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Systematic comparison of single-cell and single-nucleus transcriptomes during cardiomyocyte differentiation

ABSTRACT: Systematic comparison of single-cell and single-nucleus transcriptomes during cardiomyocyte differentiation

PROVIDER: PRJNA530131 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR8822550_1.fastq.gz	Fastqsanger.gz
	SRR8822550_2.fastq.gz	Fastqsanger.gz
	SRR8822560_1.fastq.gz	Fastqsanger.gz
	SRR8822560_2.fastq.gz	Fastqsanger.gz
	SRR8822570_1.fastq.gz	Fastqsanger.gz

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Systematic Comparison of High-throughput Single-Cell and Single-Nucleus Transcriptomes during Cardiomyocyte Differentiation.

Project description:A comprehensive reference map of all cell types in the human body is necessary for improving our understanding of fundamental biological processes and in diagnosing and treating disease. High-throughput single-cell RNA sequencing techniques have emerged as powerful tools to identify and characterize cell types in complex and heterogeneous tissues. However, extracting intact cells from tissues and organs is often technically challenging or impossible, for example in heart or brain tissue. Single-nucleus RNA sequencing provides an alternative way to obtain transcriptome profiles of such tissues. To systematically assess the differences between high-throughput single-cell and single-nuclei RNA-seq approaches, we compared Drop-seq and DroNc-seq, two microfluidic-based 3' RNA capture technologies that profile total cellular and nuclear RNA, respectively, during a time course experiment of human induced pluripotent stem cells (iPSCs) differentiating into cardiomyocytes. Clustering of time-series transcriptomes from Drop-seq and DroNc-seq revealed six distinct cell types, five of which were found in both techniques. Furthermore, single-cell trajectories reconstructed from both techniques reproduced expected differentiation dynamics. We then applied DroNc-seq to postmortem heart tissue to test its performance on heterogeneous human tissue samples. Our data confirm that DroNc-seq yields similar results to Drop-seq on matched samples and can be successfully used to generate reference maps for the human cell atlas.

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Systematic comparison of single-cell and single-nucleus transcriptomes during cardiomyocyte differentiation

Project description:Purpose:To systematically assess the differences between high-throughput single-cell and single-nuclei RNA-seq approaches, we compared Drop-seq and DroNc-seq, two microfluidic-based 3’ RNA capture technologies that profile total cellular and nuclear RNA, respectively, during a time course experiment of human induced pluripotent stem cells (iPSCs) differentiating into cardiomyocytes Conclusions: Clustering of time-series transcriptomes from Drop-seq and DroNc-seq revealed six distinct cell types, five of which were found in both techniques. Furthermore, single-cell trajectories reconstructed from both techniques reproduced expected differentiation dynamics.

2019-04-02 | GSE129096 | GEO

Cardiomyocyte Dedifferentiation and Proliferation Revealed by Massive Parallel Single-Nucleus Transciprotmic Analysis in Adult Mouse Hearts

Project description:We have demonstrated previously that adult cardiomyocytes can dedifferentiate and proliferate when cultured in vitro. To determine if cardiomyocyte dedifferentiation and cell cycling/proliferation happens in vivo, we applied here a novel multi-reporter transgenic mouse model (aMH-CMerCreMer;mT/MG;aMHC-H2BBFP) carrying reporter genes for permanent cardiomyocyte lineage mapping and maturity (dedifferentiation) reporting. With this new model, we deciphered the cellular sources and processes of cardiomyocyte dedifferentiation and proliferation in adult hearts. In this study, we used single-nucleus RNA-sequencing to tackle the challenges in analyzing the highly heterogeneous heart cell populations, and obtained datasets for a large number of cardiac single nuclei (both myocytes and non-myocytes) for control and post-infarct hearts. We identified specific cell populations in the heart using distinct transcriptomic clusters, transgenic reporters for ACM lineage and dedifferentiation, as well as cell cycle markers. The results demonstrated that the dedifferentiation and cell cycle progression of pre-existing CMs was augmented in post-infarct hearts, with a number of signaling pathways and gene sets affected. This is the first study dissecting the transcriptomic profiles and signaling pathways associated with cardiomyocyte dedifferentiation and cycling/proliferation in vivo using unbiased high-throughput single-nucleus RNA-Seq analysis, in junction with novel cell lineage (e.g. cardiomyocyte) and phenotyping (e.g. dedifferentiation) transgenic model systems.

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