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. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.

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. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.

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. We focus on strategies to handle anatomical variation across individuals and highlight the need for new technologies and analytical methods spanning multiple hierarchical scales of spatial resolution.

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. In this Review, we discuss the recent advances in the collection and integration of different data types at single-cell resolution with a focus on the integration of gene expression data with other types of single-cell measurement.

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. Through its seamless compatibility with the Seurat package, Signac facilitates the analysis of diverse multimodal single-cell chromatin data, including datasets that co-assay DNA accessibility with gene expression, protein abundance and mitochondrial genotype. We demonstrate scaling of the Signac framework to analyze datasets containing over 700,000 cells.

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. While this certainly presents a challenging task, over the past few years many new and creative methods have been developed that now enable co-assay of these different aspects of chromatin at single cell resolution. This field is entering an exciting phase, where a confluence of technological improvements, decreased sequencing costs, and computational innovation are presenting new opportunities to dissect the diversity of chromatin states present in tissues, and how these states may influence gene regulation. In this review, I discuss the spectrum of current experimental approaches for multifactorial chromatin profiling, highlight some of the experimental and analytical challenges, as well as some areas for further innovation.

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. An analysis of novel transposable element indel variation highlighted hundreds of new polymorphisms not seen in the reference sequence. DNA methylation and transposable element variation is correlated with the genome-wide amount of genetic variation present between samples. However, there was minimal evidence that novel transposon insertions or deletions are associated with nearby differential methylation. This study highlights unique relationships between genetic variation and DNA methylation variation within Brachypodium and provides a valuable map of DNA methylation across diverse resequenced accessions of this model cereal species.

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. Here, we outline step-by-step an approach for the analysis and interpretation of WGBS data. First, sequencing reads must be trimmed, quality-checked, and aligned to the genome. Second, DNA methylation levels are estimated at each cytosine position using the aligned sequence reads of the bisulfite-treated DNA. Third, regions of differential cytosine methylation between samples can be identified. Finally, these data need to be visualized and interpreted in the context of the biological question at hand.

Dictionary learning for integrative, multimodal and scalable single-cell analysis.

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.

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. NTT-seq uses recombinant Tn5 transposase fused to a set of secondary nanobodies (nb). Each nb-Tn5 fusion protein specifically binds to different immunoglobulin-G antibodies, enabling a mixture of primary antibodies binding different epitopes to be used in a single experiment. We apply bulk-cell and single-cell NTT-seq to generate high-resolution multimodal maps of chromatin states in cell culture and in human immune cells. We also extend NTT-seq to enable simultaneous profiling of cell surface protein expression and multimodal chromatin states to study cells of the immune system.

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. We apply these tools to perform an integrated analysis across nine different molecular modalities in circulating human immune cells. We demonstrate how these two approaches can characterize dynamic changes in the function of individual genomic elements across both discrete cell states and continuous developmental trajectories, nominate associated motifs and regulators that establish chromatin states and identify extensive and cell-type-specific regulatory priming. Finally, we demonstrate how our integrated reference can serve as a scaffold to map and improve the interpretation of additional scCUT&Tag datasets.

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. We developed a cross-validation approach using multi-modal information to validate fine-grained cell types and assessed the effectiveness of computational data fusion methods. Correlation analysis in individual cells revealed distinct relations between methylation and gene expression. Our integrative approach enabled joint analyses of the methylome, transcriptome, chromatin accessibility, and conformation for 63 human cortical cell types. We reconstructed regulatory lineages for cortical cell populations and found specific enrichment of genetic risk for neuropsychiatric traits, enabling the prediction of cell types that are associated with diseases.

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. Our approach outperforms existing strategies and can resolve cell types and subtle cell state differences.

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. In the second heart lineage, a Tbx1/10-Dach pathway actively suppresses the first heart lineage programme, conditioning later cell diversity in the beating heart. Finally, cross-species comparisons between Ciona and the mouse evoke the deep evolutionary origins of cardiopharyngeal networks in chordates.

A Genome-Wide Association Study of Non-Photochemical Quenching in response to local seasonal climates in Arabidopsis thaliana.

Field-grown plants have variable exposure to sunlight as a result of shifting cloud-cover, seasonal changes, canopy shading, and other environmental factors. As a result, they need to have developed a method for dissipating excess energy obtained from periodic excessive sunlight exposure. Non-photochemical quenching (NPQ) dissipates excess energy as heat, however, the physical and molecular genetic mechanics of NPQ variation are not understood. In this study, we investigated the genetic loci involved in NPQ by first growing different Arabidopsis thaliana accessions in local and seasonal climate conditions, then measured their NPQ kinetics through development by chlorophyll fluorescence. We used genome-wide association studies (GWAS) to identify 15 significant quantitative trait loci (QTL) for a range of photosynthetic traits, including a QTL co-located with known NPQ gene PSBS (AT1G44575). We found there were large alternative regulatory segments between the PSBS promoter regions of the functional haplotypes and a significant difference in PsbS protein concentration. These findings parallel studies in rice showing recurrent regulatory evolution of this gene. The variation in the PSBS promoter and the changes underlying other QTLs could give insight to allow manipulations of NPQ in crops to improve their photosynthetic efficiency and yield.

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. Here, we outline a step-by-step approach for the analysis and interpretation of WGBS data. First, sequencing reads must be trimmed, quality checked, and aligned to the genome. Second, DNA methylation levels are estimated at each cytosine position using the aligned sequence reads of the bisulfite treated DNA. Third, regions of differential cytosine methylation between samples can be identified. Finally, these data need to be visualized and interpreted in the context of the biological question at hand.

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. Of the common alleles identified, two thirds were not in linkage disequilibrium with nearby SNPs, implicating these variants as a source of novel genetic diversity. Many common TE variants were associated with significantly altered expression of nearby genes, and a major fraction of inter-accession DNA methylation differences were associated with nearby TE insertions. Overall, this demonstrates that TE variants are a rich source of genetic diversity that likely plays an important role in facilitating epigenomic and transcriptional differences between individuals, and indicates a strong genetic basis for epigenetic variation.

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. The genome of the columella root cap is the most highly methylated Arabidopsis cell characterized so far. It is hypermethylated within transposable elements (TEs), accompanied by increased abundance of transcripts encoding RNA-directed DNA methylation (RdDM) pathway components and 24-nt small RNAs (smRNAs). The absence of the nucleosome remodeller DECREASED DNA METHYLATION 1 (DDM1), required for maintenance of DNA methylation, and low abundance of histone transcripts involved in heterochromatin formation suggests that a loss of heterochromatin may occur in the columella, thus allowing access of RdDM factors to the whole genome, and producing an excess of 24-nt smRNAs in this tissue. Together, these maps provide new insights into the epigenomic diversity that exists between distinct plant somatic cell types.