Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Classification of T-cell activation via autofluorescence lifetime imaging

Nature Biomedical Engineering volume 5, pages 77–88 (2021)Cite this article

Subjects

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

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.

Similar content being viewed by others

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).

References

  1. Mosmann, T. R. & Coffman, R. L. in Advances in Immunology Vol. 46 (ed. Dixon, F. J.) 111–147 (Elsevier, 1989).

  2. Bettelli, E., Korn, T. & Kuchroo, V. K. Th17: the third member of the effector T cell trilogy. Curr. Opin. Immunol. 19, 652–657 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Pardoll, D. M. The blockade of immune checkpoints in cancer immunotherapy. Nat. Rev. Cancer 12, 252–264 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. 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).

    Article  PubMed  CAS  Google Scholar 

  6. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  7. 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).

    Article  CAS  PubMed  Google Scholar 

  8. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Georgakoudi, I. & Quinn, K. P. Optical imaging using endogenous contrast to assess metabolic state. Annu. Rev. Biomed. Eng. 14, 351–367 (2012).

    Article  CAS  PubMed  Google Scholar 

  10. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Ostrander, J. H. et al. Optical redox ratio differentiates breast cancer cell lines based on estrogen receptor status. Cancer Res. 70, 4759–4766 (2010).

    Article  CAS  PubMed  Google Scholar 

  13. 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).

    Article  CAS  PubMed  Google Scholar 

  14. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Quinn, K. P. et al. Quantitative metabolic imaging using endogenous fluorescence to detect stem cell differentiation. Sci. Rep. 3, 3432 (2013).

  16. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. 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).

    Article  CAS  PubMed  Google Scholar 

  18. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Alfonso-Garcia, A. et al. Label-free identification of macrophage phenotype by fluorescence lifetime imaging microscopy. J. Biomed. Opt. 21, 046005 (2016).

    Article  PubMed Central  Google Scholar 

  21. Szulczewski, J. M. et al. In vivo visualization of stromal macrophages via label-free FLIM-based metabolite imaging. Sci. Rep. 6, 25086 (2016).

  22. 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).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  23. Frauwirth, K. A. et al. The CD28 signaling pathway regulates glucose metabolism. Immunity 16, 769–777 (2002).

    Article  CAS  PubMed  Google Scholar 

  24. Chang, C.-H. et al. Posttranscriptional control of T cell effector function by aerobic glycolysis. Cell 153, 1239–1251 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. 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).

    Article  CAS  PubMed  Google Scholar 

  26. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  27. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. 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).

    Article  CAS  PubMed  Google Scholar 

  29. McInnes, L. & Healy, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at http://arxiv.org/abs/1802.03426v1 (2018).

  30. Zhang, Q., Piston, D. W. & Goodman, R. H. Regulation of corepressor function by nuclear NADH. Science 295, 1895–1897 (2002).

    Article  CAS  PubMed  Google Scholar 

  31. 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).

    Article  PubMed Central  Google Scholar 

  32. Wang, R. et al. The transcription factor Myc controls metabolic reprogramming upon T lymphocyte activation. Immunity 35, 871–882 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Wang, R. & Green, D. R. Metabolic checkpoints in activated T cells. Nat. Immunol. 13, 907–915 (2012).

    Article  CAS  PubMed  Google Scholar 

  34. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Liu, Z. et al. Mapping metabolic changes by noninvasive, multiparametric, high-resolution imaging using endogenous contrast. Sci. Adv. 4, eaap9302 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Sharick, J. T. et al. Protein-bound NAD(P)H lifetime is sensitive to multiple fates of glucose carbon. Sci. Rep. 8, 5456 (2018).

  37. Chang, J. T., Wherry, E. J. & Goldrath, A. W. Molecular regulation of effector and memory T cell differentiation. Nat. Immunol. 15, 1104–1115 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Kaech, S. M. & Cui, W. Transcriptional control of effector and memory CD8+ T cell differentiation. Nat. Rev. Immunol. 12, 749–761 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Krylov, S. N. et al. Correlating cell cycle with metabolism in single cells: combination of image and metabolic cytometry. Cytometry 37, 14–20 (1999).

    Article  CAS  PubMed  Google Scholar 

  42. 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).

    Google Scholar 

  43. Chen, C. L. et al. Deep learning in label-free cell classification. Sci. Rep. 6, 21471 (2016).

  44. Blasi, T. et al. Label-free cell cycle analysis for high-throughput imaging flow cytometry. Nat. Commun. 7, 10256 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Kelly, B. & O’Neill, L. A. Metabolic reprogramming in macrophages and dendritic cells in innate immunity. Cell Res. 25, 771–784 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Gavgiotaki, E. et al. Detection of the T cell activation state using non-linear optical microscopy. J. Biophotonics 12, e201800277 (2018).

  47. Janssen, E. M. et al. CD4+ T cells are required for secondary expansion and memory in CD8+ T lymphocytes. Nature 421, 852–856 (2003).

    Article  CAS  PubMed  Google Scholar 

  48. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. 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).

    CAS  PubMed  Google Scholar 

  51. Gerriets, V. A. & Rathmell, J. C. Metabolic pathways in T cell fate and function. Trends Immunol. 33, 168–173 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. 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).

    Article  CAS  PubMed  Google Scholar 

  53. Walsh, A. J. & Skala, M. C. Optical metabolic imaging quantifies heterogeneous cell populations. Biomed. Opt. Express 6, 559–573 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. 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).

Download references

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.).

Author information

Author notes

  1. Alex J. Walsh

    Present address: Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA

  2. David J. Pagliarini

    Present address: Departments of Cell Biology and Physiology, Biochemistry and Molecular Biophysics, and Genetics, Washington University School of Medicine, St Louis, MO, USA

Authors and Affiliations

  1. Morgridge Institute for Research, Madison, WI, USA

    Alex J. Walsh, Kelsey Tweed, Isabel Jones, Christine M. Walsh, Natalie M. Niemi, David J. Pagliarini & Melissa C. Skala

  2. Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA

    Katherine P. Mueller, Nicole J. Piscopo & Krishanu Saha

  3. Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA

    Katherine P. Mueller, Kelsey Tweed, Nicole J. Piscopo, Krishanu Saha & Melissa C. Skala

  4. Department of Sociology, State University of New York, Albany, NY, USA

    Christine M. Walsh

  5. Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA

    Natalie M. Niemi & David J. Pagliarini

Authors
  1. Alex J. Walsh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Katherine P. Mueller
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kelsey Tweed
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Isabel Jones
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Christine M. Walsh
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nicole J. Piscopo
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Natalie M. Niemi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. David J. Pagliarini
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Krishanu Saha
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Melissa C. Skala
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

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.

Corresponding authors

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

Ethics declarations

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.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary note, figures and tables.

Reporting Summary

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41551-020-0592-z

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing