The central problem the team set out to solve is one that has haunted cancer metabolomics for years. Untargeted high-resolution LC-MS can produce sensitive, global snapshots of the metabolites consumed and produced by cancer cells, but a tumor is not a uniform bag of cells. Genetic mutations, uneven oxygen and nutrient availability, signaling molecules, and a mixture of malignant, stromal, immune, and normal epithelial cells all shape the metabolic environment. A metabolite detected in a bulk tumor sample may come from cancer cells, from dying tissue, from fibrous scar tissue, or from fat-laden hepatocytes, and it may reflect tumorigenesis or entirely unrelated processes such as steatosis, fibrosis, necrosis, or xenobiotic metabolism. That ambiguity, the authors argue, is a key reason why candidate metabolic markers often prove highly discriminative in one patient cohort but fail to generalize to others.

To address this, the researchers defined a metabolic region as a local tissue domain characterized by the spatial presence of metabolites mechanistically associated with key metabolic or biological processes in tumor metabolism. Importantly, the concept is deliberately broader than histology: the metabolites defining a MER may be generated locally or delivered from adjacent or distant niches through transport or diffusion, so a single MER can span multiple cellular niche types with different histological appearances. The idea draws on the well-known concept of metabolic zonation in the healthy liver, in which spatially compartmentalized pathways prevent competition for shared substrates, but extends it explicitly to tumor tissue, where regions can overlap on top of one another.

The technical workflow is a four-stage pipeline that fuses the mass separation power of LC-MS/MS with the spatial separation power of MSI. First, the team systematically identified highly abundant mass features from aligned and normalized untargeted LC-MS profiles of tumor samples, annotating putative metabolites using MS/MS fragmentation patterns matched against the Human Metabolome Database and Lipid Maps. Second, these abundant metabolites were co-detected in desorption electrospray ionization mass spectrometry imaging (DESI-MSI) of tissue sections from the same tumors, and pathologist-annotated regions of interest on matching hematoxylin and eosin (H&E) images were used to train partial least-squares discriminant analysis (PLS-DA) classifiers that assign every pixel of a tissue section to a MER. Third, a new computational method called COMET’s Path (COrrelated MEtabolomics and TranscriptomicS Pathway analysis) correlates bulk RNA-seq and LC-MS profiles across samples to infer the biological processes associated with each region. Fourth, regression models trained on the resulting maps can predict the MER composition of bulk tumor samples from LC-MS data alone.

The study used 117 tumor and matched adjacent-normal tissue sections from 26 surgically resected primary liver tumors in the Precision Medicine in Liver Cancer across an Asia-Pacific Network (PLANet) cohort, 24 of them hepatocellular carcinoma (HCC) and two intrahepatic cholangiocarcinoma. Multiple sectors were harvested from each tumor to capture intratumoral heterogeneity. The LC-MS profiles of HCC tumors, iCCA tumors, and adjacent-normal tissues could be almost perfectly separated, hinting that the two cancer types disrupt liver metabolism differently and might even be distinguishable by metabolic biomarkers. Across HCC stages, more than 70 percent of mass features changed monotonically with cancer stage, and metabolomics profiles separated Stage IB from Stage III tumors more distinctly than transcriptomics profiles did, suggesting that metabolite-based markers for early-stage disease are worth pursuing.

Yet the heterogeneity data revealed a catch. Inter- and intratumoral heterogeneity scores, computed from the multi-sector samples, peaked at Stage IB for metabolomics, meaning early-stage HCC is the most metabolically diverse stage of all. Of 8742 mass features detected by LC-MS, the team focused on 265 highly abundant ions, confirmed 230 by MS/MS, and matched 206 to known metabolites, dominated by glycerophospholipids, glycerolipids, and fatty acyls. When DESI-MSI was performed on 52 sections from 11 patients, 78.7 percent of the MS/MS-confirmed abundant metabolites could be matched to the imaging data, and bulk LC-MS and averaged MSI abundances correlated significantly across samples, confirming that the imaging measurements were reproducible. But when tissue-averaged abundances were compared between tumor and normal sections, only a small fraction of the 173 co-detected metabolites changed significantly, a result the authors attribute directly to the mixing of metabolically distinct regions within each sample.

The classifiers changed that picture dramatically. Trained on 116 pathologist-annotated regions representing six histological features of HCC, three PLS-DA classifiers distinguished six MER types: metabolically normal, low-grade, high-grade, necrotic, fibrotic, and steatotic regions. The classifiers achieved balanced training accuracies of 81.5 to 99.9 percent and balanced test accuracies of 72.6 to 97.7 percent, with the normal, high-grade, and necrotic classifiers exceeding 90 percent on test data, and they outperformed support vector machine alternatives. Each MER was defined by a distinct set of discriminative metabolites, including phosphatidylcholine species such as PC(18:0/20:4) and PC(16:0/20:4), cholesterol sulfate, diacylglycerols, lysophosphatidylcholines, oleoylcarnitine, and 13′-OH-alpha-tocopherol. Notably, no single dominant metabolite emerged for any region, underscoring that single-marker approaches are insufficient to recognize these metabolic niches.

COMET’s Path analysis then attached biology to the geography. Fatty acid and lipid metabolic processes were enriched in the normal, low-grade, and necrotic regions but, surprisingly, not in the high-grade regions, while cell cycle and G2/M checkpoint processes were enriched only in low-grade regions. High-grade regions instead correlated with adaptive immune processes, including B-cell receptor, high-affinity IgE receptor, and complement activation, suggesting that transformed hepatocytes in these areas may slow proliferation while B cells drive chronic inflammation and immune suppression. The analysis also assigned previously reported HCC marker genes to specific regions: TERT, an early driver of cirrhosis-to-cancer transformation, mapped to fibrotic regions; BCL9, a Wnt pathway modulator linked to lipid formation, mapped to steatotic regions; LSM4, a strong diagnostic candidate from prior bioinformatics studies, correlated most strongly with low-grade region metabolites; and PLAUR, encoding the urokinase receptor, mapped to necrotic regions, suggesting it may mark tissue repair rather than tumorigenesis itself. Spatial transcriptomics on sections from two additional patients confirmed that most correlated gene sets were expressed in the expected histological regions.

The most consequential finding is quantitative: more than 50 percent of the highly abundant metabolites detected in bulk HCC tumors appear to originate from non-malignant regions, namely necrotic, fibrotic, or steatotic areas, and are therefore unlikely to serve as predictive or reproducible biomarkers of tumorigenesis. The maps also exposed a hidden class of markers that bulk analysis actively mislabels. Metabolites in the “ME-low-grade+/ME-necrotic-” cluster, about 21 percent of those detected, rise in low-grade tumor regions but fall sharply in necrotic regions, so at the tissue-averaged level they appear to be decreasing in tumors and would be discarded by conventional pipelines. Necrotic regions turned out to both generate and deplete the largest numbers of specific metabolites, hinting at an abrupt metabolic reprogramming when rapidly dividing hepatocytes die. Regression models trained on the maps deconvoluted bulk LC-MS profiles with coefficients of determination between 0.731 and 0.936, and the predicted region compositions aligned with pathologist-assigned Edmondson-Steiner grades, necrosis, steatosis, and Metavir fibrosis scores, though the authors caution that the high R-squared values may reflect some overfitting and require validation in independent cohorts.

The implications reach beyond liver cancer. Because the approach relies on standard histopathological features for training, it is interpretable and could be extended to other tumor types and metabolic diseases, potentially evolving into systems-level transcriptomics-proteomics-metabolomics frameworks for dissecting tumor niches. The authors acknowledge limitations, including confidence level II metabolite annotations based on accurate mass and MS/MS, possible mass discrepancies between MSI and LC-MS instruments, and the small number of spatial transcriptomics samples, and they suggest coupling ion mobility separation to MSI to resolve isomeric metabolites. Looking ahead, the team plans to test the workflow on clinically annotated HCC therapeutic cohorts, and because some predictive metabolites such as cholesterol sulfate circulate in blood, the results raise the prospect of non-invasive assays that infer the spatial metabolic composition of tumors, including unresectable ones, from a simple blood draw. If validated, SgME profiling could finally give metabolomics the spatial resolution it has needed to deliver biomarkers that survive contact with real, messy, heterogeneous tumors.

Subject of Research: Spatially guided metabolomics profiling of metabolic regions in human hepatocellular carcinoma tumor tissues

Article Title: Spatially-guided metabolomics profiling of metabolic regions in human tumor tissues

Article References: Lee, J.-Y. J., Zhang, J., Chew, S.-C., Loong, S., Xu, L., Kong, J.-W. C., Grigoryev, F., Chung, A. Y.-F., Teo, J.-Y., Cheow, P.-C., Bonney, G., Goh, B. K. P., Leow, W.-Q., Wang, Y., Loo, L.-H., & Chow, P. K.-H. (2026). Spatially-guided metabolomics profiling of metabolic regions in human tumor tissues. Molecular Systems Biology, 22(7), 1132-1160. https://doi.org/10.1038/s44320-026-00205-w

Image Credits: AI Generated

DOI: 10.1038/s44320-026-00205-w

Keywords: spatial metabolomics, mass spectrometry imaging, hepatocellular carcinoma, metabolic heterogeneity, DESI-MSI, LC-MS/MS, biomarker discovery, cancer metabolism, COMET's Path, spatial transcriptomics, tumor microenvironment, machine learning