Practical Labeling Methodology for Choline‐Derived Lipids and Applications in Live Cell Fluorescence Imaging
Bibliographic record
Abstract
Lipids of the plasma membrane participate in a variety of biological processes, and methods to probe their function and cellular location are essential to understanding biochemical mechanisms. Previous reports have established that phosphocholine-containing lipids can be labeled by alkyne groups through metabolic incorporation. Herein, we have tested alkyne, azide and ketone-containing derivatives of choline as metabolic labels of choline-containing lipids in cells. We also show that 17-octadecynoic acid can be used as a complementary metabolic label for lipid acyl chains. We provide methods for the synthesis of cyanine-based dyes that are reactive with alkyne, azide and ketone metabolic labels. Using an improved method for fluorophore conjugation to azide or alkyne-modified lipids by Cu(I)-catalyzed azide-alkyne cycloaddition (CuAAC), we apply this methodology in cells. Lipid-labeled cell membranes were then interrogated using flow cytometry and fluorescence microscopy. Furthermore, we explored the utility of this labeling strategy for use in live cell experiments. We demonstrate measurements of lipid dynamics (lateral mobility) by fluorescence photobleaching recovery (FPR). In addition, we show that adhesion of cells to specific surfaces can be accomplished by chemically linking membrane lipids to a functionalized surface. The strategies described provide robust methods for introducing bioorthogonal labels into native lipids.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".