scholarly journals Single-Cell Sequencing Analysis and Weighted Co-Expression Network Analysis Based on Public Databases Identified That TNC Is a Novel Biomarker for Keloid

2021 ◽  
Vol 12 ◽  
Author(s):  
Jiaheng Xie ◽  
Liang Chen ◽  
Yuan Cao ◽  
Dan Wu ◽  
Wenwen Xiong ◽  
...  
Keyword(s):  
Network Analysis ◽  
Single Cell ◽  
Rna Sequencing ◽  
Potential Role ◽  
Differential Expression Analysis ◽  
Sequencing Analysis ◽  
Sequencing Data ◽  
Normal Tissues ◽  
Keloid Formation ◽  
One Step

BackgroundThe pathophysiology of keloid formation is not yet understood, so the identification of biomarkers for kelod can be one step towards designing new targeting therapies which will improve outcomes for patients with keloids or at risk of developing keloids.MethodsIn this study, we performed single-cell RNA sequencing analysis, weighted co-expression network analysis, and differential expression analysis of keloids based on public databases. And 3 RNA sequencing data from keloid patients in our center were used for validation. Besides, we performed QRT-PCR on keloid tissue and adjacent normal tissues from 16 patients for further verification.ResultsWe identified the sensitive biomarker of keloid: Tenascin-C (TNC). Then, Pseudotime analysis found that the expression level of TNC decreased first, then stabilized and finally increased with fibroblast differentiation, suggesting that TNC may play an potential role in fibroblast differentiation. In addition, there were differences in the infiltration level of macrophages M0 between the TNC-high group and the TNC-low group. Macrophages M0 had a higher infiltration level in low TNC- group (P<0.05).ConclusionOur results can provide a new idea for the diagnosis and treatment of keloid.

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 IKAP—Identifying K mAjor cell Population groups in single-cell RNA-sequencing analysis

GigaScience ◽  
2019 ◽  
Vol 8 (10) ◽  
Author(s):  
Yun-Ching Chen ◽  
Abhilash Suresh ◽  
Chingiz Underbayev ◽  
Clare Sun ◽  
Komudi Singh ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Sequencing Analysis ◽  
Sequencing Data ◽  
Peripheral Blood Mononuclear ◽  
Biologically Relevant ◽  
Single Cell Rna Sequencing ◽  
Cell Groups ◽  
Cell Ontology

AbstractBackgroundIn single-cell RNA-sequencing analysis, clustering cells into groups and differentiating cell groups by differentially expressed (DE) genes are 2 separate steps for investigating cell identity. However, the ability to differentiate between cell groups could be affected by clustering. This interdependency often creates a bottleneck in the analysis pipeline, requiring researchers to repeat these 2 steps multiple times by setting different clustering parameters to identify a set of cell groups that are more differentiated and biologically relevant.FindingsTo accelerate this process, we have developed IKAP—an algorithm to identify major cell groups and improve differentiating cell groups by systematically tuning parameters for clustering. We demonstrate that, with default parameters, IKAP successfully identifies major cell types such as T cells, B cells, natural killer cells, and monocytes in 2 peripheral blood mononuclear cell datasets and recovers major cell types in a previously published mouse cortex dataset. These major cell groups identified by IKAP present more distinguishing DE genes compared with cell groups generated by different combinations of clustering parameters. We further show that cell subtypes can be identified by recursively applying IKAP within identified major cell types, thereby delineating cell identities in a multi-layered ontology.ConclusionsBy tuning the clustering parameters to identify major cell groups, IKAP greatly improves the automation of single-cell RNA-sequencing analysis to produce distinguishing DE genes and refine cell ontology using single-cell RNA-sequencing data.

scholarly journals bayNorm: Bayesian gene expression recovery, imputation and normalisation for single cell RNA-sequencing data

2018 ◽  
Author(s):  
Wenhao Tang ◽  
François Bertaux ◽  
Philipp Thomas ◽  
Claire Stefanelli ◽  
Malika Saint ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Single Molecule ◽  
Empirical Bayes ◽  
Missing Values ◽  
Likelihood Function ◽  
Differential Expression Analysis ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

Normalisation of single cell RNA sequencing (scRNA-seq) data is a prerequisite to their interpretation. The marked technical variability and high amounts of missing observations typical of scRNA-seq datasets make this task particularly challenging. Here, we introduce bayNorm, a novel Bayesian approach for scaling and inference of scRNA-seq counts. The method’s likelihood function follows a binomial model of mRNA capture, while priors are estimated from expression values across cells using an empirical Bayes approach. We demonstrate using publicly-available scRNA-seq datasets and simulated expression data that bayNorm allows robust imputation of missing values generating realistic transcript distributions that match single molecule FISH measurements. Moreover, by using priors informed by dataset structures, bayNorm improves accuracy and sensitivity of differential expression analysis and reduces batch effect compared to other existing methods. Altogether, bayNorm provides an efficient, integrated solution for global scaling normalisation, imputation and true count recovery of gene expression measurements from scRNA-seq data.

scholarly journals Differential dropout analysis captures biological variation in single-cell RNA sequencing data

2021 ◽  
Author(s):  
Gerard A. Bouland ◽  
Ahmed Mahfouz ◽  
Marcel J.T. Reinders
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Rna Sequencing ◽  
Relative Abundance ◽  
Differential Expression Analysis ◽  
Biological Variation ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Technical Artifacts ◽  
Zero Counts

AbstractSingle-cell RNA sequencing data is characterized by a large number of zero counts, yet there is growing evidence that these zeros reflect biological rather than technical artifacts. We propose differential dropout analysis (DDA), as an alternative to differential expression analysis (DEA), to identify the effects of biological variation in single-cell RNA sequencing data. Using 16 publicly available datasets, we show that dropout patterns are biological in nature and can assess the relative abundance of transcripts more robustly than counts.

scholarly journals Single-Cell RNA Sequencing Analysis of the Heterogeneity in Gene Regulatory Networks in Colorectal Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Rui-Qi Wang ◽  
Wei Zhao ◽  
Hai-Kui Yang ◽  
Jia-Mei Dong ◽  
Wei-Jie Lin ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Single Cell ◽  
Rna Sequencing ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Sequencing Analysis ◽  
Sequencing Data ◽  
Communication Analysis ◽  
Single Cell Rna Sequencing ◽  
Gene Regulatory

Colorectal cancer (CRC) manifests as gastrointestinal tumors with high intratumoral heterogeneity. Recent studies have demonstrated that CRC may consist of tumor cells with different consensus molecular subtypes (CMS). The advancements in single-cell RNA sequencing have facilitated the development of gene regulatory networks to decode key regulators for specific cell types. Herein, we comprehensively analyzed the CMS of CRC patients by using single-cell RNA-sequencing data. CMS for all malignant cells were assigned using CMScaller. Gene set variation analysis showed pathway activity differences consistent with those reported in previous studies. Cell–cell communication analysis confirmed that CMS1 was more closely related to immune cells, and that monocytes and macrophages play dominant roles in the CRC tumor microenvironment. On the basis of the constructed gene regulation networks (GRNs) for each subtype, we identified that the critical transcription factor ERG is universally activated and upregulated in all CMS in comparison with normal cells, and that it performed diverse roles by regulating the expression of different downstream genes. In summary, molecular subtyping of single-cell RNA-sequencing data for colorectal cancer could elucidate the heterogeneity in gene regulatory networks and identify critical regulators of CRC.

scholarly journals Accounting for technical noise in single-cell RNA sequencing analysis

2017 ◽  
Author(s):  
Cheng Jia ◽  
Derek Kelly ◽  
Junhyong Kim ◽  
Mingyao Li ◽  
Nancy R. Zhang
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Error Control ◽  
Empirical Bayes ◽  
Type I Error ◽  
Differential Expression Analysis ◽  
Type I ◽  
Sequencing Analysis ◽  
Batch Effects ◽  
Single Cell Rna Sequencing

ABSTRACTRecent technological breakthroughs have made it possible to measure RNA expression at the single-cell level, thus paving the way for exploring expression heterogeneity among individual cells. Current single-cell RNA sequencing (scRNA-seq) protocols are complex and introduce technical biases that vary across cells, which can bias downstream analysis without proper adjustment. To account for cell-to-cell technical differences, we propose a statistical framework, TASC (Toolkit for Analysis of Single Cell RNA-seq), an empirical Bayes approach to reliably model the cell-specific dropout rates and amplification bias by use of external RNA spike-ins. TASC incorporates the technical parameters, which reflect cell-to-cell batch effects, into a hierarchical mixture model to estimate the biological variance of a gene and detect differentially expressed genes. More importantly, TASC is able to adjust for covariates to further eliminate confounding that may originate from cell size and cell cycle differences. In simulation and real scRNA-seq data, TASC achieves accurate Type I error control and displays competitive sensitivity and improved robustness to batch effects in differential expression analysis, compared to existing methods. TASC is programmed to be computationally efficient, taking advantage of multi-threaded parallelization. We believe that TASC will provide a robust platform for researchers to leverage the power of scRNA-seq.

scholarly journals The lncRNAs Implicated in Redox Regulation in Ybx1 Deficient Zebrafish Larvae

2019 ◽  
Author(s):  
Chen Huang ◽  
Bo Zhu ◽  
Dongliang Leng ◽  
Wei Ge ◽  
Xiaohua Douglas Zhang
Keyword(s):  
Network Analysis ◽  
Rna Sequencing ◽  
Transcriptome Analysis ◽  
Redox Regulation ◽  
Differential Expression Analysis ◽  
Interaction Patterns ◽  
Vertebrate Development ◽  
Wild Type ◽  
Sequencing Data ◽  
Homozygous Mutant

AbstractYbx1 has been demonstrated as a crucial gene in embryogenesis, reproduction as well as development in various vertebrates such as mouse and zebrafish. However, the underlying lncRNA-mediated mechanisms require deep investigation. Particularly, the importance of lncRNA to vertebrate development is controversial and questionable since many studies have yielded contradictory conclusions for the same lncRNAs. In the present study, in order to disclose the lncRNAs implicated in vertebrate development, a systematic transcriptome analysis is conducted based on the RNA sequencing data derived from ybx1 homozygous mutant zebrafish on day5 (day5_ybx1-/-) as well as wild type zebrafish on day5 and day6 (day5_ybx1+/+, day6_ybx1+/+). A high-confidence dataset of zebrafish lncRNAs is detected using a stepwise filtering pipeline. Differential expression analysis and co-expression network analysis reveal that several lncRNAs probably act on duox and noxo1a, the genes related to redox (reduction–oxidation reaction) processes which are triggered by ybx1 disruption. Validation by an experimental study on three selected lncRNAs indicates that knockdown of all selected lncRNAs leads to morphological deformation of larvae. In addition, our experiments effectively support the prediction of network analysis in many interaction patterns between the selected lncRNAs and the two redox genes (duox, noxo1a). In short, our study provides new insights into the function and mechanism of lncRNAs implicated in zebrafish embryonic development and demonstrates the importance of lncRNAs in vertebrate development.Author SummaryLncRNAs has been emerged as key regulatory layers because of their multiple functions in diverse biological processes and pathways. However, there is disagreement about the roles of lncRNAs in vertebrate development Some cases demonstrated that lncRNAs was important to development. Others showed that lncRNAs just have feeble functions in development. On the other hand, Ybx1 processing multi-functions has been well demonstrated as a key protein to most vertebrate in development. Hence, aimed at disclosure of key lncRNAs implicated in vertebrate development as well as their possible roles, we performed a systematic transcriptome analysis based on the deep RNA sequencing data of ybx1 homozygous mutant zebrafish and wild type zebrafish. Our analysis successfully revealed several lncRNAs probably target on the redox-related genes. Furthermore, our experiments validated the importance of these lncRNAs to zebrafish embryo development. This study is the first to utilize Ybx1 as breakpoint to identify key lncRNA related to vertebrate development and provides new insights into underlying mechanisms of lncRNAs in development.

scholarly journals Analysis of Single-Cell RNA-Sequencing Data: A Step-by-Step Guide

2021 ◽  
Vol 2 (1) ◽  
pp. 43-61
Author(s):  
Aanchal Malhotra ◽  
Samarendra Das ◽  
Shesh N. Rai
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Rna Sequencing ◽  
Count Data ◽  
Negative Binomial ◽  
Expression Profiles ◽  
Differential Expression Analysis ◽  
Sequencing Data ◽  
Zero Inflation ◽  
Single Cell Rna Sequencing

Single-cell RNA-sequencing (scRNA-seq) technology provides an excellent platform for measuring the expression profiles of genes in heterogeneous cell populations. Multiple tools for the analysis of scRNA-seq data have been developed over the years. The tools require complicated commands and steps to analyze the underlying data, which are not easy to follow by genome researchers and experimental biologists. Therefore, we describe a step-by-step workflow for processing and analyzing the scRNA-seq unique molecular identifier (UMI) data from Human Lung Adenocarcinoma cell lines. We demonstrate the basic analyses including quality check, mapping and quantification of transcript abundance through suitable real data example to obtain UMI count data. Further, we performed basic statistical analyses, such as zero-inflation, differential expression and clustering analyses on the obtained count data. We studied the effects of excess zero-inflation present in scRNA-seq data on the downstream analyses. Our findings indicate that the zero-inflation associated with UMI data had no or minimal role in clustering, while it had significant effect on identifying differentially expressed genes. We also provide an insight into the comparative analysis for differential expression analysis tools based on zero-inflated negative binomial and negative binomial models on scRNA-seq data. The sensitivity analysis enhanced our findings in that the negative binomial model-based tool did not provide an accurate and efficient way to analyze the scRNA-seq data. This study provides a set of guidelines for the users to handle and analyze real scRNA-seq data more easily.

scholarly journals Characterization of dendritic cell subtypes in human cord blood by single-cell sequencing

2019 ◽  
Vol 5 (4) ◽  
pp. 199-208
Author(s):  
Xiaoyang Jin ◽  
Lingyuan Meng ◽  
Zhao Yin ◽  
Haisheng Yu ◽  
Linnan Zhang ◽  
...  
Keyword(s):  
T Cells ◽  
Dendritic Cell ◽  
Cord Blood ◽  
Single Cell ◽  
Rna Sequencing ◽  
Differential Expression Analysis ◽  
Sequencing Data ◽  
Human Cord Blood ◽  
Single Cell Rna Sequencing ◽  
Antigen Presenting

Abstract Dendritic cells (DCs) are professional antigen-presenting cells (APCs). The key functions of DCs include engulfing, processing and presenting antigens to T cells and regulating the activation of T cells. There are two major DC subtypes in human blood: plasmacytoid DCs (pDCs) and conventional DCs. To define the differences between the adult and infant immune systems, especially in terms of DC constitution, we enriched DCs from human cord blood and generated single-cell RNA sequencing data from about 7000 cells using the 10x Genomics Single Cell 3′ Solution. After incorporating the differential expression analysis method in our clustering process, we identified all the known dendritic cell subsets. Interestingly, we also found a group of DCs with gene expression that was a mix of megakaryocytes and pDCs. Further, we verified the expression of selected genes at both the RNA level by PCR and the protein level by flow cytometry. This study further demonstrates the power of single-cell RNA sequencing in dendritic cell research.

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