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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. Bulk approaches fail to capture the cell-to-cell heterogeneity of chromatin landscapes, while single-cell approaches provide low coverage datasets. Here, the authors present ChromSCape, a user-friendly interactive application that processes single-cell epigenomic data to assist the biological interpretation of chromatin landscapes within cell populations, as demonstrated in the context of cancer.

Background Thoroughly annotated data resources are a key requirement in phenotype dependent analysis and diagnosis of diseases in the area of precision medicine. Recent work has shown that curation and systematic annotation of human phenome data can significantly improve the quality and selectivity for the interpretation of inherited diseases. We have therefore developed PhenoDis, a comprehensive, manually annotated database providing symptomatic, genetic and imprinting information about rare cardiac diseases. Results PhenoDis includes 214 rare cardiac diseases from Orphanet and 94 more from OMIM. For phenotypic characterization of the diseases, we performed manual annotation of diseases with articles from the biomedical literature. Detailed description of disease symptoms required the use of 2247 different terms from the Human Phenotype Ontology (HPO). Diseases listed in PhenoDis frequently cover a broad spectrum of symptoms with 28% from the branch of ‘cardiovascular abnormality’ and others from areas such as neurological (11.5%) and metabolism (6%). We collected extensive information on the frequency of symptoms in respective diseases as well as on disease-associated genes and imprinting data. The analysis of the abundance of symptoms in patient studies revealed that most of the annotated symptoms (71%) are found in less than half of the patients of a particular disease. Comprehensive and systematic characterization of symptoms including their frequency is a pivotal prerequisite for computer based prediction of diseases and disease causing genetic variants. To this end, PhenoDis provides in-depth annotation for a complete group of rare diseases, including information on pathogenic and likely pathogenic genetic variants for 206 diseases as listed in ClinVar. We integrated all results in an online database ( http://mips.helmholtz-muenchen.de/phenodis/ ) with multiple search options and provide the complete dataset for download. Conclusion PhenoDis provides a comprehensive set of manually annotated rare cardiac diseases that enables computational approaches for disease prediction via decision support systems and phenotype-driven strategies for the identification of disease causing genes.

Assessing chromatin profiles at single-cell resolution is now feasible thanks to recently published experimental methods such as single cell chromatin immunoprecipitation followed by sequencing (scChIP-seq) (Grosselin et al., 2019; Rotem et al., 2015) and single-cell assay for transposase-accessibility chromatin (scATAC-seq) (Buenrostro et al., 2015; Chen et al., 2018; Cusanovich et al., 2015; Lareau et al., 2019). With these methods, we can detect the heterogeneity of epigenomic profiles within complex biological samples. Yet, existing tools used to analyze bulk epigenomic experiments are not fit for the low coverage and sparsity of single-cell epigenomic datasets. Here, we present ChromSCape: a user-friendly Shiny/R application that processes single-cell epigenomic data to help the biological interpretation of epigenomic landscapes within cell populations. The user can identify different sub-populations within heterogeneous samples, find differentially enriched regions between subpopulations and identify associated genes and pathways. ChromSCape accepts multiple samples to allow comparisons of cell populations between and within samples. ChromSCape source code is written in Shiny/R, works as a stand-alone application and is freely downloadable at https://github.com/vallotlab/ChromSCape. Here, using ChromSCape on multiple H3K27me3 scChIP-seq datasets, we deconvolve chromatin landscapes within the tumor microenvironment, identifying distinct H3K27me3 landscapes associated to cell identity and tumor subtype. pacome.prompsy@curie.fr; celine.vallot@curie.fr

Assessing chromatin profiles at single-cell resolution is now feasible thanks to recently published experimental methods such as single cell chromatin immunoprecipitation followed by sequencing (scChIP-seq) (Grosselin et al., 2019; Rotem et al., 2015) and single-cell assay for transposase-accessibility chromatin (scATAC-seq) (Buenrostro et al., 2015; Chen et al., 2018; Cusanovich et al., 2015; Lareau et al., 2019). With these methods, we can detect the heterogeneity of epigenomic profiles within complex biological samples. Yet, existing tools used to analyze bulk epigenomic experiments are not fit for the low coverage and sparsity of single-cell epigenomic datasets. Here, we present ChromSCape: a user-friendly Shiny/R application that processes single-cell epigenomic data to help the biological interpretation of epigenomic landscapes within cell populations. The user can identify different sub-populations within heterogeneous samples, find differentially enriched regions between subpopulations and identify associated genes and pathways. ChromSCape accepts multiple samples to allow comparisons of cell populations between and within samples. The application is intuitive, greatly customizable and generates annotated correlation heatmaps, t-SNE, PCA and volcano plots. ChromSCape source code is written in Shiny/R, works as a stand-alone application and is freely downloadable at https://github.com/vallotlab/ChromSCape. Dependencies and bioinformatics tools required by ChromSCape are listed on the repository page. A run command-line program achieving similar results is also available at https://github.com/vallotlab/scChIPseq. Footnotes * https://github.com/vallotlab/ChromSCape * https://github.com/vallotlab/scChIPseq