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PhyloVelo enhances transcriptomic velocity field mapping using monotonically expressed genes.

Kun Wang^1,2, Liangzhen Hou^1,3, Xin Wang¹

¹CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.

Nature Biotechnology

|July 31, 2023

Summary

PhyloVelo accurately tracks cell fate transitions using monotonically expressed genes (MEGs) to reconstruct transcriptomic dynamics. This computational framework outperforms existing methods in inferring complex lineage trajectories from single-cell RNA sequencing data.

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Area of Science:

Computational biology
Genomics
Developmental biology

Background:

Single-cell RNA sequencing (scRNA-seq) enables cellular differentiation studies.
Accurate cell fate transition tracking remains challenging, particularly in disease contexts.

Purpose of the Study:

Introduce PhyloVelo, a novel computational framework for transcriptomic dynamics.
Enhance the accuracy of cell fate transition inference using scRNA-seq data.

Main Methods:

PhyloVelo utilizes monotonically expressed genes (MEGs) to estimate transcriptomic velocity.
Integrates scRNA-seq data with lineage information to reconstruct velocity fields.
Validates performance using simulated and Caenorhabditis elegans data.

Main Results:

PhyloVelo successfully recovers linear, bifurcated, and convergent differentiation trajectories.
Demonstrates high accuracy and robustness in inferring complex lineage trajectories across seven datasets.
Outperforms existing RNA velocity methods in lineage tracing.

Conclusions:

PhyloVelo provides a robust method for analyzing transcriptomic dynamics and cell fate.
MEGs identified by PhyloVelo share conserved functions in translation and ribosome biogenesis across species.