MALDI-MS Direct Tissue Analysis of Proteins: Improving Signal Sensitivity Using Organic Treatments
Why this work is in the frame
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Bibliographic record
Abstract
Direct tissue analysis using MALDI-MS allows the generation of profiles while maintaining the integrity of the tissue, displaying cellular localizations and avoiding tedious extraction and purification steps. However, lower spectral quality can result from direct tissue analysis due to variations in section thickness, the nature of the tissue, and the limited access to peptides/proteins due to high lipid content. To improve signal sensitivity, we have developed a tissue-washing procedure using organic solvents traditionally used for lipid extraction, i.e., CHCl3, hexane, toluene, acetone, and xylene. The increased detection for peptides/proteins (m/z 5000-30,000) is close to 40% with chloroform or xylene, and 25% with hexane, while also improving sample reproducibility for each solvent used in the present study. This strategy improved matrix cocrystallization with tissue peptides/proteins and more importantly with cytoplasmic proteins without delocalization. The extracted lipids were characterized by nanoESI-QqTOF/MS/MS using the precursor ion mode, lithium adducts, or both and were identified as phospholipids including phosphatidylcholine, phosphatidylethanolamine, phosphatidylinositol, and lysophosphatidylinositol, confirming membrane lipid extraction from the tissues.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.010 | 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 it