Mesoscale organization of domains in the plasma membrane – beyond the lipid raft
Bibliographic record
Abstract
The plasma membrane is compartmentalized into several distinct regions or domains, which show a broad diversity in both size and lifetime. The segregation of lipids and membrane proteins is thought to be driven by the lipid composition itself, lipid-protein interactions and diffusional barriers. With regards to the lipid composition, the immiscibility of certain classes of lipids underlies the "lipid raft" concept of plasmalemmal compartmentalization. Historically, lipid rafts have been described as cholesterol and (glyco)sphingolipid-rich regions of the plasma membrane that exist as a liquid-ordered phase that are resistant to extraction with non-ionic detergents. Over the years the interest in lipid rafts grew as did the challenges with studying these nanodomains. The term lipid raft has fallen out of favor with many scientists and instead the terms "membrane raft" or "membrane nanodomain" are preferred as they connote the heterogeneity and dynamic nature of the lipid-protein assemblies. In this article, we will discuss the classical lipid raft hypothesis and its limitations. This review will also discuss alternative models of lipid-protein interactions, annular lipid shells, and larger membrane clusters. We will also discuss the mesoscale organization of plasmalemmal domains including visible structures such as clathrin-coated pits and caveolae.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".