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Inferring cell trajectories of spatial transcriptomics via optimal transport analysis
Xunan Shen1, Lulu Zuo2, Zhongfei Ye3
1BGI Research, Chongqing 401329, China; BGI Research, Beijing 102601, China.
Cell Systems
|February 4, 2025
Summary
SpaTrack reconstructs cell differentiation trajectories by integrating gene expression and spatial data from spatial transcriptomics. This method reveals cell dynamics during development and disease, advancing trajectory inference.
Area of Science:
- Computational Biology
- Genomics
- Developmental Biology
Background:
- Organizing cell differentiation trajectories using both transcriptomics and spatial location is complex.
- Existing methods struggle to integrate spatial transcriptomics data effectively for lineage tracing.
Purpose of the Study:
- To develop a novel computational framework, SpaTrack, for reconstructing cell differentiation trajectories.
- To integrate gene expression and spatial position data for accurate lineage inference.
Main Methods:
- SpaTrack utilizes optimal transport to unify gene expression and spatial coordinates into transition costs.
- It models cell fate based on transcription factor-influenced expression profiles over time.
Main Results:
- SpaTrack successfully reconstructs detailed spatial and temporal cell differentiation trajectories.
- It accurately traces cell dynamics across multiple samples and temporal intervals.
- The method identified malignant lineages in tumors, including those undergoing epithelial-mesenchymal transition and metastasis.
Conclusions:
- SpaTrack advances trajectory inference methods for spatial transcriptomics data.
- Provides critical insights into developmental processes and disease progression, such as cancer metastasis.
