PhyloVelo enhances transcriptomic velocity field mapping using monotonically expressed genes

Single-cell RNA sequencing (scRNA-seq) is a powerful approach for studying cellular differentiation, but accurately tracking cell fate transitions can be challenging, especially in disease conditions. Here we introduce PhyloVelo, a computational framework that estimates the velocity of transcriptomic dynamics by using monotonically expressed genes (MEGs) or genes with expression patterns that either increase or decrease, but do not cycle, through phylogenetic time. Through integration of scRNA-seq data with lineage information, PhyloVelo identifies MEGs and reconstructs a transcriptomic velocity field. We validate PhyloVelo using simulated data and Caenorhabditis elegans ground truth data, successfully recovering linear, bifurcated and convergent differentiations. Applying PhyloVelo to seven lineage-traced scRNA-seq datasets, generated using CRISPR-Cas9 editing, lentiviral barcoding or immune repertoire profiling, demonstrates its high accuracy and robustness in inferring complex lineage trajectories while outperforming RNA velocity. Additionally, we discovered that MEGs across tissues and organisms share similar functions in translation and ribosome biogenesis. A refined velocity model improves cell fate mapping with lineage-traced scRNA-seq data.

Automated summary

PhyloVelo is a computational framework that accurately estimates the velocity of transcriptomic dynamics by using monotonically expressed genes or genes with specific expression patterns. It integrates scRNA-seq data with lineage information to identify key genes and reconstruct the transcriptomic velocity field. PhyloVelo has been validated using simulated and real datasets, and has shown high accuracy and robustness in inferring complex lineage trajectories.