Hair Metabolome Research Articles

Establishing a biomonitoring baseline by characterizing the hair metabolome across age and sex using high-resolution mass spectrometry

Establishing a biomonitoring baseline by characterizing the hair metabolome across age and sex using high-resolution mass spectrometry

Pulmonary Fibrosis Diagnosis and Disease Progression Detected Via Hair Metabolome Analysis.

Fibrotic interstitial lung disease is often identified late due to non-specific symptoms, inadequate access to specialist care, and clinical unawareness precluding proper and timely treatment. Biopsy histological analysis is definitive but rarely performed due to its invasiveness. Diagnosis typically relies on high-resolution computed tomography, while disease progression is evaluated via frequent pulmonary function testing. This study tested the hypothesis that pulmonary fibrosis diagnosis and progression could be non-invasively and accurately evaluated from the hair metabolome, with the longer-term goal to minimize patient discomfort. Hair specimens collected from pulmonary fibrosis patients (n = 56) and healthy subjects (n = 14) were processed for metabolite extraction using 2DLC/MS-MS, and data were analyzed via machine learning. Metabolomic data were used to train machine learning classification models tuned via a rigorous combination of cross validation, feature selection, and testing with a hold-out dataset to evaluate classifications of diseased vs. healthy subjects and stable vs. progressed disease. Prediction of pulmonary fibrosis vs. healthy achieved AUROCTRAIN = 0.888 (0.794-0.982) and AUROCTEST = 0.908, while prediction of stable vs. progressed disease achieved AUROCTRAIN = 0.833 (0.784 - 0.882) and AUROCTEST = 0. 799. Top metabolites for diagnosis included ornithine, 4-(methylnitrosamino)-1-3-pyridyl-N-oxide-1-butanol, Thr-Phe, desthiobiotin, and proline. Top metabolites for progression included azelaic acid, Thr-Phe, Ala-Tyr, indoleacetyl glutamic acid, and cytidine. This study provides novel evidence that pulmonary fibrosis diagnosis and progression may in principle be evaluated from the hair metabolome. Longer term, this approach may facilitate non-invasive and accurate detection and monitoring of fibrotic lung diseases.

Characterization of Hair Metabolome in 5xFAD Mice and Patients with Alzheimer's Disease Using Mass Spectrometry-Based Metabolomics.

Hair emerged as a biospecimen for long-term investigation of endogenous metabolic perturbations, reflecting the chemical composition circulating in the blood over the past months. Despite its potential, the use of human hair for metabolomics in Alzheimer's disease (AD) research remains limited. Here, we performed both untargeted and targeted metabolomic approaches to profile the key metabolic pathways in the hair of 5xFAD mice, a widely used AD mouse model. Furthermore, we applied the discovered metabolites to human subjects. Hair samples were collected from 6-month-old 5xFAD mice, a stage marked by widespread accumulation of amyloid plaques in the brain, followed by sample preparation and high-resolution mass spectrometry analysis. Forty-five discriminatory metabolites were discovered in the hair of 6-month-old 5xFAD mice compared to wild-type control mice. Enrichment analysis revealed three key metabolic pathways: arachidonic acid metabolism, sphingolipid metabolism, and alanine, aspartate, and glutamate metabolism. Among these pathways, six metabolites demonstrated significant differences in the hair of 2-month-old 5xFAD mice, a stage prior to the onset of amyloid plaque deposition. These findings suggest their potential involvement in the early stages of AD pathogenesis. When evaluating 45 discriminatory metabolites for distinguishing patients with AD from nondemented controls, a combination of l-valine and arachidonic acid significantly differentiated these two groups, achieving a 0.88 area under the curve. Taken together, these findings highlight the potential of hair metabolomics in identifying disease-specific metabolic alterations and developing biomarkers for improving disease detection and monitoring.

The maternal hair metabolome is capable of discriminating intrahepatic cholestasis of pregnancy from uncomplicated pregnancy.

Intrahepatic cholestasis of pregnancy (ICP) is a pregnancy-specific liver disease associated with elevated bile acids in the blood. Diagnosis typically only occurs after the manifestation of clinical symptoms and the metabolic mechanisms underlying its development remain unclear. The aim of this study was to investigate potential specific metabolites and the underlying metabolic changes occurring during the development of ICP in the maternal plasma and hair metabolomes of women diagnosed with either ICP or having a healthy pregnancy. A total of 35 Chinese women with ICP and 42 healthy pregnancies were enrolled in our study. Plasma and hair samples, total bile acid levels (TBA), alanine transaminase levels (ALT), aspartate aminotransferase levels (AST), and additional clinical information were collected during the third trimester. Metabolites from maternal plasma and hair segments collected pre-conception and analyzed using gas chromatography-mass spectrometry (GC-MS). Three plasma metabolites (p < 0.05, q < 0.38) and 21 hair metabolites (p < 0.05, q < 0.05) were significantly different between ICP and healthy pregnancies. A combination of the eight most significant hair metabolites in a multivariate receiver operating characteristic curve model showed the best area under the curve (AUC) was 0.885, whereas the highest AUC using metabolites from plasma samples was only 0.74. Metabolic pathway analysis revealed 32 pathways were significantly (p and q values < 0.05) affected in the hair samples of patients with ICP. Pathways associated with glutathione metabolism and ABC transporters were affected. No metabolic pathways were significantly affected in plasma. Overall, this study showed that the hair metabolome could be more useful than the plasma metabolome for distinguishing ICP from normal pregnancy.

Identifying Novel Alzheimer’s disease Biomarkers Using Hair Metabolomics and Machine Learning

Identifying Novel Alzheimer’s disease Biomarkers Using Hair Metabolomics and Machine Learning

Untargeted hair lipidomics: comprehensive evaluation of the hair-specific lipid signature and considerations for retrospective analysis.

Lipidomics investigates the composition and function of lipids, typically employing blood or tissue samples as the primary study matrices. Hair has recently emerged as a potential complementary sample type to identify biomarkers in early disease stages and retrospectively document an individual's metabolic status due to its long detection window of up to several months prior to the time of sampling. However, the limited coverage of lipid profiling presented in previous studies has hindered its exploitation. This study aimed to evaluate the lipid coverage of hair using an untargeted liquid chromatography-high-resolution mass spectrometry lipidomics platform. Two distinct three-step exhaustive extraction experiments were performed using a hair metabolomics one-phase extraction technique that has been recently optimized, and the two-phase Folch extraction method which is recognized as the gold standard for lipid extraction in biological matrices. The applied lipidomics workflow improved hair lipid coverage, as only 99 species could be annotated using the one-phase extraction method, while 297 lipid species across six categories were annotated with the Folch method. Several lipids in hair were reported for the first time, including N-acyl amino acids, diradylglycerols, and coenzyme Q10. The study suggests that hair lipids are not solely derived from de novo synthesis in hair, but are also incorporated from sebum and blood, making hair a valuable matrix for clinical, forensic, and dermatological research. The improved understanding of the lipid composition and analytical considerations for retrospective analysis offers valuable insights to contextualize untargeted hair lipidomic analysis and facilitate the use of hair in translational studies.

Monitoring long-term chemical exposome by characterizing the hair metabolome using a high-resolution mass spectrometry-based suspect screening approach

Hair has recently emerged as a biospecimen for characterizing the long-term chemical exposome in biomonitoring investigations spanning several months, as chemical compounds circulating in the bloodstream accumulate in hair. Although there has been interest in using human hair as a biospecimen for exposome studies, it has yet to be widely adopted compared to blood and urine. Here, we applied a high-resolution mass spectrometry (HRMS)-based suspect screening strategy to characterize the long-term chemical exposome in human hair. Hair samples were collected from 70 subjects and cut into 3 cm segments, which were then mixed to prepare pooled samples. The pooled hair samples underwent a sample preparation procedure, and the hair extracts were further analyzed using an HRMS-based suspect screening approach. An in-house chemical suspect list containing 1227 chemical entries from National Report on Human Exposure to Environmental Chemicals (Report) published by the U.S. CDC and the Exposome-Explorer 3.0 database developed by the WHO was subsequently used to screen and filter the suspect features against the HRMS dataset. Overall, we matched 587 suspect features in the HRMS dataset to 246 unique chemical formulas in the suspect list, and the structures of 167 chemicals were further identified through a fragmentation analysis. Among these, chemicals such as mono-2-ethylhexyl phthalate, methyl paraben, and 1-naphthol, which have been detected in the urine or blood for exposure assessment, were also identified in human hair. This suggests that hair reflects the accumulation of environmental compounds to which an individual is exposed. Exposure to exogenous chemicals may exert adverse effects on cognitive function, and we discovered 15 chemicals in human hair that may contribute to the pathogenesis of Alzheimer's disease. This finding suggests that human hair may be a promising biospecimen for monitoring long-term exposure to multiple environmental chemicals and perturbations in endogenous chemicals in biomonitoring investigations.

Rat Hair Metabolomics Analysis Reveals Perturbations of Unsaturated Fatty Acid Biosynthesis, Phenylalanine, and Arachidonic Acid Metabolism Pathways Are Associated with Amyloid-β-Induced Cognitive Deficits

Hair is a noninvasive valuable biospecimen for the long-term assessment of endogenous metabolic disturbance. Whether the hair is suitable for identifying biomarkers of the Alzheimer’s disease (AD) process remains unknown. We aim to investigate the metabolism changes in hair after β-amyloid (Aβ1-42) exposure in rats using ultra-high-performance liquid chromatography-high-resolution mass spectrometry–based untargeted and targeted methods. Thirty-five days after Aβ1-42 induction, rats displayed significant cognitive deficits, and forty metabolites were changed, of which twenty belonged to three perturbed pathways: (1) phenylalanine metabolism and phenylalanine, tyrosine, and tryptophan biosynthesis—l-phenylalanine, phenylpyruvate, ortho-hydroxyphenylacetic acid, and phenyllactic acid are up-regulated; (2) arachidonic acid (ARA) metabolism—leukotriene B4 (LTB4), arachidonyl carnitine, and 5(S)-HPETE are upregulation, but ARA, 14,15-DiHETrE, 5(S)-HETE, and PGB2 are opposite; and (3) unsaturated fatty acid biosynthesis— eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), FA 18:3 + 1O, and FA 18:3 + 2O are downregulated. Linoleic acid metabolism belonging to the biosynthesis of unsaturated fatty acid includes the upregulation of 8-hydroxy-9,10-epoxystearic acid, 13-oxoODE, and FA 18:2 + 4O, and downregulation of 9(S)-HPODE and dihomo-γ-linolenic acid. In addition, cortisone and dehydroepiandrosterone belonging to steroid hormone biosynthesis are upregulated. These three perturbed metabolic pathways also correlate with cognitive impairment after Aβ1-42 stimulation. Furthermore, ARA, DHA, EPA, l-phenylalanine, and cortisone have been previously implicated in the cerebrospinal fluid of AD patients and show a similar changing trend in Aβ1-42 rats’ hair. These data suggest hair can be a useful biospecimen that well reflects the expression of non-polar molecules under Aβ1-42 stimulation, and the five metabolites have the potential to serve as novel AD biomarkers.

Segmental hair metabolomics analysis in pregnant women with pregnancy complications

Pregnancy complications, as preeclampsia (PE) and HELLP syndrome, occurring with similar pathophysiological mechanisms, have adverse effects on the health of both mother and fetus during pregnancy and thereafter, they are leading causes of maternal and fetal morbidity and mortality. The hair metabolome has been recognized as a valuable source of information in pregnancy research, as it provides stable metabolite information to be able to assist with studying biomarkers or metabolic mechanisms of pregnancy and its complications. The aim of this study was to investigate the hair metabolome profile of pregnant women with PE, HELLP syndrome and healthy women. Hair samples of new-borns' mothers (patients and controls) were investigated segmentally relevant to each trimester using a proper sample preparation and gas chromatography-mass spectrometry (GC-MS) to identify robust biomarkers that can be useful for screening, early detection, follow-up and treatment of PE and HELLP syndrome, the etiology of which are still unknown. The results showed a significant change in the metabolome profiles of the patient and control groups regarding the trimesters. A striking decrease was observed in all 100 metabolites investigated in the patient group (p < 0.000). The metabolic pathways associated with significant metabolites have also been investigated, and the most affected pathways were observed to be the urea cycle, glycine, serine, aspartate, methionine and purine metabolism, ammonia cycle, and phosphatidylethanolamine biosynthesis. The found metabolites provide us with extensive data on the ability to establish biomarkers for predicting, early detection and monitoring of PE.

Discovering Hair Biomarkers of Alzheimer’s Disease Using High Resolution Mass Spectrometry-Based Untargeted Metabolomics

Hair may be a potential biospecimen to discover biomarkers for Alzheimer's disease (AD) since it reflects the integral metabolic profiles of body burden over several months. Here, we described the AD biomarker discovery in the hair using a high-resolution mass spectrometry (HRMS)-based untargeted metabolomics approach. A total of 24 patients with AD and 24 age- and sex-matched cognitively healthy controls were recruited. The hair samples were collected 0.1-cm away from the scalp and further cut into 3-cm segments. Hair metabolites were extracted by ultrasonication with methanol/phosphate-buffered saline 50/50 (v/v) for 4 h. A total of 25 discriminatory chemicals in hair between the patients with AD and controls were discovered and identified. The AUC value achieved 0.85 (95% CI: 0.72~0.97) in patients with very mild AD compared to healthy controls using a composite panel of the 9 biomarker candidates, indicating high potential for the initiation or promotion phase of AD dementia in the early stage. A metabolic panel combined with the nine metabolites may be used as biomarkers for the early detection of AD. The hair metabolome can be used to reveal metabolic perturbations for biomarker discovery. Investigating perturbations of the metabolites will offer insight into the pathogenesis of AD.

Metabolomic profiling identifies hair as a robust biological sample for identifying women with cervical cancer

Metabolomics serves as a useful tool for identifying biomarkers of disease and uncovering pathogenic mechanisms. However, most metabolomic studies use biological fluids such as blood and urine as biospecimens, which could be dramatically influenced by daily activities and dietary variation, resulting in measurement fluctuations. In contrast, hair may serve as a robust source of stable longitudinal metabolite information. Here, we conducted a pilot study to investigate the possibility of using hair as a biospecimen for the metabolomic analysis of cervical cancer. Hair, plasma, urine, and cervical tissue samples from cervical cancer and benign tumor patients were collected. Biospecimens were then tested using a gas chromatography-mass spectrometry-based metabolomic platform. The expressions of enzymatic genes related to metabolic changes were validated using qPCR. Statistical analyses were calculated via the R-console platform. Metabolite profiles in both hair and cervical tissue samples were significantly different between cancer and control groups, while no difference was observed in plasma and urine samples. Further analysis showed that most of the altered metabolites in hair were upregulated, and they had a negative correlation with those in the cervical tissue. Eight common metabolites showed an area under the Receiver Operating Characteristic curve greater than 0.95. These metabolites primarily participated in amino acid metabolism, cofactor synthesis, ferroptosis, and glycolysis. The gene expressions (IDH1, OGDH, GLUD1, ENO1, GSS, and GPX4) associated with the shortlisted metabolic pathways were also upregulated. Our study is the first to reveal metabolomic changes of hair in cervical cancer patients and demonstrates the potential for the hair metabolome to be used for biomarker identification in cervical cancer.

The relationship between hair metabolites, air pollution exposure and gestational diabetes mellitus: A longitudinal study from pre-conception to third trimester.

Gestational diabetes mellitus (GDM) is a metabolic condition defined as glucose intolerance with first presentation during pregnancy. Many studies suggest that environmental exposures, including air pollution, contribute to the pathogenesis of GDM. Although hair metabolite profiles have been shown to reflect pollution exposure, few studies have examined the link between environmental exposures, the maternal hair metabolome and GDM. The aim of this study was to investigate the longitudinal relationship (from pre-conception through to the third trimester) between air pollution exposure, the hair metabolome and GDM in a Chinese cohort. A total of 1020 women enrolled in the Complex Lipids in Mothers and Babies (CLIMB) birth cohort were included in our study. Metabolites from maternal hair segments collected pre-conception, and in the first, second, and third trimesters were analysed using gas chromatography-mass spectrometry (GC-MS). Maternal exposure to air pollution was estimated by two methods, namely proximal and land use regression (LUR) models, using air quality data from the air quality monitoring station nearest to the participant's home. Logistic regression and mixed models were applied to investigate associations between the air pollution exposure data and the GDM associated metabolites. Of the 276 hair metabolites identified, the concentrations of fourteen were significantly different between GDM cases and non-GDM controls, including some amino acids and their derivatives, fatty acids, organic acids, and exogenous compounds. Three of the metabolites found in significantly lower concentrations in the hair of women with GDM (2-hydroxybutyric acid, citramalic acid, and myristic acid) were also negatively associated with daily average concentrations of PM2.5, PM10, SO2, NO2, CO and the exposure estimates of PM2.5 and NO2, and positively associated with O3. This study demonstrated that the maternal hair metabolome reflects the longitudinal metabolic changes that occur in response to environmental exposures and the development of GDM.

Evaluating Different Extraction Approaches for GC-MS Based Metabolomics Analysis of the Giant Pandas' Fur.

Giant pandas in zoo captivity are situated in residential areas, where environmental pollutants and anthropogenic factors have an impact on their health. Hair metabolomics has been applied in numerous environmental toxicological studies. Therefore, the panda fur metabolome could be a reliable approach to reflect endogenous and exogenous metabolic changes related to environmental exposure. However, there is no established extraction protocol to study the fur metabolome of pandas. The aim of this research was to optimize the extraction of panda fur metabolome for high-throughput metabolomics analysis using gas chromatography-mass spectrometry. Fur samples were collected from five pandas. Eight different extraction methods were investigated and evaluated for their reproducibility, metabolite coverage, and extraction efficiency, particularly in relation to the biochemical compound classes such as amino acids, tricarboxylic acid cycle derivatives, fatty acids, and secondary metabolites. Our results demonstrated that HCl + ACN were the superior extraction solvents for amino acid and secondary metabolite extraction, and NaOH + MeOH was ideal for fatty acid extraction. Interestingly, the metabolomic analysis of panda fur was capable of discriminating the longitudinal metabolite profile between black and white furs. These extraction protocols can be used in future study protocols for the analysis of the fur metabolome in pandas.

Detection of hair metabolome changes in cocaine users using untargeted metabolomics

The aim of this study was to investigate changes in the metabolome of the hair of cocaine users. Studying changes in the hair metabolome can help toxicologists to find robust markers of drug intake that are not affected by long-term perturbations such as circadian variations. Furthermore, the use of an untargeted method allowed us to search not only endogenous compounds but also possible new cocaine metabolites. An untargeted metabolomics method was applied to hair samples using liquid chromatography coupled to high-resolution time-of-flight mass spectrometry (HPLC-HRMS). Hair samples were obtained from 153 cases analyzed previously at the Center for Forensic Hair Analytics and divided in three groups according to their cocaine content (negative cohort: no cocaine detected, n = 52; below cut-off cohort: between 100 pg/mg and 500 pg/mg (the cut-off for determining a positive result in hair), n = 52; and true positive cohort: > 500 pg/mg, n = 49). Samples were decontaminated by sequential washes with DCM, acetone, deionized water and acetone and left to dry overnight. Then, the hair was cut in fine pieces and 30 mg were transferred to Eppendorf tubes and milled for 10 minutes at 30 Hz. Extraction was performed by adding 1 mL of ACN/H2O 2:8 (v/v) and ultrasonicating for 16 h. After centrifugation and evaporation of the supernatant, samples were reconstituted in 250 μL ACN/H2O 2:8 (v/v), filtered and transferred into vials in two 80 μL aliquots, pooling the rest into a QC solution. The analysis was performed using a reverse phase column [Waters XSelect HSST RP-C18 column (150 mm × 2.1 mm, 2.5 μm) in positive electrospray ionization mode]. Untargeted data processing, statistical analysis and tentative identification were performed using MSDial, R studio (version 4.1.3), and Sirius (v. 5.4.1), respectively. The true positive and the negative cohort groups were selected for a first statistical assessment. Comparing the mean areas of each feature in both groups allowed the selection of 1387 features with a fold change between the groups higher than 2-fold. A Mann–Whitney–Wilcoxon test was then performed for these selected features and 133 were found to differ significantly between groups ( P < 0.05). Identification was attempted using the Sirius software, identifying cocaine, benzoylecgonine, ecgonidine, methylecgonidine, norcocaine, the adulterant levamisole and 3 additional features with common fragments of cocaine or benzoylecgonine. Using the commercially available databases (Bio Database, COCONUT, HMDB, MeSH, NORMAN, PubChem, PubMed, YMDB, Zinc Bio…) did not provide any unambiguous identifications of endogenous metabolites, but pointed towards discerning the different metabolite classes of other features, finding amino acids and their derivatives, purine and pyrimidine derivatives, carbohydrates, peptides and lipids, for another 100 features. The identification of cocaine and its metabolites proves the general suitability of the described hair metabolomics workflow to identify differing compounds between cocaine positive and negative samples, since those are indeed the principal differing compounds between the positive and negative groups. Moreover, more features of different classes were significantly different between both groups. Approximately 65% of the features increased in concentration in cocaine positive samples while 35% decreased in concentration. Further investigation should be performed to identify them and study the applicability to discerning cocaine use via hair metabolome. The use of untargeted metabolomics analysis allowed the detection of different cocaine metabolites and possible endogenous biomarkers of cocaine use.

Current status of hair analysis on narcotics in Korea

Since hair analysis of narcotics was first introduced to Korea in 1993, and it has been 30 years already. Although hair analysis in Korea started later than Europe or the United States, it has made remarkable progress in the field of forensic science. In this study, it was reviewed the current status of hair analysis on narcotics, mainly abused in Korea. In addition, the recent research trends also were investigated. Methamphetamine (MA) and cannabis, which are mainly abused narcotics in Korea, and other drugs such as ketamine (KT), synthetic cannabinoids (new psychoactive substances, NPS) and propofol were discussed here. The analytical methods for those drugs were developed by the National Forensic Service (NFS), mainly using gas chromatography-mass spectrometry (GC/MS) and liquid chromatography-tandem mass spectrometry (LC-MS/MS). MA has been the most abused drug in Korea. Accordingly, its hair analysis was first introduced in 1993, earlier than any other drugs. In 2011, the NFS finally developed certified reference material (CRM) using Korean MA abusers’ hair. This achievement brought not only financial benefit as saving the cost of purchasing expensive standard products and it also contributed to the development of forensic science by providing free distribution to developing Asian countries. In addition, analytical method of cannabis in hair by gas chromatography-tandem mass spectrometry (GC-MS/MS) was also developed in 2011. It detects carboxytetrahydrocannabinol (THC-COOH), a metabolite of tetrahydrocannabinol (THC), following two steps using LC-MS system in two different acquisition modes (i.e., MRM and MS3). With using the method, it was found that both LOD and LOQ were 0.0001 ng/mg. After KT began to be regulated in Korea in 2005, the detection rates of KT in seized materials and urine during 2017–2020 were found 2.3 and 1.4%, respectively. Majority abusers of KT were young people in twenties. As a result of analyzing the hairs of 61 abusers, it was clear that KT was abused with other drugs such as methylenedioxymethamphetamine (MDMA) or MA rather than single administration. Since JWH-018 was first detected in Korea in 2009, the number of NPS has been rapidly increased also causing another a serious social problem. The most abused NPS is synthetic cannabinoids, accounting for 74% of the total. ABCHMINACA was the most detected synthetic cannabinoid in the hair of abusers from 2014 to May 2021. In February 2011, Korea designated propofol as a controlled drug and it was only country regulating it in the world. The detection method of the propofol in hair was finally established after two years later, 2013. For detection of the propofol in hair samples, LC-MS/MS is using to confirm propofol-glucuronide, major metabolite of the propofol. Though there is a cut-off value for each drug in hair recommended by the Society of Hair Testing (SoHT), each country has varied values due to the different analytical conditions such as method, equipment, and even cultural characteristics. Korea has been its own analytical method by referring to these international guidelines. The NFS is continuously developing new analytical method such as simultaneous analysis with increasing the number of abusers and drugs. Recently, research on hair metabolomics using animal is being actively conducted. One of findings was metabolic alterations in hair could provide insight into metabolic perturbation over a longer period than other conventional biological samples (i.e., plasma, urine). Thus, analytical techniques in hair metabolomics can be a significant approach with high potency for evaluating the animal and human pathological conditions.

Global-Scale Metabolomic Profiling of Human Hair for Simultaneous Monitoring of Endogenous Metabolome, Short- and Long-Term Exposome

Hair is a unique biological matrix that adsorbs short-term exposures (e. g., environmental contaminants and personal care products) on its surface and also embeds endogenous metabolites and long-term exposures in its matrix. In this work, we developed an untargeted metabolomics workflow to profile both temporal exposure chemicals and endogenous metabolites in the same hair sample. This analytical workflow begins with the extraction of short-term exposures from hair surfaces through washing. Further development of mechanical homogenization extracts endogenous metabolites and long-term exposures from the cleaned hair. Both solutions of hair wash and hair extract were analyzed using ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS)-based metabolomics for global-scale metabolic profiling. After analysis, raw data were processed using bioinformatic programs recently developed specifically for exposome research. Using optimized experimental conditions, we detected a total of 10,005 and 9,584 metabolic features from hair wash and extraction samples, respectively. Among them, 274 and 276 features can be definitively confirmed by MS2 spectral matching against spectral library, and an additional 3,356 and 3,079 features were tentatively confirmed as biotransformation metabolites. To demonstrate the performance of our hair metabolomics, we collected hair samples from three female volunteers and tested their hair metabolic changes before and after a 2-day exposure exercise. Our results show that 645 features from wash and 89 features from extract were significantly changed from the 2-day exposure. Altogether, this work provides a novel analytical approach to study the hair metabolome and exposome at a global scale, which can be implemented in a wide range of biological applications for a deeper understanding of the impact of environmental and genetic factors on human health.

Alterations in Pattern Baldness According to Sex: Hair Metabolomics Approach.

Pattern baldness has been associated with the male hormone, dihydrotestosterone. In this study, we tried to determine how the overall metabolic pathways of pattern baldness differ in patients and in normal controls. Our study aimed to identify alterations in hair metabolomic profiles in order to identify possible markers of pattern baldness according to sex. Untargeted metabolomics profiling in pattern baldness patients and control subjects was conducted using ultra-performance liquid chromatography-mass spectrometry. To identify significantly altered metabolic pathways, partial least squares discriminant analysis was performed. Our analysis indicated differences in steroid biosynthesis pathway in both males and females. However, there was a remarkable difference in the androgen metabolic pathway in males, and the estrogen metabolic and arachidonic acid pathways in females. For the first time, we were able to confirm the metabolic pathway in pattern baldness patients using hair samples. Our finding improves understanding of pattern baldness and highlights the need to link pattern baldness and sex-related differences.

Extraction strategies for tackling complete hair metabolome using LC-HRMS-based analysis

Over recent years, metabolomics has been featured as the state-of-the-art technology that successfully opens the paths to understanding biological mechanisms and facilitating biomarker discovery. However, the inherent dynamic and sensitive nature of the metabolome have been challenging the accuracy of capturing the timepoints of interest while using biofluids such as urine and blood. Hair has thus emerged as a valuable analytical specimen for the long-term and retrospective determinations. Unfortunately, notwithstanding the apparent interest on global hair metabolomics, very few studies have engaged in the optimisation of the extraction strategy. In this study, we systemically investigated the extraction procedures for hair metabolome using a single factor experimental design. Three pH values (acidic, neutral, and basic) in aqueous solution, six extraction solvents (methanol, acetonitrile, acetone, phosphate-buffered saline, deionised water, and dichloromethane), different compositions of selected solvent mixtures and their sequential extraction, and a series of extraction times (15, 45, 60, 120, 240, and 480 min) were evaluated. The ideal condition for hair extraction is ultrasonic-assisted extraction with methanol:phosphate-buffered saline 50:50 (v/v) under +55 °C for 240 min. This strategy may secure the true composition of the metabolome, maximise the signal abundance, and guarantee a high coverage of wide-range metabolites in a straightforward approach. The optimised extraction strategy was then coupled with structure annotation tools for hair metabolome profiling. After a single RPLC-HRMS run, hair metabolite identification was achieved as the annotations of 171 probable structures and 853 tentative structures as well as the assignments of 414 unequivocal molecular formulae. In conclusion, we established an efficient extraction strategy for untargeted hair metabolomics, which the method is deliverable to any analytical laboratories and the sample can be directly profiled by means of a conventional RPLC-HRMS gradient.

Revealing Metabolic Perturbation Following Heavy Methamphetamine Abuse by Human Hair Metabolomics and Network Analysis.

Methamphetamine (MA) is a highly addictive central nervous system stimulant. Drug addiction is not a static condition but rather a chronically relapsing disorder. Hair is a valuable and stable specimen for chronic toxicological monitoring as it retains toxicants and metabolites. The primary focus of this study was to discover the metabolic effects encompassing diverse pathological symptoms of MA addiction. Therefore, metabolic alterations were investigated in human hair following heavy MA abuse using both targeted and untargeted mass spectrometry and through integrated network analysis. The statistical analyses (t-test, variable importance on projection score, and receiver-operator characteristic curve) demonstrated that 32 metabolites (in targeted metabolomics) as well as 417 and 224 ion features (in positive and negative ionization modes of untargeted metabolomics, respectively) were critically dysregulated. The network analysis showed that the biosynthesis or metabolism of lipids, such as glycosphingolipids, sphingolipids, glycerophospholipids, and ether lipids, as well as the metabolism of amino acids (glycine, serine and threonine; cysteine and methionine) is affected by heavy MA abuse. These findings reveal crucial metabolic effects caused by MA addiction, with emphasis on the value of human hair as a diagnostic specimen for determining drug addiction, and will aid in identifying robust diagnostic markers and therapeutic targets.

Neonatal hair profiling reveals a metabolic phenotype of monochorionic twins with selective intrauterine growth restriction and abnormal umbilical artery flow

BackgroundSelective intrauterine fetal growth restriction (sIUGR) in monochorionic diamniotic twins, especially types 2&3 with abnormal umbilical artery Doppler, results in increased risk of fetal/perinatal mortality and postnatal disability. We investigate whether the hair metabolome profiles of neonates were associated with the pathophysiological differences across the different clinical forms of sIUGR in twins.MethodsHair samples were collected at delivery from 10 pairs of type 1 sIUGR twins, 8 pairs of types 2&3 sIUGR twins, and 11 pairs of twins without sIUGR. The hair metabolome was characterized using gas chromatography-mass spectrometry.ResultsOur results demonstrated that the hair metabolite profiles of the different sIUGR subclinical forms were associated with the averaged fetal growth rate after 28 weeks of gestation but not with birthweight. The hair profiles were capable of discriminating type2&3 sIUGR twins from twins without sIUGR. In particular, the metabolites 2-aminobutyric acid, cysteine, alanine, and tyrosine all displayed areas under the receiver operating characteristic curve were above 0.9. The metabolic pathway analysis highlighted the associations of sIUGR twins with abnormal umbilical artery flow with increased metabolites from a nutrient depletion pathway, glutathione metabolism, and nerve development.ConclusionThis study offers novel insight into the severity of intrauterine ischemia and hypoxia for T2&3 sIUGR twins, through evaluation of the neonatal hair metabolome.