scholarly journals Single‐Cell RNA Sequencing Analysis of the Drosophila Larval Ventral Cord

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Tho Huu Nguyen ◽  
Rosario Vicidomini ◽  
Saumitra Dey Choudhury ◽  
Steven L. Coon ◽  
James Iben ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Sequencing Analysis ◽  
Ventral Cord ◽  
Single Cell Rna Sequencing

scholarly journals Single-cell RNA sequencing analysis of SARS-CoV-2 conventional and moonlighting receptors in human organoids

2020 ◽  
Author(s):  
Rajasekaran Mahalingam ◽  
Prakash Dharmalingam ◽  
Abirami Santhanam ◽  
Gangarao Davuluri ◽  
Harry Karmouty Quintana ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Sequencing Analysis ◽  
Single Cell Rna Sequencing

scholarly journals Single-Cell RNA Sequencing Analysis of the Immunometabolic Rewiring and Immunopathogenesis of Coronavirus Disease 2019

2021 ◽  
Vol 12 ◽  
Author(s):  
Furong Qi ◽  
Wenbo Zhang ◽  
Jialu Huang ◽  
Lili Fu ◽  
Jinfang Zhao
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Mononuclear Cells ◽  
Plasma Cells ◽  
Severe Disease ◽  
Sequencing Analysis ◽  
Sequencing Data ◽  
Metabolic Remodeling ◽  
Single Cell Rna Sequencing ◽  
Antibody Secretion

Although immune dysfunction is a key feature of coronavirus disease 2019 (COVID-19), the metabolism-related mechanisms remain elusive. Here, by reanalyzing single-cell RNA sequencing data, we delineated metabolic remodeling in peripheral blood mononuclear cells (PBMCs) to elucidate the metabolic mechanisms that may lead to the progression of severe COVID-19. After scoring the metabolism-related biological processes and signaling pathways, we found that mono-CD14+ cells expressed higher levels of glycolysis-related genes (PKM, LDHA and PKM) and PPP-related genes (PGD and TKT) in severe patients than in mild patients. These genes may contribute to the hyperinflammation in mono-CD14+ cells of patients with severe COVID-19. The mono-CD16+ cell population in COVID-19 patients showed reduced transcription levels of genes related to lysine degradation (NSD1, KMT2E, and SETD2) and elevated transcription levels of genes involved in OXPHOS (ATP6V1B2, ATP5A1, ATP5E, and ATP5B), which may inhibit M2-like polarization. Plasma cells also expressed higher levels of the OXPHOS gene ATP13A3 in COVID-19 patients, which was positively associated with antibody secretion and survival of PCs. Moreover, enhanced glycolysis or OXPHOS was positively associated with the differentiation of memory B cells into plasmablasts or plasma cells. This study comprehensively investigated the metabolic features of peripheral immune cells and revealed that metabolic changes exacerbated inflammation in monocytes and promoted antibody secretion and cell survival in PCs in COVID-19 patients, especially those with severe disease.

scholarly journals Single‐cell RNA sequencing analysis of SARS‐CoV‐2 entry receptors in human organoids

2020 ◽  
Author(s):  
Rajasekaran Mahalingam ◽  
Prakash Dharmalingam ◽  
Abirami Santhanam ◽  
Sivareddy Kotla ◽  
Gangarao Davuluri ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Sequencing Analysis ◽  
Single Cell Rna Sequencing

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 Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Noa Bossel Ben-Moshe ◽  
Shelly Hen-Avivi ◽  
Natalia Levitin ◽  
Dror Yehezkel ◽  
Marije Oosting ◽  
...  
Keyword(s):  
Bacterial Infection ◽  
Single Cell ◽  
Rna Sequencing ◽  
Immune Cells ◽  
Sequencing Analysis ◽  
Single Cell Rna Sequencing ◽  
Human Immune

scholarly journals 973. Single-cell RNA Sequencing Analysis of Zika Virus Infection in Human Stem Cell-Derived Cerebral Organoids

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S33-S34
Author(s):  
Karen Ocwieja ◽  
Alexandra Stanton ◽  
Alexsia Richards ◽  
Jenna Antonucci ◽  
Travis Hughes ◽  
...  
Keyword(s):  
Stem Cell ◽  
Human Brain ◽  
Single Cell ◽  
Rna Sequencing ◽  
Zika Virus ◽  
Model Systems ◽  
Sequencing Analysis ◽  
Human Stem Cell ◽  
Zikv Infection ◽  
Single Cell Rna Sequencing

Abstract Background The molecular mechanisms underpinning the neurologic and congenital pathologies caused by Zika virus (ZIKV) infection remain poorly understood. One barrier has been the lack of relevant model systems for the developing human brain; however, thanks to advances in the stem cell field, we can now evaluate ZIKV central nervous system infections in human stem cell-derived cerebral organoids which recapitulate complex 3-dimensional neural architecture. Methods We apply Seq-Well—a simple, portable platform for massively parallel single-cell RNA sequencing—to characterize cerebral organoids infected with ZIKV. Using this sequencing method, and published transcriptional profiles, we identify multiple cellular populations in our organoids, including neuroprogenitor cells, intermediate progenitor cells, and terminally differentiated neurons. We detect and quantify host mRNA transcripts and viral RNA with single-cell resolution, defining transcriptional features of uninfected cells and infected cells. Results In this model of the developing brain, we identify preferred tropisms of ZIKV infection and pronounced effects on cell division, differentiation, and death. Our data additionally reveal differences in cellular populations and gene expression within organoids infected by historic and contemporary ZIKV strains from a variety of geographic locations. This finding might help explain phenotypic differences attributed to the viruses, including variable propensity to cause microcephaly. Conclusion 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. Disclosures All Authors: No reported Disclosures.

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