Examining the co-expression, transcriptome clustering and variation using fuzzy cluster network of testicular stem cells and pluripotent stem cells compared with other cell types. (April 2020)

• Record Type: Journal Article
• Authors: Guttula, Praveen Kumar; Gupta, Mukesh Kumar
• Is Part Of: Computational biology and chemistry. Volume 85(2020)
• Publication Date: 2020-04
• Subjects: ESCs Embryonic stem cells -- SSCs Spermatogonial stem cells -- GSCs Germ-line stem cells -- mGS multipotent adult germline stem cells -- iPSCs Induced Pluripotent stem cells -- NSCs Neural Stem Cells -- MEFs Mouse Embryonic Fibroblast cells -- SOM Self-Organizing Maps -- HC Hierarchal Clustering; Stem cells -- ESCs -- gPSCs -- AutoSOME -- Transcriptome -- Clustering; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2020.107227
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: We studied the clustering of gene expression data and co-expression modules of different cellular phenotypes. Heat maps generated reflects the distinct expression patterns of cells and also intrinsic status of the cells. The Transcriptome variation among GSCs, ESCs and gPSCs was studied using fuzzy cluster network. Abstract: Stem cells are crucial in the field of tissue regeneration and developmental biology. Embryonic stem cells (ESCs) which are pluripotent in nature are derived from the inner cell mass of blastocyst. The gene expression profiles of ESCs and Induced pluripotent stem cells (iPSCs) were compared to identify the differences. Spermatogonial stem cells (SSCs) are also known as Germ-line stem cells (GSCs) present in testis is having the capability of producing the sperm in their whole lifetime. Therefore can be reprogrammed into pluripotent cells called male germline pluripotent cells (gPSCs). It is very difficult to interpret the larger genomic data sets which are available in public databases without high computational facilities. In order to identify the similar groups We studied the co-expression, clustering of the transcriptome and variation of the transcriptome of the GSCs, gPSCs, ESCs and other cell types using fuzzy clustering using AutoSOME. The series matrix file with GSE ID GSE11274 was retrieved and subjected to the various normalization methods, corresponding rows and columns were clustered using p values, ensemble runs, and different running modes. Transcriptome analysis using the proposed approach intuitively and consistently characterized the variation in cell-cell significantly. Collectively, our results suggest that the GSCs and the ESCs displayed differential gene expression profiles, and the GSCs possessed the potential to acquire pluripotency based on the high expression of epigenetic factors and transcription factors. These data may provide novel insights into the reprogramming mechanism of GSCs.