bioRxivSynthetic BiologyPreprint
Precise engineering of chimeric antigen receptor expression levels defines T cell identity and function
Andrew RamosiD, Sylvain SimoniD, Jesús A. Siller‐FarfániD, Anusha RajaniD, Santiago RevaleiD, Elena Zanchini di Castiglionchio, Oana PeleaiD, Tudor A. FulgaiD, Stanley R. RiddelliD, Omer DushekiD, Yale S. MichaelsiD, Tatjana Sauka‐SpengleriD
Abstract
Chimeric Antigen Receptor (CAR) T therapy is a potent treatment for haematological malignancies, but T cell exhaustion reduces its efficacy in many patients. Although high CAR transgene levels appear to drive T cell exhaustion, the relationship between CAR expression levels, T cell function, and transcriptional identity is yet to be mapped at high resolution. Here, we harness a high-resolution microRNA-based control system to precisely modulate CAR transgene expression levels and assess the impact on T cell activation, gene expression and function. By post-transcriptionally modulating CAR abundance, we show that differential CAR levels significantly impact T cell proliferation, cytokine production and tonic signalling. T cells with high CAR expression become strongly activated even at low target antigen densities, while those with low CAR expression are triggered only by high concentrations of their target. Single-cell RNA sequencing of primary T cells expressing a broad range of CAR transcript levels revealed global transcriptional programmes that become dysfunctional with increased CAR abundance, expanding our understanding of T cell exhaustion. Notably, we identified a narrow CAR expression range where the exhaustion transcriptional state is not triggered, demonstrating that T cell exhaustion can be controlled by fine-tuning CAR levels. This work demonstrates that CAR expression levels are key determinants of T cell transcriptional identity and function and introduces a tractable method to precisely tune CAR expression and T cell activity.
- organism: human
- assay: scRNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 0
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