EpiScanpy: integrated single-cell epigenomic analysis.
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ABSTRACT:
SUBMITTER: Danese A
PROVIDER: S-EPMC8410937 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Chromatin modifications orchestrate the dynamic regulation of gene expression during development and in disease. Bulk approaches have characterized the wide repertoire of histone modifications across cell types, detailing their role in shaping cell identity. However, these population-based methods do not capture cell-to-cell heterogeneity of chromatin landscapes, limiting our appreciation of the role of chromatin in dynamic biological processes. Recent technological developments enable the mapping of histone marks at single-cell resolution, opening up perspectives to characterize the heterogeneity of chromatin marks in complex biological systems over time. Yet, existing tools used to analyze bulk histone modifications profiles are not fit for the low coverage and sparsity of single-cell epigenomic datasets. Here, we present ChromSCape, a user-friendly interactive Shiny/R application distributed as a Bioconductor package, that processes single-cell epigenomic data to assist the biological interpretation of chromatin landscapes within cell populations. ChromSCape analyses the distribution of repressive and active histone modifications as well as chromatin accessibility landscapes from single-cell datasets. Using ChromSCape, we deconvolve chromatin landscapes within the tumor micro-environment, identifying distinct H3K27me3 landscapes associated with cell identity and breast tumor subtype.
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Project description:Elucidation of the epigenetic basis for cell-type specific gene regulation is key to gaining a full understanding of how the distinct phenotypes of differentiated cells are achieved and maintained. Here we examined how epigenetic changes are integrated with transcriptional activation to determine cell phenotype during differentiation. We performed epigenomic profiling in conjunction with transcriptomic profiling using in vitro differentiation of human primary alveolar epithelial cells (AEC). This model recapitulates an in vivo process in which AEC transition from one differentiated cell type to another during regeneration following lung injury. Interrogation of histone marks over time revealed enrichment of specific transcription factor binding motifs within regions of changing chromatin structure. Cross-referencing of these motifs with pathways showing transcriptional changes revealed known regulatory pathways of distal alveolar differentiation, such as the WNT and transforming growth factor beta (TGFB) pathways, and putative novel regulators of adult AEC differentiation including hepatocyte nuclear factor 4 alpha (HNF4A), and the retinoid X receptor (RXR) signaling pathways. Inhibition of the RXR pathway confirmed its functional relevance for alveolar differentiation. Our incorporation of epigenetic data allowed specific identification of transcription factors that are potential direct upstream regulators of the differentiation process, demonstrating the power of this approach. Integration of epigenomic data with transcriptomic profiling has broad application for the identification of regulatory pathways in other models of differentiation.
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Project description:Current single-cell epigenome analyses are designed for single use. The cell is discarded after a single use, preventing analysis of multiple epigenetic marks in a single cell and requiring data from other cells to distinguish signal from experimental background noise in a single cell. This paper describes a method to reuse the same single cell for iterative epigenomic analyses. In this experimental method, cellular proteins are first anchored to a polyacrylamide polymer instead of crosslinking them to protein and DNA, alleviating structural bias. This critical step allows repeated experiments with the same single cell. Next, a random primer with a scaffold sequence for proximity ligation is annealed to the genomic DNA, and the genomic sequence is added to the primer by extension using a DNA polymerase. Subsequently, an antibody against an epigenetic marker and control IgG, each labeled with different DNA probes, are bound to the respective targets in the same single cell. Proximity ligation is induced between the random primer and the antibody by adding a connector DNA with complementary sequences to the scaffold sequence of the random primer and the antibody-DNA probe. This approach integrates antibody information and nearby genome sequences in a single DNA product of proximity ligation. By enabling repeated experiments with the same single cell, this method allows an increase in data density from a rare cell and statistical analysis using only IgG and antibody data from the same cell. The reusable single cells prepared by this method can be stored for at least a few months and reused later to broaden epigenetic characterization and increase data density. This method provides flexibility to researchers and their projects.
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Project description:The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce "weighted-nearest neighbor" analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.
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Project description:BackgroundATP-dependent chromatin remodelling complexes are responsible for establishing and maintaining the positions of nucleosomes. Chromatin remodellers are targeted to chromatin by transcription factors and non-coding RNA to remodel the chromatin into functional states. However, the influence of chromatin remodelling on shaping the functional epigenome is not well understood. Moreover, chromatin remodellers have not been extensively explored as a collective group across two-dimensional and three-dimensional epigenomic layers.ResultsHere, we have integrated the genome-wide binding profiles of eight chromatin remodellers together with DNA methylation, nucleosome positioning, histone modification and Hi-C chromosomal contacts to reveal that chromatin remodellers can be stratified into two functional groups. Group 1 (BRG1, SNF2H, CHD3 and CHD4) has a clear preference for binding at 'actively marked' chromatin and Group 2 (BRM, INO80, SNF2L and CHD1) for 'repressively marked' chromatin. We find that histone modifications and chromatin architectural features, but not DNA methylation, stratify the remodellers into these functional groups.ConclusionsOur findings suggest that chromatin remodelling events are synchronous and that chromatin remodellers themselves should be considered simultaneously and not as individual entities in isolation or necessarily by structural similarity, as they are traditionally classified. Their coordinated function should be considered by preference for chromatin features in order to gain a more accurate and comprehensive picture of chromatin regulation.
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Project description:The brain is a common site of metastatic disease in patients with breast cancer, which has few therapeutic options and dismal outcomes. The purpose of our study was to identify common and rare events that underlie breast cancer brain metastasis. We performed deep genomic profiling, which integrated gene copy number, gene expression and DNA methylation datasets on a collection of breast brain metastases. We identified frequent large chromosomal gains in 1q, 5p, 8q, 11q, and 20q and frequent broad-level deletions involving 8p, 17p, 21p and Xq. Frequently amplified and overexpressed genes included ATAD2, BRAF, DERL1, DNMTRB and NEK2A. The ATM, CRYAB and HSPB2 genes were commonly deleted and underexpressed. Knowledge mining revealed enrichment in cell cycle and G2/M transition pathways, which contained AURKA, AURKB and FOXM1. Using the PAM50 breast cancer intrinsic classifier, Luminal B, Her2+/ER negative, and basal-like tumors were identified as the most commonly represented breast cancer subtypes in our brain metastasis cohort. While overall methylation levels were increased in breast cancer brain metastasis, basal-like brain metastases were associated with significantly lower levels of methylation. Integrating DNA methylation data with gene expression revealed defects in cell migration and adhesion due to hypermethylation and downregulation of PENK, EDN3, and ITGAM. Hypomethylation and upregulation of KRT8 likely affects adhesion and permeability. Genomic and epigenomic profiling of breast brain metastasis has provided insight into the somatic events underlying this disease, which have potential in forming the basis of future therapeutic strategies.
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Project description:Gene expression in individual cells is epigenetically regulated by DNA modifications, histone modifications, transcription factors, and other DNA-binding proteins. It has been shown that multiple histone modifications can predict gene expression and reflect future responses of bulk cells to extracellular cues. However, the predictive ability of epigenomic analysis is still limited for mechanistic research at a single cell level. To overcome this limitation, it would be useful to acquire reliable signals from multiple epigenetic marks in the same single cell. Here, we propose a new approach and a new method for analysis of several components of the epigenome in the same single cell. The new method allows reanalysis of the same single cell. We found that reanalysis of the same single cell is feasible, provides confirmation of the epigenetic signals, and allows application of statistical analysis to identify reproduced reads using data sets generated only from the single cell. Reanalysis of the same single cell is also useful to acquire multiple epigenetic marks from the same single cells. The method can acquire at least five epigenetic marks: H3K27ac, H3K27me3, mediator complex subunit 1, a DNA modification, and a DNA-interacting protein. We can predict active signaling pathways in K562 single cells using the epigenetic data and confirm that the predicted results strongly correlate with actual active signaling pathways identified by RNA-seq results. These results suggest that the new method provides mechanistic insights for cellular phenotypes through multilayered epigenome analysis in the same single cells.
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Project description:BackgroundCell-to-cell heterogeneity is a major driver of cancer evolution, progression, and emergence of drug resistance. Epigenomic variation at the single-cell level can rapidly create cancer heterogeneity but is difficult to detect and assess functionally.ResultsWe develop a strategy to bridge the gap between measurement and function in single-cell epigenomics. Using single-cell chromatin accessibility and RNA-seq data in K562 leukemic cells, we identify the cell surface marker CD24 as co-varying with chromatin accessibility changes linked to GATA transcription factors in single cells. Fluorescence-activated cell sorting of CD24 high versus low cells prospectively isolated GATA1 and GATA2 high versus low cells. GATA high versus low cells express differential gene regulatory networks, differential sensitivity to the drug imatinib mesylate, and differential self-renewal capacity. Lineage tracing experiments show that GATA/CD24hi cells have the capability to rapidly reconstitute the heterogeneity within the entire starting population, suggesting that GATA expression levels drive a phenotypically relevant source of epigenomic plasticity.ConclusionSingle-cell chromatin accessibility can guide prospective characterization of cancer heterogeneity. Epigenomic subpopulations in cancer impact drug sensitivity and the clonal dynamics of cancer evolution.
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Project description:High-dimensional analyses of cancers can potentially be used to better define cancer subtypes, analyze the complex tumor microenvironment, and perform cancer cell pathway analyses for drug trials. Unfortunately, integrated systems that allow such analyses in serial fine needle aspirates within a day or at point-of-care currently do not exist. To achieve this, an integrated immunofluorescence single-cell analyzer (i2SCAN) for deep profiling of directly harvested cells is developed. By combining a novel cellular imaging system, highly cyclable bioorthogonal FAST antibody panels, and integrated computational analysis, it is shown that same-day analysis is possible in thousands of harvested cells. It is demonstrated that the i2SCAN approach allows comprehensive analysis of breast cancer samples obtained by fine needle aspiration or core tissues. The method is a rapid, robust, and low-cost solution to high-dimensional analysis of scant clinical specimens.
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Project description:BackgroundTranscriptional dysregulation drives cancer formation but the underlying mechanisms are still poorly understood. Renal cell carcinoma (RCC) is the most common malignant kidney tumor which canonically activates the hypoxia-inducible transcription factor (HIF) pathway. Despite intensive study, novel therapeutic strategies to target RCC have been difficult to develop. Since the RCC epigenome is relatively understudied, we sought to elucidate key mechanisms underpinning the tumor phenotype and its clinical behavior.MethodsWe performed genome-wide chromatin accessibility (DNase-seq) and transcriptome profiling (RNA-seq) on paired tumor/normal samples from 3 patients undergoing nephrectomy for removal of RCC. We incorporated publicly available data on HIF binding (ChIP-seq) in a RCC cell line. We performed integrated analyses of these high-resolution, genome-scale datasets together with larger transcriptomic data available through The Cancer Genome Atlas (TCGA).FindingsThough HIF transcription factors play a cardinal role in RCC oncogenesis, we found that numerous transcription factors with a RCC-selective expression pattern also demonstrated evidence of HIF binding near their gene body. Examination of chromatin accessibility profiles revealed that some of these transcription factors influenced the tumor's regulatory landscape, notably the stem cell transcription factor POU5F1 (OCT4). Elevated POU5F1 transcript levels were correlated with advanced tumor stage and poorer overall survival in RCC patients. Unexpectedly, we discovered a HIF-pathway-responsive promoter embedded within a endogenous retroviral long terminal repeat (LTR) element at the transcriptional start site of the PSOR1C3 long non-coding RNA gene upstream of POU5F1. RNA transcripts are induced from this promoter and read through PSOR1C3 into POU5F1 producing a novel POU5F1 transcript isoform. Rather than being unique to the POU5F1 locus, we found that HIF binds to several other transcriptionally active LTR elements genome-wide correlating with broad gene expression changes in RCC.InterpretationIntegrated transcriptomic and epigenomic analysis of matched tumor and normal tissues from even a small number of primary patient samples revealed remarkably convergent shared regulatory landscapes. Several transcription factors appear to act downstream of HIF including the potent stem cell transcription factor POU5F1. Dysregulated expression of POU5F1 is part of a larger pattern of gene expression changes in RCC that may be induced by HIF-dependent reactivation of dormant promoters embedded within endogenous retroviral LTRs.
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