Comprehensive Transcriptomic Investigation of Rett Syndrome Reveals Increasing Complexity Trends from Induced Pluripotent Stem Cells to Neurons with Implications for Enriched Pathways.
Yusuf Caglar OdabasiSena YanasikPelin Saglam-MetinerYasin KaymazOzlem Yesil-CeliktasPublished in: ACS omega (2023)
Rett syndrome (RTT) is a rare genetic neurodevelopmental disorder that has no cure apart from symptomatic treatments. While intense research efforts are required to fulfill this unmet need, the fundamental challenge is to obtain sufficient patient data. In this study, we used human transcriptomic data of four different sample types from RTT patients including induced pluripotent stem cells, differentiated neural progenitor cells, differentiated neurons, and postmortem brain tissues with an increasing in vivo-like complexity to unveil specific trends in gene expressions across the samples. Based on DEG analysis, we identified F8A3, CNTN6, RPE65, and COL19A1 to have differential expression levels in three sample types and also observed previously reported genes such as MECP2, FOXG1, CACNA1G, SATB2, GABBR2, MEF2C, KCNJ10, and CUX2 in our study. Considering the significantly enriched pathways for each sample type, we observed a consistent increase in numbers from iPSCs to NEUs where MECP2 displayed profound effects. We also validated our GSEA results by using single-cell RNA-seq data. In WGCNA, we elicited a connection among MECP2, TNRC6A, and HOXA5. Our findings highlight the utility of transcriptomic analyses to determine genes that might lead to therapeutic strategies.
Keyphrases
- induced pluripotent stem cells
- rna seq
- single cell
- genome wide
- electronic health record
- end stage renal disease
- case report
- newly diagnosed
- spinal cord
- genome wide identification
- gene expression
- chronic kidney disease
- quality improvement
- bioinformatics analysis
- peritoneal dialysis
- dna methylation
- genome wide analysis
- white matter
- multiple sclerosis
- patient reported outcomes
- subarachnoid hemorrhage
- deep learning
- long noncoding rna