一个预训练的大型生成模型,用于将单细胞转录组翻译为蛋白质组
Linjing Liu1,2,3,4, Wei Li2, Fang Wang2
1Department of Computer Science, City University of Hong Kong, Hong Kong, China.
Nature biomedical engineering
|November 5, 2025
概括
我们开发了scTranslator,这是一种新的AI模型,可以从转录组数据中推断单细胞蛋白质组. 该工具增强了多omics分析,克服了生物研究当前蛋白质组技术的局限性.
科学领域:
- 单细胞生物学 单细胞生物学
- 计算生物学是一种计算生物学.
- 蛋白质组学是指蛋白质组学.
背景情况:
- 单细胞蛋白质丰富度测量对于了解细胞过程和疾病至关重要.
- 目前的单细胞蛋白质组技术存在一些局限性,包括覆盖范围低,吞吐量低,灵敏度低,成本高.
研究的目的:
- 开发一个预训练的,大型的生成模型,scTranslator,从转录组数据中推断出缺失的单细胞蛋白质.
- 为应对现有的单细胞蛋白质组技术所面临的挑战.
主要方法:
- 利用自然语言处理和遗传中心教条原则来设计scTranslator.
- 采用预训练的大型生成模型方法,从转录数据中推断蛋白质组数据.
- 在各种数据集,分析技术,细胞类型,组织和疾病环境中验证了scTranslator.
主要成果:
- scTranslator通过从转录组中推断单细胞蛋白质组,准确地生成多组数据.
- 在各种实验技术 (CITE-seq,空间CITE-seq,REAP-seq,NEAT-seq) 和生物环境中证明了模型的准确性,稳定性和灵活性.
- 展示了scTranslator在下游应用中的有效性,例如基因/蛋白质相互作用推断,扰动预测,细胞聚类,批次校正和泛癌细胞起源识别.
结论:
- scTranslator提供了一种强大的解决方案,通过推断蛋白质组数据来增强单细胞多组组分析.
- 该模型克服了当前蛋白质组技术的关键局限性,使得高分辨率的生物见解成为可能.
- scTranslator在各种生物和疾病环境中的各种下游分析中展示了广泛的适用性和优势.
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