Classifying T cell activity in autofluorescence intensity images with convolutional neural networks.

ABSTRACT: The importance of T cells in immunotherapy has motivated developing technologies to improve therapeutic efficacy. One objective is assessing antigen-induced T cell activation because only functionally active T cells are capable of killing the desired targets. Autofluorescence imaging can distinguish T cell activity states in a non-destructive manner by detecting endogenous changes in metabolic co-enzymes such as NAD(P)H. However, recognizing robust activity patterns is computationally challenging in the absence of exogenous labels. We demonstrate machine learning methods that can accurately classify T cell activity across human donors from NAD(P)H intensity images. Using 8260 cropped single-cell images from six donors, we evaluate classifiers ranging from traditional models that use previo

SUBMITTER: Wang ZJ 

PROVIDER: S-EPMC7065628 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature