Genetically-targeted photocatalytic organic dyes for spatiotemporally controlled organic synthesis directed by specific living cells
Bibliographic record
Abstract
Catalytic reactions of a broad range of abiotic molecules and macromolecules are beyond the native capabilities of mammals. Natural enzymes from prokaryotes or plant-based eukaryotes have limited substrate scopes. Therefore, broadening the range of catalytic bond-forming reactions that function in physiological conditions would enable the syntheses of a vast array of molecules directly within biological systems. This approach may provide an alternative way to modulate cellular behaviors if such molecules can be synthesized with spatiotemporal control on specific cell types in living systems; furthermore, restricting synthesis to well-defined cells or cell-types would enable a potentially transformative approach of treating cells as separable reaction vessels within living organisms. Herein, we use genetic targeting to incorporate an organic photocatalytic dye onto specific cell types to enable in-situ light-controlled and spatially defined chemical synthesis of non-natural molecules. We demonstrate, for the first time, a photo-patterned organic coupling reaction in the extracellular matrix of living cells under dilute, aqueous, aerobic physiological conditions. A 6-fold contrast in reaction yield can be achieved between two adjacent HEK293FT cells with and without light exposure. The above photocatalysis can be initiated using mild confocal laser stimulation as low as 16 μW/mm2 at multiple wavelengths. Furthermore, the cell-type specific photocatalyzed C-H functionalization coupling reactions taking place on cell surfaces are used to demonstrate anabolic construction of non-natural products. The above findings lay an important foundation for developing future abiotic cell-type specific chemical syntheses in living organisms.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".