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Updated: Jul 1, 2025
Analysis of T-cell Receptor-Induced Calcium Influx in Primary Murine T-cells by Full Spectrum Flow Cytometry
Published on: December 16, 2022
Machine learning predictions of T cell antigen specificity from intracellular calcium dynamics
Sébastien This1,2,3, Santiago Costantino1,4, Heather J Melichar1,3,5
1Centre de recherche de l'Hôpital Maisonneuve-Rosemont, Montréal, Québec, Canada.
Abstract:
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.
Insights
Machine learning accurately predicts T cell activation from calcium signals, overcoming a key hurdle in developing T cell receptor (TCR) therapies for cancer. This advance aids in identifying TCR sequences for personalized adoptive T cell therapies.
Area of Science:
- Immunology
- Bioinformatics
- Machine Learning
Background:
- Adoptive T cell therapies require T cells with specific tumor-targeting T cell receptors (TCRs).
- Identifying effective TCR sequences is a major bottleneck in TCR-engineered cell therapy production.
- Intracellular calcium fluctuations signal T cell receptor (TCR) engagement but are highly variable.
Purpose of the Study:
- To investigate the potential of machine learning algorithms for classifying T cell activation from complex calcium signaling data.
- To develop a foundation for an antigen-specific TCR sequence identification pipeline for adoptive T cell therapies.
Main Methods:
- Utilized deep learning tools to analyze intracellular calcium fluctuations as a readout of T cell receptor (TCR) signaling.
- Trained and tested a machine learning algorithm to predict T cell activation based on calcium signaling data.
- Validated the algorithm using TCR-transgenic CD8+ T cells, distinct TCRs, and polyclonal T cells.
Main Results:
- Demonstrated accurate prediction of TCR-transgenic CD8+ T cell activation using deep learning models based on calcium fluctuations.
- Successfully tested the algorithm's performance against T cells with different TCRs and in polyclonal T cell populations.
- Established the feasibility of using machine learning to interpret variable calcium signaling events for T cell classification.
Conclusions:
- Machine learning, specifically deep learning, can effectively classify T cell activation from variable calcium signaling data.
- This approach provides a promising foundation for a novel pipeline to identify antigen-specific TCR sequences for adoptive T cell therapies.
- Overcoming the bottleneck in TCR identification can accelerate the development of personalized cancer immunotherapies.
