Cell shape determines gene expression: cardiomyocyte morphotypic transcriptomes

AbstractCardiomyocytes undergo considerable changes in cell shape. These can be due to hemodynamic constraints, including changes in preload and afterload conditions, or to mutations in genes important for cardiac function. These changes instigate significant changes in cellular architecture and lead to the addition of sarcomeres, at the same time or at a later stage. However, it is currently unknown whether changes in cell shape on their own affect gene expression and the aim of this study was to fill that gap in our knowledge. We developed a single-cell morphotyping strategy, followed by single-cell RNA sequencing, to determine the effects of altered cell shape in gene expression. This enabled us to profile the transcriptomes of individual cardiomyocytes of defined geometrical morphotypes and characterize them as either normal or pathological conditions. We observed that deviations from normal cell shapes were associated with significant downregulation of gene expression and deactivation of specific pathways, like oxidative phosphorylation, protein kinase A, and cardiac beta-adrenergic signaling pathways. In addition, we observed that genes involved in apoptosis of cardiomyocytes and necrosis were upregulated in square-like pathological shapes. Mechano-sensory pathways, including integrin and Src kinase mediated signaling, appear to be involved in the regulation of shape-dependent gene expression. Our study demonstrates that cell shape per se affects the regulation of the transcriptome in cardiac myocytes, an effect with possible implications for cardiovascular disease.

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The order and logic of CD4 versus CD8 lineage choice and differentiation in mouse thymus

Nature Communications ◽

10.1038/s41467-020-20306-w ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Mohammad M. Karimi ◽

Ya Guo ◽

Xiaokai Cui ◽

Husayn A. Pallikonda ◽

Veronika Horková ◽

...

Keyword(s):

Gene Expression ◽

T Cell ◽

Single Cell ◽

Signal Strength ◽

Gene Activity ◽

Cytotoxic T Cell ◽

Individual Selection ◽

Cell Lineages ◽

Single Cell Rna Sequencing ◽

Tcr Signals

AbstractCD4 and CD8 mark helper and cytotoxic T cell lineages, respectively, and serve as coreceptors for MHC-restricted TCR recognition. How coreceptor expression is matched with TCR specificity is central to understanding CD4/CD8 lineage choice, but visualising coreceptor gene activity in individual selection intermediates has been technically challenging. It therefore remains unclear whether the sequence of coreceptor gene expression in selection intermediates follows a stereotypic pattern, or is responsive to signaling. Here we use single cell RNA sequencing (scRNA-seq) to classify mouse thymocyte selection intermediates by coreceptor gene expression. In the unperturbed thymus, Cd4+Cd8a- selection intermediates appear before Cd4-Cd8a+ selection intermediates, but the timing of these subsets is flexible according to the strength of TCR signals. Our data show that selection intermediates discriminate MHC class prior to the loss of coreceptor expression and suggest a model where signal strength informs the timing of coreceptor gene activity and ultimately CD4/CD8 lineage choice.

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Integrated single‐cell RNA sequencing analyses suggest developmental paths of cancer‐associated fibroblasts with gene expression dynamics

Clinical and Translational Medicine ◽

10.1002/ctm2.487 ◽

2021 ◽

Vol 11 (7) ◽

Author(s):

Hee Chul Chung ◽

Eun Jeong Cho ◽

Hyeonjin Lee ◽

Won‐Kyung Kim ◽

Ji‐Hye Oh ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Cancer Associated Fibroblasts ◽

Single Cell Rna Sequencing ◽

Gene Expression Dynamics

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SCALE: modeling allele-specific gene expression by single-cell RNA sequencing

Genome Biology ◽

10.1186/s13059-017-1200-8 ◽

2017 ◽

Vol 18 (1) ◽

Cited By ~ 32

Author(s):

Yuchao Jiang ◽

Nancy R. Zhang ◽

Mingyao Li

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Specific Gene ◽

Specific Gene Expression ◽

Scale Modeling ◽

Single Cell Rna Sequencing ◽

Allele Specific

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Cryopreservation of microglia enables single-cell RNA sequencing with minimal effects on disease-related gene expression patterns

iScience ◽

10.1016/j.isci.2021.102357 ◽

2021 ◽

Vol 24 (4) ◽

pp. 102357

Author(s):

Brenda Morsey ◽

Meng Niu ◽

Shetty Ravi Dyavar ◽

Courtney V. Fletcher ◽

Benjamin G. Lamberty ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Expression Patterns ◽

Gene Expression Patterns ◽

Related Gene ◽

Single Cell Rna Sequencing ◽

Disease Related Gene

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Single-cell RNA sequencing of the mammalian pineal gland identifies two pinealocyte subtypes and cell type-specific daily patterns of gene expression

PLoS ONE ◽

10.1371/journal.pone.0205883 ◽

2018 ◽

Vol 13 (10) ◽

pp. e0205883 ◽

Cited By ~ 9

Author(s):

Joseph C. Mays ◽

Michael C. Kelly ◽

Steven L. Coon ◽

Lynne Holtzclaw ◽

Martin F. Rath ◽

...

Keyword(s):

Gene Expression ◽

Pineal Gland ◽

Single Cell ◽

Rna Sequencing ◽

Cell Type ◽

Single Cell Rna Sequencing ◽

Cell Type Specific ◽

Mammalian Pineal Gland ◽

Daily Patterns

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Bioinformatics analysis of the gene expression profile of retinal pigmental epithelial cells based in single-cell RNA sequencing in myopic mice

Archives of Medical Science ◽

10.5114/aoms/131835 ◽

2021 ◽

Vol 17 (2) ◽

pp. 574-577

Author(s):

Ya Mo ◽

Mu-Lin He ◽

Jia-Zhen Yu ◽

Xue-Jun Xie

Keyword(s):

Gene Expression ◽

Epithelial Cells ◽

Single Cell ◽

Rna Sequencing ◽

Gene Expression Profile ◽

Expression Profile ◽

Bioinformatics Analysis ◽

Single Cell Rna Sequencing

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Single-Cell RNA-Sequencing Identifies Activation of TP53 and STAT1 Pathways in Human T Lymphocyte Subpopulations in Response to Ex Vivo Radiation Exposure

International Journal of Molecular Sciences ◽

10.3390/ijms20092316 ◽

2019 ◽

Vol 20 (9) ◽

pp. 2316 ◽

Cited By ~ 4

Author(s):

Maria Moreno-Villanueva ◽

Ye Zhang ◽

Alan Feiveson ◽

Brandon Mistretta ◽

Yinghong Pan ◽

...

Keyword(s):

Gene Expression ◽

Cluster Analysis ◽

Dna Damage ◽

Radiation Exposure ◽

Single Cell ◽

T Lymphocyte ◽

Ex Vivo ◽

Lymphocyte Subpopulations ◽

Single Cell Rna Sequencing ◽

Sequencing Technique

Detrimental health consequences from exposure to space radiation are a major concern for long-duration human exploration missions to the Moon or Mars. Cellular responses to radiation are expected to be heterogeneous for space radiation exposure, where only high-energy protons and other particles traverse a fraction of the cells. Therefore, assessing DNA damage and DNA damage response in individual cells is crucial in understanding the mechanisms by which cells respond to different particle types and energies in space. In this project, we identified a cell-specific signature for radiation response by using single-cell transcriptomics of human lymphocyte subpopulations. We investigated gene expression in individual human T lymphocytes 3 h after ex vivo exposure to 2-Gy gamma rays while using the single-cell sequencing technique (10X Genomics). In the process, RNA was isolated from ~700 irradiated and ~700 non-irradiated control cells, and then sequenced with ~50 k reads/cell. RNA in each of the cells was distinctively barcoded prior to extraction to allow for quantification for individual cells. Principal component and clustering analysis of the unique molecular identifier (UMI) counts classified the cells into three groups or sub-types, which correspond to CD4+, naïve, and CD8+/NK cells. Gene expression changes after radiation exposure were evaluated using negative binomial regression. On average, BBC3, PCNA, and other TP53 related genes that are known to respond to radiation in human T cells showed increased activation. While most of the TP53 responsive genes were upregulated in all groups of cells, the expressions of IRF1, STAT1, and BATF were only upregulated in the CD4+ and naïve groups, but were unchanged in the CD8+/NK group, which suggests that the interferon-gamma pathway does not respond to radiation in CD8+/NK cells. Thus, single-cell RNA sequencing technique was useful for simultaneously identifying the expression of a set of genes in individual cells and T lymphocyte subpopulation after gamma radiation exposure. The degree of dependence of UMI counts between pairs of upregulated genes was also evaluated to construct a similarity matrix for cluster analysis. The cluster analysis identified a group of TP53-responsive genes and a group of genes that are involved in the interferon gamma pathway, which demonstrate the potential of this method for identifying previously unknown groups of genes with similar expression patterns.

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Microbial single-cell RNA sequencing by split-pool barcoding

Science ◽

10.1126/science.aba5257 ◽

2020 ◽

Vol 371 (6531) ◽

pp. eaba5257 ◽

Cited By ~ 2

Author(s):

Anna Kuchina ◽

Leandra M. Brettner ◽

Luana Paleologu ◽

Charles M. Roco ◽

Alexander B. Rosenberg ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

High Throughput ◽

Single Cell Analysis ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Growth Stages ◽

High Throughput Analysis ◽

Single Cell Rna Sequencing

Single-cell RNA sequencing (scRNA-seq) has become an essential tool for characterizing gene expression in eukaryotes, but current methods are incompatible with bacteria. Here, we introduce microSPLiT (microbial split-pool ligation transcriptomics), a high-throughput scRNA-seq method for Gram-negative and Gram-positive bacteria that can resolve heterogeneous transcriptional states. We applied microSPLiT to >25,000 Bacillus subtilis cells sampled at different growth stages, creating an atlas of changes in metabolism and lifestyle. We retrieved detailed gene expression profiles associated with known, but rare, states such as competence and prophage induction and also identified unexpected gene expression states, including the heterogeneous activation of a niche metabolic pathway in a subpopulation of cells. MicroSPLiT paves the way to high-throughput analysis of gene expression in bacterial communities that are otherwise not amenable to single-cell analysis, such as natural microbiota.

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Single-Cell Transcriptome Analysis Reveals Dynamic Cell Populations and Differential Gene Expression Patterns in Control and Aneurysmal Human Aortic Tissue

Circulation ◽

10.1161/circulationaha.120.046528 ◽

2020 ◽

Vol 142 (14) ◽

pp. 1374-1388

Author(s):

Yanming Li ◽

Pingping Ren ◽

Ashley Dawson ◽

Hernan G. Vasquez ◽

Waleed Ageedi ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Aortic Wall ◽

Genome Wide Association ◽

Aortic Tissue ◽

Sequencing Data ◽

Genome Wide ◽

Single Cell Rna Sequencing ◽

Differential Gene

Background: Ascending thoracic aortic aneurysm (ATAA) is caused by the progressive weakening and dilatation of the aortic wall and can lead to aortic dissection, rupture, and other life-threatening complications. To improve our understanding of ATAA pathogenesis, we aimed to comprehensively characterize the cellular composition of the ascending aortic wall and to identify molecular alterations in each cell population of human ATAA tissues. Methods: We performed single-cell RNA sequencing analysis of ascending aortic tissues from 11 study participants, including 8 patients with ATAA (4 women and 4 men) and 3 control subjects (2 women and 1 man). Cells extracted from aortic tissue were analyzed and categorized with single-cell RNA sequencing data to perform cluster identification. ATAA-related changes were then examined by comparing the proportions of each cell type and the gene expression profiles between ATAA and control tissues. We also examined which genes may be critical for ATAA by performing the integrative analysis of our single-cell RNA sequencing data with publicly available data from genome-wide association studies. Results: We identified 11 major cell types in human ascending aortic tissue; the high-resolution reclustering of these cells further divided them into 40 subtypes. Multiple subtypes were observed for smooth muscle cells, macrophages, and T lymphocytes, suggesting that these cells have multiple functional populations in the aortic wall. In general, ATAA tissues had fewer nonimmune cells and more immune cells, especially T lymphocytes, than control tissues did. Differential gene expression data suggested the presence of extensive mitochondrial dysfunction in ATAA tissues. In addition, integrative analysis of our single-cell RNA sequencing data with public genome-wide association study data and promoter capture Hi-C data suggested that the erythroblast transformation-specific related gene( ERG ) exerts an important role in maintaining normal aortic wall function. Conclusions: Our study provides a comprehensive evaluation of the cellular composition of the ascending aortic wall and reveals how the gene expression landscape is altered in human ATAA tissue. The information from this study makes important contributions to our understanding of ATAA formation and progression.

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