Surface Plasmon Resonance Imaging-MALDI-TOF Imaging Mass Spectrometry of Thin Tissue Sections
Bibliographic record
Abstract
Identification and quantification of proteins in imaging of biological samples are a challenge in today's science. Here, we demonstrate a novel surface plasmon resonance imaging-matrix assisted laser desorption ionization imaging mass spectrometry (SPRi-MALDI IMS) coupled technique competent for the acquisition of multiparametric information by creating a tissue section imprint on an SPRi sensor surface. Correlated images were acquired in SPRi and in MALDI IMS for abundant proteins from a single mouse kidney tissue. The spatial organization of the transferred proteins from the tissue to the SPRi surface was preserved and imaged by SPR and MALDI MS. Surface chemistry was selected to nonspecifically adsorb and retain high concentrations of proteins on the SPRi surface. The diffusion kinetics were controlled to ensure fast transfer of proteins from the tissue sections with minimal lateral diffusion to achieve high spatial fidelity transfer. Lastly, the SPRi instrument was modified to insert a tissue sample in the fluidics chamber to facilitate the real-time measurement of the transfer process. The MALDI IMS experimental conditions, such as matrix deposition and the interface between the SPRi prism and the MALDI IMS instrument, were also optimized. The results show quantitative and regioselective SPRi images correlating to MALDI IMS images of different proteins transferred from a single tissue section.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".