Resolving Single-Cell Gene Expression by Pseudotemporal Integration of Transcriptomic and Proteomic Datasets.

Single-cell omics technologies, such as single-cell RNA-Seq and single-cell proteomics, offer unprecedented insights into cellular heterogeneity and dynamic regulatory processes. However, integrating these data types to construct comprehensive transcription-translation profiles remains challenging because of their distinct and complex behaviors. This study presents a novel approach using pseudotemporal cell ordering to integrate single-cell RNA-Seq and single-cell proteomics by mass spectrometry data, facilitating the analysis of transcription-translation expression dynamics. We collected longitudinal single-cell samples following hypoxia. By leveraging key markers, we constructed pseudotemporal trajectories for each data type, revealing transcriptional and translational responses to hypoxia. This profile of unified single-cell mRNA and protein expression uncovers distinct regulatory mechanisms, including an immediate transcriptomic response, followed by delayed proteomic expression. It illustrates the use of pseudotemporal integration to integrate single-cell transcriptomic and proteomic datasets to understand the cellular phenotypes under hypoxic stress and provides a framework for future investigations into transcription-translation dynamics.