Measuring Metabolic Changes in Cancer Cells Using Two-Photon Fluorescence Lifetime Imaging Microscopy and...

Area of Science:

• Cellular Metabolism
• Biophysics
• Cancer Research

Background:

• Cellular metabolism is crucial for cancer progression and drug response.
• Tracking metabolic changes in real-time is vital for understanding cancer biology.
• Auto-fluorescent coenzymes like NAD(P)H and FAD are key metabolic indicators.

Purpose of the Study:

• To evaluate simultaneous 800 nm excitation for two-photon fluorescence lifetime imaging microscopy (2P-FLIM) of NAD(P)H and FAD.
• To compare the efficacy of different analysis methods (FLIRR, PCA, AE) for metabolic tracking.
• To assess early drug responses in cancer cells using advanced imaging techniques.

Main Methods:

• Two-photon fluorescence lifetime imaging microscopy (2P-FLIM) was employed.
• Simultaneous 800 nm excitation was compared to sequential excitation protocols.
• Analysis involved fluorescence lifetime redox ratio (FLIRR) and machine learning (PCA, AE).

Main Results:

• Simultaneous 800 nm excitation proved advantageous for metabolic imaging.
• All three analysis methods identified early drug responses.
• Machine learning models (PCA, AE) offered statistically superior and robust results.

Conclusions:

• 2P-FLIM with simultaneous excitation is effective for tracking cellular metabolism.
• Machine learning enhances the analysis of metabolic changes for drug response assessment.
• This approach provides high-resolution insights into cancer cell metabolic dynamics.