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

Wang, Zijie J.; Walsh, Alex J.; Skala, Melissa C.; Gitter, Anthony

doi:10.1002/jbio.201960050

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

Journal Article · 14 December 2019 · Journal of Biophotonics

DOI: https://doi.org/10.1002/jbio.201960050 · OSTI ID:1623563

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Univ. of Wisconsin‐Madison, Madison, WI (United States). Dept. of Computer Sciences; Morgridge Institute for Research, Madison, WI (United States); DOE/OSTI
Morgridge Institute for Research, Madison, WI (United States)
Morgridge Institute for Research, Madison, WI (United States); Univ. of Wisconsin‐Madison, Madison, WI (United States). Dept. of Biomedical Engineering
Univ. of Wisconsin‐Madison, Madison, WI (United States). Dept. of Computer Sciences; Morgridge Institute for Research Madison, WI (United States); Univ. of Wisconsin‐Madison, Madison, WI (United States). Dept. of Biostatistics and Medical Informatics

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 previously-extracted image features to convolutional neural networks (CNNs) pre-trained on general non-biological images. Adapting pre-trained CNNs for the T cell activity classification task provides substantially better performance than traditional models or a simple CNN trained with the autofluorescence images alone. Visualizing the images with dimension reduction provides intuition into why the CNNs achieve higher accuracy than other approaches. Our image processing and classifier training software is available at https://github.com/gitter-lab/t-cell-classification.

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Research Organization:: Argonne National Lab (ANL)

Sponsoring Organization:: Morgridge Institute for Research; National Institutes of Health (NIH), National Cancer Institute (NCI); USDOE Office of Science (SC); University of Wisconsin‐Madison

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1623563

Journal Information:: Journal of Biophotonics, Journal Name: Journal of Biophotonics Journal Issue: 3 Vol. 13; ISSN 1864-063X

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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

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