RT Journal Article
SR Electronic
T1 Discerning the cellular response using statistical discrimination of fluorescence images of membrane receptors
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.07.28.225144
DO 10.1101/2020.07.28.225144
A1 Munaweera, Rangika
A1 O’Neill, William D.
A1 Hu, Ying S.
YR 2020
UL http://biorxiv.org/content/early/2020/07/29/2020.07.28.225144.abstract
AB 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 StatementThe authors have declared no competing interest.