Quantifying landscape-flux via single-cell transcriptomics uncovers the underlying mechanism of cell cycle PaperPlayer biorxiv systems biology
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- Life Sciences
Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.08.01.551525v1?rss=1
Authors: Zhu, L., Wang, J.
Abstract:
Recent developments of single-cell sequencing technology enabling acquisition of the whole transcriptome data. To uncover the underlying mechanism of cell cycle function from such data, we reconstruct a continuous vector field based on the discrete single-cell RNA velocity to quantify the global non-equilibrium dynamical landscape and flux. We reveal that biological noise can make the global landscape more complex and less predictable. Genetic perturbations alter landscape-flux, thus identify key genes in maintaining cell cycle dynamics and predict the associated effects on cell cycle behaviour. Cell cycle initiation costs energy and sustaining cell cycle requires dissipation to increase oscillatory phase coherence. This approach enables the inference of cell cycle gene regulatory networks directly from single-cell transcriptomic data, including feedback mechanisms. Our study provides a new framework with insights into cell cycle regulation from single-cell transcriptome data and can be extended to other biological processes, such as differentiation-development and disease pathogenesis
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Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.08.01.551525v1?rss=1
Authors: Zhu, L., Wang, J.
Abstract:
Recent developments of single-cell sequencing technology enabling acquisition of the whole transcriptome data. To uncover the underlying mechanism of cell cycle function from such data, we reconstruct a continuous vector field based on the discrete single-cell RNA velocity to quantify the global non-equilibrium dynamical landscape and flux. We reveal that biological noise can make the global landscape more complex and less predictable. Genetic perturbations alter landscape-flux, thus identify key genes in maintaining cell cycle dynamics and predict the associated effects on cell cycle behaviour. Cell cycle initiation costs energy and sustaining cell cycle requires dissipation to increase oscillatory phase coherence. This approach enables the inference of cell cycle gene regulatory networks directly from single-cell transcriptomic data, including feedback mechanisms. Our study provides a new framework with insights into cell cycle regulation from single-cell transcriptome data and can be extended to other biological processes, such as differentiation-development and disease pathogenesis
Copy rights belong to original authors. Visit the link for more info
Podcast created by Paper Player, LLC