A time-resolved meta-analysis of consensus gene expression profiles during human T-cell activation

Author:

Rade MichaelORCID,Böhlen Sebastian,Neuhaus Vanessa,Löffler Dennis,Blumert Conny,Merz Maximilian,Köhl Ulrike,Dehmel Susann,Sewald Katherina,Reiche KristinORCID

Abstract

Abstract Background The coordinated transcriptional regulation of activated T-cells is based on a complex dynamic behavior of signaling networks. Given an external stimulus, T-cell gene expression is characterized by impulse and sustained patterns over the course. Here, we analyze the temporal pattern of activation across different T-cell populations to develop consensus gene signatures for T-cell activation. Results Here, we identify and verify general biomarker signatures robustly evaluating T-cell activation in a time-resolved manner. We identify time-resolved gene expression profiles comprising 521 genes of up to 10 disjunct time points during activation and different polarization conditions. The gene signatures include central transcriptional regulators of T-cell activation, representing successive waves as well as sustained patterns of induction. They cover sustained repressed, intermediate, and late response expression rates across multiple T-cell populations, thus defining consensus biomarker signatures for T-cell activation. In addition, intermediate and late response activation signatures in CAR T-cell infusion products are correlated to immune effector cell-associated neurotoxicity syndrome. Conclusion This study is the first to describe temporally resolved gene expression patterns across T-cell populations. These biomarker signatures are a valuable source, e.g., monitoring transcriptional changes during T-cell activation with a reasonable number of genes, annotating T-cell states in single-cell transcriptome studies, or assessing dysregulated functions of human T-cell immunity.

Funder

Fraunhofer-Institut für Zelltherapie und Immunologie

Fraunhofer-Institut für Zelltherapie und Immunologie IZI

Publisher

Springer Science and Business Media LLC

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1. Analyzing RNA-Seq Data in Complex Study Designs;Statistics in Biosciences;2024-07-14

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