Dynamical Systems Model of RNA Velocity Improves Inference of Single-cell Trajectory, Pseudo-time and Gene Regulation. Issue 15 (15th August 2022)

• Record Type: Journal Article
• Authors: Liu, Ruishan; Pisco, Angela Oliveira; Braun, Emelie; Linnarsson, Sten; Zou, James
• Is Part Of: Journal of molecular biology. Volume 434:Issue 15(2022)
• Publication Date: 2022-08-15
• Subjects: single-cell RNA sequencing -- RNA velocity -- expression dynamics -- dynamical systems model of biology -- machine learning; Molecular biology -- Periodicals; Biology -- Periodicals; Biochemistry -- Periodicals; Bacteriology -- Periodicals; Molecular Biology -- Periodicals; Biochemistry -- Periodicals; Biologie moléculaire -- Périodiques; Biologie -- Périodiques; Biochimie -- Périodiques; Moleculaire biologie; Biochemistry; Biology; Molecular biology; Periodicals; 572.805
• DOI: 10.1016/j.jmb.2022.167606
• Languages: English
• ISSNs: 0022-2836

Abstract

Graphical abstract: Highlights: RNA-ODE uses dynamical systems to model single-cell RNA expression and velocity. It provides accurate inference of cell lineages and gene regulatory networks. RNA-ODE extracts new biological insights. RNA-ODE is broadly applicable to any scRNA-seq data with velocity information. Abstract: Recent development in inferring RNA velocity from single-cell RNA-seq opens up exciting new vista into developmental lineage and cellular dynamics. However, the estimated velocity only gives a snapshot of how the transcriptome instantaneously changes in individual cells, and it does not provide quantitative predictions and insights about the whole system. In this work, we develop RNA-ODE, a principled computational framework that extends RNA velocity to quantify systems level dynamics and improve single-cell data analysis. We model the gene expression dynamics by an ordinary differential equation (ODE) based formalism. Given a snapshot of gene expression at one time, RNA-ODE is able to predict and extrapolate the expression trajectory of each cell by solving the dynamic equations. Systematic experiments on simulations and on new data from developing brain demonstrate that RNA-ODE substantially improves many aspects of standard single-cell analysis. By leveraging temporal dynamics, RNA-ODE more accurately estimates cell state lineage and pseudo-time compared to previous state-of-the-art methods. It also infers gene regulatory networks and identifies influential genes whose expression changes can decide cell fate. We expect RNA-ODE to be a Swiss army knife that aids many facets of single-cell RNA-seq analysis.