Improved Recovery and Identification of Membrane Proteins from Rat Hepatic Cells using a Centrifugal Proteomic Reactor
Bibliographic record
Abstract
Despite their importance in many biological processes, membrane proteins are underrepresented in proteomic analysis because of their poor solubility (hydrophobicity) and often low abundance. We describe a novel approach for the identification of plasma membrane proteins and intracellular microsomal proteins that combines membrane fractionation, a centrifugal proteomic reactor for streamlined protein extraction, protein digestion and fractionation by centrifugation, and high performance liquid chromatography-electrospray ionization-tandem MS. The performance of this approach was illustrated for the study of the proteome of ER and Golgi microsomal membranes in rat hepatic cells. The centrifugal proteomic reactor identified 945 plasma membrane proteins and 955 microsomal membrane proteins, of which 63 and 47% were predicted as bona fide membrane proteins, respectively. Among these proteins, >800 proteins were undetectable by the conventional in-gel digestion approach. The majority of the membrane proteins only identified by the centrifugal proteomic reactor were proteins with ≥ 2 transmembrane segments or proteins with high molecular mass (e.g. >150 kDa) and hydrophobicity. The improved proteomic reactor allowed the detection of a group of endocytic and/or signaling receptor proteins on the plasma membrane, as well as apolipoproteins and glycerolipid synthesis enzymes that play a role in the assembly and secretion of apolipoprotein B100-containing very low density lipoproteins. Thus, the centrifugal proteomic reactor offers a new analytical tool for structure and function studies of membrane proteins involved in lipid and lipoprotein metabolism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".