Publications

Successful immunotherapy relies on triggering complex responses involving T cell dynamics in tumors and the periphery. Characterizing these responses remains challenging using static human single-cell atlases or mouse models. To address this, we developed a framework for in vivo tracking of tumor-specific CD8⁺ T cells over time and at single-cell resolution. Our tools facilitate the modeling of gene program dynamics in the tumor microenvironment (TME) and the tumor-draining lymph node (tdLN). Using this approach, we characterize two modes of anti-programmed cell death protein 1 (PD-1) activity, decoupling induced differentiation of tumor-specific activated precursor cells from conventional type 1 dendritic cell (cDC1)-dependent proliferation and recruitment to the TME. We demonstrate that combining anti-PD-1 therapy with anti-4-1BB agonist enhances the recruitment and proliferation of activated precursors, resulting in tumor control. These data suggest that effective response to anti-PD-1 therapy is dependent on sufficient influx of activated precursor CD8⁺ cells to the TME and highlight the importance of understanding system-level dynamics in optimizing immunotherapies.

RNA splicing factors are recurrently mutated in clonal blood disorders, but the impact of dysregulated splicing in hematopoiesis remains unclear. To overcome technical limitations, we integrated genotyping of transcriptomes (GoT) with long-read single-cell transcriptomics and proteogenomics for single-cell profiling of transcriptomes, surface proteins, somatic mutations, and RNA splicing (GoT-Splice). We applied GoT-Splice to hematopoietic progenitors from myelodysplastic syndrome (MDS) patients with mutations in the core splicing factor SF3B1. SF3B1^mut cells were enriched in the megakaryocytic-erythroid lineage, with expansion of SF3B1^mut erythroid progenitor cells. We uncovered distinct cryptic 3 splice site usage in different progenitor populations and stage-specific aberrant splicing during erythroid differentiation. Profiling SF3B1-mutated clonal hematopoiesis samples revealed that erythroid bias and cell-type-specific cryptic 3 splice site usage in SF3B1^mut cells precede overt MDS. Collectively, GoT-Splice defines the cell-type-specific impact of somatic mutations on RNA splicing, from early clonal outgrowths to overt neoplasia, directly in human samples.

Embryonic development involves massive proliferation and differentiation of cell lineages. This must be supported by chromosome replication and epigenetic reprogramming, but how proliferation and cell fate acquisition are balanced in this process is not well understood. Here we use single cell Hi-C to map chromosomal conformations in post-gastrulation mouse embryo cells and study their distributions and correlations with matching embryonic transcriptional atlases. We find that embryonic chromosomes show a remarkably strong cell cycle signature. Despite that, replication timing, chromosome compartment structure, topological associated domains (TADs) and promoter-enhancer contacts are shown to be variable between distinct epigenetic states. About 10% of the nuclei are identified as primitive erythrocytes, showing exceptionally compact and organized compartment structure. The remaining cells are broadly associated with ectoderm and mesoderm identities, showing only mild differentiation of TADs and compartment structures, but more specific localized contacts in hundreds of ectoderm and mesoderm promoter-enhancer pairs. The data suggest that while fully committed embryonic lineages can rapidly acquire specific chromosomal conformations, most embryonic cells are showing plastic signatures driven by complex and intermixed enhancer landscapes.

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.

Mice deficient for all ten-eleven translocation (TET) genes exhibit early gastrulation lethality. However, separating cause and effect in such embryonic failure is challenging. To isolate cell-autonomous effects of TET loss, we used temporal single-cell atlases from embryos with partial or complete mutant contributions. Strikingly, when developing within a wild-type embryo, Tet-mutant cells retain near-complete differentiation potential, whereas embryos solely comprising mutant cells are defective in epiblast to ectoderm transition with degenerated mesoderm potential. We map de-repressions of early epiblast factors (e.g., Dppa4 and Gdf3) and failure to activate multiple signaling from nascent mesoderm (Lefty, FGF, and Notch) as likely cell-intrinsic drivers of TET loss phenotypes. We further suggest loss of enhancer demethylation as the underlying mechanism. Collectively, our work demonstrates an unbiased approach for defining intrinsic and extrinsic embryonic gene function based on temporal differentiation atlases and disentangles the intracellular effects of the demethylation machinery from its broader tissue-level ramifications.

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.

A fundamental characteristic of animal multicellularity is the spatial coexistence of functionally specialized cell types that are all encoded by a single genome sequence. Cell type transcriptional programs are deployed and maintained by regulatory mechanisms that control the asymmetric, differential access to genomic information in each cell. This genome regulation ultimately results in specific cellular phenotypes. However, the emergence, diversity, and evolutionary dynamics of animal cell types remain almost completely unexplored beyond a few species. Single-cell genomics is emerging as a powerful tool to build comprehensive catalogs of cell types and their associated gene regulatory programs in non-traditional model species. We review the current state of sampling efforts across the animal tree of life and challenges ahead for the comparative study of cell type programs. We also discuss how the phylogenetic integration of cell atlases can lead to the development of models of cell type evolution and a phylogenetic taxonomy of cells.

Standardized lab tests are central for patient evaluation, differential diagnosis and treatment. Interpretation of these data is nevertheless lacking quantitative and personalized metrics. Here we report on the modeling of 2.1 billion lab measurements of 92 different lab tests from 2.8 million adults over a span of 18 years. Following unsupervised filtering of 131 chronic conditions and 5,223 drugtest pairs we performed a virtual survey of lab tests distributions in healthy individuals. Age and sex alone explain less than 10% of the within-normal test variance in 89 out of 92 tests. Personalized models based on patients history explain 60% of the variance for 17 tests and over 36% for half of the tests. This allows for systematic stratification of the risk for future abnormal test levels and subsequent emerging disease. Multivariate modeling of within-normal lab tests can be readily implemented as a basis for quantitative patient evaluation.

Mouse embryonic development is a canonical model system for studying mammalian cell fate acquisition. Recently, single-cell atlases comprehensively charted embryonic transcriptional landscapes, yet inference of the coordinated dynamics of cells over such atlases remains challenging. Here, we introduce a temporal model for mouse gastrulation, consisting of data from 153 individually sampled embryos spanning 36 h of molecular diversification. Using algorithms and precise timing, we infer differentiation flows and lineage specification dynamics over the embryonic transcriptional manifold. Rapid transcriptional bifurcations characterize the commitment of early specialized node and blood cells. However, for most lineages, we observe combinatorial multi-furcation dynamics rather than hierarchical transcriptional transitions. In the mesoderm, dozens of transcription factors combinatorially regulate multifurcations, as we exemplify using time-matched chimeric embryos of Foxc1/Foxc2 mutants. Our study rejects the notion of differentiation being governed by a series of binary choices, providing an alternative quantitative model for cell fate acquisition.

Stony corals are colonial cnidarians that sustain the most biodiverse marine ecosystems on Earth: coral reefs. Despite their ecological importance, little is known about the cell types and molecular pathways that underpin the biology of reef-building corals. Using single-cell RNA sequencing, we define over 40 cell types across the life cycle of Stylophora pistillata. We discover specialized immune cells, and we uncover the developmental gene expression dynamics of calcium-carbonate skeleton formation. By simultaneously measuring the transcriptomes of coral cells and the algae within them, we characterize the metabolic programs involved in symbiosis in both partners. We also trace the evolution of these coral cell specializations by phylogenetic integration of multiple cnidarian cell type atlases. Overall, this study reveals the molecular and cellular basis of stony coral biology.

LifeTime aims to track, understand and target human cells during the onset and progression of complex diseases and their response to therapy at single-cell resolution. This mission will be implemented through the development and integration of single-cell multi-omics and imaging, artificial intelligence and patient-derived experimental disease models during progression from health to disease. Analysis of such large molecular and clinical datasets will discover molecular mechanisms, create predictive computational models of disease progression, and reveal new drug targets and therapies. Timely detection and interception of disease embedded in an ethical and patient-centered vision will be achieved through interactions across academia, hospitals, patient-associations, health data management systems and industry. Applying this strategy to key medical challenges in cancer, neurological, infectious, chronic inflammatory and cardiovascular diseases at the single-cell level will usher in cell-based interceptive medicine in Europe over the next decade.

PIC-seq characterizes cellular crosstalk by sorting and sequencing physically interacting cells.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.

Human tissues comprise trillions of cells that populate a complex space of molecular phenotypes and functions and that vary in abundance by 49 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 23 d. Sequencing and processing the data through the analytical pipeline take another 12 d.

Lung adenocarcinoma (ADC) is the most prevalent subtype of lung cancer and characterized by considerable morphological and mutational heterogeneity. However, little is known about the epigenomic intratumor variability between spatially separated histological growth patterns of ADC. In order to reconstruct the clonal evolution of histomorphological patterns, we performed global DNA methylation profiling of 27 primary tumor regions, seven matched normal tissues and six lymph node metastases from seven ADC cases. Additionally, we investigated the methylation data from 369 samples of the TCGA ADC cohort. All regions showed varying degrees of methylation changes between segments of different, but also of the same growth patterns. Similarly, copy number variations were seen between spatially distinct segments of each patient. Hierarchical clustering of promoter methylation revealed extensive heterogeneity within and between the cases. Intratumor DNA methylation heterogeneity demonstrated a branched clonal evolution of ADC regions driven by genomic instability with subclonal copy number changes. Notably, methylation profiles within tumors were not more similar to each other than to those from other individuals. In two cases, different tumor regions of the same individuals were represented in distant clusters of the TCGA cohort, illustrating the extensive epigenomic intratumor heterogeneity of ADCs. We found no evidence for the lymph node metastases to be derived from a common growth pattern. Instead, they had evolved early and separately from a particular pattern in each primary tumor. Our results suggest that extensive variation of epigenomic features contributes to the molecular and phenotypic heterogeneity of primary ADCs and lymph node metastases.

The incidence of acute myeloid leukaemia (AML) increases with age and mortality exceeds 90% when diagnosed after age 65. Most cases arise without any detectable early symptoms and patients usually present with the acute complications of bone marrow failure1. The onset of such de novo AML cases is typically preceded by the accumulation of somatic mutations in preleukaemic haematopoietic stem and progenitor cells (HSPCs) that undergo clonal expansion2,3. However, recurrent AML mutations also accumulate in HSPCs during ageing of healthy individuals who do not develop AML, a phenomenon referred to as age-related clonal haematopoiesis (ARCH)4-8. Here we use deep sequencing to analyse genes that are recurrently mutated in AML to distinguish between individuals who have a high risk of developing AML and those with benign ARCH. We analysed peripheral blood cells from 95 individuals that were obtained on average 6.3 years before AML diagnosis (pre-AML group), together with 414 unselected age- and gender-matched individuals (control group). Pre-AML cases were distinct from controls and had more mutations per sample, higher variant allele frequencies, indicating greater clonal expansion, and showed enrichment of mutations in specific genes. Genetic parameters were used to derive a model that accurately predicted AML-free survival; this model was validated in an independent cohort of 29 pre-AML cases and 262 controls. Because AML is rare, we also developed an AML predictive model using a large electronic health record database that identified individuals at greater risk. Collectively our findings provide proof-of-concept that it is possible to discriminate ARCH from pre-AML many years before malignant transformation. This could in future enable earlier detection and monitoring, and may help to inform intervention.

The dynamics of haematopoietic stem cell differentiation and the hierarchy of oligopotent stem cells in the bone marrow remain controversial. Here we dissect haematopoietic progenitor populations at single cell resolution, deriving an unbiased reference model of transcriptional states in normal and perturbed murine bone marrow. We define the signature of the naive haematopoietic stem cell and find a continuum of core progenitor states. Core cell populations mix transcription of pre-myeloid and prelymphoid programs, but do not mix erythroid or megakaryocyte programs with other fates. CRISP-seq perturbation analysis confirms our models and reveals that Cebpa regulates entry into all myeloid fates, while Irf8 and PU.1 deficiency block later differentiation towards monocyte or granulocyte fates. Our transcriptional map defines a reference network model for blood progenitors and their differentiation trajectories during normal and perturbed haematopoiesis.

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.

DNA methylation is a key regulator of embryonic stem cell (ESC) biology, dynamically changing between naive, primed, and differentiated states. The p53 tumor suppressor is a pivotal guardian of genomic stability, but its contributions to epigenetic regulation and stem cell biology are less explored. We report that, in naive mouse ESCs (mESCs), p53 restricts the expression of the de novo DNA methyltransferases Dnmt3a and Dnmt3b while up-regulating Tet1 and Tet2, which promote DNA demethylation. The DNA methylation imbalance in p53-deficient (p53(-/-)) mESCs is the result of augmented overall DNA methylation as well as increased methylation landscape heterogeneity. In differentiating p53(-/-) mESCs, elevated methylation persists, albeit more mildly. Importantly, concomitant with DNA methylation heterogeneity, p53(-/-) mESCs display increased cellular heterogeneity both in the "naive" state and upon induced differentiation. This impact of p53 loss on 5-methylcytosine (5mC) heterogeneity was also evident in human ESCs and mouse embryos in vivo. Hence, p53 helps maintain DNA methylation homeostasis and clonal homogeneity, a function that may contribute to its tumor suppressor activity.

Chromosomes are folded into highly compacted structures to accommodate physical constraints within nuclei and to regulate access to genomic information. Recently, global mapping of pairwise contacts showed that loops anchoring topological domains (TADs) are highly conserved between cell types and species. Whether pairwise loops synergize to form higher-order structures is still unclear. Here we develop a conformation capture assay to study higher-order organization using chromosomal walks (C-walks) that link multiple genomic loci together into proximity chains in human and mouse cells. This approach captures chromosomal structure at varying scales. Inter-chromosomal contacts constitute only 7-10% of the pairs and are restricted by interfacing TADs. About half of the C-walks stay within one chromosome, and almost half of those are restricted to intra-TAD spaces. C-walks that couple 2-4 TADs indicate stochastic associations between transcriptionally active, early replicating loci. Targeted analysis of thousands of 3-walks anchored at highly expressed genes support pairwise, rather than hub-like, chromosomal topology at active loci. Polycomb-repressed Hox domains are shown by the same approach to enrich for synergistic hubs. Together, the data indicate that chromosomal territories, TADs, and intra-TAD loops are primarily driven by nested, possibly dynamic, pairwise contacts.

Innate lymphoid cells (ILCs) are critical modulators of mucosal immunity, inflammation, and tissue homeostasis, but their full spectrum of cellular states and regulatory landscapes remains elusive. Here, we combine genome-wide RNA-seq, ChIP-seq, and ATAC-seq tocompare the transcriptional and epigenetic identityof small intestinal ILCs, identifying thousands ofdistinct gene profiles and regulatory elements. Single-cell RNA-seq and flow and mass cytometry analyses reveal compartmentalization of cytokine expression and metabolic activity within the three classical ILC subtypes and highlight transcriptional states beyond the current canonical classification. In addition, using antibiotic intervention and germ-free mice, we characterize the effect of the microbiome on the ILC regulatory landscape and determine the response of ILCs to microbial colonization at the single-cell level. Together, our work characterizes the spectrum of transcriptional identities of small intestinal ILCs and describes how ILCs differentially integrate signals from the microbial microenvironment to generate phenotypic and functional plasticity.

Within the bone marrow, stem cells differentiate and give rise to diverse blood cell types and functions. Currently, hematopoietic progenitors are defined using surface markers combined with functional assays that are not directly linked with in vivo differentiation potential or gene regulatory mechanisms. Here, we comprehensively map myeloid progenitor sub-populations by transcriptional sorting of single cells from the bone marrow. We describe multiple progenitor subgroups, showing unexpected transcriptional priming toward seven differentiation fates but no progenitors with a mixed state. Transcriptional differentiation is correlated with combinations of known and previously undefined transcription factors, suggesting that the process is tightly regulated. Histone maps and knockout assays are consistent with early transcriptional priming, while traditional transplantation experiments suggest that in vivo priming may still allow for plasticity given strong perturbations. These data establish a reference model and general framework for studying hematopoiesis at single-cell resolution.

Despite much evidence on epigenetic abnormalities in cancer, it is currently unclear to what extent epigenetic alterations can be associated with tumors' clonal genetic origins. Here, we show that the prostate intratumor heterogeneity in DNA methylation and copy-number patterns can be explained by a unified evolutionary process. By assaying multiple topographically distinct tumor sites, premalignant lesions, and lymph node metastases within five cases of prostate cancer, we demonstrate that both DNA methylation and copy-number heterogeneity consistently reflect the life history of the tumors. Furthermore, we show cases of genetic or epigenetic convergent evolution and highlight the diversity in the evolutionary origins and aberration spectrum between tumor and metastatic subclones. Importantly, DNA methylation can complement genetic data by serving as a proxy for activity at regulatory domains, as we show through identification of high epigenetic heterogeneity at androgen-receptor- bound enhancers. Epigenome variation thereby expands on the current genome-centric view on tumor heterogeneity.

The t(8;21) and inv(16) chromosomal aberrations generate the oncoproteins AML1-ETO (A-E) and CBFβ-SMMHC (C-S). The role of these oncoproteins in acute myeloid leukemia (AML) etiology has been well studied. Conversely, the function of native RUNX1 in promoting A-E- and C-S-mediated leukemias has remained elusive. We show that wild-type RUNX1 is required for the survival of t(8;21)-Kasumi-1 and inv(16)-ME-1 leukemic cells. RUNX1 knockdown in Kasumi-1 cells (Kasumi-1^RX1-KD) attenuates the cell-cycle mitotic checkpoint, leading to apoptosis, whereas knockdown of A-E in Kasumi-1^RX1-KD rescues these cells. Mechanistically, a delicate RUNX1/A-E balance involving competition for common genomic sites that regulate RUNX1/A-E targets sustains the malignant cell phenotype. The broad medical significance of this leukemic cell addiction to native RUNX1 is underscored by clinical data showing that an active RUNX1 allele is usually preserved in both t(8;21) or inv(16) AML patients, whereas RUNX1 is frequently inactivated in other forms of leukemia. Thus, RUNX1 and its mitotic control targets are potential candidates for new therapeutic approaches

RUNX1 transcription factor (TF) is a key regulator of megakaryocytic development and when mutated is associated with familial platelet disorder and predisposition to acute myeloid leukemia (FPD-AML). We used mice lacking Runx1 specifically in megakaryocytes (MK) to characterized Runx1-mediated transcriptional program during advanced stages of MK differentiation. Gene expression and chromatin-immunoprecipitation-sequencing (ChIP-seq) of Runx1 and p300 identified functional Runx1 bound MK enhancers. Runx1/p300 co-bound regions showed significant enrichment in genes important for MK and platelet homeostasis. Runx1 occupied genomic regions were highly enriched in RUNX and ETS motifs and to a lesser extent in GATA motif. Megakaryocytic specificity of Runx1/P300 bound enhancers was validated by transfection mutagenesis and Runx1/P300 co-bound regions of two key megakaryocytic genes Nfe2 and Selp were tested by in vivo transgenesis. The data provides the first example of genome wide Runx1/p300 occupancy in maturating primary FL-MK, unravel the Runx1-regulated program controlling MK maturation in vivo and identify a subset of its bona fide regulated genes. It advances our understanding of the molecular events that upon RUNX1mutations in human lead to the predisposition to familial platelet disorders and FPD-AML.

We review recent developments in mapping chromosomal contacts and compare emerging insights on chromosomal contact domain organization in Drosophila and mammalian cells. Potential scenarios leading to the observation of Hi-C domains and their association with the epigenomic context of the chromosomal elements involved are discussed. We argue that even though the mechanisms and precise physical structure underlying chromosomal domain demarcation are yet to be fully resolved, the implications to genome regulation and genome evolution are profound. Specifically, we hypothesize that domains are facilitating genomic compartmentalization that support the implementation of complex, modular, and tissue specific transcriptional program in metazoa.

Large-scale chromosome structure and spatial nuclear arrangement have been linked to control of gene expression and DNA replication and repair. Genomic techniques based on chromosome conformation capture (3C) assess contacts for millions of loci simultaneously, but do so by averaging chromosome conformations from millions of nuclei. Here we introduce single-cell Hi-C, combined with genome-wide statistical analysis and structural modelling of single-copy X chromosomes, to show that individual chromosomes maintain domain organization at the megabase scale, but show variable cell-to-cell chromosome structures at larger scales. Despite this structural stochasticity, localization of active gene domains to boundaries of chromosome territories is a hallmark of chromosomal conformation. Single-cell Hi-C data bridge current gaps between genomics and microscopy studies of chromosomes, demonstrating how modular organization underlies dynamic chromosome structure, and how this structure is probabilistically linked with genome activity patterns.

Regulatory DNA elements can control the expression of distant genes via physical interactions. Here we present a cost-effective methodology and computational analysis pipeline for robust characterization of the physical organization around selected promoters and other functional elements using chromosome conformation capture combined with high-throughput sequencing (4C-seq). Our approach can be multiplexed and routinely integrated with other functional genomics assays to facilitate physical characterization of gene regulation.

Mammalian CpG islands are key epigenomic elements that were first characterized experimentally as genomic fractions with low levels of DNA methylation. Currently, CpG islands are defined based on their genomic sequences alone. Here, we develop evolutionary models to show that several distinct evolutionary processes generate and maintain CpG islands. One central evolutionary regime resulting in enriched CpG content is driven by low levels of DNA methylation and consequentially low rates of CpG deamination. Another major force forming CpG islands is biased gene conversion that stabilizes constitutively methylated CpG islands by balancing rapid deamination with CpG fixation. Importantly, evolutionary analysis and population genetics data suggest that selection for high CpG content is not a significant factor contributing to conservation of CpGs in differentially methylated regions. The heterogeneous, but not selective, origins of CpG islands have direct implications for the understanding of DNA methylation patterns in healthy and diseased cells.

Profound chromatin changes occur during mitosis to allow for gene silencing and chromosome segregation followed by reactivation of memorized transcription states in daughter cells. Using genome-wide sequencing, we found H2A.Z-containing +1 nucleosomes of active genes shift upstream to occupy TSSs during mitosis, significantly reducing nucleosome-depleted regions. Single-molecule analysis confirmed nucleosome shifting and demonstrated that mitotic shifting is specific to active genes that are silenced during mitosis and, thus, is not seen on promoters, which are silenced by methylation or mitotically expressed genes. Using the GRP78 promoter as a model, we found H3K4 trimethylation is also maintained while other indicators of active chromatin are lost and expression is decreased. These key changes provide a potential mechanism for rapid silencing and reactivation of genes during the cell cycle.

A major challenge in the analysis of gene expression microarray data is to extract meaningful biological knowledge out of the huge volume of raw data. Expander (EXPression ANalyzer and DisplayER) is an integrated software platform for the analysis of gene expression data, which is freely available for academic use. It is designed to support all the stages of microarray data analysis, from raw data normalization to inference of transcriptional regulatory networks. The microarray analysis described in this protocol starts with importing the data into Expander 5.0 and is followed by normalization and filtering. Then, clustering and network-based analyses are performed. The gene groups identified are tested for enrichment in function (based on Gene Ontology), co-regulation (using transcription factor and microRNA target predictions) or co-location. The results of each analysis step can be visualized in a number of ways. The complete protocol can be executed in ∼1 h.

Multiple discrete regions at 8q24 were recently shown to contain alleles that predispose to many cancers including prostate, breast, and colon. These regions are far from any annotated gene and their biological activities have been unknown. Here we profiled a 5-megabase chromatin segment encompassing all the risk regions for RNA expression, histone modifications, and locations occupied by RNA polymerase II and androgen receptor (AR). This led to the identification of several transcriptional enhancers, which were verified using reporter assays. Two enhancers in one risk region were occupied by AR and responded to androgen treatment; one contained a single nucleotide polymorphism (rs11986220) that resides within a FoxA1 binding site, with the prostate cancer risk allele facilitating both stronger FoxA1 binding and stronger androgen responsiveness. The study reported here exemplifies an approach that may be applied to any risk-associated allele in nonprotein coding regions as it emerges from genome-wide association studies to better understand the genetic predisposition of complex diseases.

Background: Insertions and deletions (indels) are an important evolutionary force, making the evolutionary process more efficient and flexible by copying and removing genomic fragments of various lengths instead of rediscovering them by point mutations. As a mutational process, indels are known to be more active in specific sequences (like micro-satellites) but not much is known about the more general and mechanistic effect of sequence context on the insertion and deletion susceptibility of genomic loci. Results: Here we analyze a large collection of high confidence short insertions and deletions in primates and flies, revealing extensive correlations between sequence context and indel rates and building principled models for predicting these rates from sequence. According to our results, the rate of insertion or deletion of specific lengths can vary by more than 100-fold, depending on the surrounding sequence. These mutational biases can strongly influence the composition of the genome and the rate at which particular sequences appear. We exemplify this by showing how degenerate loci in human exons are selected to reduce their frame shifting indel propensity. Conclusion: Insertions and deletions are strongly affected by sequence context. Consequentially, genomes must adapt to significant variation in the mutational input at indel-prone and indel-immune loci.