A time-resolved meta-analysis of consensus gene expression profiles during human T-cell activation
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.05.03.538418v1?rss=1

Authors: Rade, M., Boehlen, S., Neuhaus, V., Loeffler, D., Blumert, C., Koehl, U., Dehmel, S., Sewald, K., Reiche, K.

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 analyzed the temporal pattern of activation across different T-cell populations to develop consensus gene signatures for T-cell activation. Methods: We applied a meta-analysis of anti-CD3/CD28 induced CD4+ T-cell activation kinetics of publicly available transcriptome-wide time series using a random effects model. We used non-negative matrix factorization, an unsupervised deconvolution method, to infer changes in biological patterns over time. For verification and to further map a wider variety of the T-cell landscape, we performed a time series of transcriptome-wide RNA sequencing on activated blood T-cells. Lastly, we matched the identified consensus biomarker signatures to single-cell RNA sequencing (scRNA-Seq) data of autologous anti-CD19 chimeric antigen receptor (CAR) T-cells from 24 patients with large B cell lymphoma (LBCL) to characterize activation status of the cell product before infusion. Results: We identified 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 early, intermediate, and late response expression rates across multiple T-cell populations, thus defining consensus biomarker signatures for T-cell activation. Intermediate and late response activation signatures in CAR T-cell infusion products were 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 for 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.

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