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Single-cell open chromatin profiling via the single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) assay has become a mainstream measurement of open chromatin in single-cells. Here we present a novel algorithm, epiAneufinder, that exploits the read count information from scATAC-seq data to extract genome-wide copy number variations (CNVs) for individual cells, allowing to explore the CNV heterogeneity present in a sample at the single-cell level. Using different cancer scATAC-seq datasets, we show how epiAneufinder can identify intratumor clonal heterogeneity in populations of single cells based on their CNV profiles. These profiles are concordant with the ones inferred from single-cell whole genome sequencing data for the same samples. epiAneufinder allows the addition of single-cell CNV information to scATAC-seq data, without the need of additional experiments, unlocking a layer of genomic variation which is otherwise unexplored.

Chromosomal instability is a hallmark of cancer, but also an instigator of aneuploidy-induced stress, reducing cellular fitness. To better understand how cells with CIN adjust to aneuploidy and adopt a malignant fate in vivo, we performed a genome-wide mutagenesis screen in mice. We find that specifically aneuploid tumors inactivate Stat1 signaling in combination with increased Myc activity. By contrast, loss of p53 is common, but not enriched in CIN tumors. Validation in another tissue type confirmed that CIN promotes immune cell infiltration, which is alleviated by Stat1 loss combined with Myc activation, but not with p53 inactivation, or Myc activation alone. Importantly, we find that this mechanism is preserved in human aneuploid cancers. We conclude that aneuploid cancers inactivate Stat1 signaling to circumvent immune surveillance.

Genetic mapping studies on crops suggest that agronomic traits can be controlled by gene–distal intergenic loci. Despite the biological importance and the potential agronomic utility of these loci, they remain virtually uncharacterized in all crop species to date. Here, we provide genetic, epigenomic and functional molecular evidence to support the widespread existence of gene–distal (hereafter, distal) loci that act as long-range transcriptional cis-regulatory elements (CREs) in the maize genome. Such loci are enriched for euchromatic features that suggest their regulatory functions. Chromatin loops link together putative CREs with genes and recapitulate genetic interactions. Putative CREs also display elevated transcriptional enhancer activities, as measured by self-transcribing active regulatory region sequencing. These results provide functional support for the widespread existence of CREs that act over large genomic distances to control gene expression.

EpiScanpy is a toolkit for the analysis of single-cell epigenomic data, namely single-cell DNA methylation and single-cell ATAC-seq data. To address the modality specific challenges from epigenomics data, epiScanpy quantifies the epigenome using multiple feature space constructions and builds a nearest neighbour graph using epigenomic distance between cells. EpiScanpy makes the many existing scRNA-seq workflows from scanpy available to large-scale single-cell data from other -omics modalities, including methods for common clustering, dimension reduction, cell type identification and trajectory learning techniques, as well as an atlas integration tool for scATAC-seq datasets. The toolkit also features numerous useful downstream functions, such as differential methylation and differential openness calling, mapping epigenomic features of interest to their nearest gene, or constructing gene activity matrices using chromatin openness. We successfully benchmark epiScanpy against other scATAC-seq analysis tools and show its outperformance at discriminating cell types., The authors present epiScanpy: a computational framework for the analysis of single-cell epigenomic data, both ATAC-seq and DNA methylation data, with examples for clustering, cell type identification, trajectory learning and atlas integration - and show its performance in distinguishing cell types.

Failure to maintain DNA methylation patterns during plant development can occasionally give rise to so-called ‘spontaneous epimutations’. These stochastic methylation changes are sometimes heritable across generations and thus accumulate in plant genomes over time. Recent evidence indicates that spontaneous epimutations have a major role in shaping patterns of methylation diversity in plant populations. Using single CG dinucleotides as units of analysis, previous work has shown that the epimutation rate is several orders of magnitude higher than the genetic mutation rate. While these large rate differences have obvious implications for understanding genome-methylome co-evolution, the functional relevance of single CG methylation changes remains questionable. In contrast to single CG, solid experimental evidence has linked methylation gains and losses in larger genomic regions with transcriptional variation and heritable phentoypic effects. Here we show that such region-level changes arise stochastically at about the same rate as those at individual CG sites, are only marginal dependent on region size and cytosine density, but strongly dependent on chromosomal location. We also find consistent evidence that region-level epimutations are not restricted to CG contexts but also frequently occur in non-CG regions at the genome-wide scale. Taken together, our results support the view that many differentially methylated regions (DMRs) in natural populations originate from epimutational events and may not be effectively tagged by proximal SNPs. This possibility reinforces the need for epigenome-wide association studies (EWAS) in plants as away to identify the epigenetic basis of adaptive traits.

Summary Innate-like T cell populations expressing conserved TCRs play critical roles in immunity through diverse developmentally acquired effector functions. Focusing on the prototypical lineage of invariant natural killer T (iNKT) cells, we sought to dissect the mechanisms and timing of fate decisions and functional effector differentiation. Utilizing induced expression of the semi-invariant NKT cell TCR on double positive thymocytes, an initially highly synchronous wave of iNKT cell development was triggered by brief homogeneous TCR signaling. After reaching a uniform progenitor state characterized by IL-4 production potential and proliferation, effector subsets emerged simultaneously, but then diverged toward different fates. While NKT17 specification was quickly completed, NKT1 cells slowly differentiated and expanded. NKT2 cells resembled maturing progenitors, which gradually diminished in numbers. Thus, iNKT subset diversification occurs in dividing progenitor cells without acute TCR input but utilizes multiple active cytokine signaling pathways. These data imply a two-step model of iNKT effector differentiation.

Single-cell RNA-seq reveals the role of pathogenic cell populations in development and progression of chronic diseases. In order to expand our knowledge on cellular heterogeneity we have developed a single-nucleus RNA-seq2 method that allows deep characterization of nuclei isolated from frozen archived tissues. We have used this approach to characterize the transcriptional profile of individual hepatocytes with different levels of ploidy, and have discovered that gene expression in tetraploid mononucleated hepatocytes is conditioned by their position within the hepatic lobe. Our work has revealed a remarkable crosstalk between gene dosage and spatial distribution of hepatocytes.

Cell atlases often include samples that span locations, labs, and conditions, leading to complex, nested batch effects in data. Thus, joint analysis of atlas datasets requires reliable data integration.Choosing a data integration method is a challenge due to the difficulty of defining integration success. Here, we benchmark 38 method and preprocessing combinations on 77 batches of gene expression, chromatin accessibility, and simulation data from 23 publications, altogether representing >1.2 million cells distributed in nine atlas-level integration tasks. Our integration tasks span several common sources of variation such as individuals, species, and experimental labs. We evaluate methods according to scalability, usability, and their ability to remove batch effects while retaining biological variation.Using 14 evaluation metrics, we find that highly variable gene selection improves the performance of data integration methods, whereas scaling pushes methods to prioritize batch removal over conservation of biological variation. Overall, BBKNN, Scanorama, and scVI perform well, particularly on complex integration tasks; Seurat v3 performs well on simpler tasks with distinct biological signals; and methods that prioritize batch removal perform best for ATAC-seq data integration. Our freely available reproducible python module can be used to identify optimal data integration methods for new data, benchmark new methods, and improve method development.

Histone 3 K4 trimethylation (depositing H3K4me3 marks) is typically associated with active promoters yet paradoxically occurs at untranscribed domains. Research to delineate the mechanisms of targeting H3K4 methyltransferases is ongoing. The oocyte provides an attractive system to investigate these mechanisms, because extensive H3K4me3 acquisition occurs in nondividing cells. We developed low-input chromatin immunoprecipitation to interrogate H3K4me3, H3K27ac and H3K27me3 marks throughout oogenesis. In nongrowing oocytes, H3K4me3 was restricted to active promoters, but as oogenesis progressed, H3K4me3 accumulated in a transcription-independent manner and was targeted to intergenic regions, putative enhancers and silent H3K27me3-marked promoters. Ablation of the H3K4 methyltransferase gene Mll2 resulted in loss of transcription-independent H3K4 trimethylation but had limited effects on transcription-coupled H3K4 trimethylation or gene expression. Deletion of Dnmt3a and Dnmt3b showed that DNA methylation protects regions from acquiring H3K4me3. Our findings reveal two independent mechanisms of targeting H3K4me3 to genomic elements, with MLL2 recruited to unmethylated CpG-rich regions independently of transcription.

Cell-cell communication is essential in healthy bone marrow (BM) and seems to be disturbed in acute myeloid leukemia (AML). To understand whether the communication patterns are different in healthy and malignant BM, we need to perform a comparative analysis--a task which most algorithms published to date are not designed to do. We solved this issue with our new computational tool which analyses differential communication in two cohorts of multiple samples and allows us to do systematic studies of communication changes in large cohorts of individuals. Our algorithm works on single-cell RNA-seq or bulk RNA-seq data of individual FACS-sorted cell types. Here we use two published datasets of human bone marrow single-cell RNA-seq containing samples from AML patients at diagnosis, as well as healthy donors. We selected 9 AML samples from van Galen, 2019 by excluding samples collected later than day 0 after diagnosis, having less than 50% blasts at diagnosis, or having less than 5 cell types captured. As a control group, we used 29 samples from healthy donors (4 from van Galen, 2019 and 25 from Oetjen, 2018). To unify the cell type annotation, we merged the original cell type annotation into bigger classes: hematopoietic stem and progenitor cells (HSPCs), monocytes, erythrocytes, B cells, T cells, natural killer cells (NK), and dendritic cells (DC). Due to low capturing rate, we excluded NK from the subsequent analysis. After filtering out lowly expressed genes and cells with low number of reads, we normalised the data using scran package (Lun, 2016). We next proceeded to calculate the communication between the 6 cell types for each sample. A communication edge represents a signal which is sent by a "sending" cell type via its ligand and is received by a "receiving" cell type via its receptor. Each edge has a weight that indicates the intensity of communication and varies between 0 (no communication) and 1 (communication of maximum intensity). We calculate edge weights using a formula which addresses the proportion of the cells within each cell type that expressed the ligand/receptor, the relative level of expression of ligand/receptor in sending/receiving cell type compared to other cell types and the discrepancy of expression between the ligand in the sending cell type and the receptor in the receiving cell type. We used iTALK (Wang, 2019) database of ligand-receptor pair interactions as a reference. After calculating communication edges in all samples, we filter low-weight edges and proceed to differential communication analysis. Here we perform Wilcoxon-Mann-Whitney test with Bonferroni correction in the two groups of samples (AML vs healthy). We identified over 4.500 communication edges in the dataset, of which 58 were significantly differentially used (adjusted p-value < 0.1). Markedly, the majority of the communication edges, as well as all significantly differential edges were down-regulated in the AML samples, indicating overall decrease in communicative activity among the investigated cell types in AML patients at diagnosis. In the significantly differential edges -- which were actively used in the healthy bone marrow, but lost in the AML -- the two most active "sending" cell types were the erythrocytes (in 19 edges) and monocytes (in 17 edges), followed by the DC (in 9 edges), B cells (in 7 edges), T cells (in 4 edges), and HSCP (in 2 edges). The most active receiving cell types were the DC (in 24 edges) and the erythrocyte (in 17 edges), followed by the monocytes (in 8 edges), B cells (in 6 edges), HSCP (in 2 edges) and T cells (in 1 edge). The top 20 significantly differential edges showed communication from erythrocytes, monocytes, B cells and T cells to DC using CALM1/3 : SELL, as well as ADAM10 : AXL interaction, indicating reduced cell-cell adhesive contact among the immune cell types in the AML. In our study, we developed a computational tool which allows us to characterize differential cell-cell communication in two cohorts of patients using scRNA-seq data. We applied our tool to the published scRNA-seq data from the bone marrow of 8 AML patients and 29 healthy donors. We identified disease-driven loss in communication patterns in the AML such as disrupted adhesion among the immune cells, which might indicate immunoevasion. This analysis is crucial to generate new data driven hypotheses for a deeper understanding of haematopoietic neoplasms. Disclosures No relevant conflicts of interest to declare.

Follicular lymphoma (FL) is a clinically and genetically heterogeneous disease. Somatic gene mutations contribute to the heterogeneous clinical course of FL. ARID1A, which encodes for a subunit of the SWI/SNF chromatin remodeling complex, is among the most commonly mutated genes in FL (up to 15% of cases). These mutations are mostly disruptive and are predicted to result in protein haplodeficiency. While we have previously shown that ARID1A mutations are predictive of treatment outcome (Pastore, 2015), the underlying biology of ARID1A loss in FL is unclear. A functional genome-wide in vitro screen showed that ARID1A loss rescued a number of cancer cell lines from FAS-L induced apoptosis (Luo, 2008). FAS-L induced apoptosis plays a critical role in normal B-cell development and homeostasis. Thus, FAS/FAS-L deficiency could contribute to FL development and disease biology. Therefore, we studied the role of ARID1A loss in FAS expression and regulation. We first tested FAS-L induced apoptosis in established lymphoma cell lines that harbor the FL-hallmark translocation t(14;18)[BCL2/IGH] plus ARID1A mutations (Karpas422, WSU-FSCCL) or no ARID1A mutations (OCI-Ly1, OCI-Ly8, SU-DHL16). ARID1A mutant (mut) cells were indeed markedly less sensitive to FAS-L (300 ng/mL/24 hrs) compared to ARID1A wild type (WT) cells (98% vs 52% mean viability by Annexin-V). FAS receptor expression on mutant cells was reduced by almost half compared to WT cells by FACS analysis (N=3, P=0.0004). To test if reduced FAS expression was directly linked to ARID1A loss, we generated single-cell derived clones (from OCI-Ly1 and OCI-Ly8) with either heterozygous (het) loss or complete knock-out (KO) of ARID1A by CRISPR/Cas9. ARID1A loss was validated by Sanger sequencing and Western blot. We consistently observed significantly reduced FAS-L induced apoptosis in het and KO clones (exemplary shown for OCI-Ly8 in Fig A). Remarkably, re-expressed of ARID1A in het cells (het+ARID1A) rescued sensitivity to FAS-L induced apoptosis (Fig A). We confirmed reduced FAS expression on mutant clones by FACS, while re-expression of ARID1A rescued its expression (Fig B). Furthermore, FAS mRNA expression was significantly reduced by qPCR in mut vs WT clones (N=4, P To understand the molecular mechanism that links ARID1A loss and reduced FAS expression, we performed ATAC sequencing (Seq) and RNA Seq on 15 single-cell derived clones (9 mut and 6 WT from OCI-Ly1 and OCI-Ly8). RNA Seq confirmed significantly lower ARID1A and FAS mRNA levels (adj p To functionally validate our model, we first confirmed reduced RUNX3 expression in ARID1A mutant clones by qPCR and Western blot, and showed that ETS1 levels were unaffected by ARID1A loss. Then, we stably overexpressed RUNX3 in ARID1A mutant clones by lentiviral transduction and could indeed show rescue of FAS surface levels by FACS (Fig D). Lastly, we wanted to validate our findings in primary patients samples. We quantified FAS expression in FL biopsies with known ARID1A mutation status by nCounter gene expression profiling (GEP; N=51, 12 mut vs 39 WT) and quantitative multispectral imaging (QMI; N=44, 10 mut vs 34 WT) (Fig E). Both approaches showed significantly reduced FAS expression in ARID1A mutant FL (P In summary, we show that ARID1A loss is directly linked to reduced FAS expression via a novel RUNX3/ETS1 transcriptional network, potentially opening avenues for therapeutic targeting of this clinically relevant perturbation. Figure 1 Figure 1. Disclosures Subklewe: Pfizer: Consultancy, Speakers Bureau; Takeda: Speakers Bureau; Klinikum der Universität München: Current Employment; Janssen: Consultancy; Seattle Genetics: Consultancy, Research Funding; Roche: Research Funding; Novartis: Consultancy, Research Funding, Speakers Bureau; MorphoSys: Research Funding; Miltenyi: Research Funding; Gilead: Consultancy, Research Funding, Speakers Bureau; Amgen: Consultancy, Research Funding, Speakers Bureau; BMS/Celgene: Consultancy, Research Funding, Speakers Bureau. von Bergwelt: Kite/Gilead: Honoraria, Research Funding, Speakers Bureau; Roche: Honoraria, Research Funding, Speakers Bureau; Novartis: Honoraria, Research Funding, Speakers Bureau; Astellas: Honoraria, Research Funding, Speakers Bureau; Miltenyi: Honoraria, Research Funding, Speakers Bureau; BMS: Honoraria, Research Funding, Speakers Bureau; Mologen: Honoraria, Research Funding, Speakers Bureau; MSD Sharpe & Dohme: Honoraria, Research Funding, Speakers Bureau. Weigert: Janssen: Speakers Bureau; Epizyme: Membership on an entity's Board of Directors or advisory committees; Roche: Research Funding.

Chromosomal instability (CIN) is a hallmark feature of cancer cells. In this study, Schukken and colleagues screen for compounds that selectively target CIN cells and identify an inhibitor of Src kinase to be selectively toxic for CIN cells., Chromosomal instability (CIN) and aneuploidy are hallmarks of cancer. As most cancers are aneuploid, targeting aneuploidy or CIN may be an effective way to target a broad spectrum of cancers. Here, we perform two small molecule compound screens to identify drugs that selectively target cells that are aneuploid or exhibit a CIN phenotype. We find that aneuploid cells are much more sensitive to the energy metabolism regulating drug ZLN005 than their euploid counterparts. Furthermore, cells with an ongoing CIN phenotype, induced by spindle assembly checkpoint (SAC) alleviation, are significantly more sensitive to the Src kinase inhibitor SKI606. We show that inhibiting Src kinase increases microtubule polymerization rates and, more generally, that deregulating microtubule polymerization rates is particularly toxic to cells with a defective SAC. Our findings, therefore, suggest that tumors with a dysfunctional SAC are particularly sensitive to microtubule poisons and, vice versa, that compounds alleviating the SAC provide a powerful means to treat tumors with deregulated microtubule dynamics.

Summary Coronavirus Disease 2019 (COVID-19) is a mild to moderate respiratory tract infection, however, a subset of patients progresses to severe disease and respiratory failure. The mechanism of protective immunity in mild forms and the pathogenesis of severe COVID-19, associated with increased neutrophil counts and dysregulated immune responses, remains unclear. In a dual-center, two-cohort study, we combined single-cell RNA-sequencing and single-cell proteomics of whole blood and peripheral blood mononuclear cells to determine changes in immune cell composition and activation in mild vs. severe COVID-19 (242 samples from 109 individuals) over time. HLA-DRhiCD11chi inflammatory monocytes with an interferon-stimulated gene signature were elevated in mild COVID-19. Severe COVID-19 was marked by occurrence of neutrophil precursors, as evidence of emergency myelopoiesis, dysfunctional mature neutrophils, and HLA-DRlo monocytes. Our study provides detailed insights into the systemic immune response to SARS-CoV-2 infection and it reveals profound alterations in the myeloid cell compartment associated with severe COVID-19., Highlights ● SARS-CoV-2 infection induces profound alterations of the myeloid compartment ● Mild COVID-19 is marked by inflammatory HLA-DRhiCD11chi CD14+ monocytes ● Dysfunctional HLA-DRloCD163hi and HLA-DRloS100Ahi CD14+ monocytes in severe COVID-19 ● Emergency myelopoiesis with immature and dysfunctional neutrophils in severe COVID-19

BackgroundPlants can transmit somatic mutations and epimutations to offspring, which in turn can affect fitness. Knowledge of the rate at which these variations arise is necessary to understand how plant development contributes to local adaption in an eco-evolutionary context, particularly in long-lived perennials.ResultsHere, we generated a new high-quality reference genome from the oldest branch of a wildPopulus trichocarpatree with two dominant stems which have been evolving independently for 330 years. By sampling multiple, age-estimated branches of this tree, we used a multi-omics approach to quantify age-related somatic changes at the genetic, epigenetic and transcriptional level. We show that the per-year somatic mutation and epimutation rates are lower than in annuals and that transcriptional variation is mainly independent of age divergence and cytosine methylation. Furthermore, a detailed analysis of the somatic epimutation spectrum indicates that transgenerationally heritable epimutations originate mainly from DNA methylation maintenance errors during mitotic rather than during meiotic cell divisions.ConclusionTaken together, our study provides unprecedented insights into the origin of nucleotide and functional variation in a long-lived perennial plant.

IntroductionHeritable changes in cytosine methylation can arise stochastically in plant genomes independently of DNA sequence alterations. These so-called ‘spontaneous epimutations’ appear to be a byproduct of imperfect DNA methylation maintenance during mitotic or meitotic cell divisions. Accurate estimates of the rate and spectrum of these stochastic events are necessary to be able to quantify how epimutational processes shape methylome diversity in the context of plant evolution, development and aging.MethodHere we describe AlphaBeta, a computational method for estimating epimutation rates and spectra from pedigree-based high-throughput DNA methylation data. The approach requires that the topology of the pedigree is known, which is typically the case in the experimental construction of mutation accumulation lines (MA-lines) in sexually or clonally reproducing species. However, this method also works for inferring somatic epimutation rates in long-lived perennials, such as trees, using leaf methylomes and coring data as input. In this case, we treat the tree branching structure as an intra-organismal phylogeny of somatic lineages and leverage information about the epimutational history of each branch.ResultsTo illustrate the method, we applied AlphaBeta to multi-generational data from selfing- and asexually-derived MA-lines in Arabidopsis and dandelion, as well as to intra-generational leaf methylome data of a single poplar tree. Our results show that the epimutation landscape in plants is deeply conserved across angiosperm species, and that heritable epimutations originate mainly during somatic development, rather than from DNA methylation reinforcement errors during sexual reproduction. Finally, we also provide the first evidence that DNA methylation data, in conjunction with statistical epimutation models, can be used as a molecular clock for age-dating trees.ConclusionAlphaBeta faciliates unprecedented quantitative insights into epimutational processes in a wide range of plant systems. Software implementing our method is available as a Bioconductor R package at http://bioconductor.org/packages/3.10/bioc/html/AlphaBeta.html

Background Genomic imprinting is an epigenetic phenomenon that allows a subset of genes to be expressed mono-allelically based on the parent of origin and is typically regulated by differential DNA methylation inherited from gametes. Imprinting is pervasive in murine extra-embryonic lineages, and uniquely, the imprinting of several genes has been found to be conferred non-canonically through maternally inherited repressive histone modification H3K27me3. However, the underlying regulatory mechanisms of non-canonical imprinting in post-implantation development remain unexplored. Results We identify imprinted regions in post-implantation epiblast and extra-embryonic ectoderm (ExE) by assaying allelic histone modifications (H3K4me3, H3K36me3, H3K27me3), gene expression, and DNA methylation in reciprocal C57BL/6 and CAST hybrid embryos. We distinguish loci with DNA methylation-dependent (canonical) and independent (non-canonical) imprinting by assaying hybrid embryos with ablated maternally inherited DNA methylation. We find that non-canonical imprints are localized to endogenous retrovirus-K (ERVK) long terminal repeats (LTRs), which act as imprinted promoters specifically in extra-embryonic lineages. Transcribed ERVK LTRs are CpG-rich and located in close proximity to gene promoters, and imprinting status is determined by their epigenetic patterning in the oocyte. Finally, we show that oocyte-derived H3K27me3 associated with non-canonical imprints is not maintained beyond pre-implantation development at these elements and is replaced by secondary imprinted DNA methylation on the maternal allele in post-implantation ExE, while being completely silenced by bi-allelic DNA methylation in the epiblast. Conclusions This study reveals distinct epigenetic mechanisms regulating non-canonical imprinted gene expression between embryonic and extra-embryonic development and identifies an integral role for ERVK LTR repetitive elements.

High-throughput next generation sequencing karyotyping has emerged as a powerful tool for the detection of genomic heterogeneity in normal tissues and cancers. Here we describe a single-cell whole genome sequencing (scWGS) platform to assess whole-chromosome aneuploidy, structural aneuploidies involving only chromosome fragments and more local small copy number alterations in individual cells. We provide a detailed protocol for the isolation, library preparation, low coverage sequencing and data analysis of single cells. Since our approach does not involve a whole-genome preamplification step, our method allows for acquisition of reliable high-resolution single-cell copy number profiles. Moreover, the protocol allows multiplexing of 384 single-cell libraries in one sequencing run, thereby significantly reducing sequencing costs and can be completed in 3-4 days starting from single cell isolation to analysis of sequencing data.

Motivation Strand-seq is a specialized single-cell DNA sequencing technique centered around the directionality of single-stranded DNA. Computational tools for Strand-seq analyses must capture the strand-specific information embedded in these data. Results Here we introduce breakpointR, an R/Bioconductor package specifically tailored to process and interpret single-cell strand-specific sequencing data obtained from Strand-seq. We developed breakpointR to detect local changes in strand directionality of aligned Strand-seq data, to enable fine-mapping of sister chromatid exchanges, germline inversion and to support global haplotype assembly. Given the broad spectrum of Strand-seq applications we expect breakpointR to be an important addition to currently available tools and extend the accessibility of this novel sequencing technique. Availability and implementation R/Bioconductor package https://bioconductor.org/packages/breakpointR. Supplementary information Supplementary data are available at Bioinformatics online.

High-throughput next generation sequencing karyotyping has emerged as a powerful tool for the detection of genomic heterogeneity in normal tissues and cancers. Here we describe a single-cell whole genome sequencing (scWGS) platform to assess whole-chromosome aneuploidy, structural aneuploidies involving only chromosome fragments and more local small copy number alterations in individual cells. We provide a detailed protocol for the isolation, library preparation, low coverage sequencing and data analysis of single cells. Since our approach does not involve a whole-genome preamplification step, our method allows for acquisition of reliable high-resolution single-cell copy number profiles. Moreover, the protocol allows multiplexing of 384 single-cell libraries in one sequencing run, thereby significantly reducing sequencing costs and can be completed in 3-4 days starting from single cell isolation to analysis of sequencing data.

Background: Whole-genome bisulfite sequencing (WGBS) has become the standard method for interrogating plant methylomes at base resolution. However, deep WGBS measurements remain cost prohibitive for large, complex genomes and for population-level studies. As a result, most published plant methylomes are sequenced far below saturation, with a large proportion of cytosines having either missing data or insufficient coverage.Results: Here we present METHimpute, a Hidden Markov Model (HMM) based imputation algorithm for the analysis of WGBS data. Unlike existing methods, METHimpute enables the construction of complete methylomes by inferring the methylation status and level of all cytosines in the genome regardless of coverage. Application of METHimpute to maize, rice and Arabidopsis shows that the algorithm infers cytosine-resolution methylomes with high accuracy from data as low as 6X, compared to data with 60X, thus making it a cost-effective solution for large-scale studies.Conclusions: METHimpute provides methylation status calls and levels for all cytosines in the genome regardless of coverage, thus yielding complete methylomes even with low-coverage WGBS datasets. The method has been extensively tested in plants, but should also be applicable to other species. An implementation is available on Bioconductor.

In the construction of recombinant inbred lines (RILs) from two divergent inbred parents certain genotype (or epigenotype) combinations may be functionally “incompatible” when brought together in the genomes of the progeny, thus resulting in sterility or lower fertility. Natural selection against these epistatic combinations during inbreeding can change haplotype frequencies and distort linkage disequilibrium (LD) relations between loci on the same or on different chromosomes. These LD distortions have received increased experimental attention, because they point to genomic regions that may drive a Dobzhansky–Muller type of reproductive isolation and, ultimately, speciation in the wild. Here we study the selection signatures of two-locus epistatic incompatibility models and quantify their impact on the genetic composition of the genomes of two-way RILs obtained by selfing. We also consider the biases introduced by breeders when trying to counteract the loss of lines by selectively propagating only viable seeds. Building on our theoretical results, we develop model-based maximum-likelihood (ML) tests that can be applied to multilocus RIL genotype data to infer the precise mode of incompatibility as well as the relative fitness of incompatible loci. We illustrate this ML approach in the context of two published Arabidopsis thaliana RIL panels. Our work lays the theoretical foundation for studying more complex systems such as RILs obtained by sibling mating and/or from multiparental crosses.

Single cell high throughput genomic measurements are revolutionizing the fields of biology and medicine, providing a means to tackle biological problems that have thus far been inaccessible, such as the systematic discovery of new cell types, the identification of cellular heterogeneity in health and disease, or the cell-fate decisions taking place during differentiation and reprogramming. Recently implemented multi–omics measurements of genomes, transcriptomes, epigenomes, proteomes and chromatin organization are opening up new avenues to begin to disentangle the causal relationship between -omics layers and how these co-determine higher-order cellular phenotypes. This technological revolution is not restricted to basic science but promises major breakthroughs in medical diagnostics and treatments. In this paper we review existing computational methods for the analysis and integration of different -omics layers and discuss what new approaches are needed to leverage the full potential of single cell multi-omics data.

Background Alzheimer’s disease (AD) is a neurodegenerative disease of the brain and the most common form of dementia in the elderly. Aneuploidy, a state in which cells have an abnormal number of chromosomes, has been proposed to play a role in neurodegeneration in AD patients. Several studies using fluorescence in situ hybridization have shown that the brains of AD patients contain an increased number of aneuploid cells. However, because the reported rate of aneuploidy in neurons ranges widely, a more sensitive method is needed to establish a possible role of aneuploidy in AD pathology. Results In the current study, we used a novel single-cell whole genome sequencing (scWGS) approach to assess aneuploidy in isolated neurons from the frontal cortex of normal control individuals (n = 6) and patients with AD (n = 10). The sensitivity and specificity of our method was shown by the presence of three copies of chromosome 21 in all analyzed neuronal nuclei of a Down’s syndrome sample (n = 36). Very low levels of aneuploidy were found in the brains from control individuals (n = 589) and AD patients (n = 893). In contrast to other studies, we observe no selective gain of chromosomes 17 or 21 in neurons of AD patients. Conclusion scWGS showed no evidence for common aneuploidy in normal and AD neurons. Therefore, our results do not support an important role for aneuploidy in neuronal cells in the pathogenesis of AD. This will need to be confirmed by future studies in larger cohorts. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-0976-2) contains supplementary material, which is available to authorized users.

Author(s): Cornish-Bowden, Athel; Cornish-Bowden, Athel; Rasnick, David; Heng, Henry H; Horne, Steven; Abdallah, Batoul; Liu, Guo; Ye, Christine J; Bloomfield, Mathew; Vincent, Mark D; Aldaz, C Marcelo; Karlsson, Jenny; Valind, Anders; Jansson, Caroline; Gisselsson, David; Graves, Jennifer A Marshall; Stepanenko, Aleksei A; Andreieva, Svitlana V; Korets, Kateryna V; Mykytenko, Dmytro O; Huleyuk, Nataliya L; Baklaushev, Vladimir P; Kovaleva, Oksana A; Chekhonin, Vladimir P; Vassetzky, Yegor S; Avdieiev, Stanislav S; Bakker, Bjorn; Taudt, Aaron S; Belderbos, Mirjam E; Porubsky, David; Spierings, Diana CJ; de Jong, Tristan V; Halsema, Nancy; Kazemier, Hinke G; Hoekstra-Wakker, Karina; Bradley, Allan; de Bont, Eveline SJM; van den Berg, Anke; Guryev, Victor; Lansdorp, Peter M; Tatche, Maria Colome; Foijer, Floris; Liehr, Thomas; Baudoin, Nicolaas C; Nicholson, Joshua M; Soto, Kimberly; Quintanilla, Isabel; Camps, Jordi; Cimini, Daniela; Durrbaum, M; Donnelly, N; Passerini, V; Kruse, C; Habermann, B; Storchova, Z; Mandrioli, Daniele; Belpoggi, Fiorella; Silbergeld, Ellen K; Perry, Melissa J; Skotheim, Rolf I; Lovf, Marthe; Johannessen, Bjarne; Hoff, Andreas M; Zhao, Sen; SveeStromme, Jonas M; Sveen, Anita; Lothe, Ragnhild A; Hehlmann, R; Voskanyan, A; Fabarius, A; Bocking, Alfred; Biesterfeld, Stefan; Berynskyy, Leonid; Borgermann, Christof; Engers, Rainer; Dietz, Josef; Fritz, A; Sehgal, N; Vecerova, J; Stojkovicz, B; Ding, H; Page, N; Tye, C; Bhattacharya, S; Xu, J

Histone modifications are epigenetic marks that play fundamental roles in many biological processes including the control of chromatin-mediated regulation of gene expression. Little is known about interindividual variability of histone modification levels across the genome and to what extent they are influenced by genetic variation. We annotated the rat genome with histone modification maps, identified differences in histone trimethyl-lysine levels among strains, and described their underlying genetic basis at the genome-wide scale using ChIP-seq in heart and liver tissues in a panel of rat recombinant inbred and their progenitor strains. We identified extensive variation of histone methylation levels among individuals and mapped hundreds of underlying cis- and trans-acting loci throughout the genome that regulate histone methylation levels in an allele-specific manner. Interestingly, most histone methylation level variation was trans-linked and the most prominent QTL identified influenced H3K4me3 levels at 899 putative promoters throughout the genome in the heart. Cis- acting variation was enriched in binding sites of distinct transcription factors in heart and liver. The integrated analysis of DNA variation together with histone methylation and gene expression levels showed that histoneQTLs are an important predictor of gene expression and that a joint analysis significantly enhanced the prediction of gene expression traits (eQTLs). Our data suggest that genetic variation has a widespread impact on histone trimethylation marks that may help to uncover novel genotype–phenotype relationships.

Quantifying the impact of heritable epigenetic variation on complex traits is an emerging challenge in population genetics. Here, we analyze a population of isogenic Arabidopsis lines that segregate experimentally induced DNA methylation changes at hundreds of regions across the genome. We demonstrate that several of these differentially methylated regions (DMRs) act as bona fide epigenetic quantitative trait loci (QTL epi ), accounting for 60 to 90% of the heritability for two complex traits, flowering time and primary root length. These QTL epi are reproducible and can be subjected to artificial selection. Many of the experimentally induced DMRs are also variable in natural populations of this species and may thus provide an epigenetic basis for Darwinian evolution independently of DNA sequence changes.

Growing evidence shows that epigenetic mechanisms contribute to complex traits, with implications across many fields of biology. In plant ecology, recent studies have attempted to merge ecological experiments with epigenetic analyses to elucidate the contribution of epigenetics to plant phenotypes, stress responses, adaptation to habitat, and range distributions. While there has been some progress in revealing the role of epigenetics in ecological processes, studies with non-model species have so far been limited to describing broad patterns based on anonymous markers of DNA methylation. In contrast, studies with model species have benefited from powerful genomic resources, which contribute to a more mechanistic understanding but have limited ecological realism. Understanding the significance of epigenetics for plant ecology requires increased transfer of knowledge and methods from model species research to genomes of evolutionarily divergent species, and examination of responses to complex natural environments at a more mechanistic level. This requires transforming genomics tools specifically for studying non-model species, which is challenging given the large and often polyploid genomes of plants. Collaboration among molecular geneticists, ecologists and bioinformaticians promises to enhance our understanding of the mutual links between genome function and ecological processes.

Histones are highly covalently modified, but the functions of many of these modifications remain unknown. In particular, it is unclear how histone marks are coupled to cellular metabolism and how this coupling affects chromatin architecture. We identified histone H3 Lys14 (H3K14) as a site of propionylation and butyrylation in vivo and carried out the first systematic characterization of histone propionylation. We found that H3K14pr and H3K14bu are deposited by histone acetyltransferases, are preferentially enriched at promoters of active genes and are recognized by acylation-state-specific reader proteins. In agreement with these findings, propionyl-CoA was able to stimulate transcription in an in vitro transcription system. Notably, genome-wide H3 acylation profiles were redefined following changes to the metabolic state, and deletion of the metabolic enzyme propionyl-CoA carboxylase altered global histone propionylation levels. We propose that histone propionylation, acetylation and butyrylation may act in combination to promote high transcriptional output and to couple cellular metabolism with chromatin structure and function.

Background Chromosome instability leads to aneuploidy, a state in which cells have abnormal numbers of chromosomes, and is found in two out of three cancers. In a chromosomal instable p53 deficient mouse model with accelerated lymphomagenesis, we previously observed whole chromosome copy number changes affecting all lymphoma cells. This suggests that chromosome instability is somehow suppressed in the aneuploid lymphomas or that selection for frequently lost/gained chromosomes out-competes the CIN-imposed mis-segregation. Results To distinguish between these explanations and to examine karyotype dynamics in chromosome instable lymphoma, we use a newly developed single-cell whole genome sequencing (scWGS) platform that provides a complete and unbiased overview of copy number variations (CNV) in individual cells. To analyse these scWGS data, we develop AneuFinder, which allows annotation of copy number changes in a fully automated fashion and quantification of CNV heterogeneity between cells. Single-cell sequencing and AneuFinder analysis reveals high levels of copy number heterogeneity in chromosome instability-driven murine T-cell lymphoma samples, indicating ongoing chromosome instability. Application of this technology to human B cell leukaemias reveals different levels of karyotype heterogeneity in these cancers. Conclusion Our data show that even though aneuploid tumours select for particular and recurring chromosome combinations, single-cell analysis using AneuFinder reveals copy number heterogeneity. This suggests ongoing chromosome instability that other platforms fail to detect. As chromosome instability might drive tumour evolution, karyotype analysis using single-cell sequencing technology could become an essential tool for cancer treatment stratification. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-0971-7) contains supplementary material, which is available to authorized users.

The field of epigenomics has rapidly progressed from the study of individual reference epigenomes to surveying epigenomic variation in populations. Recent studies in a number of species, from yeast to humans, have begun to dissect the cis- and trans-regulatory genetic mechanisms that shape patterns of population epigenomic variation at the level of single epigenetic marks, as well as at the level of integrated chromatin state maps. We show that this information is paving the way towards a more complete understanding of the heritable basis underlying population epigenomic variation. We also highlight important conceptual challenges when interpreting results from these genetic studies, particularly in plants, in which epigenomic variation can be determined both by genetic and epigenetic inheritance.

Interindividual differences in chromatin states at a locus (epialleles) can result in gene expression changes that are sometimes transmitted across generations. In this way, they can contribute to heritable phenotypic variation in natural and experimental populations independent of DNA sequence. Recent molecular evidence shows that epialleles often display high levels of transgenerational instability. This property gives rise to a dynamic dimension in phenotypic inheritance. To be able to incorporate these non-Mendelian features into quantitative genetic models, it is necessary to study the induction and the transgenerational behavior of epialleles in controlled settings. Here we outline a general experimental approach for achieving this using crosses of epigenomically perturbed isogenic lines in mammalian and plant species. We develop a theoretical description of such crosses and model the relationship between epiallelic instability, recombination, parent-of-origin effects, as well as transgressive segregation and their joint impact on phenotypic variation across generations. In the limiting case of fully stable epialleles our approach reduces to the classical theory of experimental line crosses and thus illustrates a fundamental continuity between genetic and epigenetic inheritance. We consider data from a panel of Arabidopsis epigenetic recombinant inbred lines and explore estimates of the number of quantitative trait loci for plant height that resulted from a manipulation of DNA methylation levels in one of the two isogenic founder strains.

Stochastic changes in cytosine methylation are a source of heritable epigenetic and phenotypic diversity in plants. Using the model plant Arabidopsis thaliana, we derive robust estimates of the rate at which methylation is spontaneously gained (forward epimutation) or lost (backward epimutation) at individual cytosines and construct a comprehensive picture of the epimutation landscape in this species. We demonstrate that the dynamic interplay between forward and backward epimutations is modulated by genomic context and show that subtle contextual differences have profoundly shaped patterns of methylation diversity in A. thaliana natural populations over evolutionary timescales. Theoretical arguments indicate that the epimutation rates reported here are high enough to rapidly uncouple genetic from epigenetic variation, but low enough for new epialleles to sustain long-term selection responses. Our results provide new insights into methylome evolution and its population-level consequences.

We extensively phenotyped 6000 Arabidopsis plants with experimentally perturbed DNA methylomes as well as a diverse panel of natural accessions in a common garden. We found that alterations in DNA methylation not only caused heritable phenotypic diversity but also produced heritability patterns closely resembling those of the natural accessions. Our findings indicate that epigenetically induced and naturally occurring variation in complex traits share part of their polygenic architecture and may offer complementary adaptation routes in ecological settings.

Background ChIP-seq has become a routine method for interrogating the genome-wide distribution of various histone modifications. An important experimental goal is to compare the ChIP-seq profiles between an experimental sample and a reference sample, and to identify regions that show differential enrichment. However, comparative analysis of samples remains challenging for histone modifications with broad domains, such as heterochromatin-associated H3K27me3, as most ChIP-seq algorithms are designed to detect well defined peak-like features. Results To address this limitation we introduce histoneHMM, a powerful bivariate Hidden Markov Model for the differential analysis of histone modifications with broad genomic footprints. histoneHMM aggregates short-reads over larger regions and takes the resulting bivariate read counts as inputs for an unsupervised classification procedure, requiring no further tuning parameters. histoneHMM outputs probabilistic classifications of genomic regions as being either modified in both samples, unmodified in both samples or differentially modified between samples. We extensively tested histoneHMM in the context of two broad repressive marks, H3K27me3 and H3K9me3, and evaluated region calls with follow up qPCR as well as RNA-seq data. Our results show that histoneHMM outperforms competing methods in detecting functionally relevant differentially modified regions. Conclusion histoneHMM is a fast algorithm written in C++ and compiled as an R package. It runs in the popular R computing environment and thus seamlessly integrates with the extensive bioinformatic tool sets available through Bioconductor. This makeshistoneHMM an attractive choice for the differential analysis of ChIP-seq data. Software is available from http://histonehmm.molgen.mpg.de. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0491-6) contains supplementary material, which is available to authorized users.

Studies of DNA methylation in Arabidopsis have rapidly advanced from the analysis of a single reference accession to investigations of large populations. The goal of emerging population studies is to detect differentially methylated regions (DMRs) at the genome-wide scale, and to relate this variation to gene expression and phenotypic diversity.Whole-genome bisulfite sequencing (WGBS-seq) has established itself as a gold standard in DNA methylation analysis due to its high accuracy and single cytosine measurement resolution. However, scaling up the use of this technology for large population studies is currently not only cost prohibitive but also poses nontrivial bioinformatic challenges. If the end-point of the study is to detect DMRs at the level of several hundred base pairs rather than at the level of single cytosines, low-resolution array-based methods, such as MeDIP-chip, may be entirely sufficient. However, the trade-off between measurement accuracy and experimental/analytical practicality needs to be weighted carefully. To help make such experimental choices, we conducted a side-by-side comparison between the popular dual-channel MeDIP-chip Nimblegen technology and Illumina WGBS-seq in two independent Arabidopsis lines.Our analysis shows that MeDIP-chip performs reasonably well in detecting DNA methylation at probe-level resolution, yielding a genome-wide combined false-positive and false-negative rate of about 0.21. However, detection can be susceptible to strong signal distortions resulting from a combination of dye bias and the CG content of effectively unmethylated genomic regions. We show that these issues can be easily bypassed by taking appropriate data preparation steps and applying suitable analysis tools.We conclude that MeDIP-chip is a reasonable alternative to WGBS-seq in emerging Arabidopsis population epigenetic studies.