Spectroimmunohistochemistry: A Novel Form of MALDI Mass Spectrometry Imaging Coupled to Immunohistochemistry for Tracking Antibodies
Bibliographic record
Abstract
MALDI mass spectrometry imaging (MALDI-MSI) is currently used for clinical applications, such as biomarker identification, particularly for the study of solid tumors. The ability to map specific compounds that have been determined to be biomarkers and therapeutic targets is relevant for the evaluation of the efficacy of targeted therapies. This article describes a new method called Spectro-ImmunoHistoChemistry (SIHC), which combines the use of specific antibodies against markers and mass spectrometric imaging in the MS/MS mode. SIHC is based on direct primary antibody-antigen recognition, trypsin digestion of the antibody overlaying the markers of interest in the tissue section, and MALDI-MSI of the tryptic peptides generated from the antibody. This approach has both clinical and pharmacological applications. First, it can be used as a cross-validation method to monitor the presence specifically of a marker in a tissue section. Second, SIHC could potentially be used as a novel technology for tracking specific antibodies after in vivo injection for anti-cancer treatments. Additionally, SIHC could enable novel clinical applications of MSI, such as monitoring the efficacy of cytotoxic antibody treatments.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".