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FD-seq: Droplet-based RNA sequencing of fixed single cells

ABSTRACT: FD-seq: Droplet-based RNA sequencing of fixed single cells

PROVIDER: PRJNA659731 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR12534258_1.fastq.gz	Fastqsanger.gz
	SRR12534258_2.fastq.gz	Fastqsanger.gz
	SRR12534259_1.fastq.gz	Fastqsanger.gz
	SRR12534259_2.fastq.gz	Fastqsanger.gz
	SRR12534260_1.fastq.gz	Fastqsanger.gz

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FD-seq: Droplet-based RNA sequencing of fixed single cells

Project description:Current high-throughput single-cell RNA sequencing (scRNA-seq) methods are incompatible with paraformaldehyde (PFA) fixation, a common cell/tissue preservation and viral inactivation technique, which has prevented transcriptomic analysis of rare cells that require protein staining and enrichment. Here we present FD-seq, a method for high-throughput RNA sequencing of PFA-fixed, stained and FACS-sorted single cells. We used FD-seq to address two important questions in virology. First, by analyzing a rare population of cells supporting herpesvirus reactivation, we identified TMEM119 to mediate reactivation of Kaposi’s sarcoma-associated herpesvirus (KSHV), a tumor virus. Second, we studied the innate immune activation in cells infected with the coronavirus OC43, which causes the common cold and also serves as a safer model pathogen for SARS-CoV-2 studies. We found that pro-inflammatory cytokine induction, which is a major contributor to the severe pathogenicity in SARS-CoV-2-infected individuals, is primarily driven by uninfected or lowly infected bystander cells that are exposed to the virus but fail to express high level of viral genes. FD-seq is a simple method that is suitable for characterizing the transcriptome of rare cell populations of interest, and for studying high-containment biological samples such as SARS-CoV-2-infected cells after PFA inactivation.

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Cell fixation and preservation for droplet-based single cell transcriptomics

Project description:Background: Recent developments in droplet-based microfluidics allow the transcriptional profiling of thousands of individual cells, in a quantitative, highly parallel and cost-effective way. A critical, often limiting step is the preparation of cells in an unperturbed state, not compromised by stress or ageing. Another challenge are rare cells that need to be collected over several days, or samples prepared at different times or locations. Results: Here, we used chemical fixation to overcome these problems. Methanol fixation allowed us to stabilize and preserve dissociated cells for weeks. By using mixtures of fixed human and mouse cells, we showed that individual transcriptomes could be confidently assigned to one of the two species. Single-cell gene expression from live and fixed samples correlated well with bulk mRNA-seq data. We then applied methanol fixation to transcriptionally profile primary single cells from dissociated complex tissues. Low RNA content cells from Drosophila embryos, as well as mouse hindbrain and cerebellum cells sorted by FACS, were successfully analysed after fixation, storage and single-cell droplet RNA-seq. We were able to identify diverse cell populations, including neuronal subtypes. As an additional resource, we provide 'dropbead', an R package for exploratory data analysis, visualization and filtering of Drop-seq data. Conclusions: We expect that the availability of a simple cell fixation method will open up many new opportunities in diverse biological contexts to analyse transcriptional dynamics at single cell resolution.

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Project description:Single-cell transcriptomics methods have become very popular to study the cellular composition of organs and tissues and characterize the expression profiles of the individual cells that compose them. The main critical step for single-cell transcriptomics methods is sample preparation. Several methods have been developed to preserve cells after sample dissociation to uncouple sample handling from library preparation. Yet, the suitability of these methods depends on the types of cells to be processed. In this project, we perform a systematic comparison of preservation methods for droplet-based single-cell RNA-seq (scRNA-seq) on human neural progenitor cell populations derived from induced pluripotent stem cells (iPSCs) and highlight their strengths and weaknesses. We compared the cellular composition and expression profile of single-cell suspensions from fresh NPCs with that of NPCs preserved with Dimethyl Sulfoxide (DMSO), Methanol, vivoPHIX and Acetil-methanol (ACME). Our results show that while DMSO provides the highest cell quality in terms of RNA molecules and genes detected per cell. Yet, it strongly affects the cellular composition and the expression profile of the resulting datasets. In contrast, methanol fixed samples display a cellular composition like that of fresh samples while providing a good cell quality and smaller expression biases. Taken together, our results show that methanol fixation is the method of choice for performing droplet-based single-cell transcriptomics experiments on neural cell populations.

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