A red fluorescent genetically encoded biosensor for in vivo imaging of extracellular l-lactate dynamics
Bibliographic record
Abstract
l-Lactate is increasingly recognized as an intercellular energy currency in mammals, but mysteries remain regarding the spatial and temporal dynamics of its release and uptake between cells via the extracellular environment. Here we introduce R-eLACCO2.1, a red fluorescent extracellular l-lactate biosensor that is superior to previously reported green fluorescent biosensors in in vivo sensitivity to increases in extracellular l-lactate and spectral orthogonality. R-eLACCO2.1 exhibits excellent fluorescence response in cultured cells, mouse brain slices, and live mice. R-eLACCO2.1 also serves as an effective fluorescence lifetime-based biosensor. Using R-eLACCO2.1, we monitor whisker stimulation and locomotion-induced changes in endogenous extracellular l-lactate in the somatosensory cortex of awake mice. To highlight the potential insights gained from in vivo measurements with R-eLACCO2.1, we perform dual-color imaging from the somatosensory cortex of actively locomoting mice. This enables us to simultaneously observe the neural activity, reported by a green fluorescent GCaMP calcium ion biosensor, and extracellular l-lactate. As the high-performance tool in the suite of extracellular l-lactate biosensors, R-eLACCO2.1 is ideally suited to delimit the emerging roles of l-lactate in mammalian metabolism. l-lactate is a key metabolite supporting neuronal activity, but methods to monitor l-lactate dynamics alongside neuronal activity in vivo have been limited. Here, authors report R-eLACCO2.1, a red fluorescent extracellular l-lactate biosensor enabling simultaneous, multiplexed imaging in awake mice.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".