代,多模式和可扩展的单细胞分析,用于发现和表征信号调节器
bioRxiv : the preprint server for biology
|September 5, 2025
概括
我们开发了一种结合单细胞蛋白,RNA和CRISPR数据的新方法, 这种方法成功地发现了mTOR信号的新型调节剂,进步了我们对细胞过程的理解.
科学领域:
- 分子生物学
- 系统生物学
- 基因组学
背景情况:
- 细胞信号对于细胞功能至关重要, 但识别其调节剂和后果是复杂的.
- 了解信号通路需要整合方法来将分子变化与细胞状态联系起来.
研究的目的:
- 开发和验证一个代的实验和计算框架来识别和描述信号蛋白调节剂.
- 使用化RPS6 (pRPS6) 的案例研究提名mTOR信号通路的新调节剂.
主要方法:
- 使用10x Flex测试的定制工作流进行单细胞蛋白,RNA和CRISPR扰动的联合分析.
- 通过针对性扰动生成配对蛋白-RNA测量数据集,以训练预测模型.
- 将基于转录的预测模型应用于全基因组的Perturb-seq数据集以进行*in silico*查.
主要成果:
- 成功训练了一个将转录组数据与pRPS6水平联系起来的预测模型.
- 通过全基因组*in silico*选进行mTOR信号的指定的新型调节剂.
- 实验验证证了预测,并揭示了各种调节机制,包括合成代谢活性,增殖和压力途径.
结论:
- 综合实验和计算框架使可扩展的多模式表型和信号调节器的发现成为可能.
- 这种方法通过提供一种强大的方法来识别途径调节器,从而促进了细胞信号传递的研究.
- 证明了将单细胞多组和扰动数据用于生物发现的力量.
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