Molecular probes to visualize the location, organization and dynamics of lipids
Bibliographic record
Abstract
Cellular lipids play crucial roles in the cell, including in energy storage, the formation of cellular membranes, and in signaling and vesicular trafficking. To understand the functions and characteristics of lipids within cells, various methods to image lipids have been established. In this Commentary, we discuss the four main types of molecular probes that have significantly contributed to our understanding of the cell biology of lipids. In particular, genetically encoded biosensors and antibodies will be discussed, and how they have been used extensively with traditional light and electron microscopy to determine the subcellular localization of lipids and their spatial and temporal regulation. We highlight some of the recent studies that have investigated the distribution of lipids and their ability to cluster using super-resolution and electron microscopy. We also examine methods for analyzing the movement and dynamics of lipids, including single-particle tracking (SPT), fluorescence recovery after photobleaching (FRAP) and fluorescence correlation spectroscopy (FCS). Although the combination of these lipid probes and the various microscopic techniques is very powerful, we also point out several potential caveats and limitations. Finally, we discuss the need for new probes for a variety of phospholipids and cholesterol.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".