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. HEK293F cells undergoing hypoxia