在人类固体瘤中临床区别的细胞状态和生态系统的地图
Bogdan A Luca1, Chloé B Steen2, Magdalena Matusiak3
1Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA.
Cell
|October 1, 2021
概括
机器学习工具EcoTyper可以识别人类癌症中的细胞状态和多细胞群体. 它揭示了与癌症发展和患者存活相关的十个不同的群体,
科学领域:
- 细胞生物学
- 计算生物学
- 基因组学
背景情况:
- 了解细胞组织和局部信号环境对于发育,衰老和癌症研究至关重要.
- 在生物和医学研究中, 鉴定组织内不同的细胞群体是一个关键的挑战.
研究的目的:
- 引入EcoTyper,一个用于识别和验证细胞状态和多细胞社区的机器学习框架.
- 分析细胞组织及其在人类癌症中的临床相关性.
主要方法:
- 开发了EcoTyper,一个机器学习框架,利用批量,单细胞和空间解析的基因表达数据.
- 应用EcoTyper在16种人类癌症中的12种主要细胞系.
- 分析了细胞状态共同发生的模式,以定义多细胞社区.
主要成果:
- 鉴定了69种转录定义的细胞状态,其中许多是针对瘤组织和预后的.
- 在瘤类型中发现了十种临床上不同的多细胞群体.
- 发现与不良生存,正常组织和早期癌症发展相关的特定群体.
结论:
- 在人类癌症中阐明细胞组织的基本单元.
- 证明了EcoTyper对细胞生态系统的大规模分析的能力.
- 为了解细胞群体在癌症和其他生物过程中的作用提供了一个框架.
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