阿卡迪亚通过scRNA-seq和空间蛋白质组的整合揭示了空间依赖的转录程序
Bar Rozenman1, Kevin Hoffer-Hawlik1,2, Nicholas Djedjos3
1Department of Biomedical Engineering, Columbia University, New York, NY, USA.
bioRxiv : the preprint server for biology
|December 3, 2025
概括
在没有细胞配对的情况下,ARCADIA整合了空间转录学和蛋白学,揭示了组织微环境如何影响细胞状态. 这种方法重建了组织结构,并将细胞功能与它们的空间联系起来.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 免疫学 免疫学 免疫学
背景情况:
- 单细胞RNA测序 (scRNA-seq) 缺乏空间上下文.
- 空间蛋白质组测试具有有限的标记面板.
- 整合空间转录组学和蛋白质组学是具有挑战性的,因为缺少细胞条码配对和特征对应.
研究的目的:
- 开发一种新的计算框架,ARCADIA,用于空间转录组学和蛋白质组学的交叉模式集成.
- 为了能够阐明空间如何塑造细胞转录程序.
- 通过不要求单元格条形码配对或直接的特征对特征对应,克服现有方法的局限性.
主要方法:
- 阿卡迪亚使用具有双变量自动编码器 (VAE) 的生成框架.
- 它确定了特定于模式的原型 (极端的表型状态).
- 原型通过最小化细胞类型组成差异来跨模式对齐,创建一个共享的坐标系统.
主要成果:
- 在半合成基准上,ARCADIA准确地汇总了细胞类型对应和空间结构.
- 它在整合配对的CITE-seq和合成空间数据方面优于现有的弱链方法.
- 应用于人类桃体数据,ARCADIA重建组织架构并识别空间依赖的转录程序.
结论:
- 阿卡迪亚提供了一种强大的新方法,用于整合多式模式的空间奥米克数据.
- 该框架成功地将B细胞成熟和T细胞激活/耗尽等细胞功能与其特定的微环境联系起来.
- 这种整合提高了我们对空间环境如何影响细胞状态和组织组织的理解.
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