coding variants in complex diseases. A prerequisite to apply OWAS is to choose a trait-related cell type beforehand. However, for most complex traits, the trait-relevant cell types remain elusive. In addition, many complex traits involve multiple related cell types. To address these issues, we develop OWAS-joint, an efficient framework that aggregates predicted chromatin accessibility across multiple cell types, to prioritize disease-associated genomic segments. In simulation studies, we demonstrate that OWAS-joint achieves a greater statistical power compared to OWAS. Moreover, the heritability explained by OWAS-joint segments is higher than or comparable to OWAS segments. OWAS-joint segments also have high replication rates in independent replication cohorts. Applying the method to six complex human traits, we demonstrate the advantages of OWAS-joint over a single-cell-type OWAS approach. We highlight that OWAS-joint enhances the biological interpretation of disease mechanisms, especially for non-coding regions.