Consensus guidelines for cellular label-free optical metabolic imaging: ensuring accuracy and reproducibility in metabolic profiling
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.133 | 0.165 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.018 | 0.007 |
| Research integrity | 0.017 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".