Skip to content
2000
image of Optimization of Pseudotargeted Metabolomics: Fully Integrating the Advantages of Both Targeted and Untargeted Approaches

Abstract

Introduction

This study aimed to address persistent challenges in pseudotargeted metabolomics, particularly the limited compatibility with diverse sample types, by developing an enhanced method integrating the strengths of targeted and untargeted approaches.

Methods

An upgraded pseudotargeted metabolomics method was developed, incorporating a sample-specific MS-RI library (SSMSRIL) to identify novel metabolites in new samples. Newly discovered metabolites were dynamically added to a pseudotargeted MRM list. Additionally, MRM transitions for 227 target metabolites were integrated, resulting in a final method monitoring >500 metabolites. This design facilitates the extraction and addition of new metabolites to the monitoring list. The method was established and evaluated using gas chromatography-tandem mass spectrometry (GC-MS/MS).

Results

Evaluation with new samples revealed that 33-40% of all detected metabolites were identified exclusively through the integrated targeted MRM transitions. This demonstrated their significant role in expanding metabolite coverage. Furthermore, 23-54% of metabolites detected in new sample types were absent from the initial SSMSRIL list.

Discussion

The substantial proportion (23-54%) of metabolites detected in new sample types missing from the original library underscores the critical necessity of dynamically updating the pseudotargeted MRM list when applying the method to new samples. This update mechanism is vital for maintaining broad metabolite coverage and method applicability across diverse sample matrices.

Conclusion

The enhanced pseudotargeted method significantly improves metabolite coverage and adaptability to new sample types through dynamic MRM list updating and the integration of targeted MRM transitions. While developed using GC-MS/MS, the core concept is readily transferable to liquid chromatography (LC)-based full-scan and MRM methodologies, broadening its potential impact.

Loading

Article metrics loading...

/content/journals/cac/10.2174/0115734110392891250831141601
2025-10-17
2025-12-08
Loading full text...

Full text loading...

References

  1. Nicholson J.K. Lindon J.C. Systems biology: Metabonomics. Nature 2008 455 7216 1054 1056 10.1038/4551054a 18948945
    [Google Scholar]
  2. Wang X. Shang D. Chen J. Cheng S. Chen D. Zhang Z. Liu C. Yu J. Cao H. Li L. Li L. Serum metabolomics reveals the effectiveness of human placental mesenchymal stem cell therapy for Crohn’s disease. Talanta 2024 277 126442 10.1016/j.talanta.2024.126442 38897006
    [Google Scholar]
  3. Talmor-Barkan Y. Bar N. Shaul A.A. Shahaf N. Godneva A. Bussi Y. Lotan-Pompan M. Weinberger A. Shechter A. Chezar-Azerrad C. Arow Z. Hammer Y. Chechi K. Forslund S.K. Fromentin S. Dumas M.E. Ehrlich S.D. Pedersen O. Kornowski R. Segal E. Metabolomic and microbiome profiling reveals personalized risk factors for coronary artery disease. Nat. Med. 2022 28 2 295 302 10.1038/s41591‑022‑01686‑6 35177859
    [Google Scholar]
  4. Zhang Y. Zhao H. Zhao J. Lv W. Jia X. Lu X. Zhao X. Xu G. Quantified metabolomics and lipidomics profiles reveal serum metabolic alterations and distinguished metabolites of seven chronic metabolic diseases. J. Proteome Res. 2024 23 8 3076 3087 10.1021/acs.jproteome.3c00760 38407022
    [Google Scholar]
  5. Barrett J.C. Esko T. Fischer K. Jostins-Dean L. Jousilahti P. Julkunen H. Jääskeläinen T. Kangas A. Kerimov N. Kerminen S. Kolde A. Koskela H. Kronberg J. Lundgren S.N. Lundqvist A. Mäkelä V. Nybo K. Perola M. Salomaa V. Schut K. Soikkeli M. Soininen P. Tiainen M. Tillmann T. Würtz P. Metabolomic and genomic prediction of common diseases in 700,217 participants in three national biobanks. Nat. Commun. 2024 15 1 10092 10.1038/s41467‑024‑54357‑0
    [Google Scholar]
  6. Gowda G.A.N. Zhang S. Gu H. Asiago V. Shanaiah N. Raftery D. Metabolomics-based methods for early disease diagnostics. Expert Rev. Mol. Diagn. 2008 8 5 617 633 10.1586/14737159.8.5.617 18785810
    [Google Scholar]
  7. Botello-Marabotto M. Martínez-Bisbal M.C. Pinazo-Durán M.D. Martínez-Máñez R. Tear metabolomics for the diagnosis of primary open-angle glaucoma. Talanta 2024 273 125826 10.1016/j.talanta.2024.125826 38479028
    [Google Scholar]
  8. Sun B. Fang Y. Yang H. Meng F. He C. Zhao Y. Zhao K. Zhang H. The combination of deep learning and pseudo-MS image improves the applicability of metabolomics to congenital heart defect prenatal screening. Talanta 2024 275 126109 10.1016/j.talanta.2024.126109 38648686
    [Google Scholar]
  9. Buergel T. Steinfeldt J. Ruyoga G. Pietzner M. Bizzarri D. Vojinovic D. Upmeier zu Belzen J. Loock L. Kittner P. Christmann L. Hollmann N. Strangalies H. Braunger J.M. Wild B. Chiesa S.T. Spranger J. Klostermann F. van den Akker E.B. Trompet S. Mooijaart S.P. Sattar N. Jukema J.W. Lavrijssen B. Kavousi M. Ghanbari M. Ikram M.A. Slagboom E. Kivimaki M. Langenberg C. Deanfield J. Eils R. Landmesser U. Metabolomic profiles predict individual multidisease outcomes. Nat. Med. 2022 28 11 2309 2320 10.1038/s41591‑022‑01980‑3 36138150
    [Google Scholar]
  10. Tran D.T. Dahlin A. Pharmacometabolomics: General Applications of Metabolomics in Drug Development and Personalized Medicine. Metabolomics: Recent Advances and Future Applications. Soni V. Hartman T.E. Cham Springer International Publishing 2023 127 164 10.1007/978‑3‑031‑39094‑4_
    [Google Scholar]
  11. Pang H. Hu Z. Metabolomics in drug research and development: The recent advances in technologies and applications. Acta Pharm. Sin. B 2023 13 8 3238 3251 10.1016/j.apsb.2023.05.021 37655318
    [Google Scholar]
  12. Bollard M.E. Keun H.C. Beckonert O. Ebbels T.M.D. Antti H. Nicholls A.W. Shockcor J.P. Cantor G.H. Stevens G. Lindon J.C. Holmes E. Nicholson J.K. Comparative metabonomics of differential hydrazine toxicity in the rat and mouse. Toxicol. Appl. Pharmacol. 2005 204 2 135 151 10.1016/j.taap.2004.06.031 15808519
    [Google Scholar]
  13. Clarke C.J. Haselden J.N. Metabolic profiling as a tool for understanding mechanisms of toxicity. Toxicol. Pathol. 2008 36 1 140 147 10.1177/0192623307310947 18337232
    [Google Scholar]
  14. Chen M. Environmental chemical exposomics and metabolomics in toxicology: The latest updates. Toxics 2024 12 9 647 10.3390/toxics12090647 39330575
    [Google Scholar]
  15. Dehghani F. Yousefinejad S. Walker D.I. Omidi F. Metabolomics for exposure assessment and toxicity effects of occupational pollutants: Current status and future perspectives. Metabolomics 2022 18 9 73 10.1007/s11306‑022‑01930‑7 36083566
    [Google Scholar]
  16. Bedia C. Metabolomics in environmental toxicology: Applications and challenges. Trends Environ. Anal. Chem. 2022 34 00161 10.1016/j.teac.2022.e00161
    [Google Scholar]
  17. Salam U. Ullah S. Tang Z.H. Elateeq A.A. Khan Y. Khan J. Khan A. Ali S. Plant metabolomics: An overview of the role of primary and secondary metabolites against different environmental stress factors. Life 2023 13 3 706 730 10.3390/life13030706 36983860
    [Google Scholar]
  18. Li Y. Ruan Q. Li Y. Ye G. Lu X. Lin X. Xu G. A novel approach to transforming a non-targeted metabolic profiling method to a pseudo-targeted method using the retention time locking gas chromatography/mass spectrometry-selected ions monitoring. J. Chromatogr. A 2012 1255 228 236 [https://doi.org/10.1016/j.chroma.2012.01.076].
    [Google Scholar]
  19. Zhan C. Shen S. Yang C. Liu Z. Fernie A.R. Graham I.A. Luo J. Plant metabolic gene clusters in the multi-omics era. Trends Plant Sci. 2022 27 10 981 1001 10.1016/j.tplants.2022.03.002 35365433
    [Google Scholar]
  20. Li L. Lu X. Zhao J. Zhang J. Zhao Y. Zhao C. Xu G. Lipidome and metabolome analysis of fresh tobacco leaves in different geographical regions using liquid chromatography–mass spectrometry. Anal. Bioanal. Chem. 2015 407 17 5009 5020 10.1007/s00216‑015‑8522‑8 25701418
    [Google Scholar]
  21. Zheng F. Zhao X. Zeng Z. Wang L. Lv W. Wang Q. Xu G. Development of a plasma pseudotargeted metabolomics method based on ultra-high-performance liquid chromatography–mass spectrometry. Nat. Protoc. 2020 15 8 2519 2537 10.1038/s41596‑020‑0341‑5 32581297
    [Google Scholar]
  22. Mir S.A. Rajagopalan P. Jain A.P. Khan A.A. Datta K.K. Mohan S.V. Lateef S.S. Sahasrabuddhe N. Somani B.L. Keshava Prasad T.S. Chatterjee A. Veerendra Kumar K.V. LC–MSbased serum metabolomic analysis reveals dysregulation of phosphatidylcholines in esophageal squamous cell carcinoma. J. Proteomics 2015 125 Pt A 96 102 10.1016/j.jprot.2015.05.013 25982385
    [Google Scholar]
  23. Yang J. Jin W. Liu D. Zhong Q. Zhou T. Enhanced pseudotargeted analysis using a segment data dependent acquisition strategy by LC-MS/MS for a metabolomics study of liquiritin in the treatment of depression. J. Sep. Sci. 2020 43 11 2088 2096 10.1002/jssc.202000107 32144949
    [Google Scholar]
  24. Wang M. Huang J. Fan H. He D. Zhao S. Shu Y. Li H. Liu L. Lu S. Xiao C. Liu Y. Treatment of rheumatoid arthritis using combination of methotrexate and tripterygium glycosides tablets—A quantitative plasma pharmacochemical and pseudotargeted metabolomic approach. Front. Pharmacol. 2018 9 1051 10.3389/fphar.2018.01051 30356765
    [Google Scholar]
  25. Zhou Y. Hu C. Zhao X. Luo P. Lu J. Li Q. Chen M. Yan D. Lu X. Kong H. Jia W. Xu G. Serum metabolomics study of gliclazide-modified-release-treated type 2 diabetes mellitus patients using a gas chromatography–Mass spectrometry method. J. Proteome Res. 2018 17 4 1575 1585 10.1021/acs.jproteome.7b00866 29460634
    [Google Scholar]
  26. Wang Y. Liu F. Li P. He C. Wang R. Su H. Wan J.B. An improved pseudotargeted metabolomics approach using multiple ion monitoring with time-staggered ion lists based on ultra-high performance liquid chromatography/quadrupole time-of-flight mass spectrometry. Anal. Chim. Acta 2016 927 927 82 88 10.1016/j.aca.2016.05.008 27237840
    [Google Scholar]
  27. Chen Y. Zhou Z. Yang W. Bi N. Xu J. He J. Zhang R. Wang L. Abliz Z. Development of a data-independent targeted metabolomics method for relative quantification using liquid chromatography coupled with tandem mass spectrometry. Anal. Chem. 2017 89 13 6954 6962 10.1021/acs.analchem.6b04727 28574715
    [Google Scholar]
  28. Cui Y. Xu B. Zhang X. He Y. Shao Y. Ding M. Diagnostic and therapeutic profiles of serum bile acids in women with intrahepatic cholestasis of pregnancy-A pseudo-targeted metabolomics study. Clin. Chim. Acta 2018 483 135 141 10.1016/j.cca.2018.04.035 29709452
    [Google Scholar]
  29. Wang J. Li Z. Yang G. Fang C. Yin Y. Zheng Z. Wang H. Fang S. Dai J. Wang S. Yang S. Yu B. Pseudo-targeted metabolic profile differences between emergency patients with type 1 and type 2 myocardial infarction diagnosed by optical coherence tomography. Clin. Chim. Acta 2024 554 1 117745 10.1016/j.cca.2023.117745
    [Google Scholar]
  30. Liu B. Du Z. Zhang W. Guo X. Lu Y. Jiang Y. Tu P. A pseudo-targeted metabolomics for discovery of potential biomarkers of cardiac hypertrophy in rats. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 2024 1240 124133 10.1016/j.jchromb.2024.124133 38733887
    [Google Scholar]
  31. Chen S. Kong H. Lu X. Li Y. Yin P. Zeng Z. Xu G. Pseudotargeted metabolomics method and its application in serum biomarker discovery for hepatocellular carcinoma based on ultra high-performance liquid chromatography/triple quadrupole mass spectrometry. Anal. Chem. 2013 85 17 8326 8333 10.1021/ac4016787 23889541
    [Google Scholar]
  32. Zhao C.X. Chang Y.W. Zhu Z. Lu X. Xu G.W. Metabolic responses of rice leaves and seeds under transgenic backcross breeding and pesticide stress by pseudotargeted metabolomics. J. Proteome Res. 2015 14 8 3351 3362 10.1021/acs.jproteome.5b00354
    [Google Scholar]
  33. Zhao J. Zhao Y. Hu C. Zhao C. Zhang J. Li L. Zeng J. Peng X. Lu X. Xu G. Xu G. Metabolic profiling with gas chromatography–mass spectrometry and capillary electrophoresis–mass spectrometry reveals the carbon–nitrogen status of tobacco leaves across different planting areas. J. Proteome Res. 2016 15 2 468 476 10.1021/acs.jproteome.5b00807 26784525
    [Google Scholar]
  34. Zhao J. Li L. Zhao Y. Zhao C. Chen X. Liu P. Zhou H. Zhang J. Hu C. Chen A. Liu G. Peng X. Lu X. Xu G. Metabolic changes in primary, secondary, and lipid metabolism in tobacco leaf in response to topping. Anal. Bioanal. Chem. 2018 410 3 839 851 10.1007/s00216‑017‑0596‑z 28929184
    [Google Scholar]
  35. Guo Y. Li Z. Qin X. Quality assessment of Astragali Radix based on pseudo‐targeted metabolomics and chemometric approach. J. Sep. Sci. 2023 46 11 2200985 10.1002/jssc.202200985 36965089
    [Google Scholar]
  36. Tsugawa H. Cajka T. Kind T. Ma Y. Higgins B. Ikeda K. Kanazawa M. Vander Gheynst J. Fiehn O. Arita M. MS-DIAL: Data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat. Methods 2015 12 6 523 526 10.1038/nmeth.3393 25938372
    [Google Scholar]
  37. Li Y. Pang T. Li Y. Wang X. Li Q. Lu X. Xu G. Gas chromatography‐mass spectrometric method for metabolic profiling of tobacco leaves. J. Sep. Sci. 2011 34 12 1447 1454 10.1002/jssc.201100106 21560246
    [Google Scholar]
  38. Lisec J. Schauer N. Kopka J. Willmitzer L. Fernie A.R. Gas chromatography mass spectrometry–based metabolite profiling in plants. Nat. Protoc. 2006 1 1 387 396 10.1038/nprot.2006.59 17406261
    [Google Scholar]
  39. Yong Li Pang, T.; Shi, J.L.; Lu, X.P.; Li, Y.P.; Lin, Q. Sample-specific metabolites library with retention neighbor: An improved identification and quantitation strategy for gas chromatography–mass spectrometry-based metabolomics. J. Anal. Chem. 2021 76 7 844 853 10.1134/S1061934821070108
    [Google Scholar]
  40. Szopa J. Wilczyński G. Fiehn O. Wenczel A. Willmitzer L. Identification and quantification of catecholamines in potato plants (Solanum tuberosum) by GC–MS. Phytochemistry 2001 58 2 315 320 10.1016/S0031‑9422(01)00232‑1 11551557
    [Google Scholar]
  41. Kind T. Wohlgemuth G. Lee D.Y. Lu Y. Palazoglu M. Shahbaz S. Fiehn O. FiehnLib: Mass spectral and retention index libraries for metabolomics based on quadrupole and time-of-flight gas chromatography/mass spectrometry. Anal. Chem. 2009 81 24 10038 10048 10.1021/ac9019522 19928838
    [Google Scholar]
  42. Kopka J. Schauer N. Krueger S. Birkemeyer C. Usadel B. Bergmüller E. Dörmann P. Weckwerth W. Gibon Y. Stitt M. Willmitzer L. Fernie A.R. Steinhauser D. [email protected]: The golm metabolome database. Bioinformatics 2005 21 8 1635 1638 10.1093/bioinformatics/bti236 15613389
    [Google Scholar]
  43. Tsugawa H. Tsujimoto Y. Sugitate K. Sakui N. Nishiumi S. Bamba T. Fukusaki E. Highly sensitive and selective analysis of widely targeted metabolomics using gas chromatography/triple-quadrupole mass spectrometry. J. Biosci. Bioeng. 2014 117 1 122 128 10.1016/j.jbiosc.2013.06.009 23867096
    [Google Scholar]
  44. Jayasinghe N.S. Mendis H. Roessner U. Dias D.A. Antonio C. Quantification of sugars and organic acids in biological matrices using GC-QqQ-MS. Plant. Metabolomics: Methods and Protocols. Humana Press Inc 2018 207 223
    [Google Scholar]
/content/journals/cac/10.2174/0115734110392891250831141601
Loading
/content/journals/cac/10.2174/0115734110392891250831141601
Loading

Data & Media loading...

Supplements

Supplementary material is available on the publisher’s website along with the published article.

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test