scholarly journals Novel Targets of SARS-CoV-2 Spike Protein in Human Fetal Brain Development Suggest Early Pregnancy Vulnerability

2021 ◽  
Vol 14 ◽  
Author(s):  
Parul Varma ◽  
Zane R. Lybrand ◽  
Mariah C. Antopia ◽  
Jenny Hsieh
Keyword(s):  
Gene Expression ◽  
Brain Development ◽  
Single Cell ◽  
Rna Sequencing ◽  
Fetal Brain ◽  
Spike Protein ◽  
S Protein ◽  
Single Cell Rna Sequencing ◽  
Fetal Brain Development ◽  
Low Levels

Pregnant women are at greater risk of infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), because of their altered immunity and strained cardiovascular system. Emerging studies of placenta, embryos, and cerebral organoids suggest that fetal organs including brain could also be vulnerable to coronavirus disease 2019 (COVID-19). Additionally, a case study from Paris has reported transient neurological complications in neonates born to pregnant mothers. However, it remains poorly understood whether the fetal brain expresses cellular components that interact with Spike protein (S) of coronaviruses, which facilitates fusion of virus and host cell membrane and is the primary protein in viral entry. To address this question, we analyzed the expression of known (ACE2, TMPRSS2, and FURIN) and novel (ZDHHC5, GOLGA7, and ATP1A1) S protein interactors in publicly available fetal brain bulk and single cell RNA sequencing datasets. Bulk RNA sequencing analysis across multiple regions of fetal brain spanning 8 weeks post conception (wpc)−37wpc indicates that two of the known S protein interactors are expressed at low levels with median normalized gene expression values ranging from 0.08 to 0.06 (ACE2) and 0.01–0.02 (TMPRSS2). However, the third known S protein interactor FURIN is highly expressed (11.1–44.09) in fetal brain. Interestingly, all three novel S protein interactors are abundantly expressed throughout fetal brain development with median normalized gene expression values ranging from 20.38–21.60 (ZDHHC5), 92.47–68.35 (GOLGA7), and 65.45–194.5 (ATP1A1). Moreover, the peaks of expression of novel interactors is around 12–26wpc. Using publicly available single cell RNA sequencing datasets, we further show that novel S protein interactors show higher co-expression with neurons than with neural progenitors and astrocytes. These results suggest that even though two of the known S protein interactors are present at low levels in fetal brain, novel S protein interactors are abundantly present and could play a direct or indirect role in SARS-CoV-2 fetal brain pathogenesis, especially during the 2nd and 3rd trimesters of pregnancy.

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

iScience ◽  
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

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

PLoS ONE ◽  
2018 ◽  
Vol 13 (10) ◽  
pp. e0205883 ◽  
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

scholarly journals Microbial single-cell RNA sequencing by split-pool barcoding

Science ◽  
2020 ◽  
Vol 371 (6531) ◽  
pp. eaba5257 ◽  
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.

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

Circulation ◽  
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.

scholarly journals Altered Hippocampal Gene Expression and Morphology in Fetal Piglets following Maternal Respiratory Viral Infection

2018 ◽  
Vol 40 (2) ◽  
pp. 104-119 ◽  
Author(s):  
Adrienne M. Antonson ◽  
Bindu Balakrishnan ◽  
Emily C. Radlowski ◽  
Geraldine Petr ◽  
Rodney W. Johnson
Keyword(s):  
Gene Expression ◽  
Viral Infection ◽  
Brain Development ◽  
Hippocampal Neurons ◽  
Protein A ◽  
Fetal Brain ◽  
Respiratory Syndrome Virus ◽  
Maternal Infection ◽  
Ki 67 ◽  
Fetal Brain Development

Maternal infection during pregnancy increases the risk of neurobehavioral problems in offspring. Evidence from rodent models indicates that the maternal immune response to infection can alter fetal brain development, particularly in the hippocampus. However, information on the effects of maternal viral infection on fetal brain development in gyrencephalic species is limited. Thus, the objective of this study was to assess several effects of maternal viral infection in the last one-third of gestation on hippocampal gene expression and development in fetal piglets. Pregnant gilts were inoculated with porcine reproductive and respiratory syndrome virus (PRRSV) at gestational day (GD) 76 and the fetuses were removed by cesarean section at GD 111 (3 days before anticipated parturition). The gilts infected with PRRSV had elevated plasma interleukin-6 levels and developed transient febrile and anorectic responses lasting approximately 21 days. Despite having a similar overall body weight, fetuses from the PRRSV-infected gilts had a decreased brain weight and altered hippocampal gene expression compared to fetuses from control gilts. Notably, maternal infection caused a reduction in estimated neuronal numbers in the fetal dentate gyrus and subiculum. The number of proliferative Ki-67+ cells was not altered, but the relative integrated density of GFAP+ staining was increased, in addition to an increase in GFAP gene expression, indicating astrocyte-specific gliosis. Maternal viral infection caused an increase in fetal hippocampal gene expression of the inflammatory cytokines TNF-α and IFN-γ and the myelination marker myelin basic protein. MHCII protein, a classic monocyte activation marker, was reduced in microglia, while expression of the MHCII gene was decreased in hippocampal tissue of the fetuses from PRRSV-infected gilts. Together, these data suggest that maternal viral infection at the beginning of the last trimester results in a reduction in fetal hippocampal neurons that is evident 5 weeks after infection, when fetal piglets are near full term. The neuronal reduction was not accompanied by pronounced neuroinflammation at GD 111, indicating that any activation of classic neuroinflammatory pathways by maternal viral infection, if present, is mostly resolved by parturition.

Abstract A10: Using single-cell RNA sequencing to assess the impact of pancreatic oncogenic Kras on macrophage gene expression in vitro

2019 ◽  
Author(s):  
Katelyn Donahue ◽  
Yaqing Zhang ◽  
Veerin Sirihorachai ◽  
Stephanie The ◽  
Arvind Rao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Single Cell Rna Sequencing ◽  
The Impact

scholarly journals PRIME: a probabilistic imputation method to reduce dropout effects in single-cell RNA sequencing

2020 ◽  
Vol 36 (13) ◽  
pp. 4021-4029
Author(s):  
Hyundoo Jeong ◽  
Zhandong Liu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Imputation Method ◽  
Supplementary Information ◽  
Single Cell Sequencing ◽  
Depth Analysis ◽  
Single Cell Rna Sequencing

Abstract Summary Single-cell RNA sequencing technology provides a novel means to analyze the transcriptomic profiles of individual cells. The technique is vulnerable, however, to a type of noise called dropout effects, which lead to zero-inflated distributions in the transcriptome profile and reduce the reliability of the results. Single-cell RNA sequencing data, therefore, need to be carefully processed before in-depth analysis. Here, we describe a novel imputation method that reduces dropout effects in single-cell sequencing. We construct a cell correspondence network and adjust gene expression estimates based on transcriptome profiles for the local subnetwork of cells of the same type. We comprehensively evaluated this method, called PRIME (PRobabilistic IMputation to reduce dropout effects in Expression profiles of single-cell sequencing), on synthetic and eight real single-cell sequencing datasets and verified that it improves the quality of visualization and accuracy of clustering analysis and can discover gene expression patterns hidden by noise. Availability and implementation The source code for the proposed method is freely available at https://github.com/hyundoo/PRIME. Supplementary information Supplementary data are available at Bioinformatics online.

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