Retrotransposable elements (RTEs) are common mobile genetic elements comprising ∼42% of the human genome. RTEs play critical roles in gene regulation and function, but how they are specifically involved in complex diseases is largely unknown. Here, we investigate the cellular heterogeneity of RTEs using 12 single-cell transcriptome profiles covering three neurodegenerative diseases, Alzheimer's disease (AD), Parkinson's disease, and multiple sclerosis. We identify cell type marker RTEs in neurons, astrocytes, oligodendrocytes, and oligodendrocyte precursor cells that are related to these diseases. The differential expression analysis reveals the landscape of dysregulated RTE expression, especially L1s, in excitatory neurons of multiple neurodegenerative diseases. Machine learning algorithms for predicting cell disease stage using a combination of RTE and gene expression features suggests dynamic regulation of RTEs in AD. Furthermore, we construct a single-cell atlas of retrotransposable elements in neurodegenerative disease (scARE) using these data sets and features. scARE has six feature analysis modules to explore RTE dynamics in a user-defined condition. To our knowledge, scARE represents the first systematic investigation of RTE dynamics at the single-cell level within the context of neurodegenerative diseases.

中文翻译：

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神经退行性疾病中单细胞水平反转录转座子的动态失调


逆转录转座因子 （RTE） 是常见的移动遗传元件，约占人类基因组的 42%。RTEs 在基因调控和功能中起着关键作用，但它们如何具体参与复杂疾病在很大程度上是未知的。在这里，我们使用 12 个单细胞转录组谱研究了 RTEs 的细胞异质性，这些转录组涵盖了三种神经退行性疾病、阿尔茨海默病 （AD）、帕金森病和多发性硬化症。我们在与这些疾病相关的神经元、星形胶质细胞、少突胶质细胞和少突胶质细胞前体细胞中鉴定细胞类型标志物 RTE。差异表达分析揭示了多种神经退行性疾病的兴奋性神经元中 RTE 表达失调的景观，尤其是 L1。使用 RTE 和基因表达特征的组合预测细胞疾病分期的机器学习算法表明 AD 中 RTE 的动态调节。此外，我们使用这些数据集和特征构建了神经退行性疾病 （scARE） 中逆转录转座因子的单细胞图谱。scARE 有六个特征分析模块，用于在用户定义的条件下探索 RTE 动态。据我们所知，scARE 代表了在神经退行性疾病背景下对单细胞水平 RTE 动力学的首次系统研究。