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plasmaurinefecesftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS9877mass spectrometry<p>All reported metabolite intensities were normalized to internal standards with comparable retention times and response in the MS. Metabolite identification has been based on a combination of accurate mass, (relative) retention times, ion mobility data and fragmentation spectra, compared to the analysis of a library of standards.</p><p>Metabolomics was performed as previously described, with minor adjustments<strong>[1]</strong>. In a 2 mL tube, the following amounts of internal standard dissolved in water were added to 25 µL fecal water, 25 µL urine, or 50 µL plasma: adenosine-15N5-monophosphate (5 nmol), adenosine-15N5-triphosphate (5 nmol), D4-alanine (0.5 nmol), D7-arginine (0.5 nmol), D3-aspartic acid (0.5 nmol), D3-carnitine (0.5 nmol), D4-citric acid (0.5 nmol), 13C1-citrulline (0.5 nmol), 13C6-fructose-1,6-diphosphate (1 nmol), 13C2-glycine (5 nmol), guanosine-15N5-monophosphate (5 nmol), guanosine-15N5-triphosphate (5 nmol), 13C6-glucose (10 nmol), 13C6-glucose-6-phosphate (1 nmol), D3-glutamic acid (0.5 nmol), D5-glutamine (0.5 nmol), D5-glutathione (1 nmol), 13C6-isoleucine (0.5 nmol), D3-lactic acid (1 nmol), D3-leucine (0.5 nmol), D4-lysine (0.5 nmol), D3-methionine (0.5 nmol), D6-ornithine (0.5 nmol), D5-phenylalanine (0.5 nmol), D7-proline (0.5 nmol), 13C3-pyruvate (0.5 nmol), D3-serine (0.5 nmol), D6-succinic acid (0.5 nmol), D4-thymine (1 nmol), D5-tryptophan (0.5 nmol), D4-tyrosine (0.5 nmol), D8-valine (0.5 nmol). </p><p><br></p><p>Subsequently, solvents were added to achieve a total volume of 500 µL methanol and 500 µL water. 1 mL of chloroform was added. After thorough mixing, samples were centrifuged for 10 min at 14,000 rpm. The polar top layer was transferred to a new 1.5 mL tube and dried using a vacuum concentrator at 60 °C. Dried samples were reconstituted in 100 µL 6:4 (v/v) methanol:water.</p><p><br></p><p><strong>Ref:</strong></p><p><strong>[1] </strong>Schomakers, B. V., Hermans, J., Jaspers, Y. R. J., Salomons, G., Vaz, F. M., van Weeghel, M., &amp; Houtkooper, R. H. (2022). Polar metabolomics in human muscle biopsies using a liquid-liquid extraction and full-scan LC-MS.&nbsp;STAR Protocols, 3(2), 101302.</p>Homo sapienshttps://www.ebi.ac.uk/metabolights/MTBLS9877<p>No transformation was done. Please see bioinformatic details in the article for more information regarding data processing.</p>Fecal calprotectiny.r.jaspers@amsterdamumc.nl<p>Urine and plasma samples were collected at the hospital and stored at -20 and -80 °C, respectively. Fecal samples were either collected at the hospital or patients were instructed to collect samples in provided containers and store the samples in the freezer as soon as possible and at least within 2 h of collection, and deliver these samples frozen in a cooled bag at the hospital. Aliquots of fecal samples were stored at -20 °C until analysis.</p><p><br></p><p>Patients with <strong>fecal calprotectin</strong> levels of <strong>&lt;250 mg/kg </strong>were considered as biochemically in remission (<strong>biochemical activity labelled as low</strong>) and <strong>higher levels, up to the detection limit of 1800 mg/kg</strong>, as having active disease (<strong>biochemical activity labelled as high</strong>).</p><p><br></p><p>Fecal samples were suspended in 3 parts MiliQ, vortexed thoroughly and spun down at 13,000 rcf&nbsp;for 4 min. The supernatant was collected and 500 µL was stored at -80 °C for metabolomics. Another 500 µL supernatant was collected for proteomics. To the fecal samples for proteomics, 25x cOmplete Protease Inhibitor Cocktail and sodium dodecyl sulphate (1% final concentration) were added. Samples were incubated for 20 min at 95 °C. Then, ammonium bicarbonate (100 mM final concentration), tris(2-carboxyethyl)phosophine (TCEP) (10 mM final concentration) and 2-chloroacetamide (CAA) (30 mM final concentration) was added followed by incubation of 30 min at 60 °C. Samples were stored at -80 °C before using single tube solid phase sample preparation.</p>MetaboLightsPublicMetabolomicsLiquid Chromatography MS - positive - hilicLiquid Chromatography MS - negative - hilicUrineMetabolomicsPlasma<p>Chromatographic separation was achieved using a Merck Millipore SeQuant ZIC-cHILIC column (PEEK 100 x 2.1 mm, 3 µm particle size). Column temperature was held at 30 °C. Mobile phase consisted of (A) 1:9 (v/v) acetonitrile:water and (B) 9:1 (v/v) acetonitrile:water, both containing 5 mmol/L ammonium acetate. Using a flow rate of 0.25 mL/min, the LC gradient consisted of: Dwell at 100% Solvent B, 0-2 min; Ramp to 54% Solvent B at 13.5 min; Ramp to 0% Solvent B at 13.51 min; Dwell at 0% Solvent B, 13.51-19 min; Ramp to 100% B at 19.01 min; Dwell at 100% Solvent B, 19.01-19.5 min. Column was equilibrated by increasing flow rate to 0.4 mL/min at 100% B for 19.5-21 min.&nbsp;</p>Integrated multi-omics of feces, plasma and urine can describe and differentiate pediatric active Crohn’s Disease from remission. 10.1038/s43856-025-00984-7. PMID:40629054UrineMetabolomicsPlasmaAmsterdam UMCYorrick Jaspers<p>Metabolites were analyzed as described earlier (Ref 1), using a Waters Acquity ultra-high performance liquid chromatography system coupled to a Bruker Impact II Ultra-High Resolution Qq-Time-Of-Flight mass spectrometer. MS data were acquired using negative and positive ionization in full scan mode over the range of 50-1200 m/z.&nbsp;</p><p><br></p><p>Ref 1:</p><p>Schomakers, B. V., Hermans, J., Jaspers, Y. R. J., Salomons, G., Vaz, F. M., van Weeghel, M., &amp; Houtkooper, R. H. (2022). Polar metabolomics in human muscle biopsies using a liquid-liquid extraction and full-scan LC-MS.&nbsp;STAR Protocols, 3(2), 101302</p><h4>Background</h4>This study aimed to obtain a holistic view of remission in pediatric Crohn's Disease (CD) by integrating six omics datasets from three anatomical compartments.<h4>Methods</h4>Patients with fecal calprotectin below 250 mg/kg were considered in remission (n = 27), above 250 mg/kg as having active disease (n = 31). Proteome and microbiomes (fungi and bacteria) were analyzed in feces. Metabolomes in feces, urine, and plasma. Datasets were integrated into a multi-omics model.<h4>Results</h4>The use of individual datasets shows multiple differences between remission and active disease. Integration yielded a good model (AUC of 0.8) predicting remission. The most important features in this model are fecal bacteria (40%), fecal metabolites (22%), fecal proteins (16%), plasma metabolites (12%), fecal fungi (6%), and urine metabolites (4%). The interactome reveals Ruminococcaceae and Faecalibacterium as key players, with a correlation between antifungal urine hydroxyphenyllactic acid and fecal fungi. Pathway analysis shows an association of purine metabolism with remission, independent of thiopurine use. Changes in purine metabolism are confirmed in a pediatric CD public dataset.<h4>Conclusion</h4>The pathways and correlations identified as playing a role in remission may remain undetectable if individual omics datasets or single anatomical compartments are used, highlighting the need for a holistic approach that integrates multiple datasets from multiple anatomical compartments.Integrated multi-omics of feces, plasma and urine can describe and differentiate pediatric active Crohn's Disease from remission.Koopman Nienke N, Jaspers Yorrick Y, van Leeuwen Pim T PT, Chronas Konstantinos K, Li Yim Andrew Y F AYF, Diederen Kay K, Te Velde Anje A AA, Roseboom Winfried W, Kindermann Angelika A, Benninga Marc A MA, Kramer Gertjan G, de Jonge Wouter J WJ, Brul Stanley S, Levin Evgeni E, Kemp Stephan S, Seppen Jurgen JfalseIntegrated multi-omics of feces, plasm aand urine can describe and differentiate pediatric active Crohn’s Disease from remission<p><strong>Background</strong></p><p>This study aimed to obtain a holistic view of remission in pediatric Crohn’s Disease (CD) by integrating six omics datasets from three anatomical compartments.</p><p><br></p><p><strong>Methods</strong></p><p>Patients with fecal calprotectin below 250 mg/kg were considered in remission (n = 27), and those above 250 mg/kg as having active disease (n = 31). Proteome and microbiomes (fungi and bacteria) were analyzed in feces. Metabolomes were analyzed in feces, urine, and plasma. Datasets were integrated into a multi-omics model.</p><p><br></p><p><strong>Results</strong></p><p>The use of individual datasets shows multiple differences between remission and active disease. Integration yielded a good model (AUC of 0.8) for predicting remission. The most important features in this model are fecal bacteria (40%), fecal metabolites (22%), fecal proteins (16%), plasma metabolites (12%), fecal fungi (6%), and urine metabolites (4%). The interactome reveals&nbsp;Ruminococcaceae&nbsp;and&nbsp;Faecalibacterium&nbsp;as key players, with a correlation between antifungal urine hydroxyphenyllactic acid and fecal fungi. Pathway analysis shows an association of purine metabolism with remission, independent of thiopurine use. Changes in purine metabolism are confirmed in a pediatric CD public dataset.</p><p><br></p><p><strong>Conclusion</strong></p><p>The pathways and correlations identified as playing a role in remission may remain undetectable if individual omics datasets or single anatomical compartments are used, highlighting the need for a holistic approach that integrates multiple datasets from multiple anatomical compartments.</p>2025-07-092025-07-08MTBLS987740629054