In silico analysis of single-cell RNA sequencing data from 3 and 7 days old mouse spermatogonial stem cells to identify their differentially expressed genes and transcriptional regulators

2018 ◽  
Vol 119 (9) ◽  
pp. 7556-7569 ◽  
Author(s):  
Sajjad Sisakhtnezhad
Keyword(s):  
Stem Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Differentially Expressed Genes ◽  
In Silico ◽  
In Silico Analysis ◽  
Transcriptional Regulators ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Silico Analysis

In Silico Analysis of Micro-RNA Sequencing Data

2021 ◽  
pp. 231-251
Author(s):  
Ernesto Aparicio-Puerta ◽  
Bastian Fromm ◽  
Michael Hackenberg ◽  
Marc K. Halushka
Keyword(s):  
Rna Sequencing ◽  
In Silico ◽  
In Silico Analysis ◽  
Micro Rna ◽  
Sequencing Data ◽  
Silico Analysis

scholarly journals scConsensus: combining supervised and unsupervised clustering for cell type identification in single-cell RNA sequencing data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Bobby Ranjan ◽  
Florian Schmidt ◽  
Wenjie Sun ◽  
Jinyu Park ◽  
Mohammad Amin Honardoost ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Differentially Expressed Genes ◽  
Cell Types ◽  
Unsupervised Clustering ◽  
Differentially Expressed ◽  
Consensus Clustering ◽  
Cell Type ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

Abstract Background Clustering is a crucial step in the analysis of single-cell data. Clusters identified in an unsupervised manner are typically annotated to cell types based on differentially expressed genes. In contrast, supervised methods use a reference panel of labelled transcriptomes to guide both clustering and cell type identification. Supervised and unsupervised clustering approaches have their distinct advantages and limitations. Therefore, they can lead to different but often complementary clustering results. Hence, a consensus approach leveraging the merits of both clustering paradigms could result in a more accurate clustering and a more precise cell type annotation. Results We present scConsensus, an $${\mathbf {R}}$$ R framework for generating a consensus clustering by (1) integrating results from both unsupervised and supervised approaches and (2) refining the consensus clusters using differentially expressed genes. The value of our approach is demonstrated on several existing single-cell RNA sequencing datasets, including data from sorted PBMC sub-populations. Conclusions scConsensus combines the merits of unsupervised and supervised approaches to partition cells with better cluster separation and homogeneity, thereby increasing our confidence in detecting distinct cell types. scConsensus is implemented in $${\mathbf {R}}$$ R and is freely available on GitHub at https://github.com/prabhakarlab/scConsensus.

scholarly journals Canonical and early lineage-specific stem cell types identified in planarian SirNeoblasts

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Kaimeng Niu ◽  
Hao Xu ◽  
Yuanyi Zhou Xiong ◽  
Yun Zhao ◽  
Chong Gao ◽  
...  
Keyword(s):  
Stem Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Fate ◽  
Pluripotent Stem Cells ◽  
Expression Patterns ◽  
Cell Subpopulation ◽  
Functional Study ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

Abstract Background The pluripotent stem cells in planarians, a model for tissue and cellular regeneration, remain further identification. We recently developed a method to enrich piwi-1+ cells in Schmidtea mediterranea, by staining cells with SiR-DNA and Cell Tracker Green, named SirNeoblasts that permits their propagation and subsequent functional study in vivo. Since traditional enrichment for planarian neoblasts by Hoechst 33342 staining generates X1 cells, blocking the cell cycle and inducing cytotoxicity, this method by SiR-DNA and Cell Tracker Green represents a complementary technological advance for functional investigation of cell fate and regeneration. However, the similarities in heterogeneity of cell subtypes between SirNeoblasts and X1 remain unknown. Results In this work, we performed single cell RNA sequencing of SirNeoblasts for comparison with differential expression patterns in a publicly available X1 single cell RNA sequencing data. We found first that all of the lineage-specific progenitor cells in X1 were present in comparable proportions in SirNeoblasts. In addition, SirNeoblasts contain an early muscle progenitor that is unreported in X1. Analysis of new markers for putative pluripotent stem cells identified here, with subsequent sub-clustering analysis, revealed earlier lineages of epidermal, muscular, intestinal, and pharyngeal progenitors than have been observed in X1. Using the gcm as a marker, we also identified a cell subpopulation resided in previously identified tgs-1+ neoblasts. Knockdown of gcm impaired the neoblast repopulation, suggesting a function of gcm in neoblasts. Conclusions In summary, the use of SirNeoblasts will enable broad experimental advances in regeneration and cell fate specification, given the possibility for propagation and transplantation of recombinant and mutagenized pluripotent stem cells that are not previously afforded to this rapid and versatile model system.

scholarly journals Single-Cell RNA Sequencing Reveals the Role of Phosphorylation-Related Genes in Hepatocellular Carcinoma Stem Cells

2022 ◽  
Vol 9 ◽  
Author(s):  
Fuwen Yao ◽  
Yongqiang Zhan ◽  
Changzheng Li ◽  
Ying Lu ◽  
Jiao Chen ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Stem Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Protein Kinases ◽  
Kinase Inhibitors ◽  
Underlying Mechanism ◽  
Migration And Invasion ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

Abnormal activation of protein kinases and phosphatases is implicated in various tumorigenesis, including hepatocellular carcinoma (HCC). Advanced HCC patients are treated with systemic therapy, including tyrosine kinase inhibitors, which extend overall survival. Investigation of the underlying mechanism of protein kinase signaling will help to improve the efficacy of HCC therapy. Combining single-cell RNA sequencing data and TCGA RNA-seq data, we profiled the protein kinases, phosphatases, and other phosphorylation-related genes (PRGs) of HCC patients in this study. We found nine protein kinases and PRGs with high expression levels that were mainly detected in HCC cancer stem cells, including POLR2G, PPP2R1A, POLR2L, PRC1, ITBG1BP1, MARCKSL1, EZH2, DTYMK, and AURKA. Survival analysis with the TCGA dataset showed that these genes were associated with poor prognosis of HCC patients. Further correlation analysis showed that these genes were involved in cell cycle-related pathways that may contribute to the development of HCC. Among them, AURKA and EZH2 were identified as two hub genes by Ingenuity Pathway Analysis. Treatment with an AURKA inhibitor (alisertib) and an EZH2 inhibitor (gambogenic) inhibited HCC cell proliferation, migration, and invasion. We also found that both AURKA and EZH2 were highly expressed in TP53-mutant HCC samples. Our comprehensive analysis of PRGs contributes to illustrating the mechanisms underlying HCC progression and identifying potential therapeutic targets for future clinical trials.

scholarly journals Identification of the Differentially Expressed Genes and Prognostic Associations in Single-Cell RNA Sequencing Data of Gastric Cancer

2021 ◽  
Author(s):  
Shuang Gao ◽  
Fazhan Li ◽  
Minghai Zhao ◽  
Wanqing Wu ◽  
Yuming Fu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Single Cell ◽  
Rna Sequencing ◽  
Differentially Expressed Genes ◽  
Ppi Network ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Geo Database ◽  
Differential Genes

Abstract Background: Due to the lack of effective drugs, gastric cancer(GC) has a high mortality rate among other cancers, with a low 5-year survival rate and an inferior prognosis. Thus, screening of meaningful tumor biomarkers or therapeutic targets could play a vital role in the diagnosis, treatment, prognosis, and follow-up of GC. Methods: Gene expression profiles and comprehensive clinical information of 407 patients with GC were downloaded from The Cancer Genome Atlas (TCGA) database. GC-related single-cell RNA sequencing data from the GSE118916 dataset was downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were screened from transcriptomic data in GC and normal samples by R language. The DAVID database was also used to analyze the functions and pathways of DEGs. After combining differential genes with patient survival information, target genes were identified. The interaction of DEGs in the protein-protein interaction (PPI) network was also studied. Results: Our study identified a total of 209 differential genes, which might be positively related to GC. Gene Ontology (GO) analysis indicated numerous enrichment of DEGs in the extracellular matrix organization, extracellular structure organization, and muscle contraction. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that the DEGs were mainly enriched in focal adhesion, protein digestion and absorption, AGE-RAGE signaling pathway in diabetic complications. Further analysis showed the higher expression of Carboxypeptidase vitellogenic-like gene (CPVL) was related to the better prognosis of GC patients in both TCGA and the GEO database. FAM3 metabolism regulating signaling molecule D (FAM3D) and oxidized low-density lipoprotein receptor 1 (OLR1) were significantly associated with GC patients’ prognosis only in the GEO database. Lastly, the PPI network shows the gene expression proteins that interact most closely with CPVL protein.Conclusion: Our study revealed that CPVL gene could be a promising target for the diagnosis and treatment of GC, which has a great significance for the future research on GC. In addition, we were the first to find a close relationship between FAM3D and GC.

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