Expanding Spatial Metabolomics Coverage with Lithium-Doped Nanospray Desorption Electrospray Ionization Mass Spectrometry Imaging
Bibliographic record
Abstract
Spatial metabolomics has emerged as a powerful tool capable of revealing metabolic gradients throughout complex heterogeneous tissues. While mass spectrometry imaging (MSI) technologies designed to generate spatial metabolomic data have improved significantly over time, metabolite coverage is still a significant limitation. It is possible to achieve deeper metabolite coverage by imaging in positive and negative polarities or imaging several serial sections with different targeted biomolecular classes. However, this significantly increases the number of tissue samples required for biological studies and reduces the capacity for larger sample cohorts. Herein, we introduce lithium-doped nanospray desorption electrospray ionization (nano-DESI) as a simple and robust method to increase spatial metabolomics coverage, which is achieved through enhancements to ionization efficiencies in positive ion mode for metabolites and lipids lacking basic moieties, and improved structurally diagnostic tandem mass spectra for [M + Li] + adducts. Specifically, signal intensities were found to be enhanced by 10–1000× for 96 compounds including small molecule metabolites, fatty acids, neutral lipids (e.g., diacylglycerols, DAG), and phospholipids when lithium was added to the ESI solvent. In addition, proof-of-principle results reveal that lithium-doped nano-DESI MSI was able to comprehensively visualize metabolites and lipids in the prostaglandin (PG) biosynthetic pathway with PG isomeric resolution in an ovarian tumor section. These data show colocalization of fatty acid (FA) 20:4 containing DAGs, FA 20:4 monoacylglycerols (MAGs), and FA 20:4 with PGE 2 and disparate localizations of PGD 2 . Overall, this study describes a simple and powerful approach to more comprehensively probe the spatial metabolome with MSI.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".