A Transfer Hydrogenation Approach to Activity-Based Sensing of Formate in Living Cells
Bibliographic record
Abstract
Formate is a major reactive carbon species in one-carbon metabolism, where it serves as an endogenous precursor for amino acid and nucleic acid biosynthesis and a cellular source of NAD(P)H. On the other hand, aberrant elevations in cellular formate are connected to progression of serious diseases, including cancer and Alzheimer's disease. Traditional methods for formate detection in biological environments often rely on sample destruction or extensive processing, resulting in a loss of spatiotemporal information. To help address these limitations, here we present the design, synthesis, and biological evaluation of a first-generation activity-based sensing system for live-cell formate imaging that relies on iridium-mediated transfer hydrogenation chemistry. Formate facilitates an aldehyde-to-alcohol conversion on various fluorophore scaffolds to enable fluorescence detection of this one-carbon unit, including through a two-color ratiometric response with internal calibration. The resulting two-component probe system can detect changes in formate levels in living cells with a high selectivity over potentially competing biological analytes. Moreover, this activity-based sensing system can visualize changes in endogenous formate fluxes through alterations of one-carbon pathways in cell-based models of human colon cancer, presaging the potential utility of this chemical approach to probe the continuum between one-carbon metabolism and signaling in cancer and other diseases.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".