PROFET从scRNA-seq数据中预测连续基因表达动态,以阐明癌症治疗反应的异质性
Yu-Chen Cheng1,2,3,4, Hyemin Gu5, Thomas O McDonald1,2,3,4
1Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA.
bioRxiv : the preprint server for biology
|July 9, 2025
概括
PROFET从静态单细胞RNA测序数据中重建连续基因表达轨迹. 这种计算框架揭示了细胞状态转变和乳腺癌治疗反应的异质性.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 系统生物学 系统生物学
背景情况:
- 单细胞RNA测序 (scRNA-seq) 提供了细胞基因表达的高分辨率快照.
- 目前的scRNA-seq方法无法随着时间的推移捕捉连续的基因表达动态.
- 了解动态基因表达对于破译细胞状态转变和对刺激的反应至关重要.
研究的目的:
- 开发一个计算框架,PROFET (基于粒子的生成力匹配表达轨迹的重建),用于从稀疏的scRNA-seq数据重建连续的单细胞基因表达轨迹.
- 应用PROFET来研究乳腺癌中对palbociclib治疗的细胞异质性和动态反应.
主要方法:
- PROFET使用了一种新的利普希茨调节梯度流方法,在时间标记的scRNA-seq样本之间产生粒子流.
- 用神经力匹配来学习轨迹重建的全球向量场.
- 该框架使用合成数据和老鼠/人类体外数据集进行了验证.
主要成果:
- 从静态scRNA-seq数据中,PROFET成功地重建了非线性基因表达轨迹.
- 在乳腺癌中应用到palbociclib治疗中,发现了具有显著表型变化的细胞子群.
- 鉴定了富含于治疗响应子群体的独特表面标记物.
结论:
- PROFET能够从静态数据中推断连续的单细胞表达轨迹.
- 这种计算工具对于在治疗反应期间剖析细胞状态异质性非常有价值.
- PROFET促进了对复杂生物系统中动态细胞过程的理解.
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