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Computational identification of migrating T cells in spatial transcriptomics data
Lin Zhong, Bo Li, Zhikai Chi, Siyuan Zhang, Qiwei Li, Guanghua Xiao
Lin Zhong, Bo Li, Zhikai Chi, Siyuan Zhang, Qiwei Li, Guanghua Xiao
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Research Article Immunology Oncology

Computational identification of migrating T cells in spatial transcriptomics data

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Abstract

T cells are the central players in antitumor immunity, and effective tumor killing depends on their ability to infiltrate into the tumor microenvironment (TME) while maintaining normal cytotoxicity. However, late-stage tumors develop immunosuppressive mechanisms that impede T cell movement and induce exhaustion. Investigating T cell migration in human tumors in vivo could provide insights into tumor immune escape, although it remains a challenging task. In this study, we developed ReMiTT, a computational method that leverages spatial transcriptomics data to track T cell migration patterns within tumor tissue. Applying ReMiTT to multiple tumor samples, we identified potential migration trails. On these trails, chemokines that promote T cell trafficking displayed an increasing trend. Additionally, we identified key genes and pathways enriched on these migration trails, including those involved in cytoskeleton rearrangement, leukocyte chemotaxis, cell adhesion, leukocyte migration, and extracellular matrix remodeling. Furthermore, we characterized the phenotypes of T cells along these trails, showing that the migrating T cells are highly proliferative. Our findings introduce an approach for studying T cell migration and interactions within the TME, offering valuable insights into tumor-immune dynamics.

Authors

Lin Zhong, Bo Li, Zhikai Chi, Siyuan Zhang, Qiwei Li, Guanghua Xiao

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Figure 4

Differential gene expression analysis for T cell migration trails on the lung cancer sample.

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Differential gene expression analysis for T cell migration trails on the...
(A) Volcano plot of gene (3,000 variable genes) fold change (FC) and adjusted empirical P values between those on migration trails vs. 5,000 control sets. Genes with FDR <0.05 and the absolute values of FC ≥1.2 were labeled on the plot. Genes with –log10 P value >4 shown in this plot correspond to empirical P values of 0. For visualization purposes, these P values were replaced with randomly assigned values between 10–4 and 10–8 to enable plotting on a –log10 scale. (B) Bar plot showing the top 20 enriched Gene Ontology biological process (GOBP) gene pathways computed from the 394 significantly upregulated genes on migration trails by GSEA. Colors of the text box borders indicated the type of biological processes that may involved in T cell migration. Colors of the bars indicate the adjusted P values. Statistical significance was evaluated using Fisher’s exact test, with FDR corrected by the Benjamini-Hochberg approach. (C) Bar plot showing the top 50–70 enriched GOBP gene pathways computed from the 394 significantly upregulated genes on migration trails by GSEA.

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ISSN 2379-3708

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