Elena E Nikonova

Sechenov University

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Evgeny A. Shirshin

Sechenov University

Varvara V. Dudenkova

Privolzhsky Research Medical University

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В. В. Фадеев

Lomonosov Moscow State University

Dmitry V. Komarov

Marlan O. Scully

Baylor University

Elena V. Zagaynova

Privolzhsky Research Medical University

Gleb S. Budylin

Sechenov University

Marina V. Shirmanova

Privolzhsky Research Medical University

Wolfgang Becker

Becker Technologies (Germany)

Boris P. Yakimov

Lomonosov Moscow State University

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Privolzhsky Research Medical University

Lomonosov Moscow State University

Medical Technologies (Czechia)

Sechenov University

Moscow State University

N. I. Lobachevsky State University of Nizhny Novgorod

Yaroslav-the-Wise Novgorod State University

Bellingham Technical College

Laboratoire de Biophotonique et Pharmacologie

Institute of Biomedical Technologies

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Label-free sensing of cells with fluorescence lifetime imaging: The quest for metabolic heterogeneity

Proceedings of the National Academy of Sciences (2022)

Evgeny A. Shirshin Marina V. Shirmanova Alexey V. Gayer Maria M. Lukina Elena E Nikonova

Molecular, morphological, and physiological heterogeneity is the inherent property of cells which governs differences in their response to external influence. Tumor cell metabolic heterogeneity is of a special interest due to its clinical relevance to tumor progression and therapeutic outcomes. Rapid, sensitive, and noninvasive assessment of metabolic heterogeneity of cells is a great demand for biomedical sciences. Fluorescence lifetime imaging (FLIM), which is an all-optical technique, is an emerging tool for sensing and quantifying cellular metabolism by measuring fluorescence decay parameters of endogenous fluorophores, such as NAD(P)H. To achieve accurate discrimination between metabolically diverse cellular subpopulations, appropriate approaches to FLIM data collection and analysis are needed. In this paper, the unique capability of FLIM to attain the overarching goal of discriminating metabolic heterogeneity is demonstrated. This has been achieved using an approach to data analysis based on the nonparametric analysis, which revealed a much better sensitivity to the presence of metabolically distinct subpopulations compared to more traditional approaches of FLIM measurements and analysis. The approach was further validated for imaging cultured cancer cells treated with chemotherapy. These results pave the way for accurate detection and quantification of cellular metabolic heterogeneity using FLIM, which will be valuable for assessing therapeutic vulnerabilities and predicting clinical outcomes.

Fluorescence-lifetime imaging microscopy

Cellular metabolism

10.1073/pnas.2118241119

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Citations (63)

Advanced Procedure for Estimation of Phytoplankton Fluorescence Quantum Yield Using Remote Sensing Data: A Comparative Study of the Amundsen Sea Polynyas

Springer geology (2018)

Elena E Nikonova Evgeny A. Shirshin В. В. Фадеев Maxim Y. Gorbunov

Bloom

Quantum yield

10.1007/978-3-319-77788-7_31

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