Development and characterization of phasor-based analysis for FLIM to evaluate the metabolic and epigenetic impact of HER2 inhibition on squamous cell carcinoma cultures

Abstract

. Significance : Deranged metabolism and dysregulated growth factor signaling are closely associated with abnormal levels of proliferation, a recognized hallmark in tumorigenesis. Fluorescence lifetime imaging microscopy (FLIM) of endogenous nicotinamide adenine dinucleotide (NADH), a key metabolic coenzyme, offers a non-invasive, diagnostic indicator of disease progression, and treatment response. The model-independent phasor analysis approach leverages FLIM to rapidly evaluate cancer metabolism in response to targeted therapy. Aim : We combined lifetime and phasor FLIM analysis to evaluate the influence of human epidermal growth factor receptor 2 (HER2) inhibition, a prevalent cancer biomarker, on both nuclear and cytoplasmic NAD(P)H of two squamous cell carcinoma (SCC) cultures. While better established, the standard lifetime analysis approach is relatively slow and potentially subject to intrinsic fitting errors and model assumptions. Phasor FLIM analysis offers a rapid, model-independent alternative, but the sensitivity of the bound NAD(P)H fraction to growth factor signaling must also be firmly established. Approach: Two SCC cultures with low- and high-HER2 expression, were imaged using multiphoton-excited NAD(P)H FLIM, with and without treatment of the HER2 inhibitor AG825. Cells were challenged with mitochondrial inhibition and uncoupling to investigate AG825’s impact on the overall metabolic capacity. Phasor FLIM and lifetime fitting analyses were compared within nuclear and cytoplasmic compartments to investigate epigenetic and metabolic impacts of HER2 inhibition. Results : NAD(P)H fluorescence lifetime and bound fraction consistently decreased following HER2 inhibition in both cell lines. High-HER2 SCC74B cells displayed a more significant response than low-HER2 SCC74A in both techniques. HER2 inhibition induced greater changes in nuclear than cytoplasmic compartments, leading to an increase in NAD(P)H intensity and concentration. Conclusions: The use of both, complementary FLIM analysis techniques together with quantitative fluorescence intensity revealed consistent, quantitative changes in NAD(P)H metabolism associated with inhibition of growth factor signaling in SCC cell lines. HER2 inhibition promoted increased reliance on oxidative phosphorylation in both cell lines.

Comparison of phasor analysis and biexponential decay curve fitting of autofluorescence lifetime imaging data for machine learning prediction of cellular phenotypes

“…Similarly to activated macrophages, cancer cells exhibit increased aerobic glycolysis to support proliferation even when oxygen is present, a phenomenon known as the Warburg effect ( Warburg, 1956 ). Autofluorescence lifetime imaging combined with either exponential decay fitting or phasor analysis allows analysis of metabolic phenotypes of cancer cells from FLIM data ( Periasamy et al, 2012 ; Walsh et al, 2014 ; Alam et al, 2017 ; Trinh et al, 2017 ; Pham et al, 2021 ). The phasor plots showed that glycolysis inhibition within MCF7 cells led to a shift towards more bound NADH, while the OXPHOS inhibition stimulated a shift toward more free NADH ( Figure 3G ).…”

More than double the fun with two-photon excitation microscopy

“…Using FLIM, cells or tissues can be profiled for metabolic state under various conditions such as during glucose shock (see example in Fig. 4d) 117 , infection 125,126 , differentiation [127][128][129][130] , diabetes 131 , drug treatments 117,132 , or in the context of neuro-pathophysiology [133][134][135][136][137] . While examining NADH lifetime can provide valuable insights, imaging conditions especially when fixation or embedding is required need to be carefully evaluated 138 .…”

FLUTE: a Python GUI for interactive phasor analysis of FLIM data

“…The main advantages of phasor analysis are that it provides a visual distribution of the molecular species by clustering pixels with similar decays, it allows for colour mapping of the pixels of the FLIM images, and it is linear in terms of non-interacting molecular species [12,13,17,18]. Phasor analysis of FLIM data is increasingly used in several fields of scientific research [19][20][21][22][23][24] and is particularly used for metabolic imaging with the endogenous biomarkers NAD(P)H and FAD to map their complex autofluorescence distributions in live tissues [5,13,[25][26][27][28][29][30][31][32][33].…”