The principles of single-cell polyfunctionality analysis for modelling associated variables, such as T cell efficacy.

<p>3 (n) bimodial effector molecules (A, B and C) were measured at the single-cell level identifying 2³ = 8 distinct combinatorial cell subsets, which can be stratified according to the number of simultaneous functions at the single-cell level (i). <b>A)</b> “Classical” polyfunctionality analysis generally assess only cells positive for a defined minimal number of simultaneous effector molecules (i.e. 3 simultaneous functions). <b>B)</b> Contrarily, the polyfunctionality index considers all 8 functional subsets and ingeniously parameterizes the influence of individual (φ_A>φ_B>φ_C) as well as combined (<i>q</i>) functionalities. <b>C)</b> Polyfunctionality assessed with the polyfunctionality index algorithm is therefore modulable, contrary to “classic” polyfunctional analysis, enabling a more optimal fit (green line) of model variables. <b>D)</b> Using regression analysis it is feasible to obtain proper parameter estimates (φ_A, φ_B, φ_C and q), which have biological significance. Indeed, interpretation of such parameters enables an objective evaluation of the influence of individual as well as combinatorial functions on the predictive capacity of polyfunctionality with regards to a desired model variable, such as T cell efficacy.