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Multimodal Optical Imaging Platform for Studying Cellular Metabolism
Published on: June 6, 2025
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Consensus guidelines for cellular label-free optical metabolic imaging: ensuring accuracy and reproducibility in
Irene Georgakoudi1,2,3, Melissa C Skala4,5, Kyle P Quinn3,6
1Thayer School of Engineering at Dartmouth College, Hanover, New Hampshire, United States.
Journal of Biomedical Optics
|November 13, 2025
Summary
Standardized methods for label-free optical metabolic imaging using NAD(P)H and FAD fluorescence are crucial for reproducible research. This study provides a framework for consistent data acquisition, calibration, and reporting to advance biomedical applications.
Area of Science:
- Biomedical Optics
- Cellular Metabolism
- Fluorescence Imaging
Background:
- Cellular metabolism is vital in health and disease, necessitating advanced diagnostic and therapeutic tools.
- Label-free optical metabolic imaging offers nondestructive, high-resolution insights into cellular metabolic function using endogenous fluorescence.
- Reproducibility and comparability across studies are hindered by a lack of standardized methodologies.
Purpose of the Study:
- To establish a consensus framework for the acquisition, calibration, and reporting of microscopic imaging metabolic function.
- To standardize assessments based on fluorescence intensity and lifetime measurements of reduced nicotinamide adenine dinucleotide (phosphate) [NAD(P)H] and flavin adenine dinucleotide (FAD).
Main Methods:
- Presenting best practices for calibrating and analyzing fluorescence intensity-based optical redox ratios.
- Utilizing multiexponential fitting and phasor analysis for fluorescence lifetime data.
- Providing guidelines for validation experiments and cross-system standardization.
Main Results:
- Demonstrating the critical role of calibration and normalization in intensity-based optical redox measurements.
- Highlighting the impact of analytical approaches on fluorescence lifetime-based metabolic metrics.
- Addressing signal-to-noise ratio considerations and calibration needs.
Conclusions:
- Recommending a practical framework for reproducible, label-free optical metabolic imaging.
- Facilitating robust comparisons across studies and promoting wider adoption of these technologies.
- Supporting the translation of optical metabolic imaging for biomedical research and clinical applications.

