Solid-Phase Extraction and Nanoflow Liquid Chromatography-Nanoelectrospray Ionization Mass Spectrometry for Improved Global Urine Metabolomics

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.

Advancements in Analyzing Tumor Metabolites through Chemical Derivatization-Based Chromatography

Understanding the metabolic abnormalities of tumors is crucial for early diagnosis, prognosis, and treatment. Accurate identification and quantification of metabolites in biological samples are essential to investigate the relationship between metabolite variations and tumor development. Common techniques like LC-MS and GC-MS face challenges in measuring aberrant metabolites in tumors due to their strong polarity, isomerism, or low ionization efficiency during MS detection. Chemical derivatization of metabolites offers an effective solution to overcome these challenges. This review focuses on the difficulties encountered in analyzing aberrant metabolites in tumors, the principles behind chemical derivatization methods, and the advancements in analyzing tumor metabolites using derivatization-based chromatography. It serves as a comprehensive reference for understanding the analysis and detection of tumor metabolites, particularly those that are highly polar and exhibit low ionization efficiency.

Using Omics to Study Leprosy, Tuberculosis, and Other Mycobacterial Diseases

Mycobacteria are members of the Actinomycetales order, and they are classified into one family, Mycobacteriaceae. More than 20 mycobacterial species cause disease in humans. The Mycobacterium group, called the Mycobacterium tuberculosis complex (MTBC), has nine closely related species that cause tuberculosis in animals and humans. TB can be detected worldwide and one-fourth of the world’s population is contaminated with tuberculosis. According to the WHO, about two million dies from it, and more than nine million people are newly infected with TB each year. Mycobacterium tuberculosis (M. tuberculosis) is the most potential causative agent of tuberculosis and prompts enormous mortality and morbidity worldwide due to the incompletely understood pathogenesis of human tuberculosis. Moreover, modern diagnostic approaches for human tuberculosis are inefficient and have many lacks, while MTBC species can modulate host immune response and escape host immune attacks to sustain in the human body. “Multi-omics” strategies such as genomics, transcriptomics, proteomics, metabolomics, and deep sequencing technologies could be a comprehensive strategy to investigate the pathogenesis of mycobacterial species in humans and offer significant discovery to find out biomarkers at the early stage of disease in the host. Thus, in this review, we attempt to understand an overview of the mission of “omics” approaches in mycobacterial pathogenesis, including tuberculosis, leprosy, and other mycobacterial diseases.

The promise of graphene-based transistors for democratizing multiomics studies

As biological research has synthesized genomics, proteomics, metabolomics, and transcriptomics into systems biology, a new multiomics approach to biological research has emerged. Today, multiomics studies are challenging and expensive. An experimental platform that could unify the multiple omics approaches to measurement could increase access to multiomics data by enabling more individual labs to successfully attempt multiomics studies. Field effect biosensing based on graphene transistors have gained significant attention as a potential unifying technology for such multiomics studies. This review article highlights the outstanding performance characteristics that makes graphene field effect transistor an attractive sensing platform for a wide variety of analytes important to system biology. In addition to many studies demonstrating the biosensing capabilities of graphene field effect transistors, they are uniquely suited to address the challenges of multiomics studies by providing an integrative multiplex platform for large scale manufacturing using the well-established processes of semiconductor industry. Furthermore, the resulting digital data is readily analyzable by machine learning to derive actionable biological insight to address the challenge of data compatibility for multiomics studies. A critical stage of systems biology will be democratizing multiomics study, and the graphene field effect transistor is uniquely positioned to serve as an accessible multiomics platform.

Recent advances in microscale separation techniques for lipidome analysis

This review paper highlights the recent research on liquid-phase microscale separation techniques for lipidome analysis over the last 10 years, mainly focusing on capillary liquid chromatography (LC) and capillary electrophoresis (CE) coupled with mass spectrometry (MS). Lipids are one of the most important classes of biomolecules which are involved in the cell membrane, energy storage, signal transduction, and so on. Since lipids include a variety of hydrophobic compounds including numerous structural isomers, lipidomes are a challenging target in bioanalytical chemistry. MS is the key technology that comprehensively identifies lipids; however, separation techniques like LC and CE are necessary prior to MS detection in order to avoid ionization suppression and resolve structural isomers. Separation techniques using μm-scale columns, such as a fused silica capillary and microfluidic device, are effective at realizing high-resolution separation. Microscale separation usually employs a nL-scale flow, which is also compatible with nanoelectrospray ionization-MS that achieves high sensitivity. Owing to such analytical advantages, microscale separation techniques like capillary/microchip LC and CE have been employed for more than 100 lipidome studies. Such techniques are still being evolved and achieving further higher resolution and wider coverage of lipidomes. Therefore, microscale separation techniques are promising as the fundamental technology in next-generation lipidome analysis.

Clinical significance of small molecule metabolites in the blood of patients with different types of liver injury

To understand the characteristic of changes of serum metabolites between healthy people and patients with hepatitis B virus (HBV) infection at different stages of disease, and to provide reference metabolomics information for clinical diagnosis of liver disease patients. 255 patients with different stages of HBV infection were selected. 3 mL blood was collected from each patient in the morning to detect differences in serum lysophosphatidylcholine, acetyl-l-carnitine, oleic acid amide, and glycocholic acid concentrations by UFLC-IT-TOF/MS. The diagnostic values of four metabolic substances were evaluated by receiver operating characteristic (ROC) curve. The results showed that the optimal cut-off value of oleic acid amide concentration of the liver cirrhosis and HCC groups was 23.6 mg/L, with a diagnostic sensitivity of 88.9% and specificity of 70.6%. The diagnostic efficacies of the three substances were similar in the hepatitis and HCC groups, with an optimal cut-off value of 2.04 mg/L, and a diagnostic sensitivity and specificity of 100% and 47.2%, respectively. The optimal cut-off value of lecithin of the HBV-carrier and HCC groups was 132.85 mg/L, with a diagnostic sensitivity and specificity of 88.9% and 66.7%, respectively. The optimal cut-off value of oleic acid amide of the healthy and HCC groups was 129.03 mg/L, with a diagnostic sensitivity and specificity of 88.4% and 83.3%, respectively. Lysophosphatidylcholine, acetyl-l-carnitine, and oleic acid amide were potential metabolic markers of HCC. Among them, lysophosphatidylcholine was low in the blood of HCC patients, and its diagnostic efficacy was better than that of acetyl-l-carnitine and oleic acid amide, providing reference metabolomics information in clinical diagnosis and future research.

Metabolomics of Oral/Head and Neck Cancer

Oral/head and neck cancer is the sixth most common human malignancies in the world. Despite the treatment advances in surgery, chemotherapy, and radiotherapy, the patient survival has not been significantly improved in the past several decades. As a new methodological approach, metabolomics may help reveal the metabolic reprogramming mechanisms underlying head and neck cancer cell proliferation, invasion, and metastasis and may be used to identify metabolite biomarkers for clinical applications of the disease. In this chapter, we briefly review recent metabolomic applications in head and neck cancer.

Acetaminophen metabolism revisited using non-targeted analyses: Implications for human biomonitoring

The analgesic paracetamol/acetaminophen (N-acetyl-4-aminophenol, APAP) is commonly used to relieve pain, fever and malaise. While sales have increased worldwide, a growing body of experimental and epidemiological evidence has suggested APAP as a possible risk factor for various health disorders in humans. To perform internal exposure-based risk assessment, the use of accurate and optimized biomonitoring methods is critical. However, retrospectively assessing pharmaceutical use of APAP in humans is challenging because of its short half-life. The objective of this study was to address the key issue of potential underestimation of APAP use using current standard analytical methods based on urinary analyses of free APAP and its phase II conjugates. The question we address is whether investigating additional metabolites than direct phase II conjugates could improve the monitoring of APAP. Using non-targeted analyses based on high-resolution mass spectrometry, we identified, in a controlled longitudinal exposure study with male volunteers, overlooked APAP metabolites with delayed formation and excretion rates. We postulate that these metabolites are formed via the thiomethyl shunt after the enterohepatic circulation as already observed in rodents. Importantly, these conjugated thiomethyl metabolites were (i) of comparable diagnostic sensitivity as the free APAP and its phase II conjugates detected by current methods; (ii) had delayed peak levels in blood and urine compared to other APAP metabolites and therefore potentially extend the window of exposure assessment; and (iii) provide relevant information regarding metabolic pathways of interest from a toxicological point of view. Including these metabolites in future APAP biomonitoring methods therefore provides an option to decrease potential underestimation of APAP use. Moreover, our data challenge the notion that the standard methods in biomonitoring based exclusively on the parent compound and its phase II metabolites are adequate for human biomonitoring of a non-persistent chemical such as APAP.

Untargeted metabolomics of prostate cancer zwitterionic and positively charged compounds in urine

Prostate cancer, a leading cause of cancer-related deaths worldwide, principally occurs in over 50-year-old men. Nowadays there is urgency to discover biomarkers alternative to prostate-specific antigen, as it cannot discriminate patients with benign prostatic hyperplasia from clinically significant forms of prostatic cancer. In the present paper, 32 benign prostatic hyperplasia and 41 prostatic cancer urine samples were collected and analyzed. Polar and positively charged metabolites were therein investigated using an analytical platform comprising an up to 40-fold analyte enrichment step by graphitized carbon black solid-phase extraction, HILIC separation, and untargeted high-resolution mass spectrometry analysis. These classes of compounds are often neglected in common metabolomics experiments even though previous studies reported their significance in cancer biomarker discovery. The complex metabolomics big datasets, generated by the UHPLC-HRMS, were analyzed with the ROIMCR procedure, based on the selection of the MS regions of interest data and their analysis by the Multivariate Curve-Resolution Alternating Least Squares chemometrics method. This approach allowed the resolution and tentative identification of the metabolites differentially expressed by the two data sets. Among these, amino acids and carnitine derivatives were tentatively identified highlighting the importance of the proposed methodology for cancer biomarker research.

Reviewing the metabolome coverage provided by LC-MS: Focus on sample preparation and chromatography-A tutorial

Metabolomics has become an invaluable tool for both studying metabolism and biomarker discovery. The great technical advances in analytical chemistry and bioinformatics have considerably increased the number of measurable metabolites, yet an important part of the human metabolome remains uncovered. Among the various MS hyphenated techniques available, LC-MS stands out as the most used. Here, we aimed to show the capabilities of LC-MS to uncover part of the metabolome and how to best proceed with sample preparation and LC to maximise metabolite detection. The analyses of various open metabolite databases served us to estimate the size of the already detected human metabolome, the expected metabolite composition of most used human biospecimens and which part of the metabolome can be detected when LC-MS is used. Based on an extensive review and on our experience, we have outlined standard procedures for LC-MS analysis of urine, cells, serum/plasma, tissues and faeces, to guide in the selection of the sample preparation method that best matches with one or more LC techniques in order to get the widest metabolome coverage. These standard procedures may be a useful tool to explore, at a glance, the wide spectrum of possibilities available, which can be a good starting point for most of the LC-MS metabolomic studies.

The Progress of Multi-Omics Technologies: Determining Function in Lactic Acid Bacteria Using a Systems Level Approach

Lactic Acid Bacteria (LAB) have long been recognized as having a significant impact ranging from commercial to health domains. A vast amount of research has been carried out on these microbes, deciphering many of the pathways and components responsible for these desirable effects. However, a large proportion of this functional information has been derived from a reductionist approach working with pure culture strains. This provides limited insight into understanding the impact of LAB within intricate systems such as the gut microbiome or multi strain starter cultures. Whole genome sequencing of strains and shotgun metagenomics of entire systems are powerful techniques that are currently widely used to decipher function in microbes, but they also have their limitations. An available genome or metagenome can provide an image of what a strain or microbiome, respectively, is potentially capable of and the functions that they may carry out. A top-down, multi-omics approach has the power to resolve the functional potential of an ecosystem into an image of what is being expressed, translated and produced. With this image, it is possible to see the real functions that members of a system are performing and allow more accurate and impactful predictions of the effects of these microorganisms. This review will discuss how technological advances have the potential to increase the yield of information from genomics, transcriptomics, proteomics and metabolomics. The potential for integrated omics to resolve the role of LAB in complex systems will also be assessed. Finally, the current software approaches for managing these omics data sets will be discussed.

The potential of nanoflow liquid chromatography-nano electrospray ionisation-mass spectrometry for global profiling the faecal metabolome

Faeces are comprised of a wide array of metabolites arising from the circulatory system as well as the human microbiome. A global metabolite analysis (metabolomics) of faecal extracts offers the potential to uncover new compounds which may be indicative of the onset of bowel diseases such as colorectal cancer (CRC). To date, faecal metabolomics is still in its infancy and the compounds of low abundance present in faecal extracts poorly characterised. In this study, extracts of faeces from healthy subjects were profiled using a sensitive nanoflow-nanospray LC-MS platform which resulted in highly repeatable peak retention times (<2% CV) and intensities (<15% CV). Analysis of the extracts revealed wide coverage of the faecal metabolome including detection of low abundant signalling compounds such as sex steroids and eicosanoids, alongside highly abundant pharmaceuticals and tetrapyrrole metabolites. A small pilot study investigating differences in metabolomics profiles of faecal samples obtained from 7 CRC, 25 adenomatous polyp and 26 healthy groups revealed that secondary bile acids, conjugated androgens, eicosanoids, phospholipids and an unidentified haem metabolite were potential classes of metabolites that discriminated between the CRC and control sample groups. However, much larger follow up studies are needed to confirm which components of the faecal metabolome are associated with actual CRC disease rather than dietary influences. This study reveals the potential of nanospray-nanoflow LC-MS profiling of faecal samples from large scale cohort studies for uncovering the role of the faecal metabolome in colorectal disease formation.

Challenges and emergent solutions for LC-MS/MS based untargeted metabolomics in diseases

In the past decade, advances in liquid chromatography-mass spectrometry (LC-MS) have revolutionized untargeted metabolomics analyses. By mining metabolomes more deeply, researchers are now primed to uncover key metabolites and their associations with diseases. The employment of untargeted metabolomics has led to new biomarker discoveries and a better mechanistic understanding of diseases with applications in precision medicine. However, many major pertinent challenges remain. First, compound identification has been poor, and left an overwhelming number of unidentified peaks. Second, partial, incomplete metabolomes persist due to factors such as limitations in mass spectrometry data acquisition speeds, wide-range of metabolites concentrations, and cellular/tissue/temporal-specific expression changes that confound our understanding of metabolite perturbations. Third, to contextualize metabolites in pathways and biology is difficult because many metabolites partake in multiple pathways, have yet to be described species specificity, or possess unannotated or more-complex functions that are not easily characterized through metabolomics analyses. From a translational perspective, information related to novel metabolite biomarkers, metabolic pathways, and drug targets might be sparser than they should be. Thankfully, significant progress has been made and novel solutions are emerging, achieved through sustained academic and industrial community efforts in terms of hardware, computational, and experimental approaches. Given the rapidly growing utility of metabolomics, this review will offer new perspectives, increase awareness of the major challenges in LC-MS metabolomics that will significantly benefit the metabolomics community and also the broader the biomedical community metabolomics aspire to serve.

Comparison of modified Matyash method to conventional solvent systems for polar metabolite and lipid extractions

In the last decade, metabolomics has experienced significant advances in the throughput and robustness of analytical methodologies. Yet the preparation of biofluids and low-mass tissue samples remains a laborious and potentially inconsistent manual process, and a significant bottleneck for high-throughput metabolomics. To address this, we have compared three different sample extraction solvent systems in three diverse sample types with the purpose of selecting an optimum protocol for subsequent automation of sample preparation. We have investigated and re-optimised the solvent ratios in the recently introduced methyl tert-butyl ether (MTBE)/methanol/water solvent system (here termed modified Matyash; 2.6/2.0/2.4, v/v/v) and compared it to the original Matyash method (10/3/2.5, v/v/v) and the conventional chloroform/methanol/water (stepwise Bligh and Dyer, 2.0/2.0/1.8, v/v/v) using two biofluids (human serum and urine) and one tissue (whole Daphnia magna). This is the first report of the use of the Matyash method for extracting metabolites from the US National Institutes of Health (NIH) model organism D. magna. Extracted samples were analysed by non-targeted direct infusion mass spectrometry metabolomics or LC-MS metabolomics. Overall, the modified Matyash method yielded a higher number of peaks and putatively annotated metabolites compared to the original Matyash method (1-29% more peaks and 1-30% more metabolites) and the Bligh and Dyer method (4-20% more peaks and 1-41% more metabolites). Additionally the modified Matyash method was superior when considering metabolite intensities. The reproducibility of the modified Matyash method was higher than other methods (in 10 out of 12 datasets, compared to the original Matyash method; and in 8 out of 12 datasets, compared to the Bligh and Dyer method), based upon the observation of a lower mRSD of peak intensities. In conclusion, the modified Matyash method tended to provide a higher yield and reproducibility for most sample types in this study compared to two widely used methods.

A review of nanoscale LC-ESI for metabolomics and its potential to enhance the metabolome coverage

Liquid chromatography-electrospray ionisation-mass spectrometry (LC-ESI-MS) platforms are widely used to perform high throughput untargeted profiling of biological samples for metabolomics-based approaches. However, these LC-ESI platforms usually favour the detection of metabolites present at relatively high concentrations because of analytical limitations such as ion suppression, thus reducing overall sensitivity. To counter this issue of sensitivity, the latest in terms of analytical platforms can be adopted to enable a greater portion of the metabolome to be analysed in a single analytical run. Here, nanoflow liquid chromatography-nanoelectrospray ionisation (nLC-nESI), which has previously been utilised successfully in proteomics, is explored for use in metabolomic and exposomic research. As a discovery based field, the markedly increased sensitivity of these nLC-nESI platforms offer the potential to uncover the roles played by low abundant signalling metabolites (e.g. steroids, eicosanoids) in health and disease studies, and would also enable an improvement in the detection of xenobiotics present at trace levels in biological matrices to better characterise the chemical exposome. This review aims to give an insight into the advantages associated with nLC-nESI for metabolomics-based approaches. Initially we detail the source of improved sensitivity prior to reviewing the available approaches to achieving nanoflow rates and nanospray ionisation for metabolomics. The robustness of nLC-nESI platforms was then assessed using the literature available from a metabolomic viewpoint. We also discuss the challenging point of sample preparation which needs to be addressed to fully enjoy the benefits of these nLC-nESI platforms. Finally, we assess metabolomic analysis utilising nano scale platforms and look ahead to the future of metabolomics using these new highly sensitive platforms.

Single-platform ‘multi-omic’ profiling: unified mass spectrometry and computational workflows for integrative proteomics–metabolomics analysis

Advances in instrumentation and analysis tools are permitting evermore comprehensive interrogation of diverse biomolecules and allowing investigators to move from linear signaling cascades to network models, which more accurately reflect the molecular basis of biological systems and processes.

Disruption of the Prostaglandin Metabolome and Characterization of the Pharmaceutical Exposome in Fish Exposed to Wastewater Treatment Works Effluent As Revealed by Nanoflow-Nanospray Mass Spectrometry-Based Metabolomics

Fish can be exposed to a complex mixture of chemical contaminants, including pharmaceuticals, present in discharges of wastewater treatment works (WwTWs) effluents. There is little information on the effects of effluent exposure on fish metabolism, especially the small molecule signaling compounds which are the biological target of many pharmaceuticals. We applied a newly developed sensitive nanoflow-nanospray mass spectrometry nontargeted profiling technique to identify changes in the exposome and metabolome of roach (Rutilus rutilus) exposed to a final WwTWs effluent for 15 days. Effluent exposure resulted in widespread reduction (between 50% and 90%) in prostaglandin (PG) profiles in fish tissues and plasma with disruptions also in tryptophan/serotonin, bile acid and lipid metabolism. Metabolite disruptions were not explained by altered expression of genes associated with the PG or tryptophan metabolism. Of the 31 pharmaceutical metabolites that were detected in the effluent exposome of fish, 6 were nonsteroidal anti-inflammatory drugs but with plasma concentrations too low to disrupt PG biosynthesis. PGs, bile acids, and tryptophan metabolites are important mediators regulating a diverse array of physiological systems in fish and the identity of wastewater contaminants disrupting their metabolism warrants further investigation on their exposure effects on fish health.

Methods used to increase the comprehensive coverage of urinary and plasma metabolomes by MS

Metabolomics, focusing on comprehensive analysis of all the metabolites in a biological system, provides a direct signature of biochemical activity. Using emerging technologies in MS, it is possible to simultaneously and rapidly analyze thousands of metabolites. However, due to the chemical and physical diversity of metabolites, it is difficult to acquire a comprehensive and reliable profiling of the whole metabolome. Here, we summarize the state of the art in metabolomics research, focusing on efforts to provide a more comprehensive metabolome coverage via improvements in two fundamental processes: sample preparation and MS analysis. Additionally, the reliable analysis is also highlighted via the combinations of multiple methods (e.g., targeted and untargeted approaches), and analytical quality control and calibration methods.

Use of a pre-analysis osmolality normalisation method to correct for variable urine concentrations and for improved metabolomic analyses

Metabolomics analyses of urine have the potential to provide new information on the detection and progression of many disease processes. However, urine samples can vary significantly in total solute concentration and this presents a challenge to achieve high quality metabolomic datasets and the detection of biomarkers of disease or environmental exposures. This study investigated the efficacy of pre- and post-analysis normalisation methods to analyse metabolomic datasets obtained from neat and diluted urine samples from five individuals. Urine samples were extracted by solid phase extraction (SPE) prior to metabolomic analyses using a sensitive nanoflow/nanospray LC-MS technique and the data analysed by principal component analyses (PCA). Post-analysis normalisation of the datasets to either creatinine or osmolality concentration, or to mass spectrum total signal (MSTS), revealed that sample discrimination was driven by the dilution factor of urine rather than the individual providing the sample. Normalisation of urine samples to equal osmolality concentration prior to LC-MS analysis resulted in clustering of the PCA scores plot according to sample source and significant improvements in the number of peaks common to samples of all three dilutions from each individual. In addition, the ability to identify discriminating markers, using orthogonal partial least squared-discriminant analysis (OPLS-DA), was greatly improved when pre-analysis normalisation to osmolality was compared with post-analysis normalisation to osmolality and non-normalised datasets. Further improvements for peak area repeatability were observed in some samples when the pre-analysis normalisation to osmolality was combined with a post-analysis mass spectrum total useful signal (MSTUS) or MSTS normalisation. Future adoption of such normalisation methods may reduce the variability in metabolomics analyses due to differing urine concentrations and improve the discovery of discriminating metabolites associated with sample source.