Ontology highlight
ABSTRACT:
PROVIDER: PRJNA757741 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR15610871_1.fastq.gz | Fastqsanger.gz | |||
| SRR15610871_2.fastq.gz | Fastqsanger.gz | |||
| SRR15610872_1.fastq.gz | Fastqsanger.gz | |||
| SRR15610872_2.fastq.gz | Fastqsanger.gz | |||
| SRR15610873_1.fastq.gz | Fastqsanger.gz |
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Project description:Genome editing enables sequence-function profiling of endogenous cis-regulatory elements, driving understanding of their mechanisms. However, these approaches lack direct, scalable readouts of chromatin accessibility across long single-molecule chromatin fibers. Here we leverage double-stranded DNA cytidine deaminases to profile chromatin accessibility at endogenous loci of interest through targeted PCR and long-read sequencing, a method we term targeted deaminase-accessible chromatin sequencing (TDAC-seq). With high sequence coverage at targeted loci, TDAC-seq can be integrated with CRISPR perturbations to link genetic edits and their effects on chromatin accessibility on the same single chromatin fiber at single-nucleotide resolution. We employed TDAC-seq to parse CRISPR edits that activate fetal hemoglobin in human CD34+ hematopoietic stem and progenitor cells (HSPCs) during erythroid differentiation as well as in pooled CRISPR and base-editing screens tiling an enhancer controlling the globin locus. We further scaled the method to interrogate 947 variants in a GFI1B-linked enhancer associated with myeloproliferative neoplasm risk in a single pooled CRISPR experiment in CD34+ HSPCs. Together, TDAC-seq enables high-resolution sequence-function mapping of single-molecule chromatin fibers by genome editing.
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Project description:Sequencing of CRISPR/Cas9 genome edits at imprinted genes
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Project description:Genome editing enables sequence-function profiling of endogenous cis-regulatory elements, driving understanding of their mechanisms. However, these approaches lack direct, scalable readouts of chromatin accessibility across long single-molecule chromatin fibers. Here we leverage double-stranded DNA cytidine deaminases to profile chromatin accessibility at endogenous loci of interest through targeted PCR and long-read sequencing, a method we term targeted deaminase-accessible chromatin sequencing (TDAC-seq). With high sequence coverage at targeted loci, TDAC-seq can be integrated with CRISPR perturbations to link genetic edits and their effects on chromatin accessibility on the same single chromatin fiber at single-nucleotide resolution. We employed TDAC-seq to parse CRISPR edits that activate fetal hemoglobin in human CD34+ hematopoietic stem and progenitor cells (HSPCs) during erythroid differentiation as well as in pooled CRISPR and base-editing screens tiling an enhancer controlling the globin locus. We further scaled the method to interrogate 947 variants in a GFI1B-linked enhancer associated with myeloproliferative neoplasm risk in a single pooled CRISPR experiment in CD34+ HSPCs. Together, TDAC-seq enables high-resolution sequence-function mapping of single-molecule chromatin fibers by genome editing.
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Project description:The development of modern genome editing and DNA synthesis has enabled researchers to edit DNA sequences with high precision but has left unsolved the problem of designing these edits. We introduce Ledidi, a computational method that rephrases the discrete design task of choosing which edits to make as an easily solvable continuous optimization problem. Ledidi can use any pre-trained deep learning model to guide the optimization, yielding an edited sequence that exhibits the desired outcome while explicitly minimizing the number of edits. When applied in dozens of settings, we find that Ledidi's designs can precisely control transcription factor binding, chromatin accessibility, transcription, and enhancer activity in silico. By using several deep learning models simultaneously, we design cell type-specific enhancers and experimentally validate them in cellulo. Finally, we introduce the concept of an "affinity catalog'', where the design task is repeated multiple times across continuous variants of the design target. We demonstrate how these catalogs can be used to interpret deep learning models and the impact of starting template sequences, and also to design regulatory elements that control transcriptional dosage in a cell type-specific fashion.
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Project description:Although vast numbers of putative gene regulatory elements have been cataloged, the sequence motifs and individual bases that underlie their functions remain largely unknown. Here we combine deep learning, epigenetic perturbations and base editing to dissect regulatory sequences within the exemplar immune locus encoding CD69. Focusing on a differentially accessible and acetylated upstream enhancer, we find that the complementary strategies converge on a ~150 base interval as critical for CD69 induction in stimulated Jurkat T cells. We pinpoint individual cytosine to thymine base edits that markedly reduce element accessibility and acetylation, with corresponding reduction of CD69 expression. The most potent base edits may be explained by their effect on binding competition between the transcriptional activator GATA3 and the repressor BHLHE40. Systematic analysis of GATA3 and BHLHE40 binding suggests that interplay between these factors plays a general role in rapid T cell transcriptional responses. Our study provides a framework for parsing gene regulatory elements in their endogenous chromatin contexts and identifying operative engineered variants.
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