Paper Review

A recent publication titled "Inferring cell trajectories of spatial transcriptomics via optimal transport analysis" introduced a new method "SpaTrack" to trace cell trajectories in tissue development, organ regeneration, and cancer progression, which was published in Cell Systems as the cover story.

With the booming of spatial transcriptomics technology, researchers are able to explore both transcriptomic and spatial patterns of development simultaneously. This technology supplements the missing location information during single-cell RNA (SC) sequencing. However, the incapabilities of current SC data analysis methods remain significant challenges to uncovering spatial details of cell differentiation.

SpaTrack introduces a novel framework for cellular trajectory analysis by integrating Optimal Transport (OT) with spatial distance, moving beyond traditional methods that rely solely on gene expression. By dynamically adjusting the balance between transcriptomic similarity and physical proximity, SpaTrack allows for the reconstruction of detailed and potentially discontinuous differentiation pathways across varied tissue types. This approach also enables the tracking of cell transitions over time and models the influence of transcription factors on gene expression changes during differentiation.

SpaTrack has been applied to various biological contexts, demonstrating its utility in exploring cellular dynamics. In this paper, researchers have used it to examine axolotl telencephalon regeneration, trace midbrain development in mouse embryos, and investigate tumor expansion and metastasis. In rigorous evaluations using both simulated and real-world datasets, SpaTrack consistently outperformed existing methodologies or complimented them effectively.

This innovative method leverages high-resolution data from STOmics' Stereo-seq technology, which captures both transcriptional profiles and spatial locations of individual cells in tissue samples.

Know more: https://www.cell.com/cms/10.1016/j.cels.2025.101194/attachment/cae808d9-0803-46cc-8071-90d5573f3ba3/mmc2.pdf

SpaTrack is now freely available as open-source software on GitHub (https://github.com/yzf072/SpaTrack), complete with detailed installation and user guides (https://SpaTrack.readthedocs.io/en/latest/index.html).