Analysis of the Subcellular Phosphoproteome Using a Novel Phosphoproteomic Reactor
Bibliographic record
Abstract
Protein phosphorylation is an important post-translational modification involved in the regulation of many cellular processes. Mass spectrometry has been successfully used to identify protein phosphorylation in specific pathways and for global phosphoproteomic analysis. However, phosphoproteomics approaches do not evaluate the subcellular localization of the phosphorylated forms of proteins, which is an important factor for understanding the roles of protein phosphorylation on a global scale. The in-depth mapping of protein phosphorylation at the subcellular level necessitates the development of new methods capable of specifically and efficiently enriching phosphopeptides from highly complex samples. Here, we report a novel microfluidic device called the phosphoproteomic reactor that combines efficient processing of proteins followed by phosphopeptide enrichment by Ti-IMAC. To illustrate the potential of this novel technology, we mapped the phosphoproteins in subcellular organelles of liver cells. Fifteen subcellular fractions from liver cell cultures were processed on the phosphoproteomic reactor in combination with nano-LC-MS/MS analysis. We identified thousands of phosphorylation sites in over 600 phosphoproteins in different organelles using minute amounts of starting material. Overall, this approach provides a new avenue for studying the phosphoproteome of the subcellular organelles.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".