GTAC enables parallel genotyping of multiple genomic loci with chromatin accessibility profiling in single cells.

Understanding clonal evolution and cancer development requires experimental approaches for characterizing the consequences of somatic mutations on gene regulation. However, no methods currently exist that efficiently link high-content chromatin accessibility with high-confidence genotyping in single cells. To address this, we developed Genotyping with the Assay for Transposase-Accessible Chromatin (GTAC), enabling accurate mutation detection at multiple amplified loci, coupled with robust chromatin accessibility readout. We applied GTAC to primary acute myeloid leukemia, obtaining high-quality chromatin accessibility profiles and clonal identities for multiple mutations in 88% of cells. We traced chromatin variation throughout clonal evolution, showing the restriction of different clones to distinct differentiation stages. Furthermore, we identified switches in transcription factor motif accessibility associated with a specific combination of driver mutations, which biased transformed progenitors toward a leukemia stem cell-like chromatin state. GTAC is a powerful tool to study clonal heterogeneity across a wide spectrum of pre-malignant and neoplastic conditions.

Direct correction of haemoglobin E ß-thalassaemia using base editors.

Haemoglobin E (HbE) ß-thalassaemia causes approximately 50% of all severe thalassaemia worldwide; equating to around 30,000 births per year. HbE ß-thalassaemia is due to a point mutation in codon 26 of the human HBB gene on one allele (GAG; glutamatic acid → AAG; lysine, E26K), and any mutation causing severe ß-thalassaemia on the other. When inherited together in compound heterozygosity these mutations can cause a severe thalassaemic phenotype. However, if only one allele is mutated individuals are carriers for the respective mutation and have an asymptomatic phenotype (ß-thalassaemia trait). Here we describe a base editing strategy which corrects the HbE mutation either to wildtype (WT) or a normal variant haemoglobin (E26G) known as Hb Aubenas and thereby recreates the asymptomatic trait phenotype. We have achieved editing efficiencies in excess of 90% in primary human CD34 + cells. We demonstrate editing of long-term repopulating haematopoietic stem cells (LT-HSCs) using serial xenotransplantation in NSG mice. We have profiled the off-target effects using a combination of circularization for in vitro reporting of cleavage effects by sequencing (CIRCLE-seq) and deep targeted capture and have developed machine-learning based methods to predict functional effects of candidate off-target mutations.

LanceOtron: a deep learning peak caller for genome sequencing experiments.

MOTIVATION: Genome sequencing experiments have revolutionized molecular biology by allowing researchers to identify important DNA-encoded elements genome wide. Regions where these elements are found appear as peaks in the analog signal of an assay's coverage track, and despite the ease with which humans can visually categorize these patterns, the size of many genomes necessitates algorithmic implementations. Commonly used methods focus on statistical tests to classify peaks, discounting that the background signal does not completely follow any known probability distribution and reducing the information-dense peak shapes to simply maximum height. Deep learning has been shown to be highly accurate for many pattern recognition tasks, on par or even exceeding human capabilities, providing an opportunity to reimagine and improve peak calling. RESULTS: We present the peak calling framework LanceOtron, which combines deep learning for recognizing peak shape with multifaceted enrichment calculations for assessing significance. In benchmarking ATAC-seq, ChIP-seq and DNase-seq, LanceOtron outperforms long-standing, gold-standard peak callers through its improved selectivity and near-perfect sensitivity. AVAILABILITY AND IMPLEMENTATION: A fully featured web application is freely available from LanceOtron.molbiol.ox.ac.uk, command line interface via python is pip installable from PyPI at https://pypi.org/project/lanceotron/, and source code and benchmarking tests are available at https://github.com/LHentges/LanceOtron. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Defining genome architecture at base-pair resolution.

In higher eukaryotes, many genes are regulated by enhancers that are 104-106 base pairs (bp) away from the promoter. Enhancers contain transcription-factor-binding sites (which are typically around 7-22 bp), and physical contact between the promoters and enhancers is thought to be required to modulate gene expression. Although chromatin architecture has been mapped extensively at resolutions of 1 kilobase and above; it has not been possible to define physical contacts at the scale of the proteins that determine gene expression. Here we define these interactions in detail using a chromosome conformation capture method (Micro-Capture-C) that enables the physical contacts between different classes of regulatory elements to be determined at base-pair resolution. We find that highly punctate contacts occur between enhancers, promoters and CCCTC-binding factor (CTCF) sites and we show that transcription factors have an important role in the maintenance of the contacts between enhancers and promoters. Our data show that interactions between CTCF sites are increased when active promoters and enhancers are located within the intervening chromatin. This supports a model in which chromatin loop extrusion1 is dependent on cohesin loading at active promoters and enhancers, which explains the formation of tissue-specific chromatin domains without changes in CTCF binding.