Green fluorescent protein emission obscures metabolic fluorescent lifetime imaging of NAD(P)H
Bibliographic record
Abstract
Autofluorescence of endogenous molecules can provide valuable insights in both basic research and clinical applications. One such technique is fluorescence lifetime imaging (FLIM) of NAD(P)H, which serves as a correlate of glycolysis and electron transport chain rates in metabolically active tissue. A powerful advantage of NAD(P)H-FLIM is the ability to measure cell-specific metabolism within heterogeneous tissues. Cell-type specific identification is most commonly achieved with directed green fluorescent protein (GFP) expression. However, we demonstrate that NAD(P)H-FLIM should not be analyzed in GFP-expressing cells, as GFP molecules themselves emit photons in the blue spectrum with short fluorescence lifetimes when imaged using two-photon excitation at 750 nm. This is substantially different from the reported GFP emission wavelength and lifetime after two-photon excitation at 910 nm. These blue GFP photons are indistinguishable from free NAD(P)H by both emission spectra and fluorescence lifetime. Therefore, NAD(P)H-FLIM in GFP-expressing cells will lead to incorrect interpretations of metabolic rates, and thus, these techniques should not be combined.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".