Single-cell RNA-sequencing analysis identifies host long noncoding RNA MAMDC2-AS1 as a co-factor for HSV-1 nuclear transport

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.

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Knockdown of long noncoding RNA AC245100.4 inhibits the tumorigenesis of prostate cancer cells via the STAT3/NR4A3 axis

Epigenomics ◽

10.2217/epi-2021-0293 ◽

2021 ◽

Author(s):

Chi Liu ◽

Ping Lin ◽

Jiabin Zhao ◽

Hui Xie ◽

Rou Li ◽

...

Keyword(s):

Prostate Cancer ◽

Cancer Cells ◽

Rna Sequencing ◽

Long Noncoding Rna ◽

Noncoding Rna ◽

Transcriptional Repressor ◽

Sequencing Analysis ◽

Loss Of Function ◽

Prostate Cancer Cells ◽

Rna Immunoprecipitation

Aim: To explore the role and mechanism of long noncoding RNA AC245100.4 and NR4A3 in prostate cancer (PCa). Methods: RNA-sequencing analysis was used to detect the downstream genes of AC245100.4. A series of gain- and loss-of-function approaches were used to investigate the roles of AC245100.4 and NR4A3. RNA immunoprecipitation was performed to examine the interaction between AC245100.4 and STAT3. Results: AC245100.4 was significantly upregulated in PCa cells and tissues. Knockdown of AC21500.4 significantly inhibited the tumorigenesis of PCa cells. Mechanistically, AC245100.4 deregulated the transcription of NR4A3 via increasing p-STAT3, which acted as a transcriptional repressor of NR4A3. Conclusion: Knockdown of lncRNA AC245100.4 inhibits the tumorigenesis of PCa cells via the STAT3/ NR4A3 axis.

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

Journal of Cellular Physiology ◽

10.1002/jcp.30054 ◽

2020 ◽

Cited By ~ 2

Author(s):

Rajasekaran Mahalingam ◽

Prakash Dharmalingam ◽

Abirami Santhanam ◽

Sivareddy Kotla ◽

Gangarao Davuluri ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Sequencing Analysis ◽

Single Cell Rna Sequencing

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

GigaScience ◽

10.1093/gigascience/giaa102 ◽

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.

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