Resolving single-cell gene expression by pseudo-temporal integration of transcriptomic and proteomic datasets.

One of the greatest prospects of multimodal single-cell analysis is the ability to integrate single-cell transcriptomic and proteomic datasets to compare transcription and translation profiles to investigate regulation between phenotypes. However, the integration of these complex datasets remains challenging. In this study, we demonstrate the use of pseudo-temporal cell orders as a mutual axis for integrating of scRNA-Seq and scp-MS data. We collected temporal samples of HEK293F cells undergoing hypoxia to isolate the variance of hypoxia from the tightly coupled variance of cell cycle. Markers for hypoxia were then used to construct pseudo-temporal orders in each dataset which were used to reveal transcription and translation dynamics of attenuated respiration in single cells. This approach allowed us to integrate single-cell transcriptomics and proteomics methods to understand the regulatory mechanisms governing biological phenotypes. Overall design: HEK293F cells undergoing hypoxia

多模态单细胞分析（multimodal single-cell analysis）的核心应用前景之一，在于可整合单细胞转录组与蛋白质组数据集，通过对比转录与翻译表达谱，探究不同表型间的调控机制。然而，此类复杂多组学数据集的整合仍颇具挑战。本研究证实，可采用伪时序细胞排序作为共同整合轴，实现单细胞RNA测序（scRNA-Seq）与scp-MS数据的整合。我们收集了经低氧处理的HEK293F细胞（HEK293F）的时序样本，以将低氧诱导的表达变异与细胞周期紧密耦合的表达变异分离开来。随后以低氧标志物为参照，在各数据集中构建伪时序细胞排序，借此揭示单细胞中呼吸减弱过程的转录与翻译动态变化。该方法使我们能够整合单细胞转录组学与蛋白质组学技术，解析调控生物表型的核心机制。实验整体设计：接受低氧处理的HEK293F细胞。