Free-Flow Electrophoresis of Plasma Membrane Vesicles Enriched by Two-Phase Partitioning Enhances the Quality of the Proteome from <i>Arabidopsis</i> Seedlings
Bibliographic record
Abstract
The plant plasma membrane is the interface between the cell and its environment undertaking a range of important functions related to transport, signaling, cell wall biosynthesis, and secretion. Multiple proteomic studies have attempted to capture the diversity of proteins in the plasma membrane using biochemical fractionation techniques. In this study, two-phase partitioning was combined with free-flow electrophoresis to produce a population of highly purified plasma membrane vesicles that were subsequently characterized by tandem mass spectroscopy. This combined high-quality plasma membrane isolation technique produced a reproducible proteomic library of over 1000 proteins with an extended dynamic range including plasma membrane-associated proteins. The approach enabled the detection of a number of putative plasma membrane proteins not previously identified by other studies, including peripheral membrane proteins. Utilizing multiple data sources, we developed a PM-confidence score to provide a value indicating association to the plasma membrane. This study highlights over 700 proteins that, while seemingly abundant at the plasma membrane, are mostly unstudied. To validate this data set, we selected 14 candidates and transiently localized 13 to the plasma membrane using a fluorescent tag. Given the importance of the plasma membrane, this data set provides a valuable tool to further investigate important proteins. The mass spectrometry data are available via ProteomeXchange, identifier PXD001795.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".