Quantification-Based Mass Spectrometry Imaging of Proteins by Parafilm Assisted Microdissection
Bibliographic record
Abstract
MALDI mass spectrometry imaging (MALDI-MSI) was presented as a good strategy to highlight regions presenting specific phenotypes based on molecular content. The proteins present in the different areas can be identified by MALDI MSI; however, the number of protein identifications remains low in comparison with classical MS-based proteomics approaches. To overcome this, a new strategy, involving the microdissection of tissue sections mounted on parafilm M-covered glass slides, is presented. Extraction and fractionation of proteins from a specific region of interest were investigated, leading to the identification of more than 1000 proteins from each microdissected piece. The strength of this cheap technique lies in the facile excision of millimeter-sized portions from the tissue allowing for the identification of proteins from cells of a specific phenotype obtained from the MALDI MS imaging-based molecular classification using hierarchical clustering. This approach can be extended to whole tissue sections in order to generate images of the section based on label-free quantification obtained from identification data. As a proof of concept, we have studied a tissue mounted on a parafilm M-covered glass slide, cut it into regular pieces, and submitted each piece to identification and quantification according to the developed parafilm-assisted microdissection (PAM) method. Images were then reconstructed by relative quantification of identified proteins based on spectral counting of the peptides analyzed by nanoLC-MS and MS/MS. This strategy of quantification-based MSI offers new possibilities for mapping a large number of high and low abundance proteins.
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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 | 0.001 |
| 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) | 0.021 | 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".