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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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Comparison of single-nucleus and single-cell transcriptomes in hepatocellular carcinoma tissue

Project description:Comparison of single-nucleus and single-cell transcriptomes in hepatocellular carcinoma tissue

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Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows

Project description:Background: Single-cell RNA sequencing has been widely adopted to estimate the cellular composition of heterogeneous tissues and obtain transcriptional profiles of individual cells. Multiple approaches for optimal sample dissociation and storage of single cells have been proposed as have single-nuclei profiling methods. What has been lacking is a systematic comparison of their relative biases and benefits. Results: Here, we compare gene expression and cellular composition of single cell suspensions prepared from adult mouse kidney using two tissue dissociation protocols. For each sample we also compare fresh cells to cryopreserved and methanol-fixed cells. Lastly, we compare this single-cell data to that generated using three single-nucleus RNA sequencing workflows. Our data confirms prior reports that digestion on ice avoids the stress response observed with 37°C dissociation. It also reveals cell types more abundant either in the cold or warm dissociations that may represent populations that require gentler or harsher conditions to be released intact. For cell storage, cryopreservation of dissociated cells results in a major loss of epithelial cell types; in contrast, methanol fixation maintains the cellular composition but suffers from ambient RNA leakage. Finally, cell type composition differences are observed between single-cell and single-nucleus RNA sequencing libraries. In particular, we note an under-representation of T, B and NK lymphocytes in the single-nucleus libraries. Conclusions: Systematic comparison of recovered cell types and their transcriptional profiles across the workflows has highlighted protocol-specific biases and thus enables researchers starting single cell experiments to make an informed choice.

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Comparison of single‑nucleus and single‑cell transcriptomes in hepatocellular carcinoma tissue.

Project description:Single‑nucleus RNA sequencing (snRNA‑seq) is a method used to analyze gene expression in cells for which isolation is complex, such as those in hepatocellular carcinoma (HCC) tissues. It constitutes an alternative to single‑cell RNA sequencing (scRNA‑seq) by analyzing the nucleus rather than the whole cell; however, whether it can completely replace scRNA‑seq in HCC remains to be clarified. In the present study, scRNA‑seq was compared with snRNA‑seq in tumor tissue obtained from patients with HCC, using the 10X Genomics Chromium platform. Seurat was also used to process the data and compare the differences between the two sequencing methods in identifying different cell types. In the present study, the transcriptomes of 14,349 single nuclei and 9,504 single cells were obtained from the aforementioned HCC tissue. A total of 21 discrete cell clusters, including hepatocytes, endothelial cells, fibroblasts, B cells, T cells, natural killer cells and macrophages were identified. Notably, a high number of hepatocytes were detected using snRNA‑seq, while an increased number of immunocytes were identified in the tumor microenvironment using scRNA‑seq. Results of the present study provided a comprehensive image of human HCC at a single‑cell resolution. Moreover, results of the present study further demonstrated that snRNA‑seq may be adequate in replacing scRNA‑seq in certain cases, and snRNA‑seq performs at an improved level in hepatocyte sequencing. Combined use of the two sequencing methods may contribute to the study of intercellular interactions.

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Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows

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Single nucleus transcriptomics: Apical resection in newborn pigs extends the time-window of cardiomyocyte proliferation and myocardial regeneration

Project description:Background: In this study, we have extended the myocardial regenerative window to postnatal day 28 (P28) by a double injury pig model (ARP1MIP28) of apical resection at P1 (ARP1) and LAD ligation at P28 (MIP28). However, the molecular mechanism of regenerative window extension is not known. Results: Gene-expression profiles revealed that regenerating ARP1MIP28 hearts contained different subpopulations of cardiomyocytes early after 2nd injury. The six cardiomyocyte clusters were identified (CM1-CM6) by Louvain clustering. The CM6, CM3, and CM2 clusters have mainly consisted of fetal (92.4%), CTL-P1 (95.7%), and CTL-P56 (96.2%) cardiomyocytes. In contrast, the CM1 cluster has consisted of heterogeneous cardiomyocyte groups from all other animal groups, including less than 1% of fetal and CTL-P1 cardiomyocytes. The CM5 and CM4 clusters have mainly consisted of ARP1MIP28-P35 cardiomyocytes, 97.8%, and 68.6%, respectively. Sparse modeling analysis revealed that AR and MI injury, both alone and in combination, appeared to promote the proliferative capacity of cardiomyocytes and perturb cardiomyocyte maturation. Publication: This dataseries is associated to manuscript titled 'Single nucleus transcriptomics: Apical resection in newborn pigs extends the time-window of cardiomyocyte proliferation and myocardial regeneration', published in Circulation, 2021.

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Single-nucleus RNA-Seq

Project description:Single-nucleus RNA-Seq

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