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Mapping single-cell sequencing profiles to comprehensive reference datasets provides a powerful alternative to unsupervised analysis. However, most reference datasets are constructed from single-cell RNA-sequencing data and cannot be used to annotate datasets that do not measure gene expression. Here we introduce ‘bridge integration’, a method to integrate single-cell datasets across modalities using a multiomic dataset as a molecular bridge. Each cell in the multiomic dataset constitutes an element in a ‘dictionary’, which is used to reconstruct unimodal datasets and transform them into a shared space. Our procedure accurately integrates transcriptomic data with independent single-cell measurements of chromatin accessibility, histone modifications, DNA methylation and protein levels. Moreover, we demonstrate how dictionary learning can be combined with sketching techniques to improve computational scalability and harmonize 8.6 million human immune cell profiles from sequencing and mass cytometry experiments. Our approach, implemented in version 5 of our Seurat toolkit ( http://www.satijalab.org/seurat ), broadens the utility of single-cell reference datasets and facilitates comparisons across diverse molecular modalities. Reference mapping is extended beyond scRNA-seq to single-cell epigenetic and proteomic data.

Much has been learned since the early 1960s about histone post-translational modifications (PTMs) and how they affect DNA-templated processes at the molecular level. This understanding has been bolstered in the past decade by the identification of new types of histone PTM, the advent of new genome-wide mapping approaches and methods to deposit or remove PTMs in a locally and temporally controlled manner. Now, with the availability of vast amounts of data across various biological systems, the functional role of PTMs in important processes (such as transcription, recombination, replication, DNA repair and the modulation of genomic architecture) is slowly emerging. This Review explores the contribution of histone PTMs to the regulation of genome function by discussing when these modifications play a causative (or instructive) role in DNA-templated processes and when they are deposited as a consequence of such processes, to reinforce and record the event. Important advances in the field showing that histone PTMs can exert both direct and indirect effects on genome function are also presented.Histone post-translational modifications (PTMs) have been mainly regarded as instructing DNA-templated processes. In this Review, Gonzalo Millán-Zambrano and colleagues describe how histone PTMs both affect and are affected by these DNA processes and should be viewed as components of a complex genome-regulating network.

It remains unclear why acute depletion of CTCF (CCCTC-binding factor) and cohesin only marginally affects expression of most genes despite substantially perturbing three-dimensional (3D) genome folding at the level of domains and structural loops. To address this conundrum, we used high-resolution Micro-C and nascent transcript profiling in mouse embryonic stem cells. We find that enhancer–promoter (E–P) interactions are largely insensitive to acute (3-h) depletion of CTCF, cohesin or WAPL. YY1 has been proposed as a structural regulator of E–P loops, but acute YY1 depletion also had minimal effects on E–P loops, transcription and 3D genome folding. Strikingly, live-cell, single-molecule imaging revealed that cohesin depletion reduced transcription factor (TF) binding to chromatin. Thus, although CTCF, cohesin, WAPL or YY1 is not required for the short-term maintenance of most E–P interactions and gene expression, our results suggest that cohesin may facilitate TFs to search for and bind their targets more efficiently. Micro-C and nascent transcript profiling in mouse embryonic stem cells shows that enhancer–promoter interactions are robust to acute depletion of CTCF, cohesin, WAPL or YY1. Live-cell, single-molecule imaging shows that cohesin depletion reduces transcription factor binding to chromatin.

N6-Methyladenosine (m6A) is one of the most abundant modifications of the epitranscriptome and is found in cellular RNAs across all kingdoms of life. Advances in detection and mapping methods have improved our understanding of the effects of m6A on mRNA fate and ribosomal RNA function, and have uncovered novel functional roles in virtually every species of RNA. In this Review, we explore the latest studies revealing roles for m6A-modified RNAs in chromatin architecture, transcriptional regulation and genome stability. We also summarize m6A functions in biological processes such as stem-cell renewal and differentiation, brain function, immunity and cancer progression.Boulias and Greer review the functions of N6-methyladenosine (m6A) in RNA at the molecular, genomic and organismal level. They describe the impact of m6A deposition on RNA substrates, chromatin architecture, epigenetic regulation of gene expression and genome stability, as well as key roles of m6A in stem-cell differentiation, neurogenesis and immunity.

Identification of the cis-regulatory elements controlling cell-type specific gene expression patterns is essential for understanding the origin of cellular diversity. Conventional assays to map regulatory elements via open chromatin analysis of primary tissues is hindered by sample heterogeneity. Single cell analysis of accessible chromatin (scATAC-seq) can overcome this limitation. However, the high-level noise of each single cell profile and the large volume of data pose unique computational challenges. Here, we introduce SnapATAC, a software package for analyzing scATAC-seq datasets. SnapATAC dissects cellular heterogeneity in an unbiased manner and map the trajectories of cellular states. Using the Nyström method, SnapATAC can process data from up to a million cells. Furthermore, SnapATAC incorporates existing tools into a comprehensive package for analyzing single cell ATAC-seq dataset. As demonstration of its utility, SnapATAC is applied to 55,592 single-nucleus ATAC-seq profiles from the mouse secondary motor cortex. The analysis reveals ~370,000 candidate regulatory elements in 31 distinct cell populations in this brain region and inferred candidate cell-type specific transcriptional regulators. Single cell analysis of transposase-accessible chromatin is deepening our understanding on the origins of cellular diversity, yet methods are limited by data sparsity. Here, the authors introduce SnapATAC, a pipeline to resolve cellular heterogeneity and reveal candidate regulatory elements across different cell populations.

The DNA methylation field has matured from a phase of discovery and genomic characterization to one seeking deeper functional understanding of how this modification contributes to development, ageing and disease. In particular, the past decade has seen many exciting mechanistic discoveries that have substantially expanded our appreciation for how this generic, evolutionarily ancient modification can be incorporated into robust epigenetic codes. Here, we summarize the current understanding of the distinct DNA methylation landscapes that emerge over the mammalian lifespan and discuss how they interact with other regulatory layers to support diverse genomic functions. We then review the rising interest in alternative patterns found during senescence and the somatic transition to cancer. Alongside advancements in single-cell and long-read sequencing technologies, the collective insights made across these fields offer new opportunities to connect the biochemical and genetic features of DNA methylation to cell physiology, developmental potential and phenotype. In this Review, Smith et al. describe DNA methylation landscapes that emerge over mammalian development and within key disease states, as well as how different methyltransferases interface with histone modifications and other proteins to create and maintain them.

The AID/APOBEC polynucleotide cytidine deaminases have historically been classified as either DNA mutators or RNA editors based on their first identified nucleic acid substrate preference. DNA mutators can generate functional diversity at antibody genes but also cause genomic instability in cancer. RNA editors can generate informational diversity in the transcriptome of innate immune cells, and of cancer cells. Members of both classes can act as antiviral restriction factors. Recent structural work has illuminated differences and similarities between AID/APOBEC enzymes that can catalyse DNA mutation, RNA editing or both, suggesting that the strict functional classification of members of this family should be reconsidered. As many of these enzymes have been employed for targeted genome (or transcriptome) editing, a more holistic understanding will help improve the design of therapeutically relevant programmable base editors.In this Perspective, Pecori et al. provide an overview of the AID/APOBEC cytidine deaminase family, discussing key structural features, how they contribute to viral and tumour evolution and how they can be harnessed for (potentially therapeutic) base-editing purposes.

The advent of single-cell chromatin accessibility profiling has accelerated the ability to map gene regulatory landscapes but has outpaced the development of scalable software to rapidly extract biological meaning from these data. Here we present a software suite for single-cell analysis of regulatory chromatin in R (ArchR; https://www.archrproject.com/ ) that enables fast and comprehensive analysis of single-cell chromatin accessibility data. ArchR provides an intuitive, user-focused interface for complex single-cell analyses, including doublet removal, single-cell clustering and cell type identification, unified peak set generation, cellular trajectory identification, DNA element-to-gene linkage, transcription factor footprinting, mRNA expression level prediction from chromatin accessibility and multi-omic integration with single-cell RNA sequencing (scRNA-seq). Enabling the analysis of over 1.2 million single cells within 8 h on a standard Unix laptop, ArchR is a comprehensive software suite for end-to-end analysis of single-cell chromatin accessibility that will accelerate the understanding of gene regulation at the resolution of individual cells. ArchR is a software suite that enables efficient and end-to-end analysis of single-cell chromatin accessibility data (scATAC-seq).

Emerging spatial technologies, including spatial transcriptomics and spatial epigenomics, are becoming powerful tools for profiling of cellular states in the tissue context 1 , 2 , 3 , 4 – 5 . However, current methods capture only one layer of omics information at a time, precluding the possibility of examining the mechanistic relationship across the central dogma of molecular biology. Here, we present two technologies for spatially resolved, genome-wide, joint profiling of the epigenome and transcriptome by cosequencing chromatin accessibility and gene expression, or histone modifications (H3K27me3, H3K27ac or H3K4me3) and gene expression on the same tissue section at near-single-cell resolution. These were applied to embryonic and juvenile mouse brain, as well as adult human brain, to map how epigenetic mechanisms control transcriptional phenotype and cell dynamics in tissue. Although highly concordant tissue features were identified by either spatial epigenome or spatial transcriptome we also observed distinct patterns, suggesting their differential roles in defining cell states. Linking epigenome to transcriptome pixel by pixel allows the uncovering of new insights in spatial epigenetic priming, differentiation and gene regulation within the tissue architecture. These technologies are of great interest in life science and biomedical research. The authors present two technologies for spatially resolved, genome-wide, joint profiling of the epigenome and transcriptome by cosequencing chromatin accessibility and gene expression, or histone modifications and gene expression on the same tissue section at near-single-cell resolution.

Enhancer elements in the human genome control how genes are expressed in specific cell types and harbor thousands of genetic variants that influence risk for common diseases 1 – 4 . Yet, we still do not know how enhancers regulate specific genes, and we lack general rules to predict enhancer–gene connections across cell types 5 , 6 . We developed an experimental approach, CRISPRi-FlowFISH, to perturb enhancers in the genome, and we applied it to test >3,500 potential enhancer–gene connections for 30 genes. We found that a simple activity-by-contact model substantially outperformed previous methods at predicting the complex connections in our CRISPR dataset. This activity-by-contact model allows us to construct genome-wide maps of enhancer–gene connections in a given cell type, on the basis of chromatin state measurements. Together, CRISPRi-FlowFISH and the activity-by-contact model provide a systematic approach to map and predict which enhancers regulate which genes, and will help to interpret the functions of the thousands of disease risk variants in the noncoding genome. Combining CRISPRi-FlowFISH to perturb enhancers with an activity-by-contact model to predict complex connections allows systematic mapping of enhancer–gene connections in a given cell type, on the basis of chromatin-state measurements.