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Next Generation Sequencing Facilitates Quantitative Analysis of The Transcriptome of Microbiota-Specific Memory CD4 Cells With vs Without CAMCI Treatment

ABSTRACT: Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to compare the transcriptomes of microbiota-specific memory CD4 T cells that were treated with vs without the combination of cell activation and metabolic checkpoint inhibition (CAMCI) strategy in vivo. Methods: mRNA profiles of donor-derived CBir1 TCR Tg CD4+ cells were generated by deep sequencing, with cells from 3-4 mice polling together per group, using Illumina HiSeq. The sequence reads that passed quality filters were analyzed with DESeq2 for gene expression comparisons between groups of samples. The Wald test was used to generate p-values and log2 fold changes. Results: Using an optimized data analysis workflow, we mapped about 50 million sequence reads per sample to the mouse genome (GRCm38). Depending on the groups that were compared, a range from 17-2868 transcripts showed differential expression, with an absolute log2 fold change >1 and an adjusted p value <0.01. Differentially expressed genes (DEGs) were used to generate a heatmap, a principle component analysis plot, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis.

ORGANISM(S): Mus musculus

PROVIDER: GSE161311 | GEO | 2020/11/12

REPOSITORIES: GEO

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Project description:Purpose: We applied the next-generation sequencing to explore the differential gene expression in CAD cells differentiated with dexamethasone (Dex), compared to the undifferentiated cells using Illumna -based RNA-sequencing. Methods: Total RNAs were extracted from undifferentiated CAD cells and Dex-differentiated cells (5 days). Three independent samples were collected from both experimental groups. Samples then underwent to the sequencing step performed on NextSeq with a 75 base-pair end read length. Quality filtered reads were mapped to the mouse reference genome (GRCm38.p6). Feature Counts software was implemented to obtain raw counts for all mouse genes. The gene counts were used for differential expression analysis with the DESeq2 package to identify differentially expressed genes (DEGs) in the Dex-differentiated CAD cells, as compared to the undifferentiated CAD cells. Results: The sequence reads (expression level) per sample was obtained in a range of 9.8-11.2 million. The high quality of sequence reads was mapped to the mouse reference transcriptome (GRCm38.p6). The gene counts were used for differential expression analysis. The sample-to-sample distance demonstrated that the clustering of the undifferentiated groups was completely separated from the clustering of Dex-differentiated groups. A PCA plot showed that there was a huge variation (PC 1 =99%) between the Dex-differentiated group and the undifferentiated group but less variation was observed within the same group (PC2 = 0%). These statistic profiles indicate that the transcriptomic profile between Dex-differentiated CAD cells and undifferentiated CAD cells had the different pattern. A total of 1,183 differentially expressed genes (DEGs) were identified in the Dex-differentiated CAD cells compared to the undifferentiated CAD cells (false discovery rate < 0.01 and |log2 [fold change] | > 2). Of these, 803 and 380 genes were significantly upregulated and downregulated in the Dex-differentiated group, respectively. Conclusion: Dexamethasone causes extensive changes in gene expression of CAD cells.

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Next Generation Sequencing Facilitates Quantitative Analysis of The Transcriptome of Microbiota-Specific Memory CD4 Cells With vs Without CAMCI Treatment

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