Measuring Metabolic Changes in Cancer Cells Using Two-Photon Fluorescence Lifetime Imaging Microscopy and
Jiaxin Zhang1, Horst Wallrabe1, Karsten Siller2
1The W.M. Keck Center for Cellular Imaging, University of Virginia, Charlottesville, Virginia, USA.
Journal of Biophotonics
|November 26, 2024
Summary
This study used two-photon fluorescence lifetime imaging microscopy to monitor cancer cell metabolism during drug treatment. Machine learning analysis of NAD(P)H and FAD coenzymes revealed early drug responses more robustly.
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.
