Bibliographic record
Abstract
Cells live in a close social context by having mutual communication with their local microenvironment. This complex intercellular communication activates dynamic signaling pathways and regulates specific cell fate. MS-based proteomics has been approved to be inevitable for characterizing dynamic protein expression and PTMs on a global scale. However, because of technical difficulties for targeting membrane receptors and secreted proteins, especially in a physiologically relevant manner, systematic characterization of intercellular signaling by MS-based proteomics has largely lagged behind. Here, I will review the latest proteomics technology development and its application to characterizing different modes of intercellular communication including indirect and direct cell-cell communication, and protein translocalization. I will discuss how MS-based proteomics has been applied for systems-level profiling intercellular signaling in defined biological contexts including tumor microenvironment, bacteria/virus-host cell interaction, immune cell interaction, and stem cell niche.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".