scEGOT: single-cell trajectory inference framework based on entropic Gaussian mixture optimal transport.

Area of Science:

• Developmental Biology
• Computational Biology
• Genomics

Background:

• Time-series single-cell RNA sequencing (scRNA-seq) is crucial for understanding cell differentiation.
• Optimal transport theory is a promising approach but faces challenges in interpretability and computational efficiency.

Purpose of the Study:

• To introduce scEGOT, a novel generative model framework for single-cell trajectory inference.
• To address limitations of existing methods regarding interpretability and computational cost.

Main Methods:

• Developed scEGOT, a comprehensive framework utilizing generative modeling for trajectory inference.
• Applied scEGOT to analyze human primordial germ cell-like cell (PGCLC) induction systems.

Main Results:

• scEGOT successfully identified the PGCLC progenitor population and the timing of cell fate bifurcation.
• Revealed that TFAP2A alone is insufficient for PGCLC progenitor identification, with NKX1-2 being essential.
• Highlighted the critical roles of MESP1 and GATA6 in segregating PGCLC from somatic cell lineages.

Conclusions:

• The findings elucidate the molecular mechanisms governing PGCLC segregation from somatic lineages.
• scEGOT demonstrates versatility beyond scRNA-seq, applicable to other single-cell data types like scATAC-seq.
• scEGOT has the potential to significantly advance developmental biology research.