Activity-Based Probes Linked with Laser-Cleavable Mass Tags for Signal Amplification in Imaging Mass Spectrometry: Analysis of Serine Hydrolase Enzymes in Mammalian Tissue
Bibliographic record
Abstract
A novel functional imaging mass spectrometry technology is described that utilizes activity-based probes for imaging enzyme active sites in tissue sections. We demonstrate this technology using an activity-based probe (fluorophosphate) that is specific for serine hydrolases. A dendrimer containing multiple mass tags that is attached to the activity-based probe is used to analyze the binding sites of the probe through release and measurement of the mass tags on laser irradiation. A generation 8 poly(amido amine) dendrimer with 1024 amino groups was labeled with an azide group, and then, more than 900 mass tags were attached in order to achieve signal amplification of nearly 3 orders of magnitude. The experimental protocol first involves binding of the activity-based probe containing an alkyne group to serine hydrolases in the tissue section followed by attachment of the dendrimer labeled with mass tags to the bound probe by Click chemistry. On irradiation of the labeled tissue by the laser beam in a raster pattern, the mass tags are liberated and recorded by the mass analyzer; consequently, the ion image of the mass tag reveals the distribution of serine hydrolases in the tissue. This process was shown using rat brain and mouse embryo sections. Targeted imaging has the advantage of providing high spatial resolution and high sensitivity through the use of signal amplification chemistry with high target specificity through the use of an enzyme activity probe.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
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 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".