BioProject

Purpose: The goal of this study is to unravel the differential gene expression profiles (RNA-Seq) during the early differentiation of bone marrow mesenchymal stem cells (BMSCs) into neural progenitor-like stem cells (NPCs) under the influence of different growth factor combinations using high-throughput RNA sequencing method; Group A: EGF and bFGF, Group B: EGF + bFGF and IGF-1, and Control: untreated BMSCs. More...

Purpose: The goal of this study is to unravel the differential gene expression profiles (RNA-Seq) during the early differentiation of bone marrow mesenchymal stem cells (BMSCs) into neural progenitor-like stem cells (NPCs) under the influence of different growth factor combinations using high-throughput RNA sequencing method; Group A: EGF and bFGF, Group B: EGF + bFGF and IGF-1, and Control: untreated BMSCs. Methodology: mRNA profiles of BMSCs (Control) and BMSCs-derived NPCs under the influence of growth factor combinations (Group A and Group B) were generated by deep sequencing, in triplicate, using Illumina NextSeq500. Raw reads were evaluated for quality checks in terms of sequencing quality and contamination. Reads were trimmed and filtered using BBDuk. Reads were then aligned to the latest reference genome (rn6) with GTF from Ensembl (v99) using STAR aligner. Aligned reads were transformed into read counts per gene using the RSEM tool. Pairwise differential expression analysis was performed using the DESeq2 R package. qRT–PCR validation was performed using the RT² Profiler PCR Array Rat Custom array (Array ID: CAPR13765E). Results: Using an optimized data analysis workflow, we mapped about 50 million sequence reads per sample to the Rattus norvegicus genome (Rnor_6.0 Assembly) and identified an average of 27,146 transcripts in Group A, 25,850 transcripts in Group B, and 21,683 transcripts in the control group. All transcripts have a fragment count estimation of at least 10 counts per gene. Differential analysis was performed as pairwise grouping. Comparison of Group A vs MSC identified 3,252 differentially expressed genes. Thirteen significantly expressed genes with the potential roles during neural differentiation were randomly selected for validation with qRT–PCR. RNA-seq data had a linear relationship with qRT–PCR. Hierarchical clustering of differentially expressed genes uncovered several as yet uncharacterized genes that may contribute to neural differentiation of mesenchymal stem cells. Data from the present study analysis revealed a significant overlap yet provided complementary insights in transcriptome profiling of mesenchymal stem cells derived neural progenitor-like cells. Conclusion: Our study represents the first detailed analysis of mesenchymal stem cells derived neural progenitor-like cell transcriptomes, generated by RNA-seq technology. Our data has explored crucial and novel genes involved during the early differentiation of mesenchymal stem cells into neural progenitor-like cells under the influence of different growth factor combinations. Overall design: RNA profile of BMSCs-derived NPCs under the influence of different growth factor combinations (Group A, Group B and control) were deep-sequenced on Nextseq500 instrument using High Output sequencing kit in triplicate. Group A was treated with combination of EGF and bFGF, Group B was treated with combination of EGF, bFGF and IGF-1, whereas untreated mesenchymal stem cell served as control group. Less...