MetaboLights

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ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS13432/m_MTBLS13432_LC-MS_positive_reverse-phase_metabolite_profiling_v2_maf.tsvftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS13432/m_MTBLS13432_LC-MS_negative_reverse-phase_metabolite_profiling_v2_maf.tsvftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS13432/m_MTBLS13432_LC-MS_positive_hilic_metabolite_profiling_v2_maf.tsvftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS13432/m_MTBLS13432_MSImaging___metabolite_profiling-1_v2_maf.tsvftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS13432/m_MTBLS13432_MSImaging___metabolite_profiling_v2_maf.tsvftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS13432/a_MTBLS13432_MSImaging___metabolite_profiling.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS13432/s_MTBLS13432.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS13432/i_Investigation.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS13432/a_MTBLS13432_LC-MS_negative_reverse-phase_metabolite_profiling.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS13432/a_MTBLS13432_LC-MS_positive_reverse-phase_metabolite_profiling.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS13432/a_MTBLS13432_MSImaging___metabolite_profiling-1.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS13432/a_MTBLS13432_LC-MS_positive_hilic_metabolite_profiling.txtprimaryOK200ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS13432Highly abundant mass features were identified based on the 90th-percentile and mean abundance levels of all mass features across all the tissue samples. These mass features were further subjected to data-dependent MS/MS fragmentation experiments using UPLC-ESI-time of flight (QTOF)-MS on Acquity UPLC with 2777C auto sampler coupled with Xevo G2-XS QToF mass spectrometry with the same chromatographical conditions (LC-MS data generation) on the pooled quality control samples. The mass features were fragmented using low, medium, and high level of collision energy to obtain the MS/MS fragmentation ion spectra, which were processed and analyzed using the Masslynx v4.2 software (Waters Corporation, Massachusetts, USA). A list of putative metabolites subsequently generated based on comparison of the highly abundant mass features and MS/MS fragmentation ions with in-built database using the Progenesis QI v2.0 software. Their possible structural assignments were generated out of the multiple results from the screening of mass spectrometry databases, including Human Metabolome Database (Wishart et al, 2022) and Lipid Maps (Conroy et al, 2023). The discriminative putative metabolites were further manually curated by comparing MS/MS spectra to reference spectra available online to provide reasonable annotation and structural attributions. The spatial abundance levels of highly abundant putative metabolites were determined by matching the m/z values of their LC-MS ions to the loaded DESI-MSI spectra using the MsCoreUtil package (v1.12.0). The abundance levels were further normalized by computing their log2 fold-changes with respect to the mean abundance levels of a set of highly abundant peaks that were commonly and uniformly found across all the tissue sections.MetaboLightsPublicLiquid Chromatography MS - negative - reverse phaseLiquid Chromatography MS - positive - HILICMS Imaging -Liquid Chromatography MS - positive - reverse phaseThe untargeted metabolic profiles for lipid extracts were separated using UPLC BEH C8 column (1.7μm, 2.1 x 100 mm) (Waters Corporation, Milford, MA, USA) with both positive and negative ESI modes spray voltage 2.0 kV (positive)/1.5 kV(negative). The columns were maintained at a temperature of 55°C. Solvent A consisted of 50% water, 25% acetonitrile and 25% isopropanol with 5 mM ammonium acetate, 0.05% acetic acid and 20 µM phosphoric acid. Solvent B consisted of 50% isopropanol and 50% acetonitrile with 5 mM ammonium acetate and 0.05% acetic acid. The total of 13.25 min runtime with an initial flow rate of 0.6 ml/min with 1% solvent B, increasing to 30% solvent B at 2 min, 90% solvent B at 11.5 min, 99.9% solvent B at 12.0 min, held at 99.9% solvent B for 0.5 min before returning to the initial conditions. At the end of the run 0.75 min of column equilibration with 1% mobile phase B was performed. The untargeted metabolic profiles aqueous extracts were separated using Acquity BEH HILIC column (1.7μm, 2.1 x 150mm) (Waters Corporation, Milford, MA, USA) with positive ESI mode , spray voltage 1.5 kV. Solvent A was 20 mM ammonium formate and 0.1% formic acid in water and solvent B was 0.1% formic acid in acetonitrile. The total of 12.65 min runtime with an initial flow rate of 0.6 mL/min with 95% solvent B, decreasing to 80% solvent B at 4.6 min, 50% solvent B at 5.5 min, holding 50% solvent B till 7.0 min, gradually returning to the initial conditions (Zhang et al, 2012; Want et al, 2010). At the end of the run 5.65 min of column equilibration with 95% mobile phase B was performed. The columns were maintained at a temperature of 40 °C. Spatially-guided metabolomics profiling of metabolic regions in human tumor tissues. 10.1038/s44320-026-00205-w. PMID:41922830Jia Ying Joey LEEA*STAR Bioinformatics Institutelivermass spectrometry assayAqueous and lipid metabolites were extracted using the protocols as previously described (Vorkas et al, 2015). The tissues (50−150 mg) were mixed with pre-chilled MeOH/water solution (1:1) and followed by homogenization under liquid nitrogen (Tissue homogenizing CKMix, Bertin Technologies, France). Aliquots of the supernatant were dispensed into Eppendorf tubes and dried under vacuum. The dried supernatant was then re-suspended in 100 μL UPLC-MS grade water/ACN (5:95) before analysis. The lipid metabolites were extracted by adding pre-chilled DCM/MeOH (3:1) solution proportionally into the residual pellet and followed by homogenization. A total of 100 μL of the lipid phase supernatant were collected after centrifugation and dried in an extractor hood. The dry material from the lipid extracts of the tissue sample were reconstituted in 200 µL of water/ACN/ IPA (1:1:2) before analysis. Quality control samples and dilution series were pooled samples from the extracts. Long-term references samples were made for batch variation correction for future study and following the same sample preparation procedures.Homo sapienshttps://www.ebi.ac.uk/metabolights/MTBLS13432Yulan Wang. Singapore Phenome Centre. yulan.wang@ntu.edu.sg.Jia-Ying Joey LEE. A*STAR Bioinformatics Institute. leejy@bii.a-star.edu.sg.The raw LC-MS data were processed using the Progenesis QI software, which included automatic alignment using RT, peak picking, and deconvolution. Potential drift in intensity during the analysis was corrected using regression as previously described (Lewis et al, 2016). Missing values were replaced with half minimum intensity values and all the intensity values were log transformed (Sabina Bijlsma et al, 2005). Additional features filtering was performed using the profiles obtained from various dilutions of quality control samples as detailed previously (Want et al, 2010).  The captured DESI-MSI raw data were converted and saved as imzML files. The DESI-MSI data was loaded and analyzed using the Cardinal MSI package (v3.2.1) under the R statistical software environment.Hepatocellular carcinomaleejy@bii.a-star.edu.sgSamples were obtained prospectively from the PLANET study cohort (NCT03267641). They were collected from surgical resection performed at Singapore General Hospital and National University Hospital Singapore according to a stringent protocol as previously described (Zhai et al, 2022). The resected specimens were transported immediately on ice in a temperature-controlled cooler to a pathologist, where each specimen was subjected to multi-region sampling (Zhai et al, 2022). Depending on the size of the tumor, between 2-6 sectors were collected from each patient. . All tissues were snapped frozen immediately and stored at -80 °C, unless otherwise indicated. MetabolomicsAfter performing DESI-MSI, the tissue sections were stained with H&E and scanned into whole-slide images using a Philips IntelliSite UltraFast scanner at 20x magnification.The tissues were embedded in gelatin before snapped frozen. They were then sectioned at -15 °C using the CM 1950 Microsystems cryostat-microtome with a thickness of 15 µm, thaw-mounted on glass slides and stored in a vacuum desiccator at room temperature prior to imaging.liveruntargeted analysisNanyang Technological UniversityWaters Synapt G2-Si MSHomo sapiensWaters Corporationexperimental samplemass spectrometry imagingadult hepatocellular carcinomaWaters Xevo G2-XS QTof1.4Xevo G2-XS QTofcryostatWaters High-Definition Imaging softwareliveruntargeted analysisNanyang Technological UniversityWaters Synapt G2-Si MSHomo sapiensWaters Corporationexperimental samplemass spectrometry imagingadult hepatocellular carcinomaWaters Xevo G2-XS QTof1.4Xevo G2-XS QTofWaters High-Definition Imaging softwarecryostatTissue sections were imaged using a high-resolution Waters Synapt G2-Si MS fitted with a DESI-source. Images acquisitions were performed both in positive and negative ion mode over the mass range of 50 – 1200 m/z using ionization voltage: 3 kV; cone voltage: 50 V; source temperature: 150 °C; scan time: 1 s; solvent: methanol:water (98:2) containing 0.1% formic acid; solvent flow rate: 2 μL/min; leucine enkephalin lockmass: 100 pg/μL; pixel size: 100 μm. Untargeted analysis were performed on a Xevo G2-XS QToF mass spectrometer (Waters Corporation, Massachusetts, USA) with an electrospray ionization (ESI) source in both positive mode and negative mode. Mass spectrometry instrument parameters were as follows spray voltage 2.0 kV (positive)/1.5 kV(negative), source temperature 120 °C, cone gas flow 150 L/hr, desolvation temperature 60 °C, desolvation gas flow 1000 L/h. The injection volume is 2 µL. Autosampler temperature is 4 °C . Isopropanol was used as needle wash. Bulk high-resolution mass spectrometry provides sensitive and global snapshots of metabolites involved in cancer metabolism. However, intratumoral heterogeneity obscures the cellular origins of detected metabolites, making it difficult to identify reproducible and predictive metabolic markers. Here, we present "Spatially guided MEtabolomics (SgME) profiling", a multi-modal metabolomics data analysis approach that delineates and maps metabolic regions (MERs), including overlapping regions, within tumor tissues. We applied SgME profiling to human hepatocellular carcinoma (HCC) tumors and refined potential RNA markers that were also found in previous transcriptomics or bioinformatics studies to those specifically associated with malignant regions. We further estimated that more than 50% of the highly abundant metabolites detected in bulk tumors originated from the non-malignant MERs and are therefore unlikely to be predictive and/or reproducible markers. Importantly, SgME profiling also revealed new potential metabolic markers that were not apparent in bulk analysis because they increased in low-grade tumor regions but declined sharply in necrotic regions. Together, these findings show that SgME profiling overcomes key limitations of conventional metabolomics profiling by enabling more granular, spatially resolved metabolic characterization.Spatially-guided metabolomics profiling of metabolic regions in human tumor tissues.Lee Jia-Ying Joey JJ, Zhang Jingtao J, Chew Sin-Chi SC, Loong Shihleone S, Xu Liang L, Kong Jia-Wen Carmen JC, Grigoryev Fedor F, Chung Alexander Yaw-Fui AY, Teo Jin-Yao JY, Cheow Peng-Chung PC, Bonney Glenn G, Goh Brian K P BKP, Leow Wei-Qiang WQ, Wang Yulan Y, Loo Lit-Hsin LH, Chow Pierce Kah-Hoe PKfalseSpatially-guided metabolomics profiling of heterogeneous human tumor tissuesBulk high-resolution mass spectrometry can provide sensitive and global snapshots of metabolites involved in cancer metabolism. However, intra-tumor heterogeneity (IntraTH) convolutes the cellular origins and tumor pathologies associated with the detected metabolites, thus making the elucidation of reproducible metabolic pathways and/or biomarkers very challenging. Here, we present “Spatially-guided MEtabolomics (SgME) profiling”, a multi-modal metabolomics data analysis approach to delineate IntraTH by integrating spatial and bulk metabolomics profiles from the same tumors. We applied SgME profiling to 117 tumor and adjacent normal tissues from 26 surgically resected primary liver tumors, and constructed SgME maps of metabolic regions (MERs) associated with key histopathological features. We used these maps to survey IntraTH and train regression models that accurately predict the MER compositions of bulk tumor samples. We also discovered a group of putative metabolites that increase in low-grade tumor regions but abruptly decrease in necrotic regions. SgME profiling may also be applied to heterogeneous tissues from other cancer types or metabolic diseases and provide systems-level understandings of the roles of local cellular niches in cancer metabolism and tumorigenesis.2026-04-152025-12-02MTBLS1343241922830