Ambient mass spectrometry imaging enables spatial metabolomics of optimal cutting temperature compound (OCT)-embedded tumors
Bibliographic record
Abstract
Abstract Mass spectrometry imaging (MSI) is emerging as a powerful tool for uncovering the distribution of metabolites in the tumor microenvironment and studying tumor metabolism in vivo . However, to date, MSI of primary patient biobanked tissues contextualized by patient data has been limited to peptides, proteins, and glycans – with few examples for metabolites. This is because most biobanked fresh-frozen tissue required for spatial metabolomics is embedded in optimal cutting temperature compound (OCT), which introduces high-abundance polymeric interferents. Herein, we use nanospray desorption electrospray ionization (nano-DESI) to demonstrate the MSI of metabolites in OCT-embedded tissue. Metabolite coverage and sensitivity for prepared tissue mimetic homogenates embedded in OCT and an MSI-compatible material, carboxymethylcellulose (CMC), showed excellent agreement. We apply our ambient MSI workflow to detect changes in intratumoral methionine using a preclinical cancer mouse model undergoing adoptive T-cell therapy. Eight days after tumor incubation, lymphoma-bearing mice were maintained on a complete or methionine-restricted diet for 2 days. Nano-DESI MSI revealed a heterogeneous tumor microenvironment, with multiple methionine-cycle intermediates (S-adenosylmethionine, S-adenosylhomocysteine) and related metabolites, including known T-cell modulators (1-methylnicotinamide, polyamines) localizing to tumor subregions. Methionine-restricted tumors exhibited reduced methionine levels and elevated S-adenosylmethionine, relative to the control group. Overall, this work demonstrates spatial metabolomics on fresh-frozen OCT-embedded tissue, unlocking the wealth of information stored in primary tissue biobanks and consequently accelerating our understanding of cancer metabolism and treatment.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".