Consensus guidelines for cellular label-free optical metabolic imaging: ensuring accuracy and reproducibility in metabolic profiling
Year: 2025
Type: article
Abstract: Significance: Cellular metabolism plays a central role in health and disease, making its study critical for advancing diagnostics and therapies. Label-free optical metabolic imaging using endogenous fluorescence from reduced nicotinamide adenine dinucleotide (phosphate) [NAD(P)H] and flavin adenine dinucleotide (FAD) provides nondestructive, high-resolution insights into metabolic function and heterogeneity from the sub-cellular to the tissue level. Standardized approaches are essential to ensure reproducibility and comparability across studies. Aim: We aim to establish a consensus framework for the acquisition, calibration, and reporting of microscopic imaging metabolic function assessments based on fluorescence intensity and lifetime measurements of NAD(P)H and FAD. Approach: We present best practices for calibrating, analyzing, and reporting fluorescence intensity-based optical redox ratios and fluorescence lifetime data using multiexponential fitting and phasor analysis. Guidelines for validation experiments and cross-system standardization are provided to improve accuracy and reproducibility. Results: We demonstrate the importance of calibration procedures and normalization strategies for intensity-based optical redox measurements. We highlight needed calibration, signal-to-noise ratio considerations, and the impact of distinct analytical approaches on fluorescence lifetime-based metabolic function metrics. Conclusion: We recommend a consistent, practical framework for reproducible, label-free, optical metabolic imaging, facilitating robust comparisons across studies and supporting the broader adoption of optical metabolic imaging technologies for biomedical research and clinical translation.
Source: Journal of Biomedical Optics
Authors: Irene Georgakoudi, Melissa C. Skala, Kyle P. Quinn, Chiara Stringari, Janet E. Sorrells, Ahmed A. Heikal, Lin Z. Li, He N. Xu, Sixian You, Alex J. Walsh, Rupsa Datta, Kayvan Samimi, Amani A. Gillette, Kevin W. Eliceiri, Mihaela Balu, Stephen A. Boppart, Wonsang Hwang, Xingde Li, Zhiyi Liu, Laura Marcu, Sangeeta Murugkar, Raluca Niesner, Narasimhan Rajaram, Suman Ranjit, Keyue Shen, Lingyan Shi, Belén Torrado, Alexander Vallmitjana, Michael Wang-Evers, Roger Zemp
Institutions: Dartmouth College, Tufts University, Dartmouth Health, Dartmouth Cancer Center, Morgridge Institute for Research, University of Arkansas at Fayetteville, Centre National de la Recherche Scientifique, École Polytechnique, Inserm, Laboratoire d'Optique et Biosciences, Institut Polytechnique de Paris, Washington University in St. Louis, University of Minnesota, Duluth, University of Pennsylvania, University of Wisconsin–Madison, Massachusetts Institute of Technology, Texas A&M University, University of California, Irvine, Beckman Laser Institute and Medical Clinic, University of Illinois Urbana-Champaign, Harvard University, Massachusetts General Hospital, Johns Hopkins University, Zhejiang University, University of California, Davis, Carleton University, Freie Universität Berlin, Georgetown University, University of Southern California, University of California San Diego, University of Alberta
Language: English
Subfield: Biophysics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $1,675
Funders: National Science Foundation, American Cancer Society, University of Pennsylvania, Institute for Translational Medicine and Therapeutics, Agence Nationale de la Recherche, National Institutes of Health, Perelman School of Medicine, University of Pennsylvania, National Heart, Lung, and Blood Institute, National Cancer Institute, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institute of Biomedical Imaging and Bioengineering, Leadership Lincoln, Japan Endocrine Society
Awards: G1124571864, Probe-based two photon microscopy for functional, label-free early cancer diagnosis, G2889811516, Label-free (N)on-(L)inear (O)ptical Imaging of (M)etabolism and (M)yelin distribution in (I)ntact (T)issues, CAREER: Integrating quantitative biomarkers of mitochondrial structure and function through endogenous cellular fluorescence, Redox imaging for breast cancer prognosis, G5523616634, G6183060058, Non-invasive automated wound analysis via deep learning neural networks, G9000076447, Cancer Center Support Grant, Label-free imaging of CAR T cell metabolism, Skin Biology Resource-Based Center at UCI, Functional optical imaging for rapid, label-free predictions of treatment response and clonal evolution in patient-derived cancer organoids, Center for Multiparametric Imaging of Tumor Immune Microenvironments, The National Center for Interventional Biophotonic Technologies (NCIBT), Label-Free Optical Redox Imaging for Pretreatment Prognosis of Early-Stage Triple Negative Breast Cancer, Label-free single-cell imaging for quality control of cardiomyocyte biomanufacturing, Center for Label-free Imaging and Multiscale Biophotonics (CLIMB)
OpenAlex ID: W4416114508