Biology subjects

Schwessinger, R.

Publications and source records attributed to Schwessinger, R..

9 recordsLinked to original sources

bioRxiv2023

Single-cell gene expression prediction from DNA sequence at large contexts

Human genetic variants impacting traits such as disease susceptibility frequently act through modulation of gene expression in a highly cell-type-specific manner. Computational models capable of predicting gene expression directly from DNA sequence can assist in the interpretation of expression-modulating variants, and machine learning models now operate at the large sequence contexts required for capturing long-range human transcriptional regulation. However, existing predictors have focused on bulk transcriptional measurements where gene expression heterogeneity can be drowned out in broadly defined cell types. Here, we use a transfer learning framework, seq2cells, leveraging a pre-trained epigenome model for gene expression prediction from large sequence contexts at single-cell resolution. We show that seq2cells captures cell-specific gene expression beyond the resolution of pseudo-bulked data. Using seq2cells for variant effect prediction reveals heterogeneity within annotated cell types and enables in silico transfer of variant effects between cell populations. We demonstrate the challenges and value of gene expression and variant effect prediction at single-cell resolution, and offer a path to the interpretation of genomic variation at uncompromising resolution and scale.

genomics↗

bioRxiv2021

Dynamic Runx1 chromatin boundaries affect gene expression in hematopoietic development

The transcription factor RUNX1 is a critical regulator of developmental hematopoiesis and is frequently disrupted in leukemia. Runx1 is a large, complex gene that is expressed from two alternative promoters under the spatiotemporal control of multiple hematopoietic enhancers. To dissect the dynamic regulation of Runx1 in hematopoietic development, we analyzed its three-dimensional chromatin conformation in mouse embryonic stem cell (ESC) differentiation cultures. Runx1 resides in a 1.1 Mb topologically associating domain (TAD) demarcated by convergent CTCF motifs. As ESCs differentiate to mesoderm, chromatin accessibility, Runx1 enhancer-promoter (E-P) interactions, and CTCF-CTCF interactions increased in the TAD, along with initiation of Runx1 expression from the P2 promoter. Differentiation to hematopoietic progenitor cells was associated with the formation of tissue-specific sub-TADs over Runx1, a shift in E-P interactions, P1 promoter demethylation, and robust expression from both Runx1 promoters. Deletions of promoter-proximal CTCF sites at the sub-TAD boundaries had no obvious effects on E-P interactions but led to partial loss of domain structure, mildly affected gene expression, and delayed hematopoietic development. Together, our analyses of gene regulation at a large multi-promoter developmental gene revealed that dynamic sub-TAD chromatin boundaries play a role in establishing TAD structure and coordinated gene expression.

developmental biology↗

bioRxiv2020

Targeted high-resolution chromosome conformation capture at genome-wide scale.

Chromosome conformation capture (3C) provides an adaptable tool for studying diverse biological questions. Current 3C methods provide either low-resolution interaction profiles across the entire genome, or high-resolution interaction profiles at up to several hundred loci. All 3C methods are affected to varying degrees by inefficiency, bias and noise. As such, generation of reproducible high-resolution interaction profiles has not been achieved at scale. To overcome this barrier, we systematically tested and improved upon current methods. We show that isolation of 3C libraries from intact nuclei, as well as shortening and titration of enrichment oligonucleotides used in high-resolution methods reduces noise and increases on-target sequencing. We combined these technical modifications into a new method Nuclear-Titrated (NuTi) Capture-C, which provides a >3-fold increase in informative sequencing content over current Capture-C protocols. Using NuTi Capture-C we target 8,061 promoters in triplicate, demonstrating that this method generates reproducible high-resolution genome-wide 3C interaction profiles at scale.

genomics↗

bioRxiv2020

CaptureCompendium: a comprehensive toolkit for 3C analysis.

DNA folding within nuclei is a highly ordered process, with implications for gene regulation and development. An array of chromosome conformation capture (3C) methods have been developed to investigate how DNA is packaged within nuclei and to interrogate specific interactions. While these methods use different approaches to examine target loci (many-versus-all) or the entire genome (all-versus-all), they all rely on the core principle of endonuclease digestion and proximity-based ligation to re-arrange genomic order to reflect the three-dimensional nuclear conformation. This sequence reorganization creates novel chimeric DNA fragments which require specialist bioinformatic tools to analyze and visualize. Despite this need for specialist bioinformatic skills, the core biological importance of genome folding has seen widespread methodological uptake. To service the needs of experimentalists using the many-versus-all Capture-C family of methods we have developed CaptureCompendium; a toolkit of software to simplify the design, analysis and presentation of 3C experiments.

bioinformatics↗

bioRxiv2019

Enhancers predominantly regulate gene expression in vivo via transcription initiation.

Gene transcription occurs via a cycle of linked events including initiation, promoter proximal pausing and elongation of RNA polymerase II (Pol II). A key question is how do transcriptional enhancers influence these events to control gene expression? Here we have used a new approach to quantify transcriptional initiation and pausing in vivo, while simultaneously identifying transcription start sites (TSSs) and pause-sites (TPSs) from single RNA molecules. When analyzed in parallel with nascent RNA-seq, these data show that differential gene expression is achieved predominantly via changes in transcription initiation rather than Pol II pausing. Using genetically engineered mouse models deleted for specific enhancers we show that these elements control gene expression via Pol II recruitment and/or initiation rather than via promoter proximal pause release. Together, our data show that enhancers, in general, control gene expression predominantly by Pol II recruitment and initiation rather than via pausing.

genomics↗

bioRxiv2019

An integrated platform to systematically identify causal variants and genes for polygenic human traits.

Genome-wide association studies (GWAS) have identified over 150,000 links between common genetic variants and human traits or complex diseases. Over 80% of these associations map to polymorphisms in non-coding DNA. Therefore, the challenge is to identify disease-causing variants, the genes they affect, and the cells in which these effects occur. We have developed a platform using ATAC-seq, DNaseI footprints, NG Capture-C and machine learning to address this challenge. Applying this approach to red blood cell traits identifies a significant proportion of known causative variants and their effector genes, which we show can be validated by direct in vivo modelling.

genetics↗

bioRxiv2019

Modelling erythropoiesis in congenital dyserythropoietic anaemia type I (CDA-I)

We employ and extensively characterise an ex vivo culture system to study terminal erythroid maturation of CD34+ progenitors from the peripheral blood of normal individuals and patients with Congenital Dyserythropoietic Anaemia type 1 (CDA-I). Using morphological analysis, FACS analysis and the proteomic approach CyTOF, we analysed patient-derived erythroblasts stage-matched with those from healthy donors during the expansion phase and into early differentiation. In patient cells, aspects of disordered erythropoiesis manifest midway through differentiation, including increased proliferation and changes in the DNA accessibility profile. We also show that cultured erythroblasts from CDA-I patients recapitulate the pathognomic feature of this erythroid disorder with up to 40% of the cells having abnormal spongy chromatin morphology by electron microscopy, as well as upregulation of GDF15, a marker of ineffective erythropoiesis. In the tertiary phase of culture, patient cells show significantly less enucleation and there is persistence of earlier erythroid precursors. Furthermore, the enucleation defect appears to be more severe in patients with mutations in C15orf41, as compared to the other known causative gene CDAN1, indicating a genotype/phenotype correlation in CDA-I. Such erythroblasts are a valuable resource for investigating the pathogenesis of this disease and provide the opportunity for streamlining diagnosis for CDA-I patients and ultimately other forms of unexplained anaemia.

genetics↗

bioRxiv2019

DeepC: Predicting chromatin interactions using megabase scaled deep neural networks and transfer learning.

Understanding 3D genome structure requires high throughput, genome-wide approaches. However, assays for all vs. all chromatin interaction mapping are expensive and time consuming, which severely restricts their usage for large-scale mutagenesis screens or for mapping the impact of sequence variants. Computational models sophisticated enough to grasp the determinants of chromatin folding provide a unique window into the functional determinants of 3D genome structure as well as the effects of genome variation.\n\nA chromatin interaction predictor should work at the base pair level but also incorporate large-scale genomic context to simultaneously capture the large scale and intricate structures of chromatin architecture. Similarly, to be a flexible and generalisable approach it should also be applicable to data it has not been explicitly trained on. To develop a model with these properties, we designed a deep neuronal network (deepC) that utilizes transfer learning to accurately predict chromatin interactions from DNA sequence at megabase scale. The model generalizes well to unseen chromosomes and works across cell types, Hi-C data resolutions and a range of sequencing depths. DeepC integrates DNA sequence context on an unprecedented scale, bridging the different levels of resolution from base pairs to TADs. We demonstrate how this model allows us to investigate sequence determinants of chromatin folding at genome-wide scale and to predict the importance of regulatory elements and the impact of sequence variations.

genomics↗

bioRxiv2018

Single-cell chromatin interactions reveal regulatory hubs in dynamic compartmentalized domains

The promoters of mammalian genes are commonly regulated by multiple distal enhancers, which physically interact within discrete chromatin domains. How such domains form and how the regulatory elements within them interact within single cells is not understood. To address this we developed Tri-C, a new Chromosome Conformation Capture (3C) approach to identify concurrent chromatin interactions at individual alleles within single cells. The heterogeneity of interactions observed between such cells shows that CTCF-mediated formation of chromatin domains and interactions within them are dynamic processes. Importantly, our analyses reveal higher-order structures involving simultaneous interactions between multiple enhancers and promoters within individual cells. This provides a structural basis for understanding how multiple cis-elements act together to establish robust regulation of gene expression.

genetics↗