| First Author | This S | Year | 2024 |
| Journal | Sci Adv | Volume | 10 |
| Issue | 10 | Pages | eadk2298 |
| PubMed ID | 38446885 | Mgi Jnum | J:348933 |
| Mgi Id | MGI:7611378 | Doi | 10.1126/sciadv.adk2298 |
| Citation | This S, et al. (2024) Machine learning predictions of T cell antigen specificity from intracellular calcium dynamics. Sci Adv 10(10):eadk2298 |
| abstractText | Adoptive T cell therapies rely on the production of T cells with an antigen receptor that directs their specificity toward tumor-specific antigens. Methods for identifying relevant T cell receptor (TCR) sequences, predominantly achieved through the enrichment of antigen-specific T cells, represent a major bottleneck in the production of TCR-engineered cell therapies. Fluctuation of intracellular calcium is a proximal readout of TCR signaling and candidate marker for antigen-specific T cell identification that does not require T cell expansion; however, calcium fluctuations downstream of TCR engagement are highly variable. We propose that machine learning algorithms may allow for T cell classification from complex datasets such as polyclonal T cell signaling events. Using deep learning tools, we demonstrate accurate prediction of TCR-transgenic CD8(+) T cell activation based on calcium fluctuations and test the algorithm against T cells bearing a distinct TCR as well as polyclonal T cells. This provides the foundation for an antigen-specific TCR sequence identification pipeline for adoptive T cell therapies. |
