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SpinDrop: a modular droplet microfluidic platform to maximise single-cell RNA sequencing information content

ABSTRACT: spinDrop: a modular droplet microfluidic platform to maximise single-cell RNA sequencing information content

PROVIDER: PRJNA858488 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR20172227.fastq.gz	Fastqsanger.gz
	SRR20172228.fastq.gz	Fastqsanger.gz
	SRR20172229.fastq.gz	Fastqsanger.gz
	SRR20172230.fastq.gz	Fastqsanger.gz
	SRR20172231.fastq.gz	Fastqsanger.gz

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spinDrop: a modular droplet microfluidic platform to maximise single-cell RNA sequencing information content

Project description:Droplet microfluidic methods have massively increased the throughput of single-cell RNA sequencing campaigns. The benefit of scale-up is, however, accompanied by increased background noise when processing challenging samples as well as lower overall RNA capture efficiency. These drawbacks stem from the lack of strategies to enrich for high-quality material at the moment of cell encapsulation and the lack of implementable multi-step enzymatic processes that increase RNA capture. Here we alleviate both bottlenecks by deploying fluorescence-activated droplet sorting to enrich for droplets that contain single viable cells, intact nuclei or fixed cells and use reagent addition to droplets by picoinjection to perform multi-step lysis and reverse transcription. Our methodology increases gene detection rates fivefold, while reducing background noise by up to half, depending on sample quality. We harness these unique properties to deliver a high-quality molecular atlas of mouse brain development using highly damaged input material. Our method is broadly applicable to other droplet-based workflows to deliver sensitive and accurate single-cell profiling at a reduced cost.

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spinDrop: a droplet microfluidic platform to maximise single-cell sequencing information content.

Project description:Droplet microfluidic methods have massively increased the throughput of single-cell sequencing campaigns. The benefit of scale-up is, however, accompanied by increased background noise when processing challenging samples and the overall RNA capture efficiency is lower. These drawbacks stem from the lack of strategies to enrich for high-quality material or specific cell types at the moment of cell encapsulation and the absence of implementable multi-step enzymatic processes that increase capture. Here we alleviate both bottlenecks using fluorescence-activated droplet sorting to enrich for droplets that contain single viable cells, intact nuclei, fixed cells or target cell types and use reagent addition to droplets by picoinjection to perform multi-step lysis and reverse transcription. Our methodology increases gene detection rates fivefold, while reducing background noise by up to half. We harness these properties to deliver a high-quality molecular atlas of mouse brain development, despite starting with highly damaged input material, and provide an atlas of nascent RNA transcription during mouse organogenesis. Our method is broadly applicable to other droplet-based workflows to deliver sensitive and accurate single-cell profiling at a reduced cost.

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