PT  - JOURNAL ARTICLE
AU  - Evgeny A. Shirshin
AU  - Marina V. Shirmanova
AU  - Alexey V. Gayer
AU  - Maria M. Lukina
AU  - Elena E. Nikonova
AU  - Boris P. Yakimov
AU  - Gleb S. Budylin
AU  - Varvara V. Dudenkova
AU  - Nadezhda I. Ignatova
AU  - Dmitry V. Komarov
AU  - Vladislav Yakovlev
AU  - Wolfgang Becker
AU  - Elena V. Zagaynova
AU  - Vladislav I. Shcheslavskiy
AU  - Marlan O. Scully
TI  - Label-free sensing of cells with fluorescence lifetime imaging: the quest for metabolic heterogeneity
AID  - 10.1101/2022.01.12.476038
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2022.01.12.476038
4099  - http://biorxiv.org/content/early/2022/01/13/2022.01.12.476038.short
4100  - http://biorxiv.org/content/early/2022/01/13/2022.01.12.476038.full
AB  - Molecular, morphological and physiological heterogeneity is the inherent property of cells, which governs differences in their response to external influence. The tumor cells metabolic heterogeneity is of a special interest due to its clinical relevance to the tumor progression and therapeutic outcomes. Rapid, sensitive and non-invasive assessment of metabolic heterogeneity of cells is of 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 (FDPs) of endogenous fluorophores, such as NAD(P)H. To achieve the accurate discrimination between metabolically diverse cellular subpopulations, appropriate approaches to FLIM data collection and analysis are needed. In this report, the unique capability of FLIM to attain the overarching goal of discriminating metabolic heterogeneity has been demonstrated. This has been achieved using a novel approach to data analysis based on the non-parametric analysis, which revealed a much better sensitivity to the presence of metabolically distinct subpopulations as compare more traditional approaches of FLIM measurements and analysis. The new approach was further validated for imaging cultured cancer cells treated with chemotherapy. Those results pave the way for an accurate detection and quantification of cellular metabolic heterogeneity using FLIM, which will be valuable for assessing therapeutic vulnerabilities and predicting clinical outcomes.Competing Interest StatementThe authors have declared no competing interest.