Skip to main content
SpringerLink
Log in

(Un)targeted hair metabolomics: first considerations and systematic evaluation on the impact of sample preparation

  • Research Paper
  • Published:
Analytical and Bioanalytical Chemistry Aims and scope Submit manuscript

Abstract

The interest in metabolomic studies has rapidly increased over the past few years. Changes of endogenous compounds are typically detected in plasma or urine. However, the use of hair allows for long-term monitoring of metabolomic changes and has recently started being applied to metabolomic studies. Within the proposed study, we aimed for a systematical investigation of different pre-analytical parameters on detected metabolites from different chemical classes in hair. For this purpose, three different parameters were varied: (1) multi-step decontamination (dichloromethane (DCM), acetone, H2O, acetone; H2O, acetone, DCM, acetone; and H2O, methanol/acetone), (2) homogenization (pulverization vs. cutting into snippets), and (3) extraction (acetonitrile (ACN)/buffer pH 4 vs. ACN/H2O vs. ACN/buffer pH 8.5). To include as many metabolites as possible, samples were analyzed by high-resolution time of flight mass spectrometry coupled to liquid chromatography (HPLC-HRMS) and additionally by gas chromatography high-resolution mass spectrometry (GC-HRMS) followed by untargeted-like data processing, respectively. The application of different decontamination procedures yielded similar results, although pointing to a trend towards increased washing-out effects if protic solvents were used as a first washing step. Pulverization of hair samples was favorable in terms of detected and tentatively identified metabolites. Evaluation of extraction solvents showed differences in extraction yield for the majority of investigated metabolites, yet, a prediction of metabolite extraction according to their pKa values was not possible. Overall, successive decontamination with DCM, acetone, H2O, and acetone; homogenization by pulverization; and extraction with ACN/H2O produced reliable results.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Patti GJ, Yanes O, Innovation SG. Metabolomics: the apogee of the omics trilogy. Nat Rev Mol Cell Biol. 2012;13(4):263–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Dinis-Oliveira RJ. Metabolomics of drugs of abuse: a more realistic view of the toxicological complexity. Bioanalysis. 2014;6(23):3155–9.

    Article  CAS  PubMed  Google Scholar 

  3. Dettmer K, Aronov PA, Hammock BD. Mass spectrometry-based metabolomics. Mass Spectrom Rev. 2007;26(1):51–78. https://doi.org/10.1002/mas.20108.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Nicholson JK, Lindon JC, Holmes E. ‘Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica. 1999;29(11):1181–9.

    Article  CAS  PubMed  Google Scholar 

  5. Monteiro MS, Carvalho M, Bastos ML, Guedes de Pinho P. Metabolomics analysis for biomarker discovery: advances and challenges. Curr Med Chem. 2013;20(2):257–71.

    Article  CAS  PubMed  Google Scholar 

  6. Boxler MI, Liechti ME, Schmid Y, Kraemer T, Steuer AE. First time view on human metabolome changes after a single intake of 3,4-methylenedioxymethamphetamine in healthy placebo-controlled subjects. J Proteome Res. 2017;16(9):3310–20.

    Article  CAS  PubMed  Google Scholar 

  7. Pragst F, Balikova MA. State of the art in hair analysis for detection of drug and alcohol abuse. Clin Chim Acta. 2006;370(1–2):17–49.

    Article  CAS  PubMed  Google Scholar 

  8. Binz TM, Rietschel L, Streit F, Hofmann M, Gehrke J, Herdener M, et al. Endogenous cortisol in keratinized matrices: systematic determination of baseline cortisol levels in hair and the influence of sex, age and hair color. Forensic Sci Int. 2018;284:33–8.

    Article  CAS  PubMed  Google Scholar 

  9. Binz TM, Braun U, Baumgartner MR, Kraemer T. Development of an LC-MS/MS method for the determination of endogenous cortisol in hair using (13)C3-labeled cortisol as surrogate analyte. J Chromatogr B Anal Technol Biomed Life Sci. 2016;1033-1034:65–72.

    Article  CAS  Google Scholar 

  10. Greff MJE, Levine JM, Abuzgaia AM, Elzagallaai AA, Rieder MJ, van Uum SHM. Hair cortisol analysis: an update on methodological considerations and clinical applications. Clin Biochem. 2019;63:1–9.

    Article  CAS  PubMed  Google Scholar 

  11. Rashaid AH, Harrington Pde B, Jackson GP. Profiling amino acids of Jordanian scalp hair as a tool for diabetes mellitus diagnosis: a pilot study. Anal Chem. 2015;87(14):7078–84.

    Article  CAS  PubMed  Google Scholar 

  12. Masukawa Y, Narita H, Imokawa G. Characterization of the lipid composition at the proximal root regions of human hair. J Cosmet Sci. 2005;56(1):1–16.

    CAS  PubMed  Google Scholar 

  13. Delplancke TDJ, de Seymour JV, Tong C, Sulek K, Xia Y, Zhang H, et al. Analysis of sequential hair segments reflects changes in the metabolome across the trimesters of pregnancy. Sci Rep. 2018;8(1):36.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Jones B, Han TL, Delplancke T, McKenzie EJ, de Seymour JV, Chua MC, et al. Association between maternal exposure to phthalates and lower language ability in offspring derived from hair metabolome analysis. Sci Rep. 2018;8(1):6745.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Sulek K, Han TL, Villas-Boas SG, Wishart DS, Soh SE, Kwek K, et al. Hair metabolomics: identification of fetal compromise provides proof of concept for biomarker discovery. Theranostics. 2014;4(9):953–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. He X, de Seymour JV, Sulek K, Qi H, Zhang H, Han TL, et al. Maternal hair metabolome analysis identifies a potential marker of lipid peroxidation in gestational diabetes mellitus. Acta Diabetol. 2016;53(1):119–22.

    Article  PubMed  Google Scholar 

  17. Sumner LW, Amberg A, Barrett D, Beale MH, Beger R, Daykin CA, et al. Proposed minimum reporting standards for chemical analysis chemical analysis working group (CAWG) metabolomics standards initiative (MSI). Metabolomics. 2007;3(3):211–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Deda O, Chatziioannou AC, Fasoula S, Palachanis D, Raikos N, Theodoridis GA, et al. Sample preparation optimization in fecal metabolic profiling. J Chromatogr B Anal Technol Biomed Life Sci. 2017;1047:115–23.

    Article  CAS  Google Scholar 

  19. Raterink RJ, Lindenburg PW, Vreeken RJ, Ramautar R, Hankemeier T. Recent developments in sample-pretreatment techniques for mass spectrometry-based metabolomics. TrAC Trends Anal Chem. 2014;61:157–67.

    Article  CAS  Google Scholar 

  20. Yin P, Lehmann R, Xu G. Effects of pre-analytical processes on blood samples used in metabolomics studies. Anal Bioanal Chem. 2015;407(17):4879–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Vogliardi S, Tucci M, Stocchero G, Ferrara SD, Favretto D. Sample preparation methods for determination of drugs of abuse in hair samples: a review. Anal Chim Acta. 2015;857:1–27.

    Article  CAS  PubMed  Google Scholar 

  22. Society of Hair Testing. Recommendations for hair testing in forensic cases. Forensic Sci Int. 2004;145(2–3):83–4.

    Google Scholar 

  23. Kintz P, Cirimele V, Jamey C, Ludes B. Testing for GHB in hair by GC/MS/MS after a single exposure. Application to document sexual assault. J Forensic Sci. 2003;48(1):195–200.

    Article  CAS  PubMed  Google Scholar 

  24. Choi B, Kim SP, Hwang S, Hwang J, Yang CH, Lee S. Metabolic characterization in urine and hair from a rat model of methamphetamine self- administration using LC- QTOF-MS-based metabolomics. Metabolomics. 2017. https://doi.org/10.1007/s11306-017-1257-0.

  25. Xie P, Wang TJ, Yin G, Yan Y, Xiao LH, Li Q, et al. Metabonomic study of biochemical changes in human hair of heroin abusers by liquid chromatography coupled with ion trap-time of flight mass spectrometry. J Mol Neurosci. 2016;58(1):93–101.

    Article  CAS  PubMed  Google Scholar 

  26. Madry MM, Kraemer T, Baumgartner MR. Systematic assessment of different solvents for the extraction of drugs of abuse and pharmaceuticals from an authentic hair pool. Forensic Sci Int. 2018;282:137–43.

    Article  CAS  PubMed  Google Scholar 

  27. Dunn WB, Broadhurst D, Begley P, Zelena E, Francis-McIntyre S, Anderson N, et al. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat Protoc. 2011;6(7):1060–83.

    Article  CAS  PubMed  Google Scholar 

  28. Steuer AE, Arnold K, Kamber D, Kraemer T. Suitability evaluation of new endogenous biomarkers for the identification of nitrite-based urine adulteration in mass spectrometry methods. Drug Test Anal. 2018. https://doi.org/10.1002/dta.2481.

  29. Boxler MI, Schneider TD, Kraemer T, Steuer AE. Analytical considerations for (un)-targeted metabolomic studies with special focus on forensic applications. Drug Test Anal. 2018. https://doi.org/10.1002/dta.2540.

  30. Brockbals L, Habicht M, Hajdas I, Galassi FM, Ruhli FJ, Kraemer T. Untargeted metabolomics-like screening approach for chemical characterization and differentiation of canopic jar and mummy samples from Ancient Egypt using GC-high resolution MS. Analyst. 2018;143(18):4503–12.

    Article  CAS  PubMed  Google Scholar 

  31. Linstrom PJ, Mallard WG. NIST Chemistry WebBook. NIST Standard Reference Database Number 69, 2001.

  32. Guijas C, Montenegro-Burke JR, Domingo-Almenara X, Palermo A, Warth B, Hermann G, et al. METLIN: a technology platform for identifying knowns and unknowns. Anal Chem. 2018;90(5):3156–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Kind T, Liu KH, Lee DY, DeFelice B, Meissen JK, Fiehn O. LipidBlast in silico tandem mass spectrometry database for lipid identification. Nat Methods. 2013;10(8):755–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Wishart DS, Tzur D, Knox C, Eisner R, Guo AC, Young N, et al. HMDB: the human metabolome database. Nucleic Acids Res. 2007. https://doi.org/10.1093/nar/gkl923.

  35. Zhu X, Chen Y, Subramanian R. Comparison of information-dependent acquisition, SWATH, and MS(All) techniques in metabolite identification study employing ultrahigh-performance liquid chromatography-quadrupole time-of-flight mass spectrometry. Anal Chem. 2014;86(2):1202–9.

    Article  CAS  PubMed  Google Scholar 

  36. Noppe G, de Rijke YB, Dorst K, van den Akker EL, van Rossum EF. LC-MS/MS-based method for long-term steroid profiling in human scalp hair. Clin Endocrinol. 2015;83(2):162–6.

    Article  CAS  Google Scholar 

  37. Cuykx M, Rodrigues RM, Laukens K, Vanhaecke T, Covaci A. In vitro assessment of hepatotoxicity by metabolomics: a review. Arch Toxicol. 2018;92(10):3007–29.

    Article  CAS  PubMed  Google Scholar 

  38. Cooper GA, Kronstrand R, Kintz P, Society of Hair Testing. Society of Hair Testing guidelines for drug testing in hair. Forensic Sci Int. 2012;218(1–3):20–4.

    Article  CAS  PubMed  Google Scholar 

  39. De AK, Chowdhury PP, Chattapadhyay S. Simultaneous quantification of dexpanthenol and resorcinol from hair care formulation using liquid chromatography: method development and validation. Scientifica (Cairo). 2016;2016:1537952.

    Google Scholar 

  40. Cosmetic Ingredient Review Expert P. Final report of the safety assessment of niacinamide and niacin. Int J Toxicol. 2005;24(Suppl 5):1–31.

    Google Scholar 

  41. Fiume MM, Heldreth B, Bergfeld WF, Belsito DV, Hill RA, Klaassen CD, et al. Safety assessment of triethanolamine and triethanolamine-containing ingredients as used in cosmetics. Int J Toxicol. 2013;32(3 Suppl):59S–83S.

    Article  PubMed  CAS  Google Scholar 

  42. Delgado-Povedano MM, Calderon-Santiago M, Priego-Capote F, Luque de Castro MD. Development of a method for enhancing metabolomics coverage of human sweat by gas chromatography-mass spectrometry in high resolution mode. Anal Chim Acta. 2016;905:115–25.

    Article  CAS  PubMed  Google Scholar 

  43. Camera E, Ludovici M, Galante M, Sinagra JL, Picardo M. Comprehensive analysis of the major lipid classes in sebum by rapid resolution high-performance liquid chromatography and electrospray mass spectrometry. J Lipid Res. 2010;51(11):3377–88.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Eser HP, Potsch L, Skopp G, Moeller MR. Influence of sample preparation on analytical results: drug analysis [GC/MS] on hair snippets versus hair powder using various extraction methods. Forensic Sci Int. 1997;84(1–3):271–9.

    Article  CAS  PubMed  Google Scholar 

  45. Salomone A, Baumgartner MR, Lombardo T, Alladio E, Di Corcia D, Vincenti M. Effects of various sample pretreatment procedures on ethyl glucuronide quantification in hair samples: comparison of positivity rates and appraisal of cut-off values. Forensic Sci Int. 2016;267:60–5.

    Article  CAS  PubMed  Google Scholar 

  46. Monch B, Becker R, Nehls I. Quantification of ethyl glucuronide in hair: effect of milling on extraction efficiency. Alcohol Alcohol. 2013;48(5):558–63.

    Article  PubMed  CAS  Google Scholar 

  47. Aqai P, Stolker AA, Lasaroms JJ. Effect of sample pre-treatment on the determination of steroid esters in hair of bovine calves. J Chromatogr A. 2009;1216(46):8233–9.

    Article  CAS  PubMed  Google Scholar 

  48. Becker R, Lo I, Sporkert F, Baumgartner M. The determination of ethyl glucuronide in hair: experiences from nine consecutive interlaboratory comparison rounds. Forensic Sci Int. 2018;288:67–71.

    Article  CAS  PubMed  Google Scholar 

  49. Chata C, E MH, Grova N, Appenzeller BM. Influence of pesticide physicochemical properties on the association between plasma and hair concentration. Anal Bioanal Chem. 2016;408(13):3601–12.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors would like to express their gratitude to Emma Louise Kessler, MD, for her generous legacy; she donated to the Institute of Forensic Medicine at the University of Zurich, Switzerland, for research purposes.

Author information

Authors and Affiliations

  1. Department of Forensic Pharmacology and Toxicology, Zurich Institute of Forensic Medicine, Winterthurerstrasse 190/52, 8057, Zurich, Switzerland

    Lisa Eisenbeiss, Andrea E. Steuer & Thomas Kraemer

  2. Center for Forensic Hair Analytics, Zurich Institute of Forensic Medicine, Kurvenstrasse 17, 8006, Zurich, Switzerland

    Tina M. Binz & Markus R. Baumgartner

Authors
  1. Lisa Eisenbeiss
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrea E. Steuer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tina M. Binz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Markus R. Baumgartner
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Thomas Kraemer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrea E. Steuer.

Ethics declarations

Hair samples were collected from two healthy volunteers who provided written informed consent. According to Swissethics (Humanforschungsgesetz), no further ethical approval from the cantonal ethic commission is necessary if the research is not aiming to investigate diseases or functions of the human body as is the case in the current study.

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

ESM 1

(PDF 231 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Eisenbeiss, L., Steuer, A.E., Binz, T.M. et al. (Un)targeted hair metabolomics: first considerations and systematic evaluation on the impact of sample preparation. Anal Bioanal Chem 411, 3963–3977 (2019). https://doi.org/10.1007/s00216-019-01873-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00216-019-01873-4

Keywords

Search

Navigation