Recent Advances in On‐Tissue Chemical Derivatization Strategies for Enhancing MALDI‐MSI
Bibliographic record
Abstract
Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) has rapidly advanced in biomedical research, enabling label-free, untargeted spatial detection of metabolites, lipids, proteins, and glycans in tissue sections. However, challenges such as low ionization efficiency and chemical instability limit the detection of certain molecules. To address these issues, on-tissue chemical derivatization (OTCD) has been widely applied as an effective strategy to enhance imaging capabilities. This review systematically summarizes the development of derivatization reagents targeting different reactive functional groups and their applications in MALDI-MSI, including strategies for the derivatization of amines, carbonyls, carboxyls, double bonds, hydroxyls, thiols, and platinum-based drugs. Particular attention is given to how these derivatization reagents enhance the detection range and biological relevance by increasing molecular weight, improving ionization efficiency, and reducing background noise interference. Additionally, we explore the application of OTCD in various biological samples and discuss challenges related to experimental workflows, derivatization efficiency, and tissue integrity. This review provides important theoretical support for the advancement of MSI technology and highlights its broad potential applications in biomedical research.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".