深度学习估算单细胞DNA甲基化状态,增强对精神分裂症表观遗传改变的检测。

IF 11.1 Q1 CELL BIOLOGY
Cell genomics Pub Date : 2025-03-12 Epub Date: 2025-02-21 DOI:10.1016/j.xgen.2025.100774
Jiyun Zhou, Chongyuan Luo, Hanqing Liu, Matthew G Heffel, Richard E Straub, Joel E Kleinman, Thomas M Hyde, Joseph R Ecker, Daniel R Weinberger, Shizhong Han
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引用次数: 0

摘要

DNA甲基化(DNAm)是一种重要的表观遗传标记,在基因调控、哺乳动物发育和人类疾病中起着重要作用。单细胞技术可以在单个细胞中分析胞嘧啶上的dna,但它们经常受到CpG位点覆盖率低的影响。我们介绍了scMeFormer,一个基于变压器的深度学习模型,用于输入单个细胞中每个CpG位点的DNAm状态。对人类和小鼠的5个单核DNAm数据集的综合评估表明,scMeFormer优于其他模型,即使覆盖率降低到原始CpG位点的10%,也能实现高保真的植入。将scMeFormer应用于来自精神分裂症患者和对照组前额皮质的单核dna数据集,确定了数千个与精神分裂症相关的差异甲基化区域,这些区域在没有imputation的情况下是无法检测到的,并增加了我们对精神分裂症表观遗传改变的理解粒度。我们预计scMeFormer将成为推进单细胞dna研究的宝贵工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep learning imputes DNA methylation states in single cells and enhances the detection of epigenetic alterations in schizophrenia.

DNA methylation (DNAm) is a key epigenetic mark with essential roles in gene regulation, mammalian development, and human diseases. Single-cell technologies enable profiling DNAm at cytosines in individual cells, but they often suffer from low coverage for CpG sites. We introduce scMeFormer, a transformer-based deep learning model for imputing DNAm states at each CpG site in single cells. Comprehensive evaluations across five single-nucleus DNAm datasets from human and mouse demonstrate scMeFormer's superior performance over alternative models, achieving high-fidelity imputation even with coverage reduced to 10% of original CpG sites. Applying scMeFormer to a single-nucleus DNAm dataset from the prefrontal cortex of patients with schizophrenia and controls identified thousands of schizophrenia-associated differentially methylated regions that would have remained undetectable without imputation and added granularity to our understanding of epigenetic alterations in schizophrenia. We anticipate that scMeFormer will be a valuable tool for advancing single-cell DNAm studies.

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