Establishment of single-cell transcriptional states during seed germination.

Germination involves highly dynamic transcriptional programs as the cells of seeds reactivate and express the functions necessary for establishment in the environment. Individual cell types have distinct roles within the embryo, so must therefore have cell type-specific gene expression and gene regulatory networks. We can better understand how the functions of different cell types are established and contribute to the embryo by determining how cell type-specific transcription begins and changes through germination.

Progress in multifactorial single-cell chromatin profiling methods.

Chromatin states play a key role in shaping overall cellular states and fates. Building a complete picture of the functional state of chromatin in cells requires the co-detection of several distinct biochemical aspects. These span DNA methylation, chromatin accessibility, chromosomal conformation, histone posttranslational modifications, and more.

Approaches for the Analysis and Interpretation of Whole-Genome Bisulfite Sequencing Data.

DNA methylation is a covalent modification of DNA that plays important roles in processes such as the regulation of gene expression, transcription factor binding, and suppression of transposable elements. The use of whole-genome bisulfite sequencing (WGBS) enables the genome-wide identification and quantification of DNA methylation patterns at single-base resolution and is the gold standard for the analysis of DNA methylation. However, the computational analysis of WGBS data can be particularly challenging, as many computationally intensive steps are required.

Nanobody-tethered transposition enables multifactorial chromatin profiling at single-cell resolution.

Chromatin states are functionally defined by a complex combination of histone modifications, transcription factor binding, DNA accessibility and other factors. Current methods for defining chromatin states cannot measure more than one aspect in a single experiment at single-cell resolution. Here we introduce nanobody-tethered transposition followed by sequencing (NTT-seq), an assay capable of measuring the genome-wide presence of up to three histone modifications and protein-DNA binding sites at single-cell resolution.

Single nucleus multi-omics identifies human cortical cell regulatory genome diversity.

Single-cell technologies measure unique cellular signatures but are typically limited to a single modality. Computational approaches allow the fusion of diverse single-cell data types, but their efficacy is difficult to validate in the absence of authentic multi-omic measurements. To comprehensively assess the molecular phenotypes of single cells, we devised single-nucleus methylcytosine, chromatin accessibility, and transcriptome sequencing (snmCAT-seq) and applied it to postmortem human frontal cortex tissue.

Characterizing cellular heterogeneity in chromatin state with scCUT&Tag-pro.

Technologies that profile chromatin modifications at single-cell resolution offer enormous promise for functional genomic characterization, but the sparsity of the measurements and integrating multiple binding maps represent substantial challenges. Here we introduce single-cell (sc)CUT&Tag-pro, a multimodal assay for profiling protein-DNA interactions coupled with the abundance of surface proteins in single cells. In addition, we introduce single-cell ChromHMM, which integrates data from multiple experiments to infer and annotate chromatin states based on combinatorial histone modification patterns.

geneBasis: an iterative approach for unsupervised selection of targeted gene panels from scRNA-seq.

scRNA-seq datasets are increasingly used to identify gene panels that can be probed using alternative technologies, such as spatial transcriptomics, where choosing the best subset of genes is vital. Existing methods are limited by a reliance on pre-existing cell type labels or by difficulties in identifying markers of rare cells. We introduce an iterative approach, geneBasis, for selecting an optimal gene panel, where each newly added gene captures the maximum distance between the true manifold and the manifold constructed using the currently selected gene panel.

Single-cell chromatin state analysis with Signac.

The recent development of experimental methods for measuring chromatin state at single-cell resolution has created a need for computational tools capable of analyzing these datasets. Here we developed Signac, a comprehensive toolkit for the analysis of single-cell chromatin data. Signac enables an end-to-end analysis of single-cell chromatin data, including peak calling, quantification, quality control, dimension reduction, clustering, integration with single-cell gene expression datasets, DNA motif analysis and interactive visualization.

Integrated analysis of multimodal single-cell data.

The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce "weighted-nearest neighbor" analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system.

Toward a Common Coordinate Framework for the Human Body.

Understanding the genetic and molecular drivers of phenotypic heterogeneity across individuals is central to biology. As new technologies enable fine-grained and spatially resolved molecular profiling, we need new computational approaches to integrate data from the same organ across different individuals into a consistent reference and to construct maps of molecular and cellular organization at histological and anatomical scales. Here, we review previous efforts and discuss challenges involved in establishing such a common coordinate framework, the underlying map of tissues and organs.

Comprehensive Integration of Single-Cell Data.

Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities.

A single-cell transcriptional roadmap for cardiopharyngeal fate diversification.

In vertebrates, multipotent progenitors located in the pharyngeal mesoderm form cardiomyocytes and branchiomeric head muscles, but the dynamic gene expression programmes and mechanisms underlying cardiopharyngeal multipotency and heart versus head muscle fate choices remain elusive. Here, we used single-cell genomics in the simple chordate model Ciona to reconstruct developmental trajectories forming first and second heart lineages and pharyngeal muscle precursors and characterize the molecular underpinnings of cardiopharyngeal fate choices. We show that FGF-MAPK signalling maintains multipotency and promotes the pharyngeal muscle fate, whereas signal termination permits the deployment of a pan-cardiac programme, shared by the first and second heart lineages, to define heart identity.

Integrative single-cell analysis.

The recent maturation of single-cell RNA sequencing (scRNA-seq) technologies has coincided with transformative new methods to profile genetic, epigenetic, spatial, proteomic and lineage information in individual cells. This provides unique opportunities, alongside computational challenges, for integrative methods that can jointly learn across multiple types of data. Integrated analysis can discover relationships across cellular modalities, learn a holistic representation of the cell state, and enable the pooling of data sets produced across individuals and technologies.

Approaches for the Analysis and Interpretation of Whole Genome Bisulfite Sequencing Data.

DNA methylation is a covalent modification of DNA that plays important roles in processes such as the regulation of gene expression, transcription factor binding, and suppression of transposable elements. The use of whole genome bisulfite sequencing (WGBS) enables the genome-wide identification and quantification of DNA methylation patterns at single-base resolution and is the gold standard for analysis of DNA methylation. Computational analysis of WGBS data can be particularly challenging, as many computationally intensive steps are required.

Population scale mapping of transposable element diversity reveals links to gene regulation and epigenomic variation.

Variation in the presence or absence of transposable elements (TEs) is a major source of genetic variation between individuals. Here, we identified 23,095 TE presence/absence variants between 216 Arabidopsis accessions. Most TE variants were rare, and we find these rare variants associated with local extremes of gene expression and DNA methylation levels within the population.

DNA methylation profiles of diverse Brachypodium distachyon align with underlying genetic diversity.

DNA methylation, a common modification of genomic DNA, is known to influence the expression of transposable elements as well as some genes. Although commonly viewed as an epigenetic mark, evidence has shown that underlying genetic variation, such as transposable element polymorphisms, often associate with differential DNA methylation states. To investigate the role of DNA methylation variation, transposable element polymorphism, and genomic diversity, whole-genome bisulfite sequencing was performed on genetically diverse lines of the model cereal Brachypodium distachyon Although DNA methylation profiles are broadly similar, thousands of differentially methylated regions are observed between lines.

Unique cell-type-specific patterns of DNA methylation in the root meristem.

DNA methylation is an epigenetic modification that differs between plant organs and tissues, but the extent of variation between cell types is not known. Here, we report single-base-resolution whole-genome DNA methylomes, mRNA transcriptomes and small RNA transcriptomes for six cell populations covering the major cell types of the Arabidopsis root meristem. We identify widespread cell-type-specific patterns of DNA methylation, especially in the CHH sequence context, where H is A, C or T.