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

Pulmonary Fibrosis Diagnosis and Disease Progression Detected Via Hair Metabolome Analysis

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Optimized analytical strategy based on high-resolution mass spectrometry for unveiling associations between long-term chemical exposome in hair and Alzheimer's disease

Exposure to environmental pollutants or contaminants is correlated with detrimental effects on human health, such as neurodegenerative diseases. Adopting hair as a biological matrix for biomonitoring is a significant innovation, since it can reflect the long-term chemical exposome, spanning months to years. However, only a limited number of studies have developed analytical strategies for profiling the chemical exposome in this heterogeneous biological matrix. In this study, a systematic investigation of the chemical extraction procedure from human hair was conducted, using a design of experiments and a high-resolution mass spectrometry (HRMS)-based suspect screening approach. The PlackettBurman (PB) design was applied to identify the significant variables influencing the number of detected features. Then, a central composite design was implemented to optimize the levels of each identified significant variable. Under the optimal conditions-15-minute pulverization, 25 mg of hair weight, 40 min of sonication, and a sonication temperature of 35 °C-approximately 32,000 and 15,000 aligned features were detected in positive and negative ion modes, respectively. This optimized analytical procedure was applied to hair samples from patients with Alzheimer's disease (AD) and individuals with normal cognitive function. Overall, 307 chemicals were identified using the suspect screening approach, with 37 chemicals differentiating patients with AD from controls. This study not only optimized an analytical procedure for characterizing the long-term chemical exposome in human hair but also explored the associations between AD and environmental factors.

Critical Factors in Sample Collection and Preparation for Clinical Metabolomics of Underexplored Biological Specimens

This review article compiles critical pre-analytical factors for sample collection and extraction of eight uncommon or underexplored biological specimens (human breast milk, ocular fluids, sebum, seminal plasma, sweat, hair, saliva, and cerebrospinal fluid) under the perspective of clinical metabolomics. These samples are interesting for metabolomics studies as they reflect the status of living organisms and can be applied for diagnostic purposes and biomarker discovery. Pre-collection and collection procedures are critical, requiring protocols to be standardized to avoid contamination and bias. Such procedures must consider cleaning the collection area, sample stimulation, diet, and food and drug intake, among other factors that impact the lack of homogeneity of the sample group. Precipitation of proteins and removal of salts and cell debris are the most used sample preparation procedures. This review intends to provide a global view of the practical aspects that most impact results, serving as a starting point for the designing of metabolomic experiments.

Recent advances in LC-MS-based metabolomics for clinical biomarker discovery

The employment of liquid chromatography-mass spectrometry (LC-MS) untargeted and targeted metabolomics has led to the discovery of novel biomarkers and improved the understanding of various disease mechanisms. Numerous strategies have been reported to expand the metabolite coverage in LC-MS-untargeted and targeted metabolomics. To improve the sensitivity of low-abundance or poor-ionized metabolites for reducing the amount of clinical sample, chemical derivatization methods are used to target different functional groups. Proper sample preparation is beneficial for reducing the matrix effect, maintaining the stability of the LC-MS system, and increasing the metabolite coverage. Machine learning has recently been integrated into the workflow of LC-MS metabolomics to accelerate metabolite identification and data-processing automation, and increase the accuracy of disease classification and clinical outcome prediction. Due to the rapidly growing utility of LC-MS metabolomics in discovering disease markers, this review will address the recent advances in the field and offer perspectives on various strategies for expanding metabolite coverage, chemical derivatization, sample preparation, clinical disease markers, and machining learning for disease modeling.

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.

Rapid LC-MS/MS quantification of Organophosphate non-specific metabolites in hair using alkaline extraction approach

Assessing human exposure to commonly used, highly toxic, but non-persistent organophosphates (OPs) is challenging because these toxicants are readily biotransformed into dialkyl phosphates (DAPs) and other metabolites. Growing hair accumulates toxicants and their metabolites, which makes hair a valuable non-invasively sampled matrix that can be used to retrospectively examine chemical exposure. However, the efficient quantification of hydrophilic DAP compounds in hair is challenging due to complex hair matrix effects. To improve upon existing methods, we first examined the acid dissociation constants (pKa) of DAPs and amino acids (major components in hair) and identified the best pH conditions for minimizing matrix effects. We hypothesized that under basic pH conditions DAPs and amino acids would be negatively charged and have weak interactions favorable to DAP dissociation from the matrix. To test this, we compared the efficiency of various pH conditions of suitable solvents to extract six DAPs from hair samples, and we quantified these DAPs using liquid chromatography-tandem mass spectroscopy (LC-MS/MS). As expected, a basic extraction (methanol with 2% NH4OH) approach had the highest extraction efficiency and yielded satisfactory recoveries for all six DAPs (72%-152%) without matrix effects. Additionally, the alkaline extract can be directly injected into the LC-MS/MS. This relatively rapid and simple procedure allowed us to process up to 90 samples per week with reproducible results. To our knowledge, this is the first method to quantify all six DAPs simultaneously in hair using LC-MS/MS with electrospray ionization (ESI) in negative ion mode. Finally, we demonstrated the feasibility of measuring DAP levels in hair samples from patients affected with amyotrophic lateral sclerosis (ALS), a neurodegenerative disease potentially linked to OP exposure. Due to our optimized solvent extraction process, the method we have developed is compatible with the rapidity and sensitivity needed for hair analysis applied to population biomonitoring.

UV and visible light exposure to hair leads to widespread changes in the hair lipidome

OBJECTIVE

Scalp hair is among the most exposed parts of the human body, yet the impact of visible and UV light on hair lipids, an important structural component of hair, is poorly researched. We have used lipidomics, a broad-based approach to measure lipids in samples, which has hitherto not been applied to UV-exposed hair in the published literature, and could allow for a wider understanding of how UV light impacts on specific hair lipids.

METHODS

Mixed blonde Caucasian hair switches were divided into two groups of five, with half of the hair switches exposed to UV and visible light mimicking normal daytime exposure and half left unexposed. LC-MS lipidomics was used to profile the lipids in the hair samples.

RESULTS

A total of 791 lipids and 32 lipid classes with tentative identifications were detected in the hair samples. Nineteen lipid classes and 397 lipids differed between UV-treated and non-treated hair. The main lipid classes that differed were vitamin A fatty acid esters, sterol esters, several ceramides, mono-, di- and triglycerides, phosphatidylethanolamines (all decreased in UV-exposed hair) and bismonoacylglycerolphosphates, acylcarnitines and acylglycines (all increased in UV-exposed hair). Most detected lipids were decreased in UV-exposed hair, supporting earlier work that has found that UV exposure causes oxidation of lipids which would result in a decrease in most lipid classes.

CONCLUSION

Light exposure to hair has a widespread impact on the hair lipidome. This study also adds to the emerging literature on the hair lipidome, broadening the range of lipid classes reported in hair.

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.

Evaluation of extraction parameters in authentic hair reference material using statistical design of experiments

Optimal methods for forensic hair analysis are often debated, especially regarding extraction parameters that include incubation time, temperature, and size of extracted hair particles. To assess hair pretreatment parameters for analysis of drugs of abuse, the statistical technique known as Design of Experiments (DoE) is useful. DoE evaluates both the individual roles and the combinatorial associations between multiple variables and overall drug extraction efficiency. Previous reports have focused on incorporated hair reference material (HRM), which is prepared in the lab at a specified drug concentration. In contrast, authentic HRM, which is prepared by diluting hair from drug users with blank hair to achieve specific drug concentrations, is an effective matrix for standardization of forensic hair testing, since the drug is incorporated into the hair matrix via uptake from systemic distribution. In the present study, extraction parameters for authentic HRM samples containing multiple drugs (diazepam, alprazolam, cocaine, methamphetamine, oxycodone, hydrocodone, and morphine) and metabolites (nordiazepam, cocaethylene, norcocaine, hydroxycocaine, and 6-monoacetylmorphine) were optimized based on recovery using a 2³ full factorial DoE matrix. The factors evaluated included extraction solvent volume/sample weight ratio (12.5 or 25 μL/mg), particle size (pulverized or cut into 1 mm snippets), and extraction time (2 or 24 h) using solvent swelling. DoE analysis revealed significant differences in the optimal combinations of extraction parameters for maximum recovery. However, for the majority of drugs and metabolites, the most effective extraction method consisted of pulverizing hair prior to a 2-h extraction with a 12.5 μL/mg extraction solvent volume/sample weight ratio.

Defining the Scope of Exposome Studies and Research Needs from a Multidisciplinary Perspective

The concept of the exposome was introduced over 15 years ago to reflect the important role that the environment exerts on health and disease. While originally viewed as a call-to-arms to develop more comprehensive exposure assessment methods applicable at the individual level and throughout the life course, the scope of the exposome has now expanded to include the associated biological response. In order to explore these concepts, a workshop was hosted by the Gunma University Initiative for Advanced Research (GIAR, Japan) to discuss the scope of exposomics from an international and multidisciplinary perspective. This Global Perspective is a summary of the discussions with emphasis on (1) top-down, bottom-up, and functional approaches to exposomics, (2) the need for integration and standardization of LC- and GC-based high-resolution mass spectrometry methods for untargeted exposome analyses, (3) the design of an exposomics study, (4) the requirement for open science workflows including mass spectral libraries and public databases, (5) the necessity for large investments in mass spectrometry infrastructure in order to sequence the exposome, and (6) the role of the exposome in precision medicine and nutrition to create personalized environmental exposure profiles. Recommendations are made on key issues to encourage continued advancement and cooperation in exposomics.

A Comprehensive Multi-Analyte Method for Hair Analysis: Substance-Specific Quantification Ranges and Tool for Task-Oriented Data Evaluation

The aim of the present study was to quantify a large number of analytes including opioids, stimulants, benzodiazepines, z-drugs, antidepressants and neuroleptics within a single sample workup followed by a single analytical measurement. Expected drug concentrations in hair are strongly substance dependent. Therefore, three different calibration ranges were implemented: 0.5 to 600 pg/mg (group 1), 10 to 12,000 pg/mg (group 2) and 50 to 60,000 pg/mg (group 3). In order to avoid saturation effects, different strategies were applied for selected transitions including the use of parent mass ions containing one or two 13C-isotopes and detuning of the declustering potential and/or collision energy. Drugs were extracted from pulverized hair by a two-step extraction protocol and measured by liquid chromatrography--tandem mass spectrometry (LC--MS-MS) using Scheduled MRM™ Algorithm Pro. In total, 275 MRM transitions including 43 deuterated standards were measured. The method has been fully validated according to international guidelines. A MultiQuant™ software based tool for task-oriented data evaluation was established, which allows extracting selected information from the measured data sets. The matrix effects and recoveries were within the allowed ranges for the majority of the analytes. The lower limits of quantification (LLOQs) were for ∼72% of the analytes in the low-pg/mg range (0.5-5 pg/mg) and for ∼24% of the analytes between 10 and 50 pg/mg. These LLOQs considered cut-offs by the Society of Hair Testing (SoHT), if recommended. The herein established multi-analyte approach meets the specific requirements of forensic hair testing and can be used for the rapid and robust measurement of a wide range of psychoactive substances. The analyte-specific wide concentration ranges open up a wide field of applications.

Cheating on forensic hair testing? Detection of potential biomarkers for cosmetically altered hair samples using untargeted hair metabolomics

Hair analysis has become an integral part in forensic toxicological laboratories for e.g. assessment of drug or alcohol abstinence. However, hair samples can be manipulated by cosmetic treatments, altering drug concentrations which eventually leads to false negative hair test results. In particular oxidative bleaching of hair samples under alkaline conditions significantly affects incorporated drug concentrations. To date, current techniques to detect cosmetic hair adulterations bear limitations such as the implementation of cut-off values or the requirement of specialized instrumentations. As a new approach, untargeted hair metabolomics analysis was applied to detect altered, endogenous biomolecules that could be used as biomarkers for oxidative cosmetic hair treatments. For this, genuine hair samples were treated in vitro with 9% hydrogen peroxide (H2O2) for 30 minutes. Untreated and treated hair samples were analyzed using liquid-chromatography high-resolution time-of-flight mass spectrometry. In total, 69 metabolites could be identified as significantly altered after hair bleaching. The majority of metabolites decreased after bleaching, yet totally degraded metabolites were most promising as suitable biomarkers. The formation of biomarker ratios of metabolites decreasing and increasing in concentrations improved the discrimination of untreated and treated hair samples. With the results of this study, the high variety of identified biomarkers now offers the possibility to include single biomarkers or biomarker selections into routine screening methods for improved data interpretation of hair test results.

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.

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.

Towards Best Practice in Hair Metabolomic Studies: Systematic Investigation on the Impact of Hair Length and Color

Untargeted metabolomic studies are used for large-scale analysis of endogenous compounds. Due to exceptional long detection windows of incorporated substances in hair, analysis of hair samples for retrospective monitoring of metabolome changes has recently been introduced. However, information on the general behavior of metabolites in hair samples is scarce, hampering correct data interpretation so far. The presented study aimed to investigate endogenous metabolites depending on hair color and along the hair strand and to propose recommendations for best practice in hair metabolomic studies. A metabolite selection was analyzed using untargeted data acquisition in genuine hair samples from different hair colors and after segmentation in 3 cm segments. Significant differences in metabolites among hair colors and segments were found. In conclusion, consideration of hair color and hair segments is necessary for hair metabolomic studies and, subsequently, recommendations for best practice in hair metabolomic studies were proposed.

Deep Learning Driven GC-MS Library Search and Its Application for Metabolomics

Preliminary compound identification and peak annotation in gas chromatography-mass spectrometry is usually made using mass spectral databases. There are a few algorithms that enable performing a search of a spectrum in a large mass spectral library. In many cases, a library search procedure returns a wrong answer even if a correct compound is contained in a library. In this work, we present a deep learning driven approach to a library search in order to reduce the probability of such cases. Machine learning ranking (learning to rank) is a class of machine learning and deep learning algorithms that perform a comparison (ranking) of objects. This work introduces the usage of deep learning ranking for small molecules identification using low-resolution electron ionization mass spectrometry. Instead of simple similarity measures for two spectra, such as the dot product or the Euclidean distance between vectors that represent spectra, a deep convolutional neural network is used. The deep learning ranking model outperforms other approaches and enables reducing a fraction of wrong answers (at rank-1) by 9-23% depending on the used data set. Spectra from the Golm Metabolome Database, Human Metabolome Database, and FiehnLib were used for testing the model.

Considerations for mass spectrometry-based multi-omic analysis of clinical samples

Introduction: The role of mass spectrometry in biomolecule analysis has become paramount over the last several decades ranging in the analysis across model systems and human specimens. Accordingly, the presence of mass spectrometers in clinical laboratories has also expanded alongside the number of researchers investigating the protein, lipid, and metabolite composition of an array of biospecimens. With this increase in the number of omic investigations, it is important to consider the entire experimental strategy from sample collection and storage, data collection and analysis.Areas covered: In this short review, we outline considerations for working with clinical (e.g. human) specimens including blood, urine, and cerebrospinal fluid, with emphasis on sample handling, profiling composition, targeted measurements and relevance to disease. Discussions of integrated genomic or transcriptomic datasets are not included. A brief commentary is also provided regarding new technologies with clinical relevance.Expert opinion: The role of mass spectrometry to investigate clinically related specimens is on the rise and the ability to integrate multiple omics datasets from mass spectrometry measurements will be crucial to further understanding human health and disease.

A possible new oxidation marker for hair adulteration: Detection of PTeCA (1H-pyrrole-2,3,4,5-tetracarboxylic acid) in bleached hair

Hair analysis has become a valuable tool in forensic toxicology to assess drug or alcohol abstinence. Yet, hair adulteration by cosmetic products presents a major challenge for forensic hair analysis. Oxidative treatments, e.g. bleaching, may lead to analyte loss and thereby to false negative results. Currently, the eumelanin degradation product 1H-pyrrole-2,3,5-tricarboxylic acid (PTCA) serves as a marker for oxidative hair treatment, but requires the definition of cut-off values. To investigate further eumelanin degradation products as markers for oxidative hair treatment, hair samples with and without in vitro bleaching (hydrogen peroxide (H₂ O₂ ) concentrations 1.9% up to 12%; incubation times 15 min, 30 min, 60 min) were analyzed by liquid chromatography coupled to high-resolution time of flight mass spectrometry (HPLC-HRMS). The distribution of eumelanin degradation products along the hair shaft was investigated for routine applicability after segmentation of cosmetically untreated hair samples and authentically treated hair samples. The signals of the eumelanin degradation products PTCA, 1H-pyrrole-2,3,4-tricarboxylic acid (isoPTCA), and 1H-pyrrole-2,3,4,5-tetracarboxylic acid (PTeCA) were found to be significantly elevated after in vitro bleaching already with low H₂ O₂ concentrations and after short incubation times. In contrast to PTCA and isoPTCA, PTeCA was not detectable in cosmetically untreated segments up to 12 cm from hair root and was only formed through the oxidation process. The results of the study show that the detection of PTeCA within the proximal 3 to 6 cm segment can be applied to reliably detect hair adulteration attempts through hair bleaching.