Ambient Mass Spectrometry Imaging with Picosecond Infrared Laser Ablation Electrospray Ionization (PIR-LAESI)
Bibliographic record
Abstract
A picosecond infrared laser (PIRL) is capable of cutting through biological tissues in the absence of significant thermal damage. As such, PIRL is a standalone surgical scalpel with the added bonus of minimal postoperative scar tissue formation. In this work, a tandem of PIRL ablation with electrospray ionization (PIR-LAESI) mass spectrometry is demonstrated and characterized for tissue molecular imaging, with a limit of detection in the range of 100 nM for reserpine or better than 5 nM for verapamil in aqueous solution. We characterized PIRL crater size using agar films containing Rhodamine. PIR-LAESI offers a 20-30 μm vertical resolution (∼3 μm removal per pulse) and a lateral resolution of ∼100 μm. We were able to detect 25 fmol of Rhodamine in agar ablation experiments. PIR-LAESI was used to map the distribution of endogenous methoxykaempferol glucoronide in zebra plant (Aphelandra squarrosa) leaves producing a localization map that is corroborated by the literature. PIR-LAESI was further used to image the distribution inside mouse kidneys of gadoteridol, an exogenous magnetic resonance contrast agent intravenously injected. Parallel mass spectrometry imaging (MSI) using desorption electrospray ionization (DESI) and matrix assisted laser desorption ionization (MALDI) were performed to corroborate PIR-LAESI images of the exogenous agent. We further show that PIR-LAESI is capable of desorption ionization of proteins as well as phospholipids. This comparative study illustrates that PIR-LAESI is an ion source for ambient mass spectrometry applications. As such, a future PIRL scalpel combined with secondary ionization such as ESI and mass spectrometry has the potential to provide molecular feedback to guide PIRL surgery.
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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) | 0.006 | 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".