Classification of T-cell activation via autofluorescence lifetime imaging

Alex J. Walsh, Katherine P. Mueller, Kelsey Tweed, Isabel Jones, Christine M. Walsh, Nicole J. Piscopo, Natalie M. Niemi, David J. Pagliarini, Krishanu Saha, Melissa C. Skala

Research output: Contribution to journalArticlepeer-review

69 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)77-88
Number of pages12
JournalNature Biomedical Engineering
Issue number1
StatePublished - Jan 2021

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Medicine (miscellaneous)
  • Biomedical Engineering
  • Computer Science Applications


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