Computational Identification of Migrating T cells in Spatial Transcriptomics Data

Area of Science:

• Immunology
• Computational Biology
• Oncology

Background:

• T cells are crucial for antitumor immunity, but tumor immunosuppression hinders their infiltration and function.
• Understanding T cell migration in the tumor microenvironment (TME) is vital for improving cancer therapies.
• Investigating in vivo T cell migration in human tumors presents significant challenges.

Purpose of the Study:

• To develop a computational method for tracking T cell migration patterns within tumor tissue.
• To identify migration trails and associated molecular mechanisms within the TME.
• To characterize the phenotype of T cells during migration in human tumors.

Main Methods:

• Development of ReMiTT, a computational method utilizing spatial transcriptomics data.
• Application of ReMiTT to analyze T cell migration in multiple human tumor samples.
• Identification and analysis of molecular signatures along T cell migration trails.

Main Results:

• ReMiTT successfully identified potential T cell migration trails in tumor tissues.
• Increasing trends of chemokines that promote T-cell trafficking were observed on these trails.
• Enriched genes and pathways involved in cytoskeleton rearrangement, cell adhesion, and ECM remodeling were identified.
• Migrating T cells along these trails were characterized as highly proliferative.

Conclusions:

• ReMiTT provides a novel computational approach to study T cell migration in the TME.
• The findings offer insights into tumor-immune dynamics and potential therapeutic targets.
• Understanding T cell migration pathways can inform strategies to enhance antitumor immunity.