Comprehensive Transcriptomic Investigation of Rett Syndrome Reveals Increasing Complexity Trends from Induced Pluripotent Stem Cells to Neurons with Implications for Enriched Pathways

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.

雷特综合征（Rett syndrome, RTT）是一种罕见的遗传性神经发育障碍性疾病，目前仅能通过对症治疗缓解症状，尚无根治方案。为满足这一未被满足的临床需求，亟需开展大量深入研究，但核心挑战在于获取足够的患者样本。本研究采用雷特综合征患者的四类样本的转录组数据，包括诱导多能干细胞（induced pluripotent stem cells, iPSCs）、分化神经前体细胞（neural progenitor cells）、分化神经元（neurons, NEUs）以及体内模拟复杂度依次升高的死后脑组织，以此解析不同样本间的基因表达特异性变化趋势。基于差异表达基因（Differentially Expressed Genes, DEG）分析，本研究鉴定出F8A3、CNTN6、RPE65及COL19A1在三类样本中存在差异表达；同时也验证了此前已有报道的MECP2、FOXG1、CACNA1G、SATB2、GABBR2、MEF2C、KCNJ10与CUX2等基因。针对各样本类型的显著富集通路分析结果显示，从诱导多能干细胞到神经元，富集通路的数量呈持续上升趋势，且MECP2在此过程中展现出显著的调控作用。本研究还利用单细胞RNA测序（single-cell RNA-seq）数据对基因集富集分析（Gene Set Enrichment Analysis, GSEA）的结果进行了验证。在加权基因共表达网络分析（Weighted Gene Co-expression Network Analysis, WGCNA）中，本研究揭示了MECP2、TNRC6A与HOXA5之间的调控关联。本研究结果凸显了转录组分析在筛选可用于开发治疗策略的靶基因方面的应用价值。