MALDI-MS-based biomarker analysis of extracellular vesicles from human lung carcinoma cells
Bibliographic record
Abstract
Extracellular vesicles (EVs) are actively secreted by mammalian cells. They are increasingly recognized as promising circulating biomarkers of disease progression. Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) is currently one of the most powerful techniques for the rapid analysis of biological samples, especially for discovering biomarkers for disease diagnosis and prognosis. It is unclear what cell culture medium components and EV isolation methods are suitable for MALDI-TOF MS analysis. Using a human lung carcinoma cell line (A549), we investigated and optimized the critical experimental conditions for EVs' protein profiling by combining differential ultracentrifugation and MALDI-TOF MS. The results demonstrated that medium components and ultracentrifugation procedures to extract EVs played important roles in MS detection. Compared with EV-depleted serum and normal serum medium, conditioned medium with 2% fetal bovine serum in this study maintained cell proliferation and displayed significant protein profiling of EVs. RPS27A (ribosomal protein), which plays an essential role in mRNA translation and ribosome assembly for the differentiation of cancer cells, was detected from the EVs of lung cancer cells associated with cancer cell migration and invasion. We also found the known tumor diagnosis marker, which is S100A10_S100 calcium-binding protein A10. Therefore, MALDI-TOF MS-based EV analysis with optimized experimental protocols can contribute to future development of rapid screening techniques of protein biomarkers associated with early cancer diagnosis.
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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".