Analysis of Microdissected Cells by Two-Dimensional LC-MS Approaches
Bibliographic record
Abstract
Laser capture microdissection (LCM) is a powerful tool that enables the isolation of specific cell types from tissue sections, overcoming the problem of tissue heterogeneity and contamination. We combined the LCM with isotope-coded affinity tag (ICAT) technology and two-dimensional liquid chromatography to investigate the qualitative and quantitative proteomes of hepatocellular carcinoma (HCC). The effects of three different histochemical stains on tissue sections have been compared, and toluidine blue stain was proved as the most suitable stain for LCM followed by proteomic analysis. The solubilized proteins from microdissected HCC and non-HCC hepatocytes were qualitatively and quantitatively analyzed with two-dimensional liquid chromatography tandem mass spectrometry (2D-LC-MS/MS) alone or coupled with cleavable isotope-coded affinity tag (cICAT) labeling technology. A total of 644 proteins were qualitatively identified and 261 proteins were unambiguously quantified. These results showed that the clinical proteomic method using LCM coupled with ICAT and 2D-LC-MS/MS can carry out not only large-scale but also accurate qualitative and quantitative analysis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".