A single-cell multiomics approach for simultaneous analysis of replication timing and gene expression
Anala V Shetty1, Clifford J Steer2, Walter C Low1
1Molecular, Cellular, Developmental Biology, and Genetics Graduate Program, University of Minnesota, Minneapolis, Minnesota, USA; Stem Cell Institute, University of Minnesota, Minneapolis, Minnesota, USA; Department of Neurosurgery, University of Minnesota, Minneapolis, Minnesota, USA.
The Journal of Biological Chemistry
|August 10, 2025
Summary
This study introduces a novel single-cell multiomics method for analyzing replication timing (RT) and gene expression. This approach reveals cell-specific RT and gene expression correlations, offering new insights into cellular heterogeneity in cancer.
Area of Science:
- Genomics
- Cell Biology
- Cancer Research
Background:
- Replication timing (RT) correlates with gene expression in various cellular states, including cancer.
- Previous RT studies required large cell populations (tens of thousands), limiting single-cell resolution.
- Bulk analysis obscures cell-to-cell variations in RT and gene expression patterns.
Purpose of the Study:
- To develop an affordable single-cell (sc)-multiomics approach for simultaneous RT and gene expression analysis.
- To generate high-resolution sc-RT profiles and gene expression data.
- To investigate cell-specific correlations between RT and gene expression.
Main Methods:
- Developed a novel single-cell (sc)-multiomics technique.
- Applied the method to HepG2 human liver cancer cells.
- Generated sc-RT profiles and sc-gene expression data.
Main Results:
- As few as 17 mid S-phase cells yielded pseudo-bulk RT profiles highly correlated with existing bulk data.
- Visualized individual cell progression through genome replication.
- Demonstrated novel cell-specific correlations between RT and gene expression.
- Identified both conserved and variable trends in RT and gene expression across individual cells.
Conclusions:
- The developed sc-multiomics approach enables high-resolution analysis of RT and gene expression.
- This method reveals previously undetectable cell-specific correlations and heterogeneity.
- The findings provide new insights into genome replication dynamics and gene regulation in cancer cells.
