Classification of T-cell activation via autofluorescence lifetime imaging

Abstract

The function of a T cell depends on its subtype and activation state. Here, we show that imaging of the autofluorescence lifetime signals of quiescent and activated T cells can be used to classify the cells. T cells isolated from human peripheral blood and activated in culture using tetrameric antibodies against the surface ligands CD2, CD3 and CD28 showed specific activation-state-dependent patterns of autofluorescence lifetime. Logistic regression models and random forest models classified T cells according to activation state with 97–99% accuracy, and according to activation state (quiescent or activated) and subtype (CD3+CD8+ or CD3+CD4+) with 97% accuracy. Autofluorescence lifetime imaging can be used to non-destructively determine T-cell function.

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Fig. 1: NAD(P)H and FAD autofluorescence imaging revealed metabolic differences between quiescent and activated T cells.
Fig. 2: Autofluorescence imaging features enable the classification of quiescent and activated T cells.
Fig. 3: Autofluorescence imaging reveals interdonor and intradonor T-cell heterogeneity.
Fig. 4: The T-cell population composition affects T-cell autofluorescence.
Fig. 5: Autofluorescence imaging enables the classification of quiescent and activated T cells within combined quiescent and activated T-cell populations.

Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information. The raw and analysed datasets generated during the study are too large to be publicly shared, yet they are available for research purposes from the corresponding authors on reasonable request.

Code availability

All code and algorithms generated during the study are available at GitHub (https://github.com/walshlab/T-cell-Activation-Paper).

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Acknowledgements

We thank A. Movaghar for discussions of feature selection and machine learning classification methods and R. Schmitz for her assistance with formatting figures. This research was funded by the NIH (grant nos. R01 CA185747, R01 CA205101 and R01 CA211082, to M.C.S.); the Biotechnology Training Program of the National Institute of General Medical Sciences of the National Institutes of Health (no. T32GM008349, to K.P.M.); NIH awards (nos. R01DK098672 and R35GM131795, to D.J.P.; and T32DK007665, to N.M.N.); the NSF Graduate Research Fellowship Program (no. DGE-1747503, to K.P.M); and the National Science Foundation (no. EEC-1648035, to K.S.).

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A.J.W. and M.C.S. conceived the central hypotheses and K.P.M. contributed the hypothesis on distinguishing between CD3+CD8+ naive versus memory-T-cell autofluorescence properties. K.P.M. and A.J.W. designed and performed the experiments with assistance from K.T. and N.J.P.; K.T., A.J.W. and I.J. analysed the data. N.M.N. and K.P.M. performed the Seahorse assay. C.M.W. provided statistical insight and data analysis code. K.S. and M.C.S. supervised the project. A.J.W. wrote the initial draft of the manuscript. All of the authors contributed to data interpretation and the final manuscript.

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Correspondence to Alex J. Walsh or Melissa C. Skala.

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Competing interests

A.J.W. and M.C.S. are listed as co-inventors in a patent application (Systems and methods for sorting T cells by activation state; 62/724428; August 2018; Wisconsin Alumni Research Foundation) covering devices and methods to sort T cells on the basis of fluorescence lifetime components.

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Walsh, A.J., Mueller, K.P., Tweed, K. et al. Classification of T-cell activation via autofluorescence lifetime imaging. Nat Biomed Eng (2020). https://doi.org/10.1038/s41551-020-0592-z

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