DOI QR코드

DOI QR Code

Mass Spectrometry-based Hair Metabolomics for Biomarker Discovery

  • Lee, Yu Ra (Molecular Recognition Research Center, Korea Institute of Science and Technology) ;
  • Hong, Jongki (KHU-KIST Department of Converging Science and Technology, Kyung Hee University) ;
  • Chung, Bong Chul (Molecular Recognition Research Center, Korea Institute of Science and Technology)
  • Received : 2022.01.10
  • Accepted : 2022.03.02
  • Published : 2022.03.31

Abstract

Metabolomics makes it possible to analyze the interrelationships between various signaling molecules based on the metabolic pathways involved by using high-resolution devices. This approach can also be used to obtain large-scale metabolic information to identify the relevant pathways for disease diagnosis and prognosis and search for potential biomarkers. In the fields of medicine and forensics, hair analysis is used to detect various metabolites in the body. Hair can be harvested readily in a noninvasive manner and is easier to transport and store than blood and urine. Another advantage from a forensic viewpoint is that hair reflects all the components of body fluids. In addition, because of the unique coating structure of hair, it can be used for measurements without changing or destroying its adsorbed components. In this review, the pretreatments for hair analysis, instrumental conditions and clinical applications are discussed. Especially, the clinical use of hair metabolomics in the diagnosis of various diseases and the limitations of the technique are described.

Keywords

Introduction

Interest in metabolomic studies has grown rapidly in the past few years.1 In addition, the analysis of human hair for genetic (DNA) and drug testing, which started in the 1960s, is a common technique these days, as hair is easy to collect and yields highly accurate results.

However, the hair roots are required for genetic and paternity testing. This is because the relevant biological information is present in the hair mother cells of the hair bulb. However, in the case of alcohol, tobacco, and narcotic consumption, the component of interest remains in the keratin protein layer of hair. Thus, these substances can be detected even in hair without roots.

Hair grows relatively slowly, and both endogenous compounds and those present in the environment are incorporated from the blood into the hair during its growth. This is reflected in the average chemical composition of hair over several months.2 In particular, the analysis of the slow-growing hair matrix provides a suitable index for quantitatively evaluating the integrated hormone levels over several months.3 Hair gradually forms a thick wall on its outer layer just before it is exposed to the outside environment and permanently records information related to the state of the minerals present in the human body. Normally, hair grows by 0.03 cm per day and 1 cm per month. Therefore, if one collects 3–4 cm of hair, one can obtain health-related information for the previous 3–4 months. In particular, hair stores 10–50 times more mineral information than blood and urine and can be analyzed more accurately.

Hair that grows 0.3mm daily contains a wealth of information related to the levels of essential minerals, such as magnesium, potassium, iron, sodium, and calcium, in the body as well as heavy metals such as mercury, lead, and cadmium. The essential minerals found in hair are indispensable elements and play an important role in various physiological functions such cell generation and the stimulation of cell activity. Thus, by determining the contents of these minerals, it is possible to evaluate the metabolic rate, stress, immunity, and adrenal gland and thyroid gland status.

The medical conditions that can be prevented based on the results of hair examinations include chronic fatigue, obesity, diabetes, metabolic syndrome, atopy and skin diseases, osteoporosis, arteriosclerosis, high cholesterol, and hypertension. These illnesses are caused by poor lifestyle and eating habits for long periods and vary from mental illnesses.

Hair analysis has also become increasingly important for detecting the presence of substances of abuse, both in clinical and forensic toxicology investigations.4 Hair fiber offers several advantages over other biological matrices (blood and urine), including a larger window of detection, ease of collection, and sample stability.5 Hair samples are extremely valuable for testing for long-term drug use. Moreover, as stated above, a key advantage of hair analysis is that hair samples are easy to obtain and can be acquired noninvasively. In addition, they do not have any additional storage requirements and can be kept at room temperature for long periods.

Hair is a strong matrix that is stable at room temperature, can be handled and transported with ease, cannot be tampered with readily during collection, can be collected noninvasively, and has a high resistance to decay in postmortem cases.6, 7

Metabolomics is a comprehensive technique to systematically analyze and quantify the changes in the behavior and secretion of metabolites within cells or tissues and reinterpret the metabolite network by linking the various metabolomic groups with their related physiological and pathological conditions. The main purpose of using metabolomics in current medicine is to determine the mechanism of drug action or disease; discover or measure biomarkers for diagnosis, prognosis, toxicity; and evaluate the therapeutic effects of drugs. Since human diseases and health disorders are caused by changes in the metabolism (metabolic pathways) of the body, metabolomics is increasingly being used for the discovery and identification of diagnostic biomarkers and therapeutic targets.8, 9 Metabolomics allows for a comprehensive analysis of metabolites and their associations with the metabolic processes related to various pathways.10 Therefore, in the case of unknown metabolites, it is important to study the diseases they may cause as well as their correlations. Research in this direction has already begun recently.

Finally, it has been shown based on multivariatestatistical analysis that it is possible to determine the gender of a person through the principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA)11 of hair.

E1MPSV_2022_v13n1_2_f0001.png 이미지

Figure 1. Score plots obtained from PCA and OPLS-DA of hair from healthy controls.

In this review, we briefly introduce the pretreatments performed on hair samples prior to analysis and describe the clinical applications of hair metabolomics.

Sample Preparation

Washing for decontamination

An important step in the development of a decontamination procedure is to produce artificially contaminated specimens that be used for testing the efficiency of the washing process to remove the chemicals12 and heavy metal ions13 deposited on the surface of hair. The decontamination of hair is an important step that requires further examination.14 Prior to analysis, the hair sample to be tested must be decontaminated by washing with a variety of solvents such as methanol, acetone, 15 and dichloromethane16 to remove any oil or surface contaminants present.17

Alkaline hydrolysis

Alkaline hydrolysis, which completely dissolves the hair matrix, allows for the solubilization of all the drugs present in the hair sample.18 For alkaline hydrolysis, 1 mL of 1 M NaOH is added to the hair sample and allowed to react at 80oC for 1h. The temperature can be kept at 70-90oC. After the sample has cooled to room temperature, 1 mL of acetate buffer (pH 5.2) is added to the mixture, and its pH is adjusted to 5–6 by adding 0.1mL of 2 M HCl. Finally, the mixture is extracted twice using 2.5 mL of hexane/ethyl acetate (3:2, v/v) by mechanical shaking for 10 min.19

Acidic hydrolysis

For acidic hydrolysis, 1 mL of 0.6 M HCl is added to the hair sample, and the mixture is incubated at 40oC overnight. Alternatively, 1mL of 1 M HCl is added to the hair sample, and the sample is kept overnight at 50oC.20 After heating, 0.9mL of 2 M NaOH is used to adjust the pH of the mixture to 11–12. Then, derivatization21 or extraction is performed using diethyl ether.22 Another acidic hydrolysis method used to remove the protein components involves heating the hair sample with 6 M HCl at 110oC for 16 h.23 Acid hydrolysis could be used to extract basic metabolites due to protonation of the nitrogen atoms present in the molecules.24

Ultrasonication

Sonication is used to improve the digestion performance of enzymes. The most important parameters affecting the process are the cavitation frequency, ultrasound intensity, and type of solvent, bubbled gas, and external temperature used.25 To maximize the extraction efficiency of the hair steroids, the effects of the treatment time (15, 30, 60, and 120 min at 25oC) and temperature (25, 40, and 60oC for 60 min) during sonication with methanol have been evaluated. The effect of the extraction solvent used during ultrasonication has also been evaluated. For this, hair samples were incubated with methanol (0.5mL) or methanol/ dichloromethane (1:2 or 2:1) for 60 min at 40oC.19

Enzymatic hydrolysis

For the enzymatic hydrolysis of hair, added 500 μL of a 1, 4- dithiothreitol solution (12mg/mL in tris(hydroxymethyl) aminomethane-hydrochloric acid buffer solution (0.1 M, pH 7.2)) is added. The mixture is then incubated at 40oC for 12 h.26

Instrumental Conditions

Most experimental instrument for hair analysis consists of the use of liquid chromatography-mass spectrometry. Here is one example we used to explain the instrumental conditions for untargeted profiling.27-29 An ACQUITY™ ultra-performance liquid chromatography system (Waters, Milford, MA, USA) coupled to a Q-Tof Premier™ quadrupole/time-of-flight hybrid mass spectrometer system from Waters (Milford, MA, USA) were used. The gradient elution system consisted of solvent A (water with 0.1% formic acid) and solvent B (acetonitrile with 0.1% formic acid) and was controlled as follows: 0–3 min, 5% B; 3–10 min, 5– 50% B; 10–11.5 min, 50–95% B; 11.5–12 min, 95–5% B. The gradient was then returned to the initial concentration (5% B) and held for 2 min before running the next sample. Except for previous results, instrument conditions used in other studies were showed in Table 1. also classically used gas chromatography-mass spectrometry method is also summarized in Table 2. It was confirmed that most of the GC-MS methods were analyzed using a column composed of 5% phenyl polysilphenylene-siloxane.

Table 1. Instrumental conditions for hair analysis using liquid chromatography system-mass spectrometer

E1MPSV_2022_v13n1_2_t0001.png 이미지
Table1. Continued

E1MPSV_2022_v13n1_2_t0002.png 이미지

Table 2. Instrumental conditions for hair analysis using gas chromatography system-mass spectrometer

E1MPSV_2022_v13n1_2_t0003.png 이미지

Applications of Hair Metabolomics

Forensic science

Forensic chemistry uses biological samples such as urine and hair to evaluate the authenticity of psychotropic and other drugs in order to determine whether they are suitable for use as well as to measure the contents (purities) of their various components.

Drug confirmation tests performed on hair samples obtained during drug crime investigations are not only used to complement urine analysis but also for diagnosing drug use and checking for chronic drug abuse.

Moreover, the duration of drug use can be determined several months after use based on the detection range. For example, methamphetamine is a highly addictive central nervous system stimulant, and the changes in human hair metabolites after excessive methamphetamine use have been analyzed. Through network analysis, it has been shown that the concentrations of glycosphingolipids, sphingolipids, glycerophospholipids, and ether lipids as well as the metabolism of amino acids (glycine, serine, and threonine; cysteine and methionine) are affected by methamphetamine use.60

In addition, from a forensic point of view, a method for simultaneously analyzing the drugs and steroids present in hair samples has been developed.61

However, there are a few limitations associated with hair analysis. The differences in the hair growth rate with age, gender, ethnicity, as well as the individual variations between subjects make it difficult to interpret the concentrations of metabolites in hair. In addition, it is also difficult to estimate the time and volume details from hair segment analysis, as the drug integration mechanism of the hair matrix is not yet fully understood.62 In addition, hair samples can be manipulated through cosmetic treatments, and the drug concentrations can be altered, resulting in false negatives. In particular, the oxidative bleaching of hair samples under alkaline conditions has a significant effect on drug concentration. However, recently, a method to identify the metabolites altered by oxidative beauty therapies was developed based on nontargeted hair metabolomics analysis. 63

Androgenic alopecia

Androgenic alopecia is a well-known condition that occurs because of increased male hormone secretion.64 However, androgenic alopecia in females (female-pattern baldness) differs from that in males. The causes of female pattern baldness are not as clear as those of male-pattern baldness. Therefore, in a previous study, we performed untargeted metabolomics to comprehensively analyze the metabolites and metabolic pathways of androgenic alopecia using human hair samples.29 We were able to elucidate the extensive metabolic changes associated with androgenic alopecia based on sex.

E1MPSV_2022_v13n1_2_f0002.png 이미지

Figure 2. Steroid hormone biosynthesis with significant differences in male groups. Metabolites in red are significantly different for patients and controls.

E1MPSV_2022_v13n1_2_f0003.png 이미지

Figure 3. Steroid hormone biosynthesis with significant differences in female groups. Metabolites in red are significantly different for various groups.

In the case of the male groups, steroid hormone biosynthesis and the androgen metabolic pathways were significantly altered.

On the other hand, in the case of the female groups, steroid hormone biosynthesis and the estrogen metabolic pathways were significantly altered.

Hair follicles are among the most highly proliferative tissues. Therefore, we had also analyzed the levels of the polyamines and metabolites involved in cell proliferation in hair samples taken from patients with androgenic alopecia.21,65

E1MPSV_2022_v13n1_2_f0004.png 이미지

Figure 4. Box plot of hair metabolic

Neurodegenerative diseases

Neurodegenerative diseases are associated with several types of cognitive impairment, and the cortisol concentration in hair may reflect the level of chronic stress.66 The metabolic ratio of 7β-hydroxycholesterol (7β-OHC) to cholesterol can be used as a predictable index for evaluating cognitive impairment. The ratios for the abnormal cognition (mild cognitive impairment (MCI) + Alzheimer’s disease (AD)) and vasospastic angina (VA) groups were found to be significantly different.67

In addition, an analysis of the glucocorticoid levels in hair samples from patients with Parkinson’s disease showed that the level of hair cortisone was significantly higher in the patients.68

Hair analysis during various trimesters of pregnancy

Hair metabolites are an important source of information in pregnancy research and are used to study the metabolic mechanisms and complications related to pregnancy.2, 49, 69 The 40-week gestation period is divided into three trimesters. The period from the moment of confirmation of pregnancy to 13 weeks is called the first trimester, that from 14 weeks to 28 weeks is called the second trimester, and that from 28 weeks until delivery is called the third trimester. As pregnancy progresses, the concentrations of various metabolites change. The intermediates of glycolysis and the tricarboxylic acid cycle, such as pyruvic acid, fumaric acid, citric acid, and malic acid, link the various metabolic pathways. In particular, carbohydrate metabolism is significantly altered during pregnancy.49

E1MPSV_2022_v13n1_2_f0005.png 이미지

Figure 5. Metabolic network showing relationships between trimester-related metabolic pathways and various metabolites. Red circles represent metabolites that are significantly altered throughout pregnancy while yellow circles represent those that are not.

E1MPSV_2022_v13n1_2_f0006.png 이미지

Figure 6. Principal component analysis (PCA) score plot. Red circles represent fetal growth restriction cases while green circles represent normal controls.

In addition, based on an analysis of hair samples, we were able to confirm that there is a difference in the PCA results for the group with fetal growth restriction and those for normal controls. In particular, there were significant differences in the lactate, levulinate, 2-methyloctadecanate, tyrosine, and margarate levels of these groups.2

The onset of intrahepatic cholestasis of pregnancy, a maternal liver disease, can lead to sudden consequences, including fetal death and stillbirth. Attempts are being made to predict this disease based on predictive biomarkers using maternal hair samples. However, the results obtained so far have been unsatisfactory, as the hair samples collected at the onset of the disease did not show metabolic changes, suggesting rapid development.70

Perspective and limitations

In this review, we discussed hair metabolomics, including the sample preparation techniques used and the clinical applications of the method. We believe this review provides insights for improving the currently used approaches for hair analysis. In the future, hair metabolomics will be used more widely not only in forensic science but also in many other fields.

From a forensic viewpoint, hair analysis should be a complementary approach rather than the primary technique. Since there is a possibility of false positive results owing to the external contamination of the hair sample, an efficient decontamination procedure is required.71 Hair readily adsorbs contaminants even after short-term exposure. For example, environmental substances and shampoos adhere to its surface. In addition, the hair matrix may contain endogenous metabolites and other substances related to long-term exposure. Therefore, methods are being developed to profile both transiently exposed chemicals and endogenous metabolites in the same hair sample.72

A recent study confirmed that there are differences in the levels of metabolites with the hair color, suggesting that it is necessary to consider the hair color and hair segments in subsequent studies on hair metabolites.73

Thus, owing to their various advantages, metabolomics methods based on the noninvasive analysis of hair samples have been used in many clinical studies.

Acknowledgments

This study was supported by a grant from the Korea Institute of Science and Technology Institutional Program (Project No. 2E31093).

References

  1. Eisenbeiss, L.; Steuer, A. E.; Binz, T. M.; Baumgartner, M. R.; Kraemer, T. Anal. Bioanal. Chem. 2019, 411, 3963, DOI: 10.1007/s00216-019-01873-4.
  2. Sulek, K.; Han, T. L.; Villas-Boas, S. G.; Wishart, D. S.; Soh, S. E.; Kwek, K.; Gluckman, P. D.; Chong, Y. S.; Kenny, L. C.; Baker, P. N. Theranostics 2014, 4, 953, DOI: 10.7150/thno.9265.
  3. Gao, W.; Kirschbaum, C.; Grass, J.; Stalder, T. J. Steroid Biochem. Mol. Biol. 2016, 162, 92, DOI: 10.1016/j.jsbmb.2015.12.022.
  4. Pragst, F.; Balikova, M. A. Clin. Chim. Acta 2006, 370, 17, DOI: 10.1016/j.cca.2006.02.019.
  5. Vogliardi, S.; Tucci, M.; Stocchero, G.; Ferrara, S. D.; Favretto, D. Anal. Chim. Acta 2015, 857, 1, DOI: 10.1016/j.aca.2014.06.053.
  6. Kohler, I.; Guillarme, D. Bioanalysis 2014, 6, 1255, DOI: 10.4155/bio.14.80.
  7. Niu, Z.; Zhang, W.; Yu, C.; Zhang, J.; Wen, Y. Trends Anal. Chem. 2018, 102, 123, DOI: 10.1016/j.trac.2018.02.005.
  8. Sugimoto, M.; Kawakami, M.; Robert, M.; Soga, T.; Tomita, M. Curr. Bioinform. 2012, 7, 96, DOI: 10.2174/157489312799304431.
  9. Worley, B.; Powers, R. Curr. Metabolomics 2013, 1, 92, DOI: 10.2174/2213235X11301010092.
  10. Muthubharathi, B. C.; Gowripriya, T.; Balamurugan, K. Mol. Omics 2021, 17, 210, DOI: 10.1039/D0MO00176G.
  11. Dong, Z.; Wang, C.; Zhang, J.; Wang, Z. Anal. Bioanal. Chem. 2017, 409, 4751, DOI: 10.1007/s00216-017-0419-2.
  12. Duca, R. C.; Hardy, E.; Salquebre, G.; Appenzeller, B. M. Drug Test Anal. 2014, 6 Suppl 1, 55, DOI: 10.1002/dta.1649.
  13. Zhang, H.; Carrillo-Navarrete, F.; Lopez-Mesas, M.; Palet, C. Water 2020, 12, 1263, DOI: 10.3390/w12051263.
  14. Collins, S.; James, T.; Southworth, F.; Davidson, L.; Williams, N.; Orchard, E.; Marczylo, T.; Amlot, R. Sci. Rep. 2020, 10, 20822, DOI: 10.1038/s41598-020-77930-1.
  15. Janda, I.; Weinmann, W.; Kuehnle, T.; Lahode, M.; Alt, A. Forensic Sci. Int. 2002, 128, 59, DOI: 10.1016/s0379-0738(02)00163-9.
  16. Muller, A.; Iwersen-Bergmann, S. Drug Test Anal. 2020, 12, 948, DOI: 10.1002/dta.2791.
  17. Balikova, M. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub 2005, 149, 199. https://doi.org/10.5507/bp.2005.026
  18. Cirimele, V.; Kintz, P.; Mangin, P. Biomed. Chromatogr. 1996, 10, 179, DOI: 10.1002/(SICI)1099-0801(199607)10:4<179::AID-BMC586>3.0.CO;2-N.
  19. Jung, H. J.; Kim, S. J.; Lee, W. Y.; Chung, B. C.; Choi, M. H. Rapid Commun. Mass Spectrom. 2011, 25, 1184, DOI: 10.1002/rcm.4975.
  20. Kim, J. Y.; Jung, K. S.; Kim, M. K.; Lee, J. I.; In, M. K. Rapid Commun. Mass Spectrom. 2007, 21, 1705, DOI: 10.1002/rcm.3010.
  21. Lee, Y. R.; Lew, B. L.; Sim, W. Y.; Lee, J.; Hong, J.; Chung, B. C. J. Dermatol. 2019, 46, 985, DOI: 10.1111/1346-8138.15063.
  22. Choi, M. H.; Kim, K. R.; Chung, B. C. J. Chromatogr. A 2000, 897, 295, DOI: 10.1016/S0021-9673(00)00800-1.
  23. Ito, S.; Miyake, S.; Maruyama, S.; Suzuki, I.; Commo, S.; Nakanishi, Y.; Wakamatsu, K. Pigment Cell Melanoma Res. 2018, 31, 393, DOI: 10.1111/pcmr.12673.
  24. Ferreira, C.; Paulino, C.; Quintas, A. Chem. Res. Toxicol. 2019, 32, 2367, DOI: 10.1021/acs.chemrestox.9b00301.
  25. Bermejo, P.; Capelo, J.; Mota, A.; Madrid, Y.; Camara, C. Trends Anal. Chem. 2004, 23, 654, DOI:10.1016/j.trac.2004.06.007.
  26. Miguez-Framil, M.; Moreda-Pineiro, A.; BermejoBarrera, P.; Lopez, P.; Tabernero, M. J.; Bermejo, A. M. Anal. Chem. 2007, 79, 8564, DOI: 10.1021/ac071337n.
  27. Lee, Y. R.; Im, E.; Kim, H.; Lew, B. L.; Sim, W. Y.; Lee, J.; Oh, H. B.; Paeng, K. J.; Hong, J.; Chung, B. C. Metabolites 2020, 10, DOI: 10.3390/metabo10040131.
  28. Lee, Y. R.; An, K. Y.; Jeon, J.; Kim, N. K.; Lee, J. W.; Hong, J.; Chung, B. C. Metabolites 2020, 10, 487,DOI: 10.3390/metabo10120487.
  29. Lee, Y. R.; Lew, B. L.; Sim, W. Y.; Hong, J.; Chung, B. C. Metabolites 2021, 11, 178, DOI: 10.3390/metabo11030178.
  30. Cuypers, E.; Flinders, B.; Boone, C. M.; Bosman, I. J.; Lusthof, K. J.; van Asten, A. C.; Tytgat, J.; Heeren, R. M. Anal. Chem. 2016, 88, 3091, DOI: 10.1021/acs.analchem.5b03979.
  31. Fabresse, N.; Larabi, I. A.; Stratton, T.; Mistrik, R.; Pfau, G.; Lorin de la Grandmaison, G.; Etting, I.; Grassin Delyle, S.; Alvarez, J. C. Drug Test Anal. 2019, 11, 697, DOI: 10.1002/dta.2535.
  32. Kronstrand, R.; Forsman, M.; Roman, M. Forensic Sci. Int. 2018, 283, 9 DOI: 10.1016/j.forsciint.2017.12.001.
  33. Fernandez Mdel, M.; Di Fazio, V.; Wille, S. M.; Kummer, N.; Samyn, N. J. Chromatogr. B 2014, 965, 7, DOI: 10.1016/j.jchromb.2014.05.055.
  34. Duvivier, W. F.; van Beek, T. A.; Pennings, E. J.; Nielen, M. W. Rapid Commun. Mass Spectrom. 2014, 28, 682 DOI: 10.1002/rcm.6831.
  35. Strano-Rossi, S.; Odoardi, S.; Fisichella, M.; Anzillotti, L.; Gottardo, R.; Tagliaro, F. J. Chromatogr. A 2014, 1372C, 145, DOI: 10.1016/j.chroma.2014.10.106.
  36. Gaudl, A.; Kratzsch, J.; Bae, Y. J.; Kiess, W.; Thiery, J.; Ceglarek, U. J. Chromatogr. A 2016, 1464, 64, DOI: 10.1016/j.chroma.2016.07.087.
  37. DiBattista, A.; Rampersaud, D.; Lee, H.; Kim, M.; Britz-McKibbin, P. Anal. Chem. 2017, 89, 11853, DOI: 10.1021/acs.analchem.7b03590.
  38. Montesano, C.; Simeoni, M. C.; Vannutelli, G.; Gregori, A.; Ripani, L.; Sergi, M.; Compagnone, D.; Curini, R. J. Chromatogr. A 2015, 1406, 192, DOI: 10.1016/j.chroma.2015.06.021.
  39. Chang, Y. J.; Chao, M. R.; Chen, S. C.; Chen, C. H.; Chang, Y. Z. Anal. Bioanal. Chem. 2014, 406, 2445, DOI: 10.1007/s00216-014-7669-z.
  40. Beasley, E.; Francese, S.; Bassindale, T. Anal. Chem. 2016, 88, 10328, DOI: 10.1021/acs.analchem.6b03551.
  41. Wu, Y.; Yang, J.; Duan, C.; Chu, L.; Chen, S.; Qiao, S.; Li, X.; Deng, H. J. Chromatogr. B 2018, 1083, 209, DOI: 10.1016/j.jchromb.2018.03.021.
  42. Inukai, T.; Kaji, S.; Kataoka, H. J. Pharm. Biomed. Anal. 2018, 156, 272, DOI: 10.1016/j.jpba.2018.04.032.
  43. Pichini, S.; Marchei, E.; Garcia-Algar, O.; Gomez, A.; Di Giovannandrea, R.; Pacifici, R. J. Pharm. Biomed. Anal. 2014, 100, 284, DOI: 10.1016/j.jpba.2014.08.006.
  44. Shu, I.; Alexander, A.; Jones, M.; Jones, J.; Negrusz, A. J. Chromatogr. B 2016, 1028, 145, DOI: 10.1016/j.jchromb.2016.06.015.
  45. Pichini, S.; Cortes, L.; Marchei, E.; Solimini, R.; Pacifici, R.; Gomez-Roig, M. D.; Garcia-Algar, O. J. Pharm. Biomed. Anal. 2016, 118, 9, DOI: 10.1016/j.jpba.2015.10.016.
  46. Rodriguez-Gomez, R.; Martin, J.; Zafra-Gomez, A.; Alonso, E.; Vilchez, J. L.; Navalon, A. Chemosphere 2017, 168, 676, DOI: 10.1016/j.chemosphere.2016.11.008.
  47. Xie, P.; Wang, T. J.; Yin, G.; Yan, Y.; Xiao, L. H.; Li, Q.; Bi, K. S. J. Mol. Neurosci. 2016, 58, 93, DOI: 10.1007/s12031-015-0655-x.
  48. Martin, J.; Santos, J. L.; Aparicio, I.; Alonso, E. Anal. Chim. Acta 2016, 945, 95, DOI: 10.1016/j.aca.2016.10.004.
  49. Delplancke, T. D. J.; de Seymour, J. V.; Tong, C.; Sulek, K.; Xia, Y.; Zhang, H.; Han, T. L.; Baker, P. N. Sci. Rep. 2018, 8, 36, DOI: 10.1038/s41598-017-18317-7.
  50. Martin, J.; Moder, M.; Gaudl, A.; Alonso, E.; Reemtsma, T. Anal. Bioanal. Chem. 2015, 407, 8725, DOI: 10.1007/s00216-015-9026-2.
  51. Pego, A. M.; Roveri, F. L.; Kuninari, R. Y.; Leyton, V.; Miziara, I. D.; Yonamine, M. Forensic Sci. Int. 2017, 274, 83, DOI: 10.1016/j.forsciint.2016.12.024.
  52. Roveri, F. L.; Paranhos, B. A.; Yonamine, M. Forensic Sci. Int. 2016, 265, 75, DOI: 10.1016/j.forsciint.2015.12.033.
  53. Rodrigues, A.; Yegles, M.; van Elsue, N.; Schneider, S. Forensic Sci. Int. 2018, 292, 163, DOI: 10.1016/j.forsciint.2018.09.015.
  54. Kieliba, T.; Lerch, O.; Andresen-Streichert, H.; Rothschild, M. A.; Beike, J. Drug Test Anal. 2019, 11, 267, DOI: 10.1002/dta.2490.
  55. Breidi, S. E.; Barker, J.; Petroczi, A.; Naughton, D. P. J. Anal. Methods Chem. 2012, 2012, 907893, DOI: 10.1155/2012/907893.
  56. Emidio, E. S.; Prata Vde, M.; Dorea, H. S. Anal. Chim. Acta 2010, 670, 63, DOI: 10.1016/j.aca.2010.04.023.
  57. Guthery, B.; Bassindale, T.; Bassindale, A.; Pillinger, C. T.; Morgan, G. H. J. Chromatogr. A 2010, 1217, 4402, DOI: 10.1016/j.chroma.2010.04.020.
  58. Pinho, S.; Oliveira, A.; Costa, I.; Gouveia, C. A.; Carvalho, F.; Moreira, R. F.; Dinis-Oliveira, R. J. Biomed. Chromatogr. 2013, 27, 1003, DOI: 10.1002/bmc.2894.
  59. Salquebre, G.; Schummer, C.; Millet, M.; Briand, O.; Appenzeller, B. M. Anal. Chim. Acta 2012, 710, 65, DOI: 10.1016/j.aca.2011.10.029.
  60. Kim, S.; Jang, W. J.; Yu, H.; Kim, J.; Lee, S. K.; Jeong, C. H.; Lee, S. Int. J. Mol. Sci. 2020, 21, DOI: 10.3390/ijms21176041.
  61. Shah, I.; Petroczi, A.; Uvacsek, M.; Ranky, M.; Naughton, D. P. Chem. Cent. J. 2014, 8, 73, DOI: 10.1186/s13065-014-0073-0.
  62. Wennig, R. Forensic Sci. Int. 2000, 107, 5, DOI: 10.1016/s0379-0738(99)00146-2.
  63. Eisenbeiss, L.; Binz, T. M.; Baumgartner, M. R.; Kraemer, T.; Steuer, A. E. Analyst 2020, 145, 6586, DOI: 10.1039/d0an01265c.
  64. Orentreich, N. J. Soc. Cosmet. Chemists 1960, 11, 479.
  65. Lee, Y. R.; Lee, J.; Lew, B. L.; Sim, W. Y.; Hong, J.; Chung, B. C. Exp. Dermatol. 2019, 28, 1083, DOI: 10.1111/exd.13998.
  66. Lee, D. Y.; Kim, E.; Choi, M. H. BMB Rep. 2015, 48, 209, DOI: 10.5483/bmbrep.2015.48.4.275.
  67. Son, H. H.; Lee, D. Y.; Seo, H. S.; Jeong, J.; Moon, J. Y.; Lee, J. E.; Chung, B. C.; Kim, E.; Choi, M. H. J. Steroid Biochem. Mol. Biol. 2016, 155, 9, DOI: 10.1016/j.jsbmb.2015.09.024.
  68. van den Heuvel, L. L.; du Plessis, S.; Stalder, T.; Acker, D.; Kirschbaum, C.; Carr, J.; Seedat, S. Psychoneuroendocrinology 2020, 117, 104704, DOI: 10.1016/j.psyneuen.2020.104704.
  69. He, X.; de Seymour, J. V.; Sulek, K.; Qi, H.; Zhang, H.; Han, T. L.; Villas-Boas, S. G.; Baker, P. N. Acta Diabetol. 2016, 53, 119, DOI: 10.1007/s00592-015-0737-9.
  70. de Seymour, J. V.; Tu, S.; He, X.; Zhang, H.; Han, T. L.; Baker, P. N.; Sulek, K. Metabolomics 2018, 14, 79, DOI: 10.1007/s11306-018-1371-7.
  71. Barroso, M.; Gallardo, E. Bioanalysis 2014, 6, 1, DOI: 10.4155/bio.13.291.
  72. Chen, Y.; Guo, J.; Xing, S.; Yu, H.; Huan, T. Front. Chem. 2021, 9, 674265, DOI: 10.3389/fchem.2021.674265.
  73. Eisenbeiss, L.; Binz, T. M.; Baumgartner, M. R.; Kraemer, T.; Steuer, A. E. Metabolites 2020, 10, 381, DOI: 10.3390/metabo10100381.