Abstract
We demonstrate a statistical modeling technique to recognize T cell responses to different external environmental conditions using membrane distributions of T cell receptors. We transformed fluorescence images of T cell receptors from each T cell into estimated model parameters of a partial differential equation. The model parameters enabled the construction of an accurate classification model using linear discrimination techniques. We further demonstrated that the technique successfully differentiated immobilized T cells on non-activating and activating surfaces. Compared to machine learning techniques, our statistical technique relies upon robust image-derived statistics and achieves effective classification with a limited sample size and a minimal computational footprint. The technique provides an effective strategy to quantitatively characterize the global distribution of membrane receptors and other intracellular proteins under various physiological and pathological conditions.
Competing Interest Statement
The authors have declared no competing interest.
