EpiScanpy integrated single-cell epigenomic analysis, bioRxiv, 2019-05-25
ABSTRACTEpigenetic single-cell measurements reveal a layer of regulatory information not accessible to single-cell transcriptomics, however single-cell-omics analysis tools mainly focus on gene expression data. To address this issue, we present epiScanpy, a computational framework for the analysis of single-cell DNA methylation and single-cell ATAC-seq data. EpiScanpy makes the many existing RNA-seq workflows from scanpy available to large-scale single-cell data from other -omics modalities. We introduce and compare multiple feature space constructions for epigenetic data and show the feasibility of common clustering, dimension reduction and trajectory learning techniques. We benchmark epiScanpy by interrogating different single-cell brain mouse atlases of DNA methylation, ATAC-seq and transcriptomics. We find that differentially methylated and differentially open markers between cell clusters enrich transcriptome-based cell type labels by orthogonal epigenetic information.
biorxiv bioinformatics 0-100-users 2019The genetic makeup of the electrocardiogram, bioRxiv, 2019-05-25
AbstractSince its original description in 1893 by Willem van Einthoven, the electrocardiogram (ECG) has been instrumental in the recognition of a wide array of cardiac disorders1,2. Although many electrocardiographic patterns have been well described, the underlying biology is incompletely understood. Genetic associations of particular features of the ECG have been identified by genome wide studies. This snapshot approach only provides fragmented information of the underlying genetic makeup of the ECG. Here, we follow the effecs of individual genetic variants through the complete cardiac cycle the ECG represents. We found that genetic variants have unique morphological signatures not identfied by previous analyses. By exploiting identified abberations of these morphological signatures, we show that novel genetic loci can be identified for cardiac disorders. Our results demonstrate how an integrated approach to analyse high-dimensional data can further our understanding of the ECG, adding to the earlier undertaken snapshot analyses of individual ECG components. We anticipate that our comprehensive resource will fuel in silico explorations of the biological mechanisms underlying cardiac traits and disorders represented on the ECG. For example, known disease causing variants can be used to identify novel morphological ECG signatures, which in turn can be utilized to prioritize genetic variants or genes for functional validation. Furthermore, the ECG plays a major role in the development of drugs, a genetic assessment of the entire ECG can drive such developments.
biorxiv genetics 0-100-users 2019A single bacterial genus maintains root development in a complex microbiome, bioRxiv, 2019-05-24
AbstractPlants grow within a complex web of species interacting with each other and with the plant. Many of these interactions are governed by a wide repertoire of chemical signals, and the resulting chemical landscape of the rhizosphere can strongly affect root health and development. To understand how microbe-microbe interactions influence root development in Arabidopsis, we established a model system for plant-microbe-microbe-environment interactions. We inoculated seedlings with a 185-member bacterial synthetic community (SynCom), manipulated the abiotic environment, and measured bacterial colonization of the plant. This enabled classification of the SynCom into four modules of co-occurring strains. We deconstructed the SynCom based on these modules, identifying microbe-microbe interactions that determine root phenotypes. These interactions primarily involve a single bacterial genus, Variovorax, which completely reverts severe root growth inhibition (RGI) induced by a wide diversity of bacterial strains as well as by the entire 185-member community. We demonstrate that Variovorax manipulate plant hormone levels to balance this ecologically realistic root community’s effects on root development. We identify a novel auxin degradation operon in the Variovorax genome that is necessary and sufficient for RGI reversion. Therefore, metabolic signal interference shapes bacteria-plant communication networks and is essential for maintaining the root’s developmental program. Optimizing the feedbacks that shape chemical interaction networks in the rhizosphere provides a promising new ecological strategy towards the development of more resilient and productive crops.
biorxiv microbiology 100-200-users 2019Can education be personalised using pupils’ genetic data?, bioRxiv, 2019-05-24
AbstractThe predictive power of polygenic scores for some traits now rivals that of more classical phenotypic measures, and as such they have been promoted as a potential tool for genetically informed policy. However, how predictive polygenic scores are conditional on other easily available phenotypic data is not well understood. Using data from a UK cohort study, the Avon Longitudinal Study of Parents and Children, we investigated how well polygenic scores for education predict individuals’ realised attainment over and above phenotypic data available to schools. Across our sample children’s polygenic scores predicted their educational outcomes almost as well as parent’s socioeconomic position or education. There was high overlap between the polygenic score and attainment distributions, leading to weak predictive accuracy at the individual level. Furthermore, conditional on prior attainment the polygenic score was not predictive of later attainment. Our results suggest that polygenic scores are informative for identifying group level differences, but they currently have limited use in predicting individual attainment.
biorxiv genetics 100-200-users 2019Clonal replacement of tumor-specific T cells following PD-1 blockade, bioRxiv, 2019-05-24
AbstractImmunotherapies that block inhibitory checkpoint receptors on T cells have transformed the clinical care of cancer patients. However, which tumor-specific T cells are mobilized following checkpoint blockade remains unclear. Here, we performed paired single-cell RNA- and T cell receptor (TCR)-sequencing on 79,046 cells from site-matched tumors from patients with basal cell carcinoma (BCC) or squamous cell carcinoma (SCC) pre- and post-anti-PD-1 therapy. Tracking TCR clones and transcriptional phenotypes revealed a coupling of tumor-recognition, clonal expansion, and T cell dysfunction the T cell response to treatment was accompanied by clonal expansions of CD8+CD39+ T cells, which co-expressed markers of chronic T cell activation and exhaustion. However, this expansion did not derive from pre-existing tumor infiltrating T cell clones; rather, it comprised novel clonotypes, which were not previously observed in the same tumor. Clonal replacement of T cells was preferentially observed in exhausted CD8+ T cells, compared to other distinct T cell phenotypes, and was evident in BCC and SCC patients. These results, enabled by single-cell multi-omic profiling of clinical samples, demonstrate that pre-existing tumor-specific T cells may be limited in their capacity for re-invigoration, and that the T cell response to checkpoint blockade relies on the expansion of a distinct repertoire of T cell clones that may have just recently entered the tumor.
biorxiv immunology 100-200-users 2019Enabling high-accuracy long-read amplicon sequences using unique molecular identifiers with Nanopore or PacBio sequencing, bioRxiv, 2019-05-24
AbstractHigh-throughput amplicon sequencing of large genomic regions remains challenging for short-read technologies. Here, we report a high-throughput amplicon sequencing approach combining unique molecular identifiers (UMIs) with Oxford Nanopore Technologies or Pacific Biosciences CCS sequencing, yielding high accuracy single-molecule consensus sequences of large genomic regions. Our approach generates amplicon and genomic sequences of >10,000 bp in length with a mean error-rate of 0.0049-0.0006% and chimera rate <0.022%.
biorxiv molecular-biology 200-500-users 2019