Single-cell RNA sequencing to identify cellular heterogeneity and targets in cardiovascular diseases: from bench to bedside

Xu, Xinjie; Hua, Xiumeng; Han, Mei; Hu, Shengshou; Song, Jiangping

doi:10.1007/s00395-022-00972-1

Cited by 15 publications

(6 citation statements)

References 193 publications

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“…These independent or combined "single" technologies have helped us not only to create a high-resolution SC type map of human tissues [127] and three-dimensional nuclear architecture, gene expression, and epigenetic modifications, [128] but also to assess and analyze immune cell differentiation, [129,130] to uncover cellular and molecular mechanisms and to discover new therapeutic targets for human diseases, [49,[131][132][133] in particular COVID-19 and CVDs, as well as to trace cancer progression. [134] It can be said that this is indeed a new "single" era of biomedicine and its current implications and research in both CVDs and COVID-19 [135][136][137][138][139][140][141][142][143][144][145][146][147][148][149][150] as well as others will definitely open a new beautiful future.…”

Section: Cavdmentioning

confidence: 99%

A new "single" era of biomedicine and implications in disease research

2023

J Bio-X Res

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Section: Cavdmentioning

confidence: 99%

A new "single" era of biomedicine and implications in disease research

2023

J Bio-X Res

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“…Single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (RNA-seq) have emerged as essential techniques for exploring cellular heterogeneity, differentiation, and disease mechanisms [1][2][3][4][5][6] . These technologies facilitate numerous applications, including the conversion of bulk-seq data into single-seq analyses 7 , performing differential expression analysis 8 , pathway enrichment 9 , gene co-expression network analysis in bulk RNA-seq 10 , cell annotation 11 , cell interaction analysis 12 , celltrajectory inference 13 , evaluation of cell-state in gene sets, and prediction of drug response in scRNA-seq 14 .…”

Section: Mainmentioning

confidence: 99%

OmicVerse: A single pipeline for exploring the entire transcriptome universe

Zeng¹,

Ma²,

Hu³

et al. 2023

Preprint

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Single-cell sequencing often results in cellular "interruptions" due to limitations in sequencing throughput, while bulk RNA-seq may contain these potential "interrupted" cells. Here, we propose the BulkTrajBlend algorithm in OmicVerse that combines Beta-Variational AutoEncoder for deconvolution and graph neural networks for overlapping community discovery to effectively interpolate and restore the continuity of "interrupted" cells in the original scRNA-seq data. Additionally, OmicVerse serves as a comprehensive tool, providing standardized and consistent access to a wide range of methods for bulk and single cell RNA-seq analysis, enhancing computational efficiency and enabling beautiful visualization, and helping researchers to explore the transcriptomic universe, unravel novel biological insights, and propel scientific discoveries.

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“…Moreover, based on the differentially expressed genes of the cluster, activated pathways can be statistically determined using specific algorithms, such as gene set enrichment analysis (GSEA) (19). A growing number of studies implement scRNA-seq to accelerate the understanding of different disorders (20)(21)(22). Furthermore, it also facilitates drug development and transition from the bench to the bedside (20,21).…”

Section: Introductionmentioning

confidence: 99%

“…A growing number of studies implement scRNA-seq to accelerate the understanding of different disorders (20)(21)(22). Furthermore, it also facilitates drug development and transition from the bench to the bedside (20,21). In this study, we have delved into the transcriptomic landscape of immune cells in sepsis through scRNA-seq analysis.…”

Section: Introductionmentioning

confidence: 99%

Transcriptomic profiling of immune cells in murine polymicrobial sepsis

Murao,

Jha,

Aziz

et al. 2024

Front. Immunol.

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IntroductionVarious immune cell types play critical roles in sepsis with numerous distinct subsets exhibiting unique phenotypes even within the same cell population. Single-cell RNA sequencing (scRNA-seq) enables comprehensive transcriptome profiling and unbiased cell classification. In this study, we have unveiled the transcriptomic landscape of immune cells in sepsis through scRNA-seq analysis.MethodsWe induced sepsis in mice by cecal ligation and puncture. 20 h after the surgery, the spleen and peritoneal lavage were collected. Single-cell suspensions were processed using a 10× Genomics pipeline and sequenced on an Illumina platform. Count matrices were generated using the Cell Ranger pipeline, which maps reads to the mouse reference transcriptome, GRCm38/mm10. Subsequent scRNA-seq analysis was performed using the R package Seurat.ResultsAfter quality control, we subjected the entire data set to unsupervised classification. Four major clusters were identified as neutrophils, macrophages, B cells, and T cells according to their putative markers. Based on the differentially expressed genes, we identified activated pathways in sepsis for each cell type. In neutrophils, pathways related to inflammatory signaling, such as NF-κB and responses to pathogen-associated molecular patterns (PAMPs), cytokines, and hypoxia were activated. In macrophages, activated pathways were the ones related to cell aging, inflammatory signaling, and responses to PAMPs. In B cells, pathways related to endoplasmic reticulum stress were activated. In T cells, activated pathways were the ones related to inflammatory signaling, responses to PAMPs, and acute lung injury. Next, we further classified each cell type into subsets. Neutrophils consisted of four clusters. Some subsets were activated in inflammatory signaling or cell metabolism, whereas others possessed immunoregulatory or aging properties. Macrophages consisted of four clusters, namely, the ones with enhanced aging, lymphocyte activation, extracellular matrix organization, or cytokine activity. B cells consisted of four clusters, including the ones possessing the phenotype of cell maturation or aging. T cells consisted of six clusters, whose phenotypes include molecular translocation or cell activation.ConclusionsTranscriptomic analysis by scRNA-seq has unveiled a comprehensive spectrum of immune cell responses and distinct subsets in the context of sepsis. These findings are poised to enhance our understanding of sepsis pathophysiology, offering avenues for targeting novel molecules, cells, and pathways to combat infectious diseases.

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Single-cell RNA sequencing to identify cellular heterogeneity and targets in cardiovascular diseases: from bench to bedside

Cited by 15 publications

References 193 publications

A new "single" era of biomedicine and implications in disease research

A new "single" era of biomedicine and implications in disease research

OmicVerse: A single pipeline for exploring the entire transcriptome universe

Transcriptomic profiling of immune cells in murine polymicrobial sepsis

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