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TooManyPeaks identifies drug-resistant-specific regulatory elements from single-cell leukemic epigenomes [ATAC-seq]

ABSTRACT: TooManyPeaks identifies drug-resistant-specific regulatory elements from single-cell leukemic epigenomes [ATAC-seq]

PROVIDER: PRJNA656191 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR12415917_1.fastq.gz	Fastqsanger.gz
	SRR12415917_2.fastq.gz	Fastqsanger.gz
	SRR12415918_1.fastq.gz	Fastqsanger.gz
	SRR12415918_2.fastq.gz	Fastqsanger.gz
	SRR12415919_1.fastq.gz	Fastqsanger.gz

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TooManyPeaks identifies drug-resistant-specific regulatory elements from single-cell leukemic epigenomes [ChIP-seq]

Project description:TooManyPeaks identifies drug-resistant-specific regulatory elements from single-cell leukemic epigenomes [ChIP-seq]

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TooManyPeaks identifies drug-resistant-specific regulatory elements from single-cell leukemic epigenomes.

Project description:Emerging single-cell epigenomic assays are used to investigate the heterogeneity of chromatin activity and its function. However, identifying cells with distinct regulatory elements and clearly visualizing their relationships remains challenging. To this end, we introduce TooManyPeaks to address the need for the simultaneous study of chromatin state heterogeneity in both rare and abundant subpopulations. Our analyses of existing data from three widely used single-cell assays for transposase-accessible chromatin using sequencing (scATAC-seq) show the superior performance of TooManyPeaks in delineating and visualizing pure clusters of rare and abundant subpopulations. Furthermore, the application of TooManyPeaks to new scATAC-seq data from drug-naive and drug-resistant leukemic T cells clearly visualizes relationships among these cells and stratifies a rare "resistant-like" drug-naive sub-clone with distinct cis-regulatory elements.

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TooManyPeaks identifies drug-resistant-specific regulatory elements from single-cell leukemic epigenomes [ATAC-seq]

Project description:Purpose: To characterize epigenomic heterogeneity in GSI-responsive and -resistant T-ALL cells Method: We generated GSI-resistant T-ALL DND41 cells and profiled their epigenome using single-cell RNA sequencing. Result: We report here the presence of resistant-like subpopulation in drug naïve DND41 T-ALL cells. Conclusion: While other popular scRNA-seq clustering algorithms failed, TooManyPeaks, a suite of clustering and visualization algorithm developed in our lab, could identify rare resistant-like DND41 T-ALL cells.

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TooManyPeaks identifies drug-resistant-specific regulatory elements from single-cell leukemic epigenomes [ChIP-seq]

Project description:Purpose: To investigate the impact of GSI resistance on epigenome of Notch-mutated T-ALL Methods: We generated scATAC-seq and H3K27ac ChIP-seq in parental and GSI-resistant cells Results: Using TooManyPeaks, a novel algorithm for scATAC-seq analysis, we identified subpopulation of epigenetically resistant-like parental cell.

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