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

Single-cell omics technologies, such as single-cell RNA sequencing (scRNA-seq) and single-cell proteomics (scp-MS), offer unprecedented insights into cellular heterogeneity and dynamic regulatory processes. However, integrating these data types to construct comprehensive transcription-translation pr...

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Veröffentlicht in:Molecular & cellular proteomics S. 101475
Hauptverfasser: Barry, Craig P, Talbo, Gert H, Beauglehole, Aiden, Ovchinnikov, Dmitry, Munro, Trent, Mahler, Stephen, Baker, Kym, Nielsen, Lars K, Mercer, Tim R, Marcellin, Esteban
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 27.11.2025
Schlagworte:
multi-omics integration
hypoxia
scRNA-seq
single cell proteomics
ISSN:1535-9484, 1535-9484
Online-Zugang:Volltext
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Zusammenfassung:Single-cell omics technologies, such as single-cell RNA sequencing (scRNA-seq) and single-cell proteomics (scp-MS), offer unprecedented insights into cellular heterogeneity and dynamic regulatory processes. However, integrating these data types to construct comprehensive transcription-translation profiles remains challenging due to their distinct and complex behaviors. This study presents a novel approach using pseudo-temporal cell ordering to integrate scRNA-seq and scp-MS data, facilitating the analysis of transcription-translation expression dynamics. We collected longitudinal single-cell samples following hypoxia. By leveraging key markers, we constructed pseudo-temporal 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 pseudo-temporal 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.
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ISSN:1535-9484
1535-9484
DOI:10.1016/j.mcpro.2025.101475