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

Abstract

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.

Keywords: cancer; fluorescence lifetime; heterogeneity; imaging; metabolism.

Fig. 1.

Illustration of tumor metabolic heterogeneity evaluation using FLIM. (A) Cancer cells are examined using metabolic FLIM, which provides the kinetics of fluorescence decay from each pixel of the image. The obtained fluorescence decay signal depends on the metabolic state of the cell and can further be analyzed using various parametric and nonparametric methods, which can accurately predict metabolically distinct subpopulations. (B) Automatic segmentation of cells in FLIM images using artificial intelligence (AI)-based approaches allows for the assessment of metabolic heterogeneity on a single cell level.

Fig. 2.

Schematic representation of bimodality assessment using (A) the distribution of mean fluorescence lifetime, (B) population density of phasor plot, and (C) K-means clustering of fluorescence decay curves. The data were obtained using numerical simulation. Under the assumption of the presence of two subpopulations, two modes are detected for each method of FLIM data analysis, and then the median value (µ) and SD (σ) for each mode are used to calculate the BI. The results of bimodality assessment for two values of Δτ_mean (50 and 350 ps) using the BI estimation from (D) the distribution of mean fluorescence lifetime, (E) population density of phasor plot, and (F) K-means clustering of fluorescence decay curves. The analysis was performed over segmented cells.

Fig. 3.

(A) The dependence of the fraction of cells correctly attributed to its cluster on the distance between the median fluorescence lifetimes in the clusters ($\mathrm{\Delta }{\tau }_{\mathit{mean}}$) obtained using biexponential fitting for whole-image analysis (red) and for segmented cells (black). (B) The dependence of the fraction of cells correctly attributed to its cluster on the distance between the median fluorescence lifetimes in the clusters ($\mathrm{\Delta }{\tau }_{\mathit{mean}}$) obtained using biexponential fitting (red), phasor plot analysis (green), and K-means clustering (blue). The gray horizontal line corresponds to precision of 90% in attribution of cells to the correct cluster. The dependencies presented in A and B were calculated for fraction of cells belonging to Cluster 1 equal to 90%, 70%, and 50%.

Fig. 4.

Experimental assessment of cellular heterogeneity in the human colorectal carcinoma cell line HCT116 treated with 5-FU. (A) Representative FLIM images of NAD(P)H fluorescence of untreated (Top) and treated cells (4 µM for 24 h, Bottom). Orange and blue contours correspond to two clusters as determined by the K-means algorithm applied for the fluorescence decay curves of the contoured cell. (B and C) The distributions of the mean fluorescence lifetime τ_mean for the untreated (B) and treated (C) cells and their fits to two Gaussians. (D and E) The results of K-means clustering for the untreated (D) and treated (E) samples. The two clusters of fluorescence decay curves with centroids are shown by dashed lines. (F) Evolution of the mean fluorescence lifetime distribution with the drug concentration. Orange and blue contours correspond to two clusters as determined in the calculation of the BI. (G) The dependence of BI, calculated from fluorescence lifetime distributions (red) and using K-means clustering (blue), on the drug concentration. (H) The dependence of the cells’ area on the 5-FU concentration.

Fig. 5.

Experimental assessment of cellular heterogeneity in the primary colon cancer cell cultures. (A) Representative FLIM images of NAD(P)H fluorescence of primary cell cultures exhibiting unimodal (Top) and bimodal (Bottom) metabolism. (B and C) The distributions of the mean fluorescence lifetime for the primary cell cultures exhibiting unimodal (B) and bimodal (C) metabolism and its fits to two Gaussians. (D and E) The results of K-means clustering for the primary cell cultures exhibiting unimodal (D) and bimodal (E) distribution of FDPs. The two clusters of fluorescence decay curves with centroids are shown by dashed lines. (F) Changes of the mean fluorescence lifetime distribution upon treatment with 5-FU for the patient’s cells, which exhibited no bimodality. (G) Changes of the mean fluorescence lifetime distribution upon treatment with 5-FU for the patient’s cells, which exhibited bimodality. Orange and blue contours in the A and I correspond to two clusters as determined by the K-means algorithm.

Fig. 6.

Schematic of the U-Net model-based algorithm for cell segmentation in the FLIM data.