A Polymeric Nanocarrier Platform for Rapid and Precise Fatty Acid Tracing in Cells
Bibliographic record
Abstract
Stable isotope tracing provides insights into metabolism by tracking the movement of isotopically labeled precursors through metabolic networks. Fatty acid tracers, such as uniformly labeled 13 C-palmitate, are used to study lipid biosynthesis, energy storage, and/or signaling. These tracers are complexed with BSA to improve solubility; yet, this approach is limited by transport bottlenecks, toxicity, and immunogenicity. Here, we developed biodegradable nanocarriers that improve hydrophobic tracer delivery and benchmarked performance against BSA with metabolomics and lipidomics. Nanocarriers accumulated U– 13 C-palmitate to higher intracellular levels, and more rapidly, than BSA-conjugated controls. Once inside the cell, nanocarrier-delivered tracers exhibited first-order depletion kinetics, ensuring predictable and efficient metabolism. In contrast, BSA produced delayed or biphasic tracer depletion due to transport limitations, which hindered the bioavailability. Entrance of nanocarrier-delivered U– 13 C-palmitate into the cellular metabolic network manifested through 13 C-labeled desaturated and elongated fatty acids and incorporation into complex lipids without material-mediated aberrations. Our results demonstrate that nanocarrier-assisted tracing captures key metabolic trends with enhanced labeling while overcoming limitations of BSA-mediated delivery. This versatile, customizable platform enables opportunities for metabolic tracing in complex systems.
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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 | 0.001 | 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".