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Amos Tanay and Eitan Yaffe report methods to correct biases in the Hi-C method for mapping chromosomal contacts on a genome-wide scale. Their analysis of Hi-C data shows interchromosomal aggregation of hypersensitive sites, transcriptionally active foci and other epigenetic markers of active chromatin. Hi-C experiments measure the probability of physical proximity between pairs of chromosomal loci on a genomic scale. We report on several systematic biases that substantially affect the Hi-C experimental procedure, including the distance between restriction sites, the GC content of trimmed ligation junctions and sequence uniqueness. To address these biases, we introduce an integrated probabilistic background model and develop algorithms to estimate its parameters and renormalize Hi-C data. Analysis of corrected human lymphoblast contact maps provides genome-wide evidence for interchromosomal aggregation of active chromatin marks, including DNase-hypersensitive sites and transcriptionally active foci. We observe extensive long-range (up to 400 kb) cis interactions at active promoters and derive asymmetric contact profiles next to transcription start sites and CTCF binding sites. Clusters of interacting chromosomal domains suggest physical separation of centromere-proximal and centromere-distal regions. These results provide a computational basis for the inference of chromosomal architectures from Hi-C experiments.

Crosstalk between neighboring cells underlies many biological processes, including cell signaling, proliferation and differentiation. Current single-cell genomic technologies profile each cell separately after tissue dissociation, losing information on cell–cell interactions. In the present study, we present an approach for sequencing physically interacting cells (PIC-seq), which combines cell sorting of physically interacting cells (PICs) with single-cell RNA-sequencing. Using computational modeling, PIC-seq systematically maps in situ cellular interactions and characterizes their molecular crosstalk. We apply PIC-seq to interrogate diverse interactions including immune–epithelial PICs in neonatal murine lungs. Focusing on interactions between T cells and dendritic cells (DCs) in vitro and in vivo, we map T cell–DC interaction preferences, and discover regulatory T cells as a major T cell subtype interacting with DCs in mouse draining lymph nodes. Analysis of T cell–DC pairs reveals an interaction-specific program between pathogen-presenting migratory DCs and T cells. PIC-seq provides a direct and broadly applicable technology to characterize intercellular interaction-specific pathways at high resolution. PIC-seq characterizes cellular crosstalk by sorting and sequencing physically interacting cells.

Scaling scRNA-seq to profile millions of cells is crucial for constructing high-resolution maps of transcriptional manifolds. Current analysis strategies, in particular dimensionality reduction and two-phase clustering, offer only limited scaling and sensitivity to define such manifolds. We introduce Metacell-2, a recursive divide-and-conquer algorithm allowing efficient decomposition of scRNA-seq datasets of any size into small and cohesive groups of cells called metacells. Metacell-2 improves outlier cell detection and rare cell type identification, as shown with human bone marrow cell atlas and mouse embryonic data. Metacell-2 is implemented over the scanpy framework for easy integration in any analysis pipeline.

Propagation of clonal regulatory programs contributes to cancer development. It is poorly understood how epigenetic mechanisms interact with genetic drivers to shape this process. Here, we combine single-cell analysis of transcription and DNA methylation with a Luria–Delbrück experimental design to demonstrate the existence of clonally stable epigenetic memory in multiple types of cancer cells. Longitudinal transcriptional and genetic analysis of clonal colon cancer cell populations reveals a slowly drifting spectrum of epithelial-to-mesenchymal transcriptional identities that is seemingly independent of genetic variation. DNA methylation landscapes correlate with these identities but also reflect an independent clock-like methylation loss process. Methylation variation can be explained as an effect of global trans -acting factors in most cases. However, for a specific class of promoters—in particular, cancer–testis antigens—de-repression is correlated with and probably driven by loss of methylation in cis . This study indicates how genetic sub-clonal structure in cancer cells can be diversified by epigenetic memory. Longitudinal single-cell analysis of transcription and DNA methylation dynamics in cancer cell lines suggests a clonally stable epigenetic memory. Colon cancer cells show a spectrum of epithelial-to-mesenchymal identities that seems independent of genetic variation.

Human tissues comprise trillions of cells that populate a complex space of molecular phenotypes and functions and that vary in abundance by 4–9 orders of magnitude. Relying solely on unbiased sampling to characterize cellular niches becomes infeasible, as the marginal utility of collecting more cells diminishes quickly. Furthermore, in many clinical samples, the relevant cell types are scarce and efficient processing is critical. We developed an integrated pipeline for index sorting and massively parallel single-cell RNA sequencing (MARS-seq2.0) that builds on our previously published MARS-seq approach. MARS-seq2.0 is based on >1 million cells sequenced with this pipeline and allows identification of unique cell types across different tissues and diseases, as well as unique model systems and organisms. Here, we present a detailed step-by-step procedure for applying the method. In the improved procedure, we combine sub-microliter reaction volumes, optimization of enzymatic mixtures and an enhanced analytical pipeline to substantially lower the cost, improve reproducibility and reduce well-to-well contamination. Data analysis combines multiple layers of quality assessment and error detection and correction, graphically presenting key statistics for library complexity, noise distribution and sequencing saturation. Importantly, our combined FACS and single-cell RNA sequencing (scRNA-seq) workflow enables intuitive approaches for depletion or enrichment of cell populations in a data-driven manner that is essential to efficient sampling of complex tissues. The experimental protocol, from cell sorting to a ready-to-sequence library, takes 2–3 d. Sequencing and processing the data through the analytical pipeline take another 1–2 d. This single-cell RNA-sequencing protocol combines FACS-based index sorting and an analytical pipeline into a single framework. MARS-seq2.0 can handle tens of thousands of cells with a dynamic range suitable for the analysis of complex tissues.

scRNA-seq profiles each represent a highly partial sample of mRNA molecules from a unique cell that can never be resampled, and robust analysis must separate the sampling effect from biological variance. We describe a methodology for partitioning scRNA-seq datasets into metacells : disjoint and homogenous groups of profiles that could have been resampled from the same cell. Unlike clustering analysis, our algorithm specializes at obtaining granular as opposed to maximal groups. We show how to use metacells as building blocks for complex quantitative transcriptional maps while avoiding data smoothing. Our algorithms are implemented in the MetaCell R/C++ software package.

Chromosomes in proliferating metazoan cells undergo marked structural metamorphoses every cell cycle, alternating between highly condensed mitotic structures that facilitate chromosome segregation, and decondensed interphase structures that accommodate transcription, gene silencing and DNA replication. Here we use single-cell Hi-C (high-resolution chromosome conformation capture) analysis to study chromosome conformations in thousands of individual cells, and discover a continuum of cis -interaction profiles that finely position individual cells along the cell cycle. We show that chromosomal compartments, topological-associated domains (TADs), contact insulation and long-range loops, all defined by bulk Hi-C maps, are governed by distinct cell-cycle dynamics. In particular, DNA replication correlates with a build-up of compartments and a reduction in TAD insulation, while loops are generally stable from G1 to S and G2 phase. Whole-genome three-dimensional structural models reveal a radial architecture of chromosomal compartments with distinct epigenomic signatures. Our single-cell data therefore allow re-interpretation of chromosome conformation maps through the prism of the cell cycle. Single-cell Hi-C analysis in thousands of mouse embryonic stem cells shows that chromosomal compartments, topological-associated domains and long-range loops all have distinct cell-cycle dynamics. Chromosomal organization dynamics Eukaryotic chromosomes undergo a cycle of compaction and decondensation during the cell cycle. Here, Peter Fraser and colleagues have developed an improved single-cell Hi-C method to characterize the 3D organization of chromosomes through the cell cycle in thousands of individual mouse embryonic stem cells. They find that chromosomal compartments, topological-associated domains and loops are each governed by distinct dynamics and reveal a continuum of dynamic chromosomal structural features throughout the cell cycle. The results will be a new point of reference for interpreting chromosome conformation Hi-C maps.

A hallmark of metazoan evolution is the emergence of genomic mechanisms that implement cell-type-specific functions. However, the evolution of metazoan cell types and their underlying gene regulatory programmes remains largely uncharacterized. Here, we use whole-organism single-cell RNA sequencing to map cell-type-specific transcription in Porifera (sponges), Ctenophora (comb jellies) and Placozoa species. We describe the repertoires of cell types in these non-bilaterian animals, uncovering diverse instances of previously unknown molecular signatures, such as multiple types of peptidergic cells in Placozoa. Analysis of the regulatory programmes of these cell types reveals variable levels of complexity. In placozoans and poriferans, sequence motifs in the promoters are predictive of cell-type-specific programmes. By contrast, the generation of a higher diversity of cell types in ctenophores is associated with lower specificity of promoter sequences and the existence of distal regulatory elements. Our findings demonstrate that metazoan cell types can be defined by networks of transcription factors and proximal promoters, and indicate that further genome regulatory complexity may be required for more diverse cell type repertoires. Analysis of cell-type-specific transcription in non-bilaterian animals provides insight into the evolution of the gene regulatory networks that underlie metazoan cell types.

DNA methylation is aberrant in cancer, but the dynamics, regulatory role and clinical implications of such epigenetic changes are still poorly understood. Here, reduced representation bisulfite sequencing (RRBS) profiles of 1538 breast tumors and 244 normal breast tissues from the METABRIC cohort are reported, facilitating detailed analysis of DNA methylation within a rich context of genomic, transcriptional, and clinical data. Tumor methylation from immune and stromal signatures are deconvoluted leading to the discovery of a tumor replication-linked clock with genome-wide methylation loss in non-CpG island sites. Unexpectedly, methylation in most tumor CpG islands follows two replication-independent processes of gain (MG) or loss (ML) that we term epigenomic instability. Epigenomic instability is correlated with tumor grade and stage, TP53 mutations and poorer prognosis. After controlling for these global trans-acting trends, as well as for X-linked dosage compensation effects, cis -specific methylation and expression correlations are uncovered at hundreds of promoters and over a thousand distal elements. Some of these targeted known tumor suppressors and oncogenes. In conclusion, this study demonstrates that global epigenetic instability can erode cancer methylomes and expose them to localized methylation aberrations in-cis resulting in transcriptional changes seen in tumors. Understanding the molecular mechanisms underlying DNA methylation in cancer and its clinical relevance remains crucial. Here, the authors study RRBS-based profiles of 1538 breast tumours and 244 normal breast tissues from the METABRIC cohort and report epigenomic instability and cis-regulatory effects.

Three of the most fundamental questions in biology are how individual cells differentiate to form tissues, how tissues function in a coordinated and flexible fashion and which gene regulatory mechanisms support these processes. Single-cell genomics is opening up new ways to tackle these questions by combining the comprehensive nature of genomics with the microscopic resolution that is required to describe complex multicellular systems. Initial single-cell genomic studies provided a remarkably rich phenomenology of heterogeneous cellular states, but transforming observational studies into models of dynamics and causal mechanisms in tissues poses fresh challenges and requires stronger integration of theoretical, computational and experimental frameworks.