scholarly journals SARS-CoV-2 entry related genes are comparably expressed in children’s lung as adults

2020 ◽  
Author(s):  
Yue Tao ◽  
Ruwen Yang ◽  
Chen Wen ◽  
Jue Fan ◽  
Jing Ma ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Viral Entry ◽  
Expression Levels ◽  
Single Cell Rna Sequencing ◽  
Key Genes

AbstractTo explore whether the expression levels of viral-entry associated genes might contribute to the milder symptoms in children, we analysed the expression of these genes in both children and adults’ lung tissues by single cell RNA sequencing (scRNA-seq) and immunohistochemistry (IHC). Both scRNA-seq and IHC analyses showed comparable expression of the key genes for SARS-CoV-2 entry in children and adults, including ACE2, TMPRSS2 and FURIN, suggesting that instead of lower virus intrusion rate, other factors are more likely to be the key reasons for the milder symptoms of SARS-CoV-2 infected children.

scholarly journals Single-cell RNA Expression of SARS-CoV-2 Cell Entry Factors in Human Endometrium during Preconception

2020 ◽  
Author(s):  
Felipe Vilella ◽  
Wanxin Wang ◽  
Inmaculada Moreno ◽  
Stephen R. Quake ◽  
Carlos Simon
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Viral Entry ◽  
Embryo Implantation ◽  
Human Endometrium ◽  
Cell Entry ◽  
Rna Expression ◽  
Endometrial Cells ◽  
Single Cell Rna Sequencing ◽  
Low Efficiency

AbstractWe investigated potential SARS-CoV-2 tropism in human endometrium by single-cell RNA-sequencing of viral entry-associated genes in healthy women. Percentages of endometrial cells expressing ACE2, TMPRSS2, CTSB, or CTSL were <2%, 12%, 80%, and 80%, respectively, with 0.7% of cells expressing all four genes. Our findings imply low efficiency of SARS-CoV-2 infection in the endometrium before embryo implantation, providing information to assess preconception risk in asymptomatic carriers.

scholarly journals Single-Cell RNA Sequencing Reveals Endothelial Cell Transcriptome Heterogeneity under Homeostatic Laminar Flow

2020 ◽  
Author(s):  
Ziqing Liu ◽  
Dana L Ruter ◽  
Kaitlyn Quigley ◽  
Yuchao Jiang ◽  
Victoria L Bautch
Keyword(s):  
Endothelial Cells ◽  
Endothelial Cell ◽  
Laminar Flow ◽  
Single Cell ◽  
Rna Sequencing ◽  
Protein Translation ◽  
Flow Conditions ◽  
Expression Levels ◽  
Cell Behaviors ◽  
Single Cell Rna Sequencing

ABSTRACTObjectiveEndothelial cells that form the innermost layer of all vessels exhibit heterogeneous cell behaviors and responses to pro-angiogenic signals that are critical for vascular sprouting and angiogenesis. Once vessels form, remodeling and blood flow lead to endothelial cell quiescence, and homogeneity in cell behaviors and signaling responses. These changes are important for the function of mature vessels, but whether and at what level endothelial cells regulate overall expression heterogeneity during this transition is poorly understood. Here we profiled endothelial cell transcriptomic heterogeneity, and expression heterogeneity of selected proteins, under homeostatic laminar flow.Approach and ResultsSingle-cell RNA sequencing and fluorescence microscopy were used to characterize heterogeneity in RNA and protein gene expression levels of human endothelial cells under homeostatic laminar flow compared to non-flow conditions. Analysis of transcriptome variance, Gini coefficient, and coefficient of variation showed that more genes increased RNA heterogeneity under laminar flow relative to genes whose expression became more homogeneous. Analysis of a subset of genes for relative protein expression revealed that most protein profiles showed decreased heterogeneity under flow. In contrast, the magnitude of expression level changes in RNA and protein was coordinated among endothelial cells in flow vs. non-flow conditions.ConclusionsEndothelial cells exposed to homeostatic laminar flow showed increased cohort heterogeneity in RNA expression levels, while cohort expression heterogeneity of selected cognate proteins decreased under laminar flow. These findings suggest that EC homeostasis is imposed at the level of protein translation and/or stability rather than transcriptionally.

scholarly journals Regulatory network-based imputation of dropouts in single-cell RNA sequencing data

2019 ◽  
Author(s):  
Ana Carolina Leote ◽  
Xiaohui Wu ◽  
Andreas Beyer
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Regulatory Network ◽  
Missing Values ◽  
Transcriptional Regulatory Network ◽  
Imputation Method ◽  
Sequencing Data ◽  
Expression Levels ◽  
Single Cell Rna Sequencing ◽  
Baseline Approach

AbstractSingle-cell RNA sequencing (scRNA-seq) methods are typically unable to quantify the expression levels of all genes in a cell, creating a need for the computational prediction of missing values (‘dropout imputation’). Most existing dropout imputation methods are limited in the sense that they exclusively use the scRNA-seq dataset at hand and do not exploit external gene-gene relationship information.Here, we show that a transcriptional regulatory network learned from external, independent gene expression data improves dropout imputation. Using a variety of human scRNA-seq datasets we demonstrate that our network-based approach outperforms published state-of-the-art methods. The network-based approach performs particularly well for lowly expressed genes, including cell-type-specific transcriptional regulators. Additionally, we tested a baseline approach, where we imputed missing values using the sample-wide average expression of a gene. Unexpectedly, up to 48% of the genes were better predicted using this baseline approach, suggesting negligible cell-to-cell variation of expression levels for many genes. Our work shows that there is no single best imputation method; rather, the best method depends on gene-specific features, such as expression level and expression variation across cells. We thus implemented an R-package called ADImpute (available from https://github.com/anacarolinaleote/ADImpute) that automatically determines the best imputation method for each gene in a dataset.

scholarly journals Single-Cell RNA Sequencing Reveals Endothelial Cell Transcriptome Heterogeneity Under Homeostatic Laminar Flow

2021 ◽  
Vol 41 (10) ◽  
pp. 2575-2584
Author(s):  
Ziqing Liu ◽  
Dana L. Ruter ◽  
Kaitlyn Quigley ◽  
Natalie T. Tanke ◽  
Yuchao Jiang ◽  
...  
Keyword(s):  
Laminar Flow ◽  
Single Cell ◽  
Rna Sequencing ◽  
Protein Gene ◽  
Expression Levels ◽  
Cell Behaviors ◽  
Innermost Layer ◽  
Single Cell Rna Sequencing ◽  
Cell Transcriptome ◽  
Gene Expression Levels

Objective: Endothelial cells (ECs) that form the innermost layer of all vessels exhibit heterogeneous cell behaviors and responses to pro-angiogenic signals that are critical for vascular sprouting and angiogenesis. Once vessels form, remodeling and blood flow lead to EC quiescence, and homogeneity in cell behaviors and signaling responses. These changes are important for the function of mature vessels, but whether and at what level ECs regulate overall expression heterogeneity during this transition is poorly understood. Here, we profiled EC transcriptomic heterogeneity, and expression heterogeneity of selected proteins, under homeostatic laminar flow. Approach and Results: Single-cell RNA sequencing and fluorescence microscopy were used to characterize heterogeneity in RNA and protein gene expression levels of human ECs under homeostatic laminar flow compared to nonflow conditions. Analysis of transcriptome variance, Gini coefficient, and coefficient of variation showed that more genes increased RNA heterogeneity under laminar flow relative to genes whose expression became more homogeneous, although small subsets of cells did not follow this pattern. Analysis of a subset of genes for relative protein expression revealed little congruence between RNA and protein heterogeneity changes under flow. In contrast, the magnitude of expression level changes in RNA and protein was more coordinated among ECs in flow versus nonflow conditions. Conclusions: ECs exposed to homeostatic laminar flow showed overall increased heterogeneity in RNA expression levels, while expression heterogeneity of selected cognate proteins did not follow RNA heterogeneity changes closely. These findings suggest that EC homeostasis is imposed post-transcriptionally in response to laminar flow.

scholarly journals SCMarker: ab initio marker selection for single cell transcriptome profiling

2018 ◽  
Author(s):  
Fang Wang ◽  
Shaoheng Liang ◽  
Tapsi Kumar ◽  
Nicholas Navin ◽  
Ken Chen
Keyword(s):  
Ab Initio ◽  
Single Cell ◽  
Rna Sequencing ◽  
Mrna Transcript ◽  
Cell Type ◽  
Sequencing Data ◽  
Transcript Levels ◽  
Expression Levels ◽  
Marker Selection ◽  
Single Cell Rna Sequencing

AbstractSingle-cell RNA-sequencing data generated by a variety of technologies, such as Drop-seq and SMART-seq, can reveal simultaneously the mRNA transcript levels of thousands of genes in thousands of cells. It is often important to identify informative genes or cell-type-discriminative markers to reduce dimensionality and achieve informative cell typing results. We present an ab initio method that performs unsupervised marker selection by identifying genes that have subpopulation-discriminative expression levels and are co- or mutually-exclusively expressed with other genes. Consistent improvements in cell-type classification and biologically meaningful marker selection are achieved by applying SCMarker on various datasets in multiple tissue types, followed by a variety of clustering algorithms. The source code of SCMarker is publicly available at https://github.com/KChen-lab/SCMarker.Author SummarySingle cell RNA-sequencing technology simultaneously provides the mRNA transcript levels of thousands of genes in thousands of cells. A frequent requirement of single cell expression analysis is the identification of markers which may explain complex cellular states or tissue composition. We propose a new marker selection strategy (SCMarker) to accurately delineate cell types in single cell RNA-sequencing data by identifying genes that have bi/multi-modally distributed expression levels and are co- or mutually-exclusively expressed with some other genes. Our method can determine the cell-type-discriminative markers without referencing to any known transcriptomic profiles or cell ontologies, and consistently achieves accurate cell-type-discriminative marker identification in a variety of scRNA-seq datasets.

scTSSR: gene expression recovery for single-cell RNA sequencing using two-side sparse self-representation

2020 ◽  
Vol 36 (10) ◽  
pp. 3131-3138
Author(s):  
Ke Jin ◽  
Le Ou-Yang ◽  
Xing-Ming Zhao ◽  
Hong Yan ◽  
Xiao-Fei Zhang
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Differential Expression Analysis ◽  
Supplementary Information ◽  
Expression Levels ◽  
Single Cell Rna Sequencing ◽  
Downstream Analysis ◽  
Gene Expression Levels

Abstract Motivation Single-cell RNA sequencing (scRNA-seq) methods make it possible to reveal gene expression patterns at single-cell resolution. Due to technical defects, dropout events in scRNA-seq will add noise to the gene-cell expression matrix and hinder downstream analysis. Therefore, it is important for recovering the true gene expression levels before carrying out downstream analysis. Results In this article, we develop an imputation method, called scTSSR, to recover gene expression for scRNA-seq. Unlike most existing methods that impute dropout events by borrowing information across only genes or cells, scTSSR simultaneously leverages information from both similar genes and similar cells using a two-side sparse self-representation model. We demonstrate that scTSSR can effectively capture the Gini coefficients of genes and gene-to-gene correlations observed in single-molecule RNA fluorescence in situ hybridization (smRNA FISH). Down-sampling experiments indicate that scTSSR performs better than existing methods in recovering the true gene expression levels. We also show that scTSSR has a competitive performance in differential expression analysis, cell clustering and cell trajectory inference. Availability and implementation The R package is available at https://github.com/Zhangxf-ccnu/scTSSR. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Single cell transcriptome of CD8+ T cells in multiple cancers reveals comprehensive exhaustion associated mechanisms

2019 ◽  
Author(s):  
Yunmeng Bai ◽  
Zixi Chen ◽  
Xiaoshi Chen ◽  
Ziqing He ◽  
Jie Long ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cd8 T Cells ◽  
T Cell Exhaustion ◽  
Cell Exhaustion ◽  
Multiple Cancers ◽  
Single Cell Rna Sequencing ◽  
Key Genes

AbstractT cell exhaustion is one of the mechanisms that cancer cells get rid of control from the immune system. Single-cell RNA sequencing has showed superiority on immunity mechanism in recent studies. Here, we collected more than 6000 single CD8+ T cells from three cancers including CRC, HCC and NSCLC, and identified five clusters of each cancer. We obtained 71 and 159 DEGs for pre_exhausted or exhausted vs. effector comparison in all three cancers, respectively. Specially, we found some key genes including the four exhaustion associated genes of PDCD1, HAVCR2, TIGIT and TOX, and two vital genes of CD69 and JUN in the interaction network. Additionally, we identified the gene SAMSN1 which highly expressed in the exhausted cells had a poor overall survival and played a negative role in immunity. We summarized the putative interrelated mechanisms of above key genes identified in this study by integrating the reported knowledge. Furthermore, we explored the heterogeneous and preference of exhausted CD8+ T cells in each patient and found only one exhausted sub-cluster existed in the most of patients, especially in CRC and HCC. As far as we know, this is the first time to study the mechanism of T cell exhaustion with the data of single-cell RNA sequencing of multiple cancers. Our study may facilitate the understanding of the mechanism of T cell exhaustion, and provide a new way for functional research of single-cell RNA sequencing data across cancers.

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