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Release Notes

Release Notes: Multiomics Tool Now Live

25 Febuary 2025

A common question we receive from our customers is “When will the Integrated Bioinformatics Platform allow me to upload my multiomic data?”. The great news is that the answer is “Today!”.

Behind the scenes, our bioinformaticians and software developers have been working to implement a new Multiomics Tool that allow you to upload your own multiomics data, integrate it within an existing project, and perform predictive modelling, pathway enrichment, and interpretative analyses alongside your metabolomics data. The features are now available for immediate use for Metabolon customers, and contacts with a demo license will be able to explore the features using demo data.

Please note: This release is part of an open beta.

The Multiomics Tool, available within the Integrated Bioinformatics Platform is still under active development and as such, has been released as part of an open beta. All existing projects that access the tool and projects that are booked during the beta phase will have perpetual access to the Multiomics Tool at no additional cost, for all other projects we may close the beta or adjust how you access the Multiomics Tools in the future as we finalize our development roadmap and continue to bring additional functionality.

Visit the Multiomics Tools Page 

  • Integrated Bioinformatics Platform now supports multiomics analysis – upload Compare groups from your sample metadata and launch predictive modelling analyses at the click of a button.
  • Integrated predictive modeling (logistic regression and random forest) delivers comprehensive model performance metrics and feature importance insights.
  • DIABLO‐based Latent Factor Analysis provides interactive sample mapping, loadings, and visualization tools.
  • Multiomic pathway analysis now offers Over-Representation Analysis (ORA) and PathIntegrate methods using a curated mapping of metabolites to the REACTOME database to maximise coverage of pathways.

For more information, please visit the Multiomics Tools page.

Release Notes: Library Expansion

19 June 2024

“Please, sir, I want some more…”

Metabolon leads the world in the identification of small molecules in biological samples.  Do we let that stop us from continuing to deliver even more impactful insights?  Say it with me: No, we do not!

By combining the world’s most powerful metabolomics library and cutting-edge machine learning techniques, we regularly increase the number of compounds we can routinely identify in samples.  The most recent improvements to our industry-leading library include additional dipeptide and lipid classes of molecules, resulting in increased coverage in these areas for many studies.

What this means for you: If you are a repeat user of Metabolon’s Global Discovery Panel, you may see new compounds in your data that you have not seen before (winning!).  Honestly, who doesn’t want to see more compounds?  If you are not a repeat user, congratulations and welcome, heroic science person!  Keep on doing what you do to make the world a better place.

If you have questions about your study, please get in touch with your Metabolon representative.

Release Notes: Global Discovery Panel

13 March 2023

What is the new methodology? 

Metabolon’s current Global Discovery Panel utilized four distinct LC/MS methods, namely Neg, Pos Early, Pos Late, and Polar. In this upgrade, the Polar method is being retired for two new methods: NOS and HILIC, see graphic below. We have essentially divided the original all HILIC-based separation, “Polar” method, into these two new methods: one shorter HILIC separation method and one new reverse phase (RP) method. These two new methods provide improved stability and consistency of detection for those molecules originally detected by the Polar method. These molecules include small organic acids such as TCA cycle metabolites (now on NOS method) and sugars (now on HILIC method) such as glycolysis pathway metabolites.  

 

To what level were NOS and HILIC methods validated? 

Metabolon is ISO 9001 certified, and as such we have an extensive quality management system and required validation procedures. The new methods have undergone the same rigor of analytical and biological validation as our previous methods, including analyzing a variety of different matrices to ensure data quality. As a result of this thorough validation process, you can expect to receive the same high quality metabolomics data as Metabolon has always produced. 

 

What compounds may be affected by the new release? 

Over 99% of compounds currently detected and identified on the Global Discovery Panel have similar or improved detection using the new methods. Only compounds that were previously detected by the Polar method (library ID 305) may be affected by this version upgrade. The two new methods replacing the Polar method provide improved stability and consistency of detection over time for those molecules historically called from the Polar method. 

    Release Notes: Client Data Tables

    4 October 2022

    BETTER, FASTER, STRONGER

    Welcome to Metabolon’s latest set of release notes. How’s that science thing going? We hope you are changing the world today. You are a hero, and we’re proud of you; never forget that. Here are a few insights about what’s new at Metabolon.

    CLIENT PORTAL

    Bug fixes

    • Pathway map metabolite fold change sizing fixed.
    • Fixes for direct Discovery Panel link for expired tokens.

    General Improvements to the Portal:

    • Quality of life improvements to sign-up and login processes.
    • Updated code to support the upcoming Manifest Upload page.

     

    MACHINE LEARNING & ARTIFICIAL INTELLIGENCE

    If you’re too young to know what SkyNet is, we recommend that you binge-watch the entire Terminator movie series now (or tonight after work; keep your job because the world needs more scientists).

    • From human plasma to maize kernels and everything in between, our machine learning now looks for metabolites in whatever species matrix you send us.
    • For any type of human cell, we just expanded our automated QC to provide more oversight on the core machine learning algorithm—we watch the watchmen.

    SERVICES

    And by “services,” we mean cool stuff like Metabolon Discovery Panels, Targeted Panels, and Single Analyte Assays (Team Oxford Comma!) that help you explore new things.

    Compound Naming Update

    • We were recently adding compounds to our already impressive, biologically focused, super-library when we noticed that one of the compound names was a bit ambiguous. There was a more appropriate name to describe this compound’s structure. As we strive to ensure that investigators know what actual molecule on which we are reporting (including having associated database links), we are updating the name of the molecule O-methyltyrosine to tyrosine methyl ester. Don’t worry, the chemical ID remains the same, so you will know you are still tracking the same compound from previous studies. Also, to round out the story, we are adding two new compounds to the library to fill in this molecular family, per the chart below. Enjoy!

    Chemical ID Old Name New Name Structure
    100002078 O-methyl tyrosine Tyrosine methyl ester
    100022738 O-methyltyrosine
    100022739 Metyrosine

     

    Release Notes: Client Portal

    30 December 2022

    • Bug fixes
    • Small molecules, appropriately sized circles. Added a bug fix that more granularly resizes metabolite circles based on fold change in the Pathway Explorer tab of the Portal.
    • Introduced a “fast clicker” bug fix which keeps the Pathway Explorer from crashing when clicking around too fast in the Enrichment tab.
    • Accessibility Improvements
    • In order to make our heatmap deliverable readable by all, expect blue and red instead of green and red cells in our heatmap deliverable soon. Bonus: This color update will match what you see in the Stats Table in the Portal!
      ✕

      References

      1. Zgoda-Pols, J.R., et al., Metabolomics analysis reveals elevation of 3-indoxyl sulfate in plasma and brain during chemically-induced acute kidney injury in mice: investigation of nicotinic acid receptor agonists. Toxicol Appl Pharmacol, 2011. 255(1): p. 48-56.

      2. Bryant, J.A., et al., The impact of an oral purified microbiome therapeutic on the gastrointestinal microbiome. Nat Med, 2026. 32(1): p. 186-196

      3. McGovern, B .H., et al., SER-109, an Investigational Microbiome Drugto Reduce Recurrence After Clostridioides difficile Infection: Lessons Learned From a Phase 2 Trial. Clin Infect Dis, 2021. 72(12): p. 2132-2140.

      4. Feuerstadt, P., et al., SER-109, an Oral Microbiome Therapy for Recurrent Clostridioides difficile Infection. N Engl J Med, 2022. 386(3): p. 220-229.

      5. Hu, Z., et al., Targeted metabolomics reveals novel diagnostic biomarkers for colorectal cancer. Mol Oncol, 2025. 19(6): p. 1737-1750.

      6. Butler, F.M., et al., Vegetarian Dietary Patterns and Diet-Related Metabolites Are Associated With Kidney Function in the Adventist Health Study-2 Cohort. J Ren Nutr, 2025.

      7. Stanford, J., et al., Metabolomic Profiling and Diet Quality Scoring in a Randomized Crossover Trial of Healthy and Typical Dietary Patterns. Mol Nutr Food Res, 2025 . 69(23): p. e70271.

      8. O’Connor, L.E., et al., Metabolomic Profiling of an Ultraprocessed Dietary Pattern in a Domiciled Randomized Controlled Crossover Feeding Trial. J Nutr, 2023. 153(8): p. 2181-2192.

      9. Fritsch, D.A., et al., Microbiome function underpins the efficacy of a fiber-supplemented dietary intervention in dogs with chronic large bowel diarrhea. BMC Vet Res, 2022. 18(1): p. 245.

      10. Leal, L.N., et al., Preweaning nutrient supply improves lactation productivity and reduces the risk of culling in Holstein cows. J Dairy Sci, 2025. 108(6): p. 5875-5888.

      11. Ahsin, M., et al., Soil and pasture health underlie improved beef nutrient density determined by untargeted metabolomics in Southern US grass finished beef systems. NPJ Sci Food, 2025. 9(1): p. 151.

      12. Yin, W., et al., Plasma lipid profiling across species for the identification of optimal animal models of human dyslipidemia. J Lipid Res, 2012. 53(1): p. 51-65.

      13. Porter, F .D., et al., Cholesterol oxidation products are sensitive and specific blood-based biomarkers for Niemann-Pick C1 disease. Sci Transl Med, 2010. 2(56): p. 56ra81.

      14. Needham, B .D., et al., Plasma and Fecal Metabolite Profiles in Autism Spectrum Disorder. Biol Psychiatry, 2021. 89(5): p. 451-462

      15. Li, C., et al., Estradiol and mTORC2 cooperate to enhance prostaglandin biosynthesis and tumorigenesis in TSC2-deficient LAM cells. J Exp Med, 2014. 211(1): p. 15-28.

      16. Green, P.G., et al., Metabolic flexibility and reverse remodelling of the failing human heart. Eur Heart J, 2025. 46(25): p. 2422-2433.

      17. Maekawa, H., et al., SGLT2 inhibition protects kidney function by SAM-dependent epigenetic repression of inflammatory genes under metabolic stress. J Clin Invest, 2025. 135(19).

      18. Wu, D., et al., Integrated screens reveal that guanine nucleotide depletion, which is irreversible via targeting IMPDH2, inhibits pancreatic cancer and potentiates KRAS inhibition. Gut, 2026.

      19. Schwerdtfeger, L.A., et al., Gut microbiota and metabolites are linked to disease progression in multiple sclerosis. Cell Rep Med, 2025. 6(4): p. 102055.

      20. Wu, H., et al., Microbiome-metabolome dynamics associated with impaired glucose control and responses to lifestyle changes. Nat Med, 2025. 31(7): p. 2222-2231.

      21. Jacobs, J.P., et al., Cognitive behavioral therapy for irritable bowel syndrome induces bidirectional alterations in the brain-gut-microbiome axis associated with gastrointestinal symptom improvement. Microbiome, 2021. 9(1): p. 236.

      22. Pietzner, M., et al., Plasma metabolites to profile pathways in noncommunicable disease multimorbidity. Nat Med, 2021. 27(3): p. 471-479.

      23. Faquih, T.O., et al., Robust Metabolomic Age Prediction Based on a Wide Selection of Metabolites. J Gerontol A Biol Sci Med Sci, 2025. 80(3).

      24. Scherer, N., et al., Coupling metabolomics and exome sequencing reveals graded effects of rare damaging heterozygous variants on gene function and human traits. Nat Genet, 2025. 57(1): p. 193-205.

      25. Holmes, Z.C., et al., Untargeted metabolomic analysis of human milk from healthy mothers reveals drivers of metabolite variability. Sci Rep, 2024. 14(1): p. 20827.

      26. Titz, B., et al., Implications of Ocular Confounding Factors for Aqueous Humor Proteomic and Metabolomic Analyses in Retinal Diseases. Transl Vis Sci Technol, 2024. 13(6): p. 17.

      27. Bloom, S.M., et al., Cysteine dependence of Lactobacillus iners is a potential therapeutic target for vaginal microbiota modulation. Nat Microbiol, 2022. 7(3): p. 434-450.

      28. Leimer, E.M., et al., Lipid profile of human synovial fluid following intra-articular ankle fracture. J Orthop Res, 2017. 35(3): p. 657-666.