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.

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.

#71: Single-cell RNA Sequencing Analysis of Zika Virus Infection in Human Stem Cell-Derived Neuroprogenitor Cells and Cerebral Organoids

2021 ◽  
Vol 10 (Supplement_1) ◽  
pp. S14-S14
Author(s):  
K E Ocwieja ◽  
T K Hughes ◽  
J M Antonucci ◽  
A L Richards ◽  
A C Stanton ◽  
...  
Keyword(s):  
Stem Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
Zika Virus ◽  
Molecular Mechanisms ◽  
Sequencing Analysis ◽  
Human Stem Cell ◽  
Zikv Infection ◽  
Single Cell Sequencing ◽  
Single Cell Rna Sequencing

Abstract Background The molecular mechanisms underpinning the neurologic and congenital pathologies caused by Zika virus (ZIKV) infection remain poorly understood. It is also unclear why congenital ZIKV disease was not observed prior to the recent epidemics in French Polynesia and the Americas, despite evidence that the Zika virus has actively circulated in parts of Africa and Asia since 1947 and 1966, respectively. Methods Due to advances in stem cell-based technologies, we can now model ZIKV infections of the central nervous system in human stem cell-derived neuroprogenitor cells and cerebral organoids, which recapitulate complex three-dimensional neural architecture. We apply Seq-Well—a simple, portable platform for massively parallel single-cell RNA sequencing—to characterize these neural models infected with ZIKV. We detect and quantify host mRNA transcripts and viral RNA with single-cell resolution, thereby defining transcriptional features of both uninfected and infected cells. Results In neuroprogenitor cells, single-cell sequencing reveals that while uninfected bystander cells strongly upregulate interferon pathway genes, these are largely suppressed in cells infected with ZIKV within the same culture dish. In our organoid model, single-cell sequencing allows us to identify multiple cellular populations, including neuroprogenitor cells, intermediate progenitor cells, and terminally differentiated neurons. In this model of the developing brain, we identify preferred tropisms of ZIKV infection. Our data additionally reveal differences in cell-type frequencies and gene expression within organoids infected by historic and contemporary ZIKV strains from a variety of geographic locations. Conclusions These findings may help explain phenotypic differences attributed to the viruses, including variable propensities to cause microcephaly. Overall, our work provides insight into normal and diseased human brain development and suggests that both virus replication and host response mechanisms underlie the neuropathology of ZIKV infection.

scholarly journals Single-cell RNA sequencing of Trypanosoma brucei from tsetse salivary glands unveils metacyclogenesis and identifies potential transmission blocking antigens

2020 ◽  
Vol 117 (5) ◽  
pp. 2613-2621 ◽  
Author(s):  
Aurélien Vigneron ◽  
Michelle B. O’Neill ◽  
Brian L. Weiss ◽  
Amy F. Savage ◽  
Olivia C. Campbell ◽  
...  
Keyword(s):  
Single Cell ◽  
Salivary Glands ◽  
Rna Sequencing ◽  
Molecular Mechanisms ◽  
Developmental Process ◽  
Surface Glycoprotein ◽  
Mammalian Host ◽  
Transmission Blocking ◽  
African Trypanosomes ◽  
Single Cell Rna Sequencing

Tsetse-transmitted African trypanosomes must develop into mammalian-infectious metacyclic cells in the fly’s salivary glands (SGs) before transmission to a new host. The molecular mechanisms that underlie this developmental process, known as metacyclogenesis, are poorly understood. Blocking the few metacyclic parasites deposited in saliva from further development in the mammal could prevent disease. To obtain an in-depth perspective of metacyclogenesis, we performed single-cell RNA sequencing (scRNA-seq) from a pool of 2,045 parasites collected from infected tsetse SGs. Our data revealed three major cell clusters that represent the epimastigote, and pre- and mature metacyclic trypanosome developmental stages. Individual cell level data also confirm that the metacyclic pool is diverse, and that each parasite expresses only one of the unique metacyclic variant surface glycoprotein (mVSG) coat protein transcripts identified. Further clustering of cells revealed a dynamic transcriptomic and metabolic landscape reflective of a developmental program leading to infectious metacyclic forms preadapted to survive in the mammalian host environment. We describe the expression profile of proteins that regulate gene expression and that potentially play a role in metacyclogenesis. We also report on a family of nonvariant surface proteins (Fam10) and demonstrate surface localization of one member (named SGM1.7) on mature metacyclic parasites. Vaccination of mice with recombinant SGM1.7 reduced parasitemia early in the infection. Future studies are warranted to investigate Fam10 family proteins as potential trypanosome transmission blocking vaccine antigens. Our experimental approach is translationally relevant for developing strategies to prevent other insect saliva-transmitted parasites from infecting and causing disease in mammalian hosts.

Goals and approaches for each processing step for single-cell RNA sequencing data

2020 ◽  
Author(s):  
Zilong Zhang ◽  
Feifei Cui ◽  
Chunyu Wang ◽  
Lingling Zhao ◽  
Quan Zou
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cellular Level ◽  
Sequencing Data ◽  
Analysis Tools ◽  
Processing Step ◽  
Study Gene Expression ◽  
Single Cell Rna Sequencing ◽  
Cell Data

Abstract Single-cell RNA sequencing (scRNA-seq) has enabled researchers to study gene expression at the cellular level. However, due to the extremely low levels of transcripts in a single cell and technical losses during reverse transcription, gene expression at a single-cell resolution is usually noisy and highly dimensional; thus, statistical analyses of single-cell data are a challenge. Although many scRNA-seq data analysis tools are currently available, a gold standard pipeline is not available for all datasets. Therefore, a general understanding of bioinformatics and associated computational issues would facilitate the selection of appropriate tools for a given set of data. In this review, we provide an overview of the goals and most popular computational analysis tools for the quality control, normalization, imputation, feature selection and dimension reduction of scRNA-seq data.

LRSK: a low-rank self-representation K-means method for clustering single-cell RNA-sequencing data

2020 ◽  
Vol 16 (5) ◽  
pp. 465-473
Author(s):  
Ye-Sen Sun ◽  
Le Ou-Yang ◽  
Dao-Qing Dai
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Quantitative Research ◽  
Cellular Level ◽  
Low Rank ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

The development of single-cell RNA-sequencing (scRNA-seq) technologies brings tremendous opportunities for quantitative research and analyses at the cellular level.

scholarly journals Harnessing Single-Cell RNA Sequencing to Better Understand How Diseased Cells Behave the Way They Do in Cardiovascular Disease

2020 ◽  
Author(s):  
Farwah Iqbal ◽  
Adrien Lupieri ◽  
Masanori Aikawa ◽  
Elena Aikawa
Keyword(s):  
Cardiovascular Disease ◽  
Single Cell ◽  
Rna Sequencing ◽  
Molecular Mechanisms ◽  
Cell Types ◽  
Specific Cell ◽  
Calcific Aortic Valve Disease ◽  
Cardiovascular Tissues ◽  
Single Cell Rna Sequencing ◽  
Cell Phenotypes

The transition of healthy arteries and cardiac valves into dense, cell-rich, calcified, and fibrotic tissues is driven by a complex interplay of both cellular and molecular mechanisms. Specific cell types in these cardiovascular tissues become activated following the exposure to systemic stimuli including circulating lipoproteins or inflammatory mediators. This activation induces multiple cascades of events where changes in cell phenotypes and activation of certain receptors may trigger multiple pathways and specific alterations to the transcriptome. Modifications to the transcriptome and proteome can give rise to pathological cell phenotypes and trigger mechanisms that exacerbate inflammation, proliferation, calcification, and recruitment of resident or distant cells. Accumulating evidence suggests that each cell type involved in vascular and valvular diseases is heterogeneous. Single-cell RNA sequencing is a transforming medical research tool that enables the profiling of the unique fingerprints at single-cell levels. Its applications have allowed the construction of cell atlases including the mammalian heart and tissue vasculature and the discovery of new cell types implicated in cardiovascular disease. Recent advances in single-cell RNA sequencing have facilitated the identification of novel resident cell populations that become activated during disease and has allowed tracing the transition of healthy cells into pathological phenotypes. Furthermore, single-cell RNA sequencing has permitted the characterization of heterogeneous cell subpopulations with unique genetic profiles in healthy and pathological cardiovascular tissues. In this review, we highlight the latest groundbreaking research that has improved our understanding of the pathological mechanisms of atherosclerosis and future directions for calcific aortic valve disease.

scholarly journals Transcriptional evaluation of the ductus arteriosus at the single-cell level uncovers a requirement for vimentin for complete closure

2021 ◽  
Author(s):  
Jocelynda Salvador ◽  
Gloria E Hernandez ◽  
Feiyang Ma ◽  
Cyrus W Abrahamson ◽  
Robert D Goldman ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Vascular Remodeling ◽  
Molecular Mechanisms ◽  
Heart Defects ◽  
Ductus Arteriosus ◽  
Fibroblast Cell ◽  
Management Options ◽  
Complete Closure ◽  
Single Cell Rna Sequencing

OBJECTIVE: Failure to close the ductus arteriosus immediately post-birth, patent ductus arteriosus (PDA), accounts for up to 10% of all congenital heart defects. Despite significant advances in PDA management options, including pharmacological treatment targeting the prostaglandin pathway, a proportion of patients fail to respond and must undergo surgical intervention. Thus, further refinement of the cellular and molecular mechanisms that govern vascular remodeling of this vessel is required. APPROACH AND RESULTS: As anticipated, single-cell RNA sequencing on the ductus arteriosus in mouse embryos at E18.5, P0.5, and P5, revealed broad transcriptional alterations in the endothelial, smooth muscle, and fibroblast cell compartments. Making use of these data sets, vimentin emerged as an interesting candidate for further investigation. Subsequent studies demonstrated that, in fact, mice with genetic deletion of vimentin fail to complete vascular remodeling of the ductus arteriosus, as per presence of a functional lumen. CONCLUSIONS: Through single-cell RNA-sequencing and by tracking closure of the ductus arteriosus postnatally in mice, we uncovered the unexpected contribution of vimentin in driving complete closure of the ductus arteriosus potentially through regulation of the Notch signaling pathway.

scholarly journals Single-cell RNA sequencing in cardiovascular development, disease and medicine

2020 ◽  
Vol 17 (8) ◽  
pp. 457-473 ◽  
Author(s):  
David T. Paik ◽  
Sangkyun Cho ◽  
Lei Tian ◽  
Howard Y. Chang ◽  
Joseph C. Wu
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cardiovascular Development ◽  
Single Cell Rna Sequencing

scholarly journals Recent Developments and Applications of Single-Cell RNA Sequencing Technology in Cell Classification

2021 ◽  
Vol 2 (12) ◽  
pp. 1283-1290
Author(s):  
Safir Ullah Khan ◽  
Munir Ullah Khan
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Clinical Medicine ◽  
Cell Types ◽  
Sequencing Platform ◽  
Single Cell Rna Sequencing ◽  
Recent Developments ◽  
A New Technique ◽  
Multicellular Organisms ◽  
Different Cell Types

Multicellular organisms have many cell types and are complex, and heterogeneity is common among cells. Single-Cell RNA Sequencing (scRNA-SEQ) is a new technique for studying the transcriptional activity of a single cell that is still in its early stages of development. It generates transcriptional profiles from thousands of parallel cells to reveal the differential expression of individual cell genomes. They reflect the heterogeneity between cells to identify different cell types and form cell maps of tissues or organs, which play an essential role in biology and clinical medicine. Based on the introduction and comparison of the scRNA-SEQ sequencing platform, this paper focuses on the application of scRNA-SEQ in the exploration of cell types in the nervous system and immune system and summarizes the research results of the combination of scRNA-SEQ and spatial transcriptome technology.

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.

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