Mpath maps multi-branching single-cell trajectories revealing progenitor cell progression during development

Area of Science:

• Developmental Biology
• Computational Biology
• Genomics

Background:

• Single-cell RNA sequencing (scRNA-seq) provides high-resolution insights into cell differentiation.
• Existing computational tools for reconstructing complex, multi-branching cell lineages from scRNA-seq data are limited.

Purpose of the Study:

• To introduce Mpath, a novel algorithm for inferring multi-branching developmental trajectories from scRNA-seq data.
• To demonstrate Mpath's capability in identifying distinct cell lineages and regulatory dynamics during differentiation.

Main Methods:

• Mpath algorithm utilizes neighborhood-based cell state transitions to construct developmental trajectories.
• Application of Mpath to scRNA-seq data from mouse conventional dendritic cell (cDC) progenitors and human myoblasts.

Main Results:

• Mpath successfully constructed multi-branching trajectories for mouse cDC progenitors, identifying distinct pre-dendritic cell (preDC) subsets committed to cDC1 or cDC2 lineages.
• Analysis revealed sequential gene regulation waves and temporal coupling between cell cycle and cDC differentiation.
• Mpath accurately recapitulated human myoblast differentiation and identified a non-muscle cell differentiation branch.

Conclusions:

• Mpath is an effective computational tool for reconstructing complex cell lineages and uncovering developmental dynamics from single-cell data.
• The algorithm aids in understanding cell fate decisions and gene regulatory networks during differentiation processes.