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Single-cell, multi-omic analysis identifies regulatory programs in mixed phenotype acute leukemia

ABSTRACT: Single-cell, multi-omic analysis identifies regulatory programs in mixed phenotype acute leukemia

PROVIDER: PRJNA579391 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR10343100.fastq.gz	Fastqsanger.gz
	SRR10343100_1.fastq.gz	Fastqsanger.gz
	SRR10343100_2.fastq.gz	Fastqsanger.gz
	SRR10343101.fastq.gz	Fastqsanger.gz
	SRR10343101_1.fastq.gz	Fastqsanger.gz

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Single-cell, multi-omic analysis identifies regulatory programs in mixed phenotype acute leukemia

Project description:We present a single-cell framework that integrates highly multiplexed protein quantification, transcriptome profiling, and chromatin accessibility analysis. Using this approach, we establish a normal epigenetic baseline for healthy blood development, which we then use to deconvolve aberrant molecular features within blood from mixed-phenotype acute leukemia (MPAL) patients.

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Project description:Identifying the causes of human diseases requires deconvolution of abnormal molecular phenotypes spanning DNA accessibility, gene expression and protein abundance1-3. We present a single-cell framework that integrates highly multiplexed protein quantification, transcriptome profiling and analysis of chromatin accessibility. Using this approach, we establish a normal epigenetic baseline for healthy blood development, which we then use to deconvolve aberrant molecular features within blood from patients with mixed-phenotype acute leukemia4,5. Despite widespread epigenetic heterogeneity within the patient cohort, we observe common malignant signatures across patients as well as patient-specific regulatory features that are shared across phenotypic compartments of individual patients. Integrative analysis of transcriptomic and chromatin-accessibility maps identified 91,601 putative peak-to-gene linkages and transcription factors that regulate leukemia-specific genes, such as RUNX1-linked regulatory elements proximal to the marker gene CD69. These results demonstrate how integrative, multiomic analysis of single cells within the framework of normal development can reveal both distinct and shared molecular mechanisms of disease from patient samples.

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