Lipid raft proteomics: Analysis of in‐solution digest of sodium dodecyl sulfate‐solubilized lipid raft proteins by liquid chromatography‐matrix‐assisted laser desorption/ionization tandem mass spectrometry
Bibliographic record
Abstract
Lipid rafts are glycolipid- and cholesterol-enriched membrane microdomains implicated in membrane signaling and trafficking. The highly hydrophobic nature of lipid raft proteins pose significant problems of solubilization and recovery that hinder analysis by mass spectrometry (MS) and may under-report the composition of lipid rafts. In a previous investigation of the monocyte lipid raft in which proteins were digested with trypsin following polyacrylamide gel electrophoresis we identified 52 proteins. Here we report the development of a sodium dodecyl sulfate (SDS)-aided approach in which proteins are digested in solution and examined by high-performance liquid chromatography-matrix-assisted laser desorption/ionization-tandem mass spectrometry (HPLC-MALDI-MS/MS) using a novel LC-MALDI interface thereby circumventing the need to separate proteins on gels. Using this approach we identified 71 proteins in the lipid raft, 45 of which were not detected using in-gel digestion. Among the new proteins are alpha- and beta-tubulin, tubulinspecific chaperone A, a folding protein involved in tubulin dimer assembly, and KIF13, a microtubule motor protein indicating that proteins involved in microtubule assembly and trafficking are more readily detected using an in-solution approach. To investigate why tubulin was not identified by in-gel digestion, we compared the distribution of alpha-tubulin and the raft marker flotillin-2 in buoyant density gradients before and after separation on SDS-gels. Both proteins were present in the raft fractions, but tubulin was selectively lost following separation on SDS-gels. Assemblies of cytoskeletal proteins with lipid rafts may therefore be resolved using in-solution digestion that would be missed using gel-based approaches.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".