Machine learning predictions of T cell antigen specificity from intracellular calcium dynamics

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.

Fig. 1.. In vitro T cell activation model for the study of intracellular Ca²⁺ dynamics.

(A) Schematic representation of the analysis pipeline. (B) Flow cytometry assessment of surface TCRβ down-regulation, CD69 expression, and 4-1BB expression, 3 or 15 hours after coculture with OVA peptide. Error bars indicate SD (n = 6 to 10 independent wells over four independent experiments; Mann-Whitney U test). (C) Distribution of intracellular Ca²⁺ concentration for all time points (left) or the average Ca²⁺ concentration over the entire time lapse (right) according to antigen specificity assignment (n = 7173 antigen-specific and n = 7564 nonspecific T cells over eight independent experiments). a.u., arbitrary units. (D) Frequency of T cells manually labeled as antigen-reactive. Horizontal lines show the median and numbers below show the average. Individual fields of view are represented in gray (n = 111 fields of view over eight independent experiments). (E) Proportion of antigen-specific and nonspecific cells among those manually labeled as antigen-reactive. (F) Distribution of the average intracellular Ca²⁺ concentration according to manual assignment and genotype (n = 5038 OT-I and n = 347 P14 cells labeled as antigen-reactive; n = 9351 cells labeled as nonreactive over eight independent experiments). (G) Correlation between the frequency of cells manually labeled as antigen-reactive and the frequency of cells down-regulating surface TCRβ or expressing 4-1BB, as measured by flow cytometry, after 3 or 15 hours of incubation. For each well, data for antigen-specific and nonspecific T cells are shown, and the frequency of manual labeling for all fields of view per well is averaged. Error bars indicate SEM, full line shows linear regression on antigen-specific T cells, and dotted lines show 95% confidence interval [TCRβ: n = 13 independent wells over four independent experiments, coefficient of determination (R²) = 0.9534, ρ = 0.9764; 4-1BB: n = 21 independent wells over six independent experiments; R² = 0.442, ρ = 0.649]. OT-I, antigen-specific; P14, nonspecific. [Ca²⁺]_i, intracellular calcium concentration. **P < 0.01 and ***P < 0.005.

Fig. 2.. CNNs allow for efficient and accurate classification of T cell activation based on intracellular Ca²⁺ dynamics.

(A) Model efficiency (frequency of cells manually labeled as antigen-reactive predicted as antigen-specific) and false discovery rate (FDR; frequency of nonreactive T cells predicted as antigen-specific) of all ML algorithms tested, for both OVA (left) and gp33 (right) time lapses. Performance of the selected models detailed in table S1 (black circles) is plotted along with the performance of the thresholding approach (fig. S6, B to D). Gray dots represent other models generated during the systematic evaluation of ML structures. The optimal optCNN_man model is shown in magenta. (B) Performance of optCNN_man (see Materials and Methods for the performance metrics) for varying thresholds (see fig. S6) on the probability of being antigen-specific (P_antigen-spe). a.u., arbitrary units. (C) Receiver operating characteristic curve of optCNN_man for both OVA (full line) and gp33 (dotted line) time lapses. Area under the curve (AUC) represents the overall performance of optCNN_man. (D) Detailed performance of optCNN_man using the prediction probability threshold of 0.47. Horizontal lines in the violin plot show the median and numbers below show the average of the distribution. Individual fields of view are represented in gray (n = 73 OVA and n = 15 gp33 fields of view over nine OVA and two gp33 independent experiments; Mann-Whitney U test). (E) Distribution of the overall efficiency (frequency of antigen-specific T cells predicted as antigen-specific) and FDR across the evaluation dataset for optCNN_man and the manual labeling process (n = 73 OVA and n = 15 gp33 fields of view over nine OVA and two gp33 independent experiments). (F and G) Distribution of intracellular Ca²⁺ concentration (F) and prediction probability P_antigen-spe (G) of the nonspecific cells mispredicted as antigen-specific (n = 174 OVA and n = 32 gp33 cells over nine OVA and two gp33 independent experiments). Not significant (ns), P > 0.05.

Fig. 3.. Biological validation of antigen specificity predictions based on intracellular Ca²⁺ dynamics.

Critical parameters of the coculture (e.g., number of antigen-presenting BMDCs and total number of BMDCs) are modulated, and optCNN_man is used to predict the frequency of antigen-specific T cells. For each independent culture well, the prediction percentage is averaged over three fields of view; wells are then harvested and analyzed by flow cytometry for expression of selected activation markers. (A) Correlation between ML prediction and the frequency of cells down-regulating surface TCRβ expression or expressing CD69 or 4-1BB, as measured by flow cytometry, after 3 or 15 hours of incubation. Error bars indicate SEM. Linear regression of the antigen-specific conditions is shown (full line) with 95% confidence error (dotted lines) (TCRβ: n = 12 independent wells over three independent experiments, R² = 0.959, ρ = 0.989; CD69: n = 12 independent wells over three independent experiments, R² = 0.866, ρ = 0.930; 4-1BB and OVA peptide: n = 21 independent wells over four independent experiments, R² = 0.493, ρ = 0.702; 4-1BB and gp33 peptide: n = 8 independent wells over four independent experiments, R² = 0.746, ρ = 0.863). (B) Percentage of antigen-specific and nonspecific T cells predicted as antigen-specific as a function of the ratio of antigen-presenting to non-presenting BMDCs. Full lines and dotted lines represent a sigmoidal curve fitted to the data. The nonspecific group pools data from both 1 × 10⁵ and 2 × 10⁵ BMDC conditions. Error bars indicate SD. (C) Model efficiency (compared to manual labeling) and FDR of optCNNman when predicting T cell specificity to lower affinity antigens. Affinity (K_d) of the altered peptide ligands (APLs) for the OT-I TCR is indicated (37) (n = 11 independent fields of view over three independent experiments).

Fig. 4.. Deep learning models predict polyclonal T cell responses.

(A) Schematic representation of the mixed lymphocyte reaction (MLR) setup. Purified C57B/6J naïve CD8⁺ T cells, stained with Indo-1, were overlaid on MHC-matched C57B/6J BMDCs (autologous condition) or MHC-mismatched BALB/c BMDCs (allogeneic condition). (B) Flow cytometry–based measurement of TCRβ, CD69, and 4-1BB expression 3 or 20 hours after coculture. Error bars show SD (n = 4 to 10 culture wells over three to five independent experiments; Mann-Whitney U test). (C) Frequency of cells predicted as antigen-specific (Mann-Whitney U test). (D) Correlation between ML prediction of antigen specificity and the frequency of cells expressing CD69 or 4-1BB (B), as measured by flow cytometry after 3 or 20 hours of incubation, respectively. Each point is the average of the prediction of three independent fields of view and the average of two to six independent culture wells for flow cytometry data. Error bars indicate SEM of prediction and flow cytometry data. Linear regression is shown (full line) with 95% confidence error (dotted lines) and its correlation coefficient (R²) and slope (three to five independent experiments). (E) Probability distribution of the average Ca²⁺ concentration over the entire time lapse for all the cells according to the prediction by optCNN_man and culture condition, comparing prediction for MLR cultures and monoclonal T cell culture with OVA N4 and OVA Q4 antigens (replotted from fig. S8) (MLR: n = 3219 nonspecific and n = 541 antigen-specific cells over five independent experiments; OVA: n = 7266 nonspecific and n = 3333 antigen-specific cells over three independent experiments; Q4: n = 1161 nonspecific and n = 972 antigen-specific cells over three independent experiments). a.u., arbitrary units. ns, P > 0.05; **P < 0.01; and ***P < 0.005.