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.
This is a preview of subscription content, access via your institution
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$99.00 per year
only $8.25 per issue
Rent or buy this article
Prices vary by article type
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
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.
All code and algorithms generated during the study are available at GitHub (https://github.com/walshlab/T-cell-Activation-Paper).
Mosmann, T. R. & Coffman, R. L. in Advances in Immunology Vol. 46 (ed. Dixon, F. J.) 111–147 (Elsevier, 1989).
Bettelli, E., Korn, T. & Kuchroo, V. K. Th17: the third member of the effector T cell trilogy. Curr. Opin. Immunol. 19, 652–657 (2007).
Pardoll, D. M. The blockade of immune checkpoints in cancer immunotherapy. Nat. Rev. Cancer 12, 252–264 (2012).
Restifo, N. P., Dudley, M. E. & Rosenberg, S. A. Adoptive immunotherapy for cancer: harnessing the T cell response. Nat. Rev. Immunol. 12, 269–281 (2012).
Canavan, J. B. et al. Developing in vitro expanded CD45RA+ regulatory T cells as an adoptive cell therapy for Crohn’s disease. Gut 65, 584–594 (2015).
Marek-Trzonkowska, N. et al. Administration of CD4+CD25highCD127− regulatory T cells preserves β-cell function in type 1 diabetes in children. Diabetes Care 35, 1817–1820 (2012).
Chance, B., Schoener, B., Oshino, R., Itshak, F. & Nakase, Y. Oxidation-reduction ratio studies of mitochondria in freeze-trapped samples. NADH and flavoprotein fluorescence signals. J. Biol. Chem. 254, 4764–4771 (1979).
Lakowicz, J. R., Szmacinski, H., Nowaczyk, K. & Johnson, M. L. Fluorescence lifetime imaging of free and protein-bound NADH. Proc. Natl Acad. Sci. USA 89, 1271–1275 (1992).
Georgakoudi, I. & Quinn, K. P. Optical imaging using endogenous contrast to assess metabolic state. Annu. Rev. Biomed. Eng. 14, 351–367 (2012).
Huang, S., Heikal, A. A. & Webb, W. W. Two-photon fluorescence spectroscopy and microscopy of NAD(P)H and flavoprotein. Biophys. J. 82, 2811–2825 (2002).
Varone, A. et al. Endogenous two-photon fluorescence imaging elucidates metabolic changes related to enhanced glycolysis and glutamine consumption in precancerous epithelial tissues. Cancer Res. 74, 3067–3075 (2014).
Ostrander, J. H. et al. Optical redox ratio differentiates breast cancer cell lines based on estrogen receptor status. Cancer Res. 70, 4759–4766 (2010).
Nakashima, N., Yoshihara, K., Tanaka, F. & Yagi, K. Picosecond fluorescence lifetime of the coenzyme of d-amino acid oxidase. J. Biol. Chem. 255, 5261–5263 (1980).
Skala, M. C. et al. In vivo multiphoton microscopy of NADH and FAD redox states, fluorescence lifetimes, and cellular morphology in precancerous epithelia. Proc. Natl Acad. Sci. USA 104, 19494–19499 (2007).
Quinn, K. P. et al. Quantitative metabolic imaging using endogenous fluorescence to detect stem cell differentiation. Sci. Rep. 3, 3432 (2013).
Walsh, A. J. et al. Quantitative optical imaging of primary tumor organoid metabolism predicts drug response in breast cancer. Cancer Res. 74, 5184–5194 (2014).
Walsh, A. J., Castellanos, J. A., Nagathihalli, N. S., Merchant, N. B. & Skala, M. C. Optical imaging of drug-induced metabolism changes in murine and human pancreatic cancer organoids reveals heterogeneous drug response. Pancreas 45, 863–869 (2016).
Walsh, A. J. et al. Optical metabolic imaging identifies glycolytic levels, subtypes, and early treatment response in breast cancer. Cancer Res. 73, 6164–6174 (2013).
Stringari, C. et al. Phasor approach to fluorescence lifetime microscopy distinguishes different metabolic states of germ cells in a live tissue. Proc. Natl Acad. Sci. USA 108, 13582–13587 (2011).
Alfonso-Garcia, A. et al. Label-free identification of macrophage phenotype by fluorescence lifetime imaging microscopy. J. Biomed. Opt. 21, 046005 (2016).
Szulczewski, J. M. et al. In vivo visualization of stromal macrophages via label-free FLIM-based metabolite imaging. Sci. Rep. 6, 25086 (2016).
Pavillon, N., Hobro, A. J., Akira, S. & Smith, N. I. Noninvasive detection of macrophage activation with single-cell resolution through machine learning. Proc. Natl Acad. Sci. USA 115, E2676–E2685 (2018).
Frauwirth, K. A. et al. The CD28 signaling pathway regulates glucose metabolism. Immunity 16, 769–777 (2002).
Chang, C.-H. et al. Posttranscriptional control of T cell effector function by aerobic glycolysis. Cell 153, 1239–1251 (2013).
Michalek, R. D. et al. Cutting edge: distinct glycolytic and lipid oxidative metabolic programs are essential for effector and regulatory CD4+ T cell subsets. J. Immunol. 186, 3299–3303 (2011).
Van der Windt, G. J. W. et al. CD8 memory T cells have a bioenergetic advantage that underlies their rapid recall ability. Proc. Natl Acad. Sci. USA 110, 14336–14341 (2013).
Tarasenko, T. N. et al. Cytochrome c oxidase activity is a metabolic checkpoint that regulates cell fate decisions during T cell activation and differentiation. Cell Metab. 25, 1254–1268 (2017).
Gubser, P. M. et al. Rapid effector function of memory CD8+ T cells requires an immediate-early glycolytic switch. Nat. Immunol. 14, 1064–1072 (2013).
McInnes, L. & Healy, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at http://arxiv.org/abs/1802.03426v1 (2018).
Zhang, Q., Piston, D. W. & Goodman, R. H. Regulation of corepressor function by nuclear NADH. Science 295, 1895–1897 (2002).
Hou, J. et al. Correlating two-photon excited fluorescence imaging of breast cancer cellular redox state with seahorse flux analysis of normalized cellular oxygen consumption. J. Biomed. Opt. 21, 060503 (2016).
Wang, R. et al. The transcription factor Myc controls metabolic reprogramming upon T lymphocyte activation. Immunity 35, 871–882 (2011).
Wang, R. & Green, D. R. Metabolic checkpoints in activated T cells. Nat. Immunol. 13, 907–915 (2012).
Kishton, R. J., Sukumar, M. & Restifo, N. P. Metabolic regulation of T cell longevity and function in tumor immunotherapy. Cell Metab. 26, 94–109 (2017).
Liu, Z. et al. Mapping metabolic changes by noninvasive, multiparametric, high-resolution imaging using endogenous contrast. Sci. Adv. 4, eaap9302 (2018).
Sharick, J. T. et al. Protein-bound NAD(P)H lifetime is sensitive to multiple fates of glucose carbon. Sci. Rep. 8, 5456 (2018).
Chang, J. T., Wherry, E. J. & Goldrath, A. W. Molecular regulation of effector and memory T cell differentiation. Nat. Immunol. 15, 1104–1115 (2014).
Kaech, S. M. & Cui, W. Transcriptional control of effector and memory CD8+ T cell differentiation. Nat. Rev. Immunol. 12, 749–761 (2012).
Palmer, M. J., Mahajan, V. S., Chen, J., Irvine, D. J. & Lauffenburger, D. A. Signaling thresholds govern heterogeneity in IL-7-receptor-mediated responses of naive CD8+ T cells. Immunol. Cell Biol. 89, 581–594 (2011).
Tubo, N. J. et al. Single naive CD4+ T cells from a diverse repertoire produce different effector cell types during infection. Cell 153, 785–796 (2013).
Krylov, S. N. et al. Correlating cell cycle with metabolism in single cells: combination of image and metabolic cytometry. Cytometry 37, 14–20 (1999).
Heaster, T. M., Walsh, A. J., Zhao, Y., Hiebert, S. W. & Skala, M. C. Autofluorescence imaging identifies tumor cell-cycle status on a single-cell level. J. Biophotonics 11, e201600276 (2017).
Chen, C. L. et al. Deep learning in label-free cell classification. Sci. Rep. 6, 21471 (2016).
Blasi, T. et al. Label-free cell cycle analysis for high-throughput imaging flow cytometry. Nat. Commun. 7, 10256 (2016).
Kelly, B. & O’Neill, L. A. Metabolic reprogramming in macrophages and dendritic cells in innate immunity. Cell Res. 25, 771–784 (2015).
Gavgiotaki, E. et al. Detection of the T cell activation state using non-linear optical microscopy. J. Biophotonics 12, e201800277 (2018).
Janssen, E. M. et al. CD4+ T cells are required for secondary expansion and memory in CD8+ T lymphocytes. Nature 421, 852–856 (2003).
Takahashi, T. et al. Immunologic self-tolerance maintained by CD25+CD4+ regulatory T cells constitutively expressing cytotoxic T lymphocyte-associated antigen 4. J. Exp. Med. 192, 303–310 (2000).
Dieckmann, D., Plottner, H., Berchtold, S., Berger, T. & Schuler, G. Ex vivo isolation and characterization of CD4+CD25+ T cells with regulatory properties from human blood. J. Exp. Med. 193, 1303–1310 (2001).
Naito, Y. et al. CD8+ T cells infiltrated within cancer cell nests as a prognostic factor in human colorectal cancer. Cancer Res. 58, 3491–3494 (1998).
Gerriets, V. A. & Rathmell, J. C. Metabolic pathways in T cell fate and function. Trends Immunol. 33, 168–173 (2012).
Bird, D. K. et al. Metabolic mapping of MCF10A human breast cells via multiphoton fluorescence lifetime imaging of the coenzyme NADH. Cancer Res. 65, 8766–8773 (2005).
Walsh, A. J. & Skala, M. C. Optical metabolic imaging quantifies heterogeneous cell populations. Biomed. Opt. Express 6, 559–573 (2015).
Walsh, A. J. & Skala, M. C. An automated image processing routine for segmentation of cell cytoplasms in high-resolution autofluorescence images. in Proc. Multiphoton Microscopy in the Biomedical Sciences XIV (Eds Periasamy, A. et al.) 161–166 (SPIE, 2014).
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.).
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.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Walsh, A.J., Mueller, K.P., Tweed, K. et al. Classification of T-cell activation via autofluorescence lifetime imaging. Nat Biomed Eng 5, 77–88 (2021). https://doi.org/10.1038/s41551-020-0592-z
This article is cited by
Nature Biotechnology (2023)
On-chip label-free cell classification based directly on off-axis holograms and spatial-frequency-invariant deep learning
Scientific Reports (2023)
Scientific Reports (2023)
Nature Photonics (2023)
Evolution of cisplatin resistance through coordinated metabolic reprogramming of the cellular reductive state
British Journal of Cancer (2023)