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High-throughput combinatorial indexing enables scalable single-cell chromatin accessibility profiling [Blood]

ABSTRACT: High-throughput combinatorial indexing enables scalable single-cell chromatin accessibility profiling [Blood]

PROVIDER: PRJNA509252 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR8310530_1.fastq.gz	Fastqsanger.gz
	SRR8310530_2.fastq.gz	Fastqsanger.gz
	SRR8310540_1.fastq.gz	Fastqsanger.gz
	SRR8310540_2.fastq.gz	Fastqsanger.gz
	SRR8310550_1.fastq.gz	Fastqsanger.gz

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Project description:Technical advances have enabled the collection of genome and transcriptome data sets with single-cell resolution. However, single-cell characterization of the epigenome has remained challenging. Furthermore, because cells must be physically separated prior to biochemical processing, conventional single-cell preparatory methods scale linearly. We applied combinatorial cellular indexing to measure chromatin accessibility in thousands of single cells per assay, circumventing the need for compartmentalization of individual cells. We report chromatin accessibility profiles from over 15,000 single cells and use these data to cluster cells on the basis of chromatin accessibility landscapes. We identify modules of coordinately regulated chromatin accessibility at the level of single cells both between and within cell types, with a scalable method that may accelerate progress toward a human cell atlas. 3 replicates from GM12878 and HL-60 cell lines collected for differential gene expression analysis.

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Project description:Cell atlas projects and high-throughput perturbation screens require single-cell sequencing at a scale that is challenging with current technology. To enable cost-effective single-cell sequencing for millions of individual cells, we developed “single-cell combinatorial fluidic indexing” (scifi). The scifi-RNA-seq assay combines one-step combinatorial pre-indexing of entire transcriptomes inside permeabilized cells with subsequent single-cell RNA-seq using microfluidics. Pre-indexing allows us to load multiple cells per droplet and bioinformatically demultiplex their individual expression profiles. Thereby, scifi-RNA-seq massively increases the throughput of droplet-based single-cell RNA-seq, and it provides a straightforward way of multiplexing thousands of samples in a single experiment. Compared to multi-round combinatorial indexing, scifi-RNA-seq provides an easier, faster, and more efficient workflow. In contrast to cell hashing methods, which flag and discard droplets containing more than one cell, scifi-RNA-seq resolves and retains individual transcriptomes from overloaded droplets.

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