scholarly journals Single-Cell RNA Sequencing of the Adult Mammalian Heart—State-of-the-Art and Future Perspectives

2021 ◽  
Vol 18 (2) ◽  
pp. 64-70
Author(s):  
Monika M. Gladka
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Molecular Mechanisms ◽  
State Of The Art ◽  
Future Perspectives ◽  
Cell Level ◽  
Mammalian Heart ◽  
Cardiac Pathophysiology ◽  
Single Cell Rna Sequencing ◽  
Downstream Analysis

Abstract Purpose of the Review Cardiovascular disease remains the leading cause of death worldwide, resulting in cardiac dysfunction and, subsequently, heart failure (HF). Single-cell RNA sequencing (scRNA-seq) is a rapidly developing tool for studying the transcriptional heterogeneity in both healthy and diseased hearts. Wide applications of techniques like scRNA-seq could significantly contribute to uncovering the molecular mechanisms involved in the onset and progression to HF and contribute to the development of new, improved therapies. This review discusses several studies that successfully applied scRNA-seq to the mouse and human heart using various methods of tissue processing and downstream analysis. Recent Findings The application of scRNA-seq in the cardiovascular field is continuously expanding, providing new detailed insights into cardiac pathophysiology. Summary Increased understanding of cardiac pathophysiology on the single-cell level will contribute to the development of novel, more effective therapeutic strategies. Here, we summarise the possible application of scRNA-seq to the adult mammalian heart.

Critical downstream analysis steps for single-cell RNA sequencing data

2021 ◽  
Author(s):  
Zilong Zhang ◽  
Feifei Cui ◽  
Chen Lin ◽  
Lingling Zhao ◽  
Chunyu Wang ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Noisy Data ◽  
Single Cell Level ◽  
Cell Type ◽  
Sequencing Data ◽  
Cell Level ◽  
Bioinformatics Tool ◽  
Single Cell Rna Sequencing ◽  
Downstream Analysis

Abstract Single-cell RNA sequencing (scRNA-seq) has enabled us to study biological questions at the single-cell level. Currently, many analysis tools are available to better utilize these relatively noisy data. In this review, we summarize the most widely used methods for critical downstream analysis steps (i.e. clustering, trajectory inference, cell-type annotation and integrating datasets). The advantages and limitations are comprehensively discussed, and we provide suggestions for choosing proper methods in different situations. We hope this paper will be useful for scRNA-seq data analysts and bioinformatics tool developers.

scholarly journals PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data

2020 ◽  
Author(s):  
Dongyuan Song ◽  
Jingyi Jessica Li
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Molecular Mechanisms ◽  
Real Data ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Ill Posed ◽  
Differential Gene ◽  
Cell Trajectory ◽  
Downstream Analysis

AbstractIn the investigation of molecular mechanisms underlying cell state changes, a crucial analysis is to identify differentially expressed (DE) genes along a continuous cell trajectory, which can be estimated by pseudotime inference from single-cell RNA-sequencing (scRNA-seq) data. However, existing methods that identify DE genes based on inferred pseudotime do not account for the uncertainty in pseudotime inference. Also, they either have ill-posed p-values that hinder the control of false discovery rate (FDR) or have restrictive models that reduce the power of DE gene identification. To overcome these drawbacks, we propose PseudotimeDE, a robust method that accounts for the uncertainty in pseudotime inference and thus identifies DE genes along cell pseudotime with well-calibrated p-values. PseudotimeDE is flexible in allowing users to specify the pseudotime inference method and to choose the appropriate model for scRNA-seq data. Comprehensive simulations and real-data applications verify that PseudotimeDE provides well-calibrated p-values essential for controlling FDR and downstream analysis and that PseudotimeDE is more powerful than existing methods to identify DE genes.

scholarly journals Understanding the pathogenesis of infectious diseases by single-cell RNA sequencing

2021 ◽  
Vol 8 (9) ◽  
pp. 208-222
Author(s):  
Wanqiu Huang ◽  
Danni Wang ◽  
Yu-Feng Yao
Keyword(s):  
Infectious Diseases ◽  
Single Cell ◽  
Rna Sequencing ◽  
Molecular Mechanisms ◽  
Transcriptional Profiling ◽  
Single Cell Level ◽  
Cell Level ◽  
Disease Treatment ◽  
Single Cell Rna Sequencing ◽  
Cell Diversity

Infections are highly orchestrated and dynamic processes, which involve both pathogen and host. Transcriptional profiling at the single-cell level enables the analysis of cell diversity, heterogeneity of the immune response, and detailed molecular mechanisms underlying infectious diseases caused by bacteria, viruses, fungi, and parasites. Herein, we highlight recent remarkable advances in single-cell RNA sequencing (scRNA-seq) technologies and their applications in the investigation of host-pathogen interactions, current challenges and potential prospects for disease treatment are discussed as well. We propose that with the aid of scRNA-seq, the mechanism of infectious diseases will be further revealed thus inspiring the development of novel interventions and therapies.

scholarly journals A single-cell RNA-sequencing training and analysis suite using the Galaxy framework

GigaScience ◽  
2020 ◽  
Vol 9 (10) ◽  
Author(s):  
Mehmet Tekman ◽  
Bérénice Batut ◽  
Alexander Ostrovsky ◽  
Christophe Antoniewski ◽  
Dave Clements ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Biology ◽  
High Performance ◽  
Learning Experience ◽  
Sequencing Analysis ◽  
Teaching Resources ◽  
Single Cell Rna Sequencing ◽  
The Galaxy ◽  
Downstream Analysis

Abstract Background The vast ecosystem of single-cell RNA-sequencing tools has until recently been plagued by an excess of diverging analysis strategies, inconsistent file formats, and compatibility issues between different software suites. The uptake of 10x Genomics datasets has begun to calm this diversity, and the bioinformatics community leans once more towards the large computing requirements and the statistically driven methods needed to process and understand these ever-growing datasets. Results Here we outline several Galaxy workflows and learning resources for single-cell RNA-sequencing, with the aim of providing a comprehensive analysis environment paired with a thorough user learning experience that bridges the knowledge gap between the computational methods and the underlying cell biology. The Galaxy reproducible bioinformatics framework provides tools, workflows, and trainings that not only enable users to perform 1-click 10x preprocessing but also empower them to demultiplex raw sequencing from custom tagged and full-length sequencing protocols. The downstream analysis supports a range of high-quality interoperable suites separated into common stages of analysis: inspection, filtering, normalization, confounder removal, and clustering. The teaching resources cover concepts from computer science to cell biology. Access to all resources is provided at the singlecell.usegalaxy.eu portal. Conclusions The reproducible and training-oriented Galaxy framework provides a sustainable high-performance computing environment for users to run flexible analyses on both 10x and alternative platforms. The tutorials from the Galaxy Training Network along with the frequent training workshops hosted by the Galaxy community provide a means for users to learn, publish, and teach single-cell RNA-sequencing analysis.

scholarly journals VASC: dimension reduction and visualization of single cell RNA sequencing data by deep variational autoencoder

2017 ◽  
Author(s):  
Dongfang Wang ◽  
Jin Gu
Keyword(s):  
Dimension Reduction ◽  
Single Cell ◽  
Rna Sequencing ◽  
Original Data ◽  
Marker Genes ◽  
Single Cell Level ◽  
Sequencing Data ◽  
Cell Level ◽  
Variational Autoencoder ◽  
Single Cell Rna Sequencing

AbstractSingle cell RNA sequencing (scRNA-seq) is a powerful technique to analyze the transcriptomic heterogeneities in single cell level. It is an important step for studying cell sub-populations and lineages based on scRNA-seq data by finding an effective low-dimensional representation and visualization of the original data. The scRNA-seq data are much noiser than traditional bulk RNA-Seq: in the single cell level, the transcriptional fluctuations are much larger than the average of a cell population and the low amount of RNA transcripts will increase the rate of technical dropout events. In this study, we proposed VASC (deep Variational Autoencoder for scRNA-seq data), a deep multi-layer generative model, for the unsupervised dimension reduction and visualization of scRNA-seq data. It can explicitly model the dropout events and find the nonlinear hierarchical feature representations of the original data. Tested on twenty datasets, VASC shows superior performances in most cases and broader dataset compatibility compared with four state-of-the-art dimension reduction methods. Then, for a case study of pre-implantation embryos, VASC successfully re-establishes the cell dynamics and identifies several candidate marker genes associated with the early embryo development.

scholarly journals Single-Cell RNA Sequencing to Disentangle the Blood System

2021 ◽  
Vol 41 (3) ◽  
pp. 1012-1018
Author(s):  
Jean Acosta ◽  
Daniel Ssozi ◽  
Peter van Galen
Keyword(s):  
Blood Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
Molecular Mechanisms ◽  
Single Cells ◽  
Cell Types ◽  
Blood System ◽  
Differentiated Cells ◽  
Tissue Organization ◽  
Single Cell Rna Sequencing

The blood system is often represented as a tree-like structure with stem cells that give rise to mature blood cell types through a series of demarcated steps. Although this representation has served as a model of hierarchical tissue organization for decades, single-cell technologies are shedding new light on the abundance of cell type intermediates and the molecular mechanisms that ensure balanced replenishment of differentiated cells. In this Brief Review, we exemplify new insights into blood cell differentiation generated by single-cell RNA sequencing, summarize considerations for the application of this technology, and highlight innovations that are leading the way to understand hematopoiesis at the resolution of single cells. Graphic Abstract: A graphic abstract is available for this article.

scholarly journals Controlling for confounding effects in single cell RNA sequencing studies using both control and target genes

2016 ◽  
Author(s):  
Mengjie Chen ◽  
Xiang Zhou
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Target Genes ◽  
Expectation Maximization Algorithm ◽  
Data Sets ◽  
Single Cell Rna Sequencing ◽  
Sequencing Studies ◽  
Order Of Magnitude ◽  
The Rich ◽  
Downstream Analysis

Single cell RNA sequencing (scRNAseq) technique is becoming increasingly popular for unbiased and high-resolutional transcriptome analysis of heterogeneous cell populations. Despite its many advantages, scRNAseq, like any other genomic sequencing technique, is susceptible to the influence of confounding effects. Controlling for confounding effects in scRNAseq data is thus a crucial step for proper data normalization and accurate downstream analysis. Several recent methodological studies have demonstrated the use of control genes for controlling for confounding effects in scRNAseq studies; the control genes are used to infer the confounding effects, which are then used to normalize target genes of primary interest. However, these methods can be suboptimal as they ignore the rich information contained in the target genes. Here, we develop an alternative statistical method, which we refer to as scPLS, for more accurate inference of confounding effects. Our method is based on partial least squares and models control and target genes jointly to better infer and control for confounding effects. To accompany our method, we develop a novel expectation maximization algorithm for scalable inference. Our algorithm is an order of magnitude faster than standard ones, making scPLS applicable to hundreds of cells and hundreds of thousands of genes. With extensive simulations and comparisons with other methods, we demonstrate the effectiveness of scPLS. Finally, we apply scPLS to analyze two scRNAseq data sets to illustrate its benefits in removing technical confounding effects as well as for removing cell cycle effects.

EnImpute: imputing dropout events in single-cell RNA-sequencing data via ensemble learning

2019 ◽  
Vol 35 (22) ◽  
pp. 4827-4829 ◽  
Author(s):  
Xiao-Fei Zhang ◽  
Le Ou-Yang ◽  
Shuo Yang ◽  
Xing-Ming Zhao ◽  
Xiaohua Hu ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Ensemble Learning ◽  
R Package ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
The Individual ◽  
Downstream Analysis ◽  
Shiny Application

Abstract Summary Imputation of dropout events that may mislead downstream analyses is a key step in analyzing single-cell RNA-sequencing (scRNA-seq) data. We develop EnImpute, an R package that introduces an ensemble learning method for imputing dropout events in scRNA-seq data. EnImpute combines the results obtained from multiple imputation methods to generate a more accurate result. A Shiny application is developed to provide easier implementation and visualization. Experiment results show that EnImpute outperforms the individual state-of-the-art methods in almost all situations. EnImpute is useful for correcting the noisy scRNA-seq data before performing downstream analysis. Availability and implementation The R package and Shiny application are available through Github at https://github.com/Zhangxf-ccnu/EnImpute. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Review of Single-Cell RNA Sequencing in the Heart

2020 ◽  
Vol 21 (21) ◽  
pp. 8345
Author(s):  
Shintaro Yamada ◽  
Seitaro Nomura
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Molecular Mechanisms ◽  
Clinical Medicine ◽  
Basic Research ◽  
Cellular Level ◽  
Cardiovascular Development ◽  
Biological Mechanisms ◽  
Technological Advances ◽  
Single Cell Rna Sequencing

Single-cell RNA sequencing (scRNA-seq) technology is a powerful, rapidly developing tool for characterizing individual cells and elucidating biological mechanisms at the cellular level. Cardiovascular disease is one of the major causes of death worldwide and its precise pathology remains unclear. scRNA-seq has provided many novel insights into both healthy and pathological hearts. In this review, we summarize the various scRNA-seq platforms and describe the molecular mechanisms of cardiovascular development and disease revealed by scRNA-seq analysis. We then describe the latest technological advances in scRNA-seq. Finally, we discuss how to translate basic research into clinical medicine using scRNA-seq technology.

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