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Single-cell analysis of non-myocytes during heart regeneration reveals sequential coordinated responses from distinct cell populations.

ABSTRACT: Single-cell analysis of non-myocytes during heart regeneration reveals sequential coordinated responses from distinct cell populations.

PROVIDER: PRJNA608906 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR11186408_1.fastq.gz	Fastqsanger.gz
	SRR11186408_2.fastq.gz	Fastqsanger.gz
	SRR11186409_1.fastq.gz	Fastqsanger.gz
	SRR11186409_2.fastq.gz	Fastqsanger.gz
	SRR11186410_1.fastq.gz	Fastqsanger.gz

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Single-cell analysis of non-myocytes during heart regeneration reveals sequential coordinated responses from distinct cell populations [scRNA-seq]

Project description:Single-cell analysis of non-myocytes during heart regeneration reveals sequential coordinated responses from distinct cell populations [scRNA-seq]

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Single-cell analysis of non-myocytes during heart regeneration reveals sequential coordinated responses from distinct cell populations [bulk RNA-seq]

Project description:Single-cell analysis of non-myocytes during heart regeneration reveals sequential coordinated responses from distinct cell populations [bulk RNA-seq]

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Single-cell analysis of non-myocytes during heart regeneration reveals sequential coordinated responses from distinct cell populations [scRNA-seq]

Project description:Cardiac regeneration occurs primarily through proliferation of existing cardiomyocytes, yet the regenerative response also involves complex interactions between distinct cardiac cell types including not only cardiomyocytes, but also non-cardiomyocytes (nonCMs). However, the molecular features and cellular functions of the highly heterogeneous populations of nonCMs and how these populations cooperate to regenerate the injured heart remain largely unexplored. Using the newly developed LIGER algorithm that allows flexible modeling across highly diverse single-cell datasets, we analyzed the transcriptome dynamics of 61,977 individual nonCMs isolated at multiple time points during zebrafish heart regeneration. Combining single-cell analysis and in situ hybridization, we identified major nonCM cell types, including multiple novel subpopulations with unique tempo-spatial distributions and highly cooperative interactions in the regenerating heart. Interestingly, genetic perturbation of macrophage function by kit knockout led to accumulation of fibrotic deposits and severely compromised cardiomyocyte proliferation and myocardium regeneration. Our single-cell transcriptomic analysis of nonCMs during cardiac regeneration provides a blueprint for interrogating the molecular and cellular basis of cardiac regeneration.

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Single-cell analysis of non-myocytes during heart regeneration reveals sequential coordinated responses from distinct cell populations.

Project description:This SuperSeries is composed of the SubSeries listed below.

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Single-cell analysis of non-myocytes during heart regeneration reveals sequential coordinated responses from distinct cell populations [bulk RNA-seq]

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Proteogenomic single cell analysis of skeletal muscle myocytes

Project description:Proteogenomic single cell analysis of skeletal muscle myocytes

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Sequential dendritic cell activation with innate and adaptive signals

Project description:How sequential stimulation regulates immune cell function remain largely unexplored. We used human primary dendritic cells as a cellular model to asses the impact of sequential activation on cell function at the systems level. We found that innate priming modified the response to sequential activation by CD40L at the transcriptional and functional level. Moreover, we found that sequential activation modulted DC diversity.

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Project description:Background: Skeletal muscle myocytes have evolved into slow and fast-twitch types. These types are functionally distinct as a result of differential gene and protein expression. However, an understanding of the complexity of gene and protein variation between myofibers is unknown. Methods: We performed deep, whole cell, single cell RNA-seq on intact and fragments of skeletal myocytes from the mouse flexor digitorum brevis muscle. We compared the genomic expression data of 171 of these cells with two human proteomic datasets. The first was a spatial proteomics survey of mosaic patterns of protein expression utilizing the Human Protein Atlas (HPA) and the HPASubC tool. The second was a mass-spectrometry (MS) derived proteomic dataset of single human muscle fibers. Immunohistochemistry and RNA-ISH were used to understand variable expression. Results: scRNA-seq identified three distinct clusters of myocytes (a slow/fast 2A cluster and two fast 2X clusters). Utilizing 1,605 mosaic patterned proteins from visual proteomics, and 596 differentially expressed proteins by MS methods, we explore this fast 2X division. Only 36 genes/proteins were mosaic across all three studies, of which nine are newly described as variable between fast/slow twitch myofibers. An additional 414 genes/proteins were identified by two methods. Immunohistochemistry and RNA-ISH generally validated variable expression across methods presumably due to species-related differences. Conclusions: In this first integrated proteogenomic analysis of mature skeletal muscle myocytes we validate the main fiber types and greatly expand the known repertoire of twitch-type specific genes/proteins. We also demonstrate the importance of integrating genomic and proteomic datasets.

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