MALDI-MS Direct Tissue Analysis of Proteins: Improving Signal Sensitivity Using Organic Treatments
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".