Analytical considerations for (un)-targeted metabolomic studies with special focus on forensic applications

Development and thorough evaluation of a multi-omics sample preparation workflow for comprehensive LC-MS/MS-based metabolomics, lipidomics and proteomics datasets

The importance of sample preparation selection if often overlooked particularly for untargeted multi-omics approaches that gained popularity in recent years. To minimize issues with sample heterogeneity and additional freeze-thaw cycles during sample splitting, multiple -omics datasets (e.g. metabolomics, lipidomics and proteomics) should ideally be generated from the same set of samples. For sample extraction, commonly biphasic organic solvent systems are used that require extensive multi-step protocols. Individual studies have recently also started to investigate monophasic (all-in-one) extraction procedures. The aim of the current study was to develop and systematically compare ten different mono- and biphasic extraction solvent mixtures for their potential to aid in the most comprehensive metabolomics, lipidomics and proteomics datasets. As the focus was on human postmortem tissue samples (muscle and liver tissue), four tissue homogenization parameters were also evaluated. Untargeted liquid chromatography mass spectrometry-based metabolomics, lipidomic and proteomics methods were utilized along with 1D sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and bicinchoninic acid (BCA) assay results. Optimal homogenization was found to be achieved by bead-homogenizing 20 mg of muscle or liver tissue with 200 μL (1:10 ratio) Water:Methanol (1:2) using 3 × 30 s pulses. The supernatant of the homogenate was further extracted. Comprehensive ranking, taking nine different processing parameters into account, showed that the monophasic extraction solvents, overall, showed better scores compared to the biphasic solvent systems, despite their recommendation for one or all of the -omics extractions. The optimal extraction solvent was found to be Methanol:Acetone (9:1), resulting in the most comprehensive metabolomics, lipidomics and proteomics datasets, showing the potential to be automated, hence, allowing for high-throughput analysis of samples and opening the door for comprehensive multi-omics results from routine clinical cases in the future.

Impact of three different peak picking software tools on the quality of untargeted metabolomics data

Data quality and control parameters are becoming more important in metabolomics. For peak picking, open-source or commercial solutions are used. Other publications consider different software solutions or data acquisition types for peak picking, a combination, including proposed and new quality parameters for the process of peak picking, does not exist. This study tries to examine the performance of three different software in terms of reproducibility and quality of their output while also considering new quality parameters to gain a better understanding of resulting feature lists in metabolomics data. We saw best recovery of spiked analytes in MS-DIAL. Reproducibility over multiple projects was good among all software. The total number of features found was consistent for DDA and full scan acquisition in MS-DIAL but full scan data leading to considerably more features in MZmine and Progenesis Qi. Feature linearity proved to be a good quality parameter. Features in MS-DIAL and MZmine, showed good linearity while Progenesis Qi produced large variation, especially in full scan data. Peak width proved to be a very powerful filtering criteria revealing many features in MZmine and Progenesis Qi to be of questionable peak width. Additionally, full scan data appears to produce a disproportionally higher number of short features. This parameter is not yet available in MS-DIAL. Finally, the manual classification of true positive features proved MS-DIAL to perform significantly better in DDA data (62 % true positive) than the two other software in either mode. We showed that currently popular solutions MS-DIAL and MZmine perform well in targeted analysis of spiked analytes as well as in classic untargeted analysis. The commercially available solution Progenesis Qi does not hold any advantage over the two in terms of quality parameters, of which we proposed peak width as a new parameter and showed that already proposed parameters such as feature linearity in samples of increasing concentration are advisable to use.

Assessing the influence of sleep and sampling time on metabolites in oral fluid: implications for metabolomics studies

INTRODUCTION

The human salivary metabolome is a rich source of information for metabolomics studies. Among other influences, individual differences in sleep-wake history and time of day may affect the metabolome.

OBJECTIVES

We aimed to characterize the influence of a single night of sleep deprivation compared to sufficient sleep on the metabolites present in oral fluid and to assess the implications of sampling time points for the design of metabolomics studies.

METHODS

Oral fluid specimens of 13 healthy young males were obtained in Salivette® devices at regular intervals in both a control condition (repeated 8-hour sleep) and a sleep deprivation condition (total sleep deprivation of 8 h, recovery sleep of 8 h) and their metabolic contents compared in a semi-targeted metabolomics approach.

RESULTS

Analysis of variance results showed factor 'time' (i.e., sampling time point) representing the major influencer (median 9.24%, range 3.02-42.91%), surpassing the intervention of sleep deprivation (median 1.81%, range 0.19-12.46%). In addition, we found about 10% of all metabolic features to have significantly changed in at least one time point after a night of sleep deprivation when compared to 8 h of sleep.

CONCLUSION

The majority of significant alterations in metabolites' abundances were found when sampled in the morning hours, which can lead to subsequent misinterpretations of experimental effects in metabolomics studies. Beyond applying a within-subject design with identical sample collection times, we highly recommend monitoring participants' sleep-wake schedules prior to and during experiments, even if the study focus is not sleep-related (e.g., via actigraphy).

Postmortem metabolomics: influence of time since death on the level of endogenous compounds in human femoral blood. Necessary to be considered in metabolome study planning?

INTRODUCTION

The (un)targeted analysis of endogenous compounds has gained interest in the field of forensic postmortem investigations. The blood metabolome is influenced by many factors, and postmortem specimens are considered particularly challenging due to unpredictable decomposition processes.

OBJECTIVES

This study aimed to systematically investigate the influence of the time since death on endogenous compounds and its relevance in designing postmortem metabolome studies.

METHODS

Femoral blood samples of 427 authentic postmortem cases, were collected at two time points after death (854 samples in total; t1: admission to the institute, 1.3-290 h; t2: autopsy, 11-478 h; median ∆t = 71 h). All samples were analyzed using an untargeted metabolome approach, and peak areas were determined for 38 compounds (acylcarnitines, amino acids, phospholipids, and others). Differences between t2 and t1 were assessed by Wilcoxon signed-ranked test (p < 0.05). Moreover, all samples (n = 854) were binned into time groups (6 h, 12 h, or 24 h intervals) and compared by Kruskal-Wallis/Dunn's multiple comparison tests (p < 0.05 each) to investigate the effect of the estimated time since death.

RESULTS

Except for serine, threonine, and PC 34:1, all tested analytes revealed statistically significant changes between t1 and t2 (highest median increase 166%). Unpaired analysis of all 854 blood samples in-between groups indicated similar results. Significant differences were typically observed between blood samples collected within the first and later than 48 h after death, respectively.

CONCLUSIONS

To improve the consistency of comprehensive data evaluation in postmortem metabolome studies, it seems advisable to only include specimens collected within the first 2 days after death.

Easy and convenient millimole-scale synthesis of new, potential biomarkers for gamma-hydroxybutyric acid (GHB) intake - feasible for analytical laboratories

New biomarkers indicating the abuse of drugs and alcohol are still of major interest for clinical and forensic sciences. The endogenous neurotransmitter and approved drug, gamma-hydroxybutyric acid (GHB), is often illegally used for drug-facilitated crimes by spiking GHB into alcoholic beverages. Analytical detection windows of only 6 hours in blood and 12 hours in urine often too short to provide reliable proof of GHB ingestion. Therefore, new biomarkers are needed to prove exogenous GHB administration. Previously, amino-acid GHB conjugates were discovered in an untargeted metabolomics screening and fatty acid esters with GHB were recently discussed as promising biomarkers to enlarge the analytical detection time windows. However, the development of analytical methods is still slowed down since reference compounds for targeted screenings are still missing. In this paper, we describe simple procedures for the rapid synthesis and purification of amino acid-GHB conjugates as well as fatty acid esters, which can be adopted in analytical and clinical/forensic laboratories. Structural characterization data, together with IR, ¹ H-NMR, ¹³ C-NMR, high resolution mass spectra (MS), and MS/MS spectra in positive and negative ionization mode are reported for all obtained GHB-conjugates and GHB-conjugate precursors.

Untargeted Metabolomics in Forensic Toxicology: A New Approach for the Detection of Fentanyl Intake in Urine Samples

The misuse of fentanyl, and novel synthetic opioids (NSO) in general, has become a public health emergency, especially in the United States. The detection of NSO is often challenged by the limited diagnostic time frame allowed by urine sampling and the wide range of chemically modified analogues, continuously introduced to the recreational drug market. In this study, an untargeted metabolomics approach was developed to obtain a comprehensive "fingerprint" of any anomalous and specific metabolic pattern potentially related to fentanyl exposure. In recent years, in vitro models of drug metabolism have emerged as important tools to overcome the limited access to positive urine samples and uncertainties related to the substances actually taken, the possible combined drug intake, and the ingested dose. In this study, an in vivo experiment was designed by incubating HepG2 cell lines with either fentanyl or common drugs of abuse, creating a cohort of 96 samples. These samples, together with 81 urine samples including negative controls and positive samples obtained from recent users of either fentanyl or "traditional" drugs, were subjected to untargeted analysis using both UHPLC reverse phase and HILIC chromatography combined with QTOF mass spectrometry. Data independent acquisition was performed by SWATH in order to obtain a comprehensive profile of the urinary metabolome. After extensive processing, the resulting datasets were initially subjected to unsupervised exploration by principal component analysis (PCA), yielding clear separation of the fentanyl positive samples with respect to both controls and samples positive to other drugs. The urine datasets were then systematically investigated by supervised classification models based on soft independent modeling by class analogy (SIMCA) algorithms, with the end goal of identifying fentanyl users. A final single-class SIMCA model based on an RP dataset and five PCs yielded 96% sensitivity and 74% specificity. The distinguishable metabolic patterns produced by fentanyl in comparison to other opioids opens up new perspectives in the interpretation of the biological activity of fentanyl.

The Future of Analytical and Interpretative Toxicology: Where are We Going and How Do We Get There?

(Forensic) toxicology has faced many challenges, both analytically and interpretatively, especially in relation to an increase in potential drugs of interest. Analytical toxicology and its application to medicine and forensic science have progressed rapidly within the past centuries. Technological innovations have enabled detection of more substances with increasing sensitivity in a variety of matrices. Our understanding of the effects (both intended and unintended) have also increased along with determination and degree of toxicity. However, it is clear there is even more to understand and consider. The analytical focus has been on typical matrices such as blood and urine but other matrices could further increase our understanding, especially in postmortem (PM) situations. Within this context, the role of PM changes and potential redistribution of drugs requires further research and identification of markers of its occurrence and extent. Whilst instrumentation has improved, in the future, nanotechnology may play a role in selective and sensitive analysis as well as bioassays. Toxicologists often only have an advisory impact on pre-analytical and pre-interpretative considerations. The collection of appropriate samples at the right time in an appropriate way as well as obtaining sufficient circumstance background is paramount in ensuring an effective analytical strategy to provide useful results that can be interpreted within context. Nevertheless, key interpretative considerations such as pharmacogenomics and drug-drug interactions as well as determination of tolerance remain and in the future, analytical confirmation of an individual's metabolic profile may support a personalized medicine and judicial approach. This should be supported by the compilation and appropriate application of drug data pursuant to the situation. Specifically, in PM circumstances, data pertaining to where a drug was not/may have been/was contributory will be beneficial with associated pathological considerations. This article describes the challenges faced within toxicology and discusses progress to a future where they are being addressed.

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.

Current Situation of the Metabolomics Techniques Used for the Metabolism Studies of New Psychoactive Substances

PURPOSE

The purpose of this short overview is to summarize and discuss the English-written and PubMed-listed review articles and original studies published between January 2015 and April 2019 on the use of metabolomics techniques for investigating the metabolism of new psychoactive substances (NPS). First, a brief introduction is given on the metabolism of NPS and metabolomics techniques in general. Afterward, the selected original studies are summarized and discussed. Finally, a section dedicated to the studies on NPS beyond metabolism using metabolomics techniques is provided. Thereafter, both sections are concluded and perspectives are given.

METHODS

PubMed was searched for English-written literature published between January 1, 2015 and April 1, 2019.

RESULTS

The present short overview found that the current use of metabolomics techniques in investigating the metabolism of NPS is rather limited, but these techniques can support and facilitate traditional metabolism studies.

CONCLUSIONS

Thus, there may be a certain potential for using metabolomics techniques in the field of NPS research, but a great challenge remains to thoroughly adopt the existing metabolomics methods.

Interpretable machine learning model to detect chemically adulterated urine samples analyzed by high resolution mass spectrometry

OBJECTIVES

Urine sample manipulation including substitution, dilution, and chemical adulteration is a continuing challenge for workplace drug testing, abstinence control, and doping control laboratories. The simultaneous detection of sample manipulation and prohibited drugs within one single analytical measurement would be highly advantageous. Machine learning algorithms are able to learn from existing datasets and predict outcomes of new data, which are unknown to the model.

METHODS

Authentic human urine samples were treated with pyridinium chlorochromate, potassium nitrite, hydrogen peroxide, iodine, sodium hypochlorite, and water as control. In total, 702 samples, measured with liquid chromatography coupled to quadrupole time-of-flight mass spectrometry, were used. After retention time alignment within Progenesis QI, an artificial neural network was trained with 500 samples, each featuring 33,448 values. The feature importance was analyzed with the local interpretable model-agnostic explanations approach.

RESULTS

Following 10-fold cross-validation, the mean sensitivity, specificity, positive predictive value, and negative predictive value was 88.9, 92.0, 91.9, and 89.2%, respectively. A diverse test set (n=202) containing treated and untreated urine samples could be correctly classified with an accuracy of 95.4%. In addition, 14 important features and four potential biomarkers were extracted.

CONCLUSIONS

With interpretable retention time aligned liquid chromatography high-resolution mass spectrometry data, a reliable machine learning model could be established that rapidly uncovers chemical urine manipulation. The incorporation of our model into routine clinical or forensic analysis allows simultaneous LC-MS analysis and sample integrity testing in one run, thus revolutionizing this field of drug testing.

Towards Extending the Detection Window of Gamma-Hydroxybutyric Acid-An Untargeted Metabolomics Study in Serum and Urine Following Controlled Administration in Healthy Men

In forensic toxicology, gamma-hydroxybutyrate (GHB) still represents one of the most challenging drugs of abuse in terms of analytical detection and interpretation. Given its rapid elimination, the detection window of GHB in common matrices is short (maximum 12 h in urine). Additionally, the differentiation from naturally occurring endogenous GHB, is challenging. Thus, novel biomarkers to extend the detection window of GHB are urgently needed. The present study aimed at searching new potential biomarkers of GHB use by means of mass spectrometry (MS) metabolomic profiling in serum (up to 16.5 h) and urine samples (up to 8 h after intake) collected during a placebo-controlled crossover study in healthy men. MS data acquired by different analytical methods (reversed phase and hydrophilic interaction liquid chromatography; positive and negative electrospray ionization each) were filtered for significantly changed features applying univariate and mixed-effect model statistics. Complementary to a former study, conjugates of GHB with glycine, glutamate, taurine, carnitine and pentose (ribose) were identified in urine, with particularly GHB-pentose being promising for longer detection. None of the conjugates were detectable in serum. Therein, mainly energy metabolic substrates were identified, which may be useful for more detailed interpretation of underlying pathways but are too unspecific as biomarkers.

Hyphenated high-resolution mass spectrometry-the "all-in-one" device in analytical toxicology?

This trend article reviews papers with hyphenated high-resolution mass spectrometry (HRMS) approaches applied in analytical toxicology, particularly in clinical and forensic toxicology published since 2016 and referenced in PubMed. The article focuses on the question of whether HRMS has or will become the all-in-one device in these fields as supposed by the increasing number of HRMS presentations at scientific meetings, corresponding original papers, and review articles. Typical examples for the different application fields are discussed such as targeted or untargeted drug screening, quantification, drug metabolism studies, and metabolomics approaches. Considering the reviewed papers, HRMS is currently the only technique that fulfills the criteria of an all-in-one device for the various applications needed in analytical toxicology.

Graphical abstract

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.

Comparative Untargeted Metabolomics Analysis of the Psychostimulants 3,4-Methylenedioxy-Methamphetamine (MDMA), Amphetamine, and the Novel Psychoactive Substance Mephedrone after Controlled Drug Administration to Humans

Psychoactive stimulants are a popular drug class which are used recreationally. Over the last decade, large numbers of new psychoactive substances (NPS) have entered the drug market and these pose a worldwide problem to human health. Metabolomics approaches are useful tools for simultaneous detection of endogenous metabolites affected by drug use. They allow identification of pathways or characteristic metabolites, which might support the understanding of pharmacological actions or act as indirect biomarkers of consumption behavior or analytical detectability. Herein, we performed a comparative metabolic profiling of three psychoactive stimulant drugs 3,4-methylenedioxymethamphetamine (MDMA), amphetamine and the NPS mephedrone by liquid chromatography-high resolution mass spectrometry (LC-HRMS) in order to identify common pathways or compounds. Plasma samples were obtained from controlled administration studies to humans. Various metabolites were identified as increased or decreased based on drug intake, mainly belonging to energy metabolism, steroid biosynthesis and amino acids. Linoleic acid and pregnenolone-sulfate changed similarly in response to intake of all drugs. Overall, mephedrone produced a profile more similar to that of amphetamine than MDMA in terms of affected energy metabolism. These data can provide the basis for further in-depth targeted metabolome studies on pharmacological actions and search for biomarkers of drug use.

Interpol review of toxicology 2016-2019

This review paper covers the forensic-relevant literature in toxicology from 2016 to 2019 as a part of the 19th Interpol International Forensic Science Managers Symposium. The review papers are also available at the Interpol website at: https://www.interpol.int/content/download/14458/file/Interpol%20Review%20.Papers%202019.pdf.

Zwitterionic HILIC stationary phase as a valuable alternative in separative techniques: Application to the analysis of gamma-hydroxybutyric acid and its metabolite in hair

In this work, the physical and chemical properties of a novel zwitterionic LC stationary phase are applied to the development, validation and application of a new fast and reliable method devoted to the analysis of GHB (gamma-hydroxybutyric acid) and its relatively new discovered glucuronide metabolite in hair. The obtained sensitivity, expressed as limit of detection (LOD) and quantification (LOQ), were 0.033 and 0.10 ng/mg for GHB and 0.11 and 0.37 ng/mg, for GHB-glucuronide respectively. Linearity was assessed between LOQ and 50 ng/mg for both compounds. GHB and GHB-glucuronide extraction from hair matrix was maintained simple and consisted in an acidified-solvent incubation. No samples purification was required before LC-MS/MS analysis. The method was finally applied to 65 real hair sample, 60 adults and 5 children below 2 years old. The obtained results highlighted that GHB concentrations were in the range 0.11-0.96 ng/mg (average 0.38 ± 0.25 ng/mg) in 44 cases (68%) while in 21 samples GHB concentrations were in the range between LOD and LOQ (0.033-0.1 ng/mg). GHB-glucuronide was detected in few samples (n. 3) at levels below LOQ. The interest on these molecules relies on the fact that GHB is both a naturally occurring inhibitory neurotransmitter in the central nervous system and an illicit drug often experienced by victims of drug-facilitated sexual assault. GHB-glucuronide was firstly identified in urine by the group of Petersen in 2013 and, as per analogy to ethyl glucuronide, it was proposed as a longer biomarker for GHB intoxication.

Metabolomic Strategies in Biomarker Research-New Approach for Indirect Identification of Drug Consumption and Sample Manipulation in Clinical and Forensic Toxicology?

Drug of abuse (DOA) consumption is a growing problem worldwide, particularly with increasing numbers of new psychoactive substances (NPS) entering the drug market. Generally, little information on their adverse effects and toxicity are available. The direct detection and identification of NPS is an analytical challenge due to their ephemerality on the drug scene. An approach that does not directly focus on the structural detection of an analyte or its metabolites, would be beneficial for this complex analytical scenario and the development of alternative screening methods could help to provide fast response on suspected NPS consumption. A metabolomics approach might represent such an alternative strategy for the identification of biomarkers for different questions in DOA testing. Metabolomics is the monitoring of changes in small (endogenous) molecules (<1,000 Da) in response to a certain stimulus, e.g., DOA consumption. For this review, a literature search targeting "metabolomics" and different DOAs or NPS was conducted. Thereby, different applications of metabolomic strategies in biomarker research for DOA identification were identified: (a) as an additional tool for metabolism studies bearing the major advantage that particularly a priori unknown or unexpected metabolites can be identified; and (b) for identification of endogenous biomarker or metabolite patterns, e.g., for synthetic cannabinoids or also to indirectly detect urine manipulation attempts by chemical adulteration or replacement with artificial urine samples. The majority of the currently available literature in that field, however, deals with metabolomic studies for DOAs to better assess their acute or chronic effects or to find biomarkers for drug addiction and tolerance. Certain changes in endogenous compounds are detected for all studied DOAs, but often similar compounds/pathways are influenced. When evaluating these studies with regard to possible biomarkers for drug consumption, the observed changes appear, albeit statistically significant, too small to reliably work as biomarker for drug consumption. Further, different drugs were shown to affect the same pathways. In conclusion, metabolomic approaches possess potential for detection of biomarkers indicating drug consumption. More studies, including more sensitive targeted analyses, multi-variant statistical models or deep-learning approaches are needed to fully explore the potential of omics science in DOA testing.