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

doi:10.1038/s41551-020-0592-z

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 journal › Article › peer-review

88 Scopus citations

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.

Original language	English (US)
Pages (from-to)	77-88
Number of pages	12
Journal	Nature Biomedical Engineering
Volume	5
Issue number	1
DOIs	https://doi.org/10.1038/s41551-020-0592-z
State	Published - Jan 2021

ASJC Scopus subject areas

Biotechnology
Bioengineering
Medicine (miscellaneous)
Biomedical Engineering
Computer Science Applications

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10.1038/s41551-020-0592-z

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title = "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.",

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

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doi = "10.1038/s41551-020-0592-z",

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AU - Mueller, Katherine P.

AU - Tweed, Kelsey

AU - Jones, Isabel

AU - Walsh, Christine M.

AU - Piscopo, Nicole J.

AU - Niemi, Natalie M.

AU - Pagliarini, David J.

AU - Saha, Krishanu

AU - Skala, Melissa C.

PY - 2021/1

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AB - 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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Classification of T-cell activation via autofluorescence lifetime imaging

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