Improved Data-Dependent Acquisition for Untargeted Metabolomics Using Gas-Phase Fractionation with Staggered Mass Range

Integrated strategy facilitates rapid in-depth chemome characterization of traditional Chinese medicine prescriptions: Shengbai oral liquid as a case

Chemome characterization is the prerequisite for either therapeutic mechanism clarification or quality control of traditional Chinese medicine prescriptions (TCMPs). Liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS) currently serves as the most popular analytical tool; however, chemome characterization is still challenged by MS/MS spectral acquisition and post-acquisition data processing. Here, an integrated strategy was proposed for in-depth chemome clarification of Shengbai oral liquid (SBOL). Gas phase ion fractionation with staggered mass ranges was demonstrated to be the superior acquisition method regarding MS2 spectrum coverage in this study, and narrower mass range further advanced coverage. To facilitate information extraction, all ingredient materials were measured in parallel to form an in-house library, where each MS1 -MS2 item generated a square mass-to-charge ratio (m/z) frame to capture the tagged identity and each chemical family produced a pentagon frame for mass defect features to accomplish chemical analogs-targeted quasi-molecular ion extraction. Square m/z frame imprinting captured 355 identities, while mass defect frames extracted 275 compounds. Attributing to comprehensive MS2 spectrum acquisition and efficient data processing, 355 components were captured and tentatively identified, resulting in a clarified chemical composition for SBOL. Therefore, the proposed strategy should be meaningful for the chemome characterization of TCMPs.

Improving the Data Quality of Untargeted Metabolomics through a Targeted Data-Dependent Acquisition Based on an Inclusion List of Differential and Preidentified Ions

Metabolomics based on high-resolution mass spectrometry has become a powerful technique in biomedical research. The development of various analytical tools and online libraries has promoted the identification of biomarkers. However, how to make mass spectrometry collect more data information is an important but underestimated research topic. Herein, we combined full-scan and data-dependent acquisition (DDA) modes to develop a new targeted DDA based on the inclusion list of differential and preidentified ions (dpDDA). In this workflow, the MS1 datasets for statistical analysis and metabolite preidentification were first obtained using full-scan, and then, the MS/MS datasets for metabolite identification were obtained using targeted DDA of quality control samples based on the inclusion list. Compared with the current methods (DDA, data-independent acquisition, targeted DDA with time-staggered precursor ion list, and iterative exclusion DDA), dpDDA showed better stability, higher characteristic ion coverage, higher differential metabolites' MS/MS coverage, and higher quality MS/MS spectra. Moreover, the same trend was verified in the analysis of large-scale clinical samples. More surprisingly, dpDDA can distinguish patients with different severities of coronary heart disease (CHD) based on the Canadian Cardiovascular Society angina classification, which we cannot distinguish through conventional metabolomics data collection. Finally, dpDDA was employed to differentiate CHD from healthy control, and targeted metabolomics confirmed that dpDDA could identify a more complete metabolic pathway network. At the same time, four unreported potential CHD biomarkers were identified, and the area under the receiver operating characteristic curve was greater than 0.85. These results showed that dpDDA would expand the discovery of biomarkers based on metabolomics, more comprehensively explore the key metabolites and their association with diseases, and promote the development of precision medicine.

Optimization of Mass Spectrometric Parameters in Data Dependent Acquisition for Untargeted Metabolomics on the Basis of Putative Assignments

Optimization of mass spectrometric parameters for a data dependent acquisition (DDA) experiment is essential to increase the MS/MS coverage and hence increase metabolite identifications in untargeted metabolomics. We explored the influence of mass spectrometric parameters including mass resolution, radio frequency (RF) level, signal intensity threshold, number of MS/MS events, cycle time, collision energy, maximum ion injection time (MIT), dynamic exclusion, and automatic gain control (AGC) target value on metabolite annotations on an Exploris 480-Orbitrap mass spectrometer. Optimal annotation results were obtained by performing ten data dependent MS/MS scans with a mass isolation window of 2.0 m/z and a minimum signal intensity threshold of 1 × 104 at a mass resolution of 180,000 for MS and 30,000 for MS/MS, while maintaining the RF level at 70%. Furthermore, combining an AGC target value of 5 × 106 and MIT of 100 ms for MS and an AGC target value of 1 × 105 and an MIT of 50 ms for MS/MS scans provided an improved number of annotated metabolites. A 10 s exclusion duration and a two stepped collision energy were optimal for higher spectral quality. These findings confirm that MS parameters do influence metabolomics results, and propose strategies for increasing metabolite coverage in untargeted metabolomics. A limitation of this work is that our parameters were only optimized for one RPLC method on single matrix and may be different for other protocols. Additionally, no metabolites were identified at level 1 confidence. The results presented here are based on metabolite annotations and need to be validated with authentic standards.

Mass spectrometry DDA parameters and global coverage of the metabolome: Spectral molecular networks of momordica cardiospermoides plants

INTRODUCTION

Molecular networking (MN) has emerged as a key strategy to organize and annotate untargeted tandem mass spectrometry (MS/MS) data generated using either data independent- or dependent acquisition (DIA or DDA). The latter presents a time-efficient approach where full scan (MS¹) and MS² spectra are obtained with shorter cycle times. However, there are limitations related to DDA parameters, some of which are (i) intensity threshold and (ii) collision energy. The former determines ion prioritization for fragmentation, and the latter defines the fragmentation of selected ions. These DDA parameters inevitably determine the coverage and quality of spectral data, which would affect the outputs of MN methods.

OBJECTIVES

This study assessed the extent to which the quality of the tandem spectral data relates to MN topology and subsequent implications in the annotation of metabolites and chemical classification relative to the different DDA parameters employed.

METHODS

Herein, characterising the metabolome of Momordica cardiospermoides plants, we employ classical MN performance indicators to investigate the effects of collision energies and intensity thresholds on the topology of generated MN and propagated annotations.

RESULTS

We demonstrated that the lowest predefined intensity thresholds and collision energies result in comprehensive molecular networks. Comparatively, higher intensity thresholds and collision energies resulted in fewer MS² spectra acquisition, subsequently fewer nodes, and a limited exploration of the metabolome through MN.

CONCLUSION

Contributing to ongoing efforts and conversations on improving DDA strategies, this study proposes a framework in which multiple DDA parameters are utilized to increase the coverage of ions acquired and improve the global coverage of MN, propagated annotations, and the chemical classification performed.

A novel strategy integrating gas phase fractionation with staggered mass range and LC-MS/MS molecular network for comprehensive metabolites profiling of Gui Ling Ji in rats

Metabolite detection from complex biological samples faces challenges due to interference from endogenous substrates and the inherent limitation of multiple subsequent tandem scanning rates of instruments. Here, a new integrated approach based on gas-phase fractionation with a staggered mass range (sGPF) and a liquid chromatography-tandem mass spectrometry (LC-MS/MS) molecular network was developed to accelerate the data processing of the targeted and untargeted constituents absorbed in rats after oral administration of the traditional Chinese medicine (TCM) prescription Gui Ling Ji (GLJ). Compared with three conventional acquisition methods, sGPF at 3, 5, and 7 mass fractions could enhance MS/MS coverage with an increased MS/MS triggering rate of 29.4-206.2% over data-dependent acquisition (DDA), fast DDA and gas-phase fractionation. A mass range fraction setting of five optimized the performance. Based on the similar diagnostic fragment ions and characteristic neutral loss behaviors in the DDA-MS/MS spectrum, an initial molecular network of GLJ was created with the help of the global natural products social molecular networking (GNPS) platform. Furthermore, to remove the endogenous interference nodes, Cytoscape software was adopted to produce a clean and concise molecular network of prototype compounds and their corresponding metabolites. Using this strategy, a total of 210 compounds, including 59 prototype constituents and 151 metabolites, was unambiguously or tentatively identified in GLJ. This first systematic metabolic study of GLJ in vivo elucidated the potential pharmacodynamic basis of GLJ in clinical treatment. More importantly, this work can serve as a practical example and establish a guide for rapidly identifying TCM metabolites in biological matrices.

Rapid tryptic peptide mapping of human serum albumin using DI-MS/MSALL

In recent decades, proteinic drugs, in particular monoclonal antibodies, are taking the leading role of small molecule drugs, and peptide mapping relying on liquid chromatography-tandem mass spectrometry (LC-MS/MS) is an emerging approach to substitute the role of a ligand-binding assay for the quality control of the proteinic drugs. However, such LC-MS/MS approaches extensively suffer from time-intensive measurements, leading to a limited throughput. To achieve accelerated measurements, here, the potential of DI-MS/MS^ALL towards tryptic peptide mapping was evaluated through comparing with well-defined LC-MS/MS means, and human serum albumin (HSA) was employed as the representative protein for applicability illustration. Among the 55 tryptic peptides theoretically suggested by Skyline software, 47 were successfully captured by DI-MS/MS^ALL through acquiring the desired MS² spectra, in comparison to 51 detected by LC-MS/MS. DI-MS/MS^ALL measurements merely took 5 min, which was dramatically superior to the LC-MS/MS assay. Noteworthily, different from fruitful multi-charged MS¹ signals for LC-MS/MS, most quasi-molecular ions received lower charged states. DI-MS/MS^ALL also possessed advantages such as lower solvent consumption and facile instrumentation; however, more sample was consumed. In conclusion, DI-MS/MS^ALL is eligible to act as an alternative analytical tool for LC-MS/MS towards the peptide mapping of proteinic drugs, particularly when a heavy measurement workload.

DI-MS/MS^ALL records MS² spectrum at each 1 Da mass window through gas phase ion fractionation theory, and is eligible to act as an alternative analytical tool for LC-MS/MS towards the peptide mapping of proteinic drugs.

Global identification and determination of the major constituents in Kai-Xin-San by ultra-performance liquid chromatography-quadrupole-Orbitrap mass spectrometry and gas chromatography-mass spectrometry

Kai-Xin-San (KXS) is a traditional Chinese medicine (TCM) formula containing four herbal medicines: Ginseng Radix Rhizoma, Polygalae Radix, Poria and Acori Tatarinowii Rhizoma. A large number of pharmacological studies in vitro and in vivo have shown that KXS is characterized by anti-depression, anti-Alzheimer's disease, anti-oxidation and other activities. However, the pharmacodynamic substance basis studies of KXS are hitherto quite limited. Here, KXS was identified and determined by ultra-performance liquid chromatography-quadrupole-Orbitrap mass spectrometry (UPLC-Q-Orbitrap MS) and gas chromatography-mass spectrometry (GC-MS). Firstly, the data-dependent acquisition mode (DDA) of UPLC-Q-Orbitrap MS combined with the inclusion list were used to collected the chemical composition. The chemical constituents of KXS were identified by local database on compound discoverer™ 3.1 software and Xcalibur 4.1 software. With the use of this approach, a total of 211 compounds were identified from KXS. Wherein 60 compounds were from Ginseng Radix Rhizoma, 40 compounds were from Poria, and 111 compounds were from Polygala Radix, respectively. Secondly, 105 volatile constituents were identified by GC-MS analysis, which were mainly derived from Acori Tatarinowii Rhizoma. Besides, an adjusted parallel reaction monitoring method was established and validated to quantify the seventeen major compounds in different herbal medicines of KXS, which were chosen as the benchmarked substances to evaluate the quality of KXS. In conclusion, this study provided a generally applicable strategy for global metabolite identification of the complicated components and determination of multi-component content in traditional Chinese medicines.

Fecal multi-omics analysis reveals diverse molecular alterations of gut ecosystem in COVID-19 patients

Gut ecosystem has profound effects on host physiology and health. Gastrointestinal (GI) symptoms were frequently observed in patients with COVID-19. Compared with other organs, gut antiviral response can result in more complicated immune responses because of the interactions between the gut microbiota and host immunity. However, there are still large knowledge gaps in the impact of COVID-19 on gut molecular profiles and commensal microbiome, hindering our comprehensive understanding of the pathogenesis of SARS-CoV-2 and the treatment of COVID-19. We performed longitudinal stool multi-omics profiling to systemically investigate the molecular phenomics alterations of gut ecosystem in COVID-19. Gut proteomes of COVID-19 were characterized by disturbed immune, proteolysis and redox homeostasis. The expression and glycosylation of proteins involved in neutrophil degranulation and migration were suppressed, while those of proteases were upregulated. The variable domains of Ig heavy chains were downregulated and the overall glycosylation of IgA heavy chain constant regions, IgGFc-binding protein, and J chain were suppressed with glycan-specific variations. There was a reduction of beneficial gut bacteria and an enrichment of bacteria derived deleterious metabolites potentially associated with multiple types of diseases (such as ethyl glucuronide). The reduction of Ig heave chain variable domains may contribute to the increase of some Bacteroidetes species. Many bacteria ceramide lipids with a C17-sphingoid based were downregulated in COVID-19. In many cases, the gut phenome did not restore two months after symptom onset. Our study indicates widely disturbed gut molecular profiles which may play a role in the development of symptoms in COVID-19. Our findings also emphasis the need for ongoing investigation of the long-term gut molecular and microbial alterations during COVID-19 recovery process. Considering the gut ecosystem as a potential target could offer a valuable approach in managing the disease.

Direct infusion-tandem mass spectrometry combining with data mining strategies enables rapid chemome characterization of medicinal plants: A case study of Polygala tenuifolia

Data-independent MS² spectrum acquisition after fragmenting the precursor ion cohort with 1 Da bin, termed as MS/MS^{ALL ®}, offers an opportunity to achieve rapid chemome characterization when being coupled with direct infusion (DI). Some post-acquisition data processing strategies, such as mass defect filtering (MDF), diagnostic fragment ion filtering (DFIF), and neutral loss filtering (NLF), facilitate data extraction from massive dataset, and moreover, molecular weight (MW) imprinting allows rapid capturing those reported components. Here, DI-MS/MS^{ALL ®} was employed to acquire cubic spectral dataset, and the strategies such as MW imprinting, MDF, DFIF, and NLF, were subsequently applied to filter the structural information. The integrated pipeline was utilized for the chemome characterization of Polygala tenuifolia, a famous edible medicinal plant. To aid information filtering, an in-house chemical library was built by comprehensively collecting structural information from some available databases. A single analytical run was completed within 5 min. For MS¹ spectrum processing, MW imprinting was firstly applied to capture the compounds in the chemical library, and "five-point" MDF frames were employed to pursue saponins, oligosaccharide esters, and xanthones. Regarding MS² spectral plot, DFIF and NLF were deployed to search information-of-interest. Structural identification was accomplished by carefully correlating precursor ions and MS² spectra, applying the well-defined mass cracking rules, and referring to literature information as well as available databases. A total of 109 compounds, mainly saponins (40 ones), oligosaccharide esters (29 ones), and xanthones (19 ones), were captured and structurally annotated. MS¹ spectra were also implemented for chemome comparison between Polygala tenuifolia and several similar plants belonging to Polygala genus, resulting in the observation of significant inter- and intra-species differences. Above all, DI-MS/MS^{ALL ®} is a promising choice for high-throughput chemome profiling of, but not limited to, medicinal plants, in particular when being integrated with post-acquisition data processing strategies.

Analytical aspects of meet-in-metabolite analysis for molecular pathway reconstitution from exposure to adverse outcome

To explore the etiology of diseases is one of the major goals in epidemiological study. Meet-in-metabolite analysis reconstitutes biomonitoring-based adverse outcome (AO) pathways from environmental exposure to a disease, in which the chemical exposome-related metabolism responses are transmitted to incur the AO-related metabolism phenotypes. However, the ongoing data-dependent acquisition of non-targeted biomonitoring by high-resolution mass spectrometry (HRMS) is biased against the low abundance molecules, which forms the major of molecular internal exposome, i.e., the totality of trace levels of environmental pollutants and/or their metabolites in human samples. The recent development of data-independent acquisition protocols for HRMS screening has opened new opportunities to enhance unbiased measurement of the extremely low abundance molecules, which can encompass a wide range of analytes and has been applied in metabolomics, DNA, and protein adductomics. In addition, computational MS for small molecules is urgently required for the top-down exposome databases. Although a holistic analysis of the exposome and endogenous metabolites is plausible, multiple and flexible strategies, instead of "putting one thing above all" are proposed.

Shotgun chemome characterization of Artemisia rupestris L. Using direct infusion-MS/MSALL

In comparison of liquid chromatography, direct infusion is a superior choice to achieve high-throughput measurements. The specificity and selectivity of tandem mass spectrometry (MS/MS) actually result in a so-called MS separation potential when chemical characterization of herbal medicines. Here, a MS/MS^ALL program was introduced to promote DI-MS/MS to be an eligible tool for shotgun chemome characterization of Artemisia rupestris L. that is currently drawing worldwide interests because of the promising antiviral activity. After MS¹ spectral acquisition for the crude extract, the gas phase fractionation concept enabled the precursor ion cohort sequentially entered the collision cell with a stepped unit mass window (step-size as 1 Da) to generate MS² spectra, thus generating a unique property integrating the advantages of both data-dependent and data-independent acquisition manners. Even though being free of chromatographic separation, spectrometric separations were accomplished for by MS/MS^ALL program unless the components shared identical nominal molecular weights. Extensive efforts such as the correlations of MS¹ signals with MS² spectra, structural annotations of fragment ion species, information retrieval in some accessible databases, and referring to the literature data, were devoted for chemical characterization, and as a result, 44 compounds, in total, were structurally identified from 50% aqueous methanol exact of A. rupestris, including 8 caffeoyl quinic acid derivatives, 13 flavonoids, 15 monomeric and dimeric sesquiterpenoids, 4 fatty acids, 2 penylpropanoids, along with 2 other compounds. However, isomers were assigned as an isomeric mixture because their precursor ions always co-existed in a single mass window. Above all, DI-MS/MS^ALL provides an alternative tool for chemome characterization of herbal medicines, in particular when the great measurement workload for a large sample cohort, attributing to the high-throughput advantage.

An integrated strategy for comprehensive characterization of metabolites and metabolic profiles of bufadienolides from Venenum Bufonis in rats

Comprehensive characterization of metabolites and metabolic profiles in plasma has considerable significance in determining the efficacy and safety of traditional Chinese medicine (TCM) in vivo. However, this process is usually hindered by the insufficient characteristic fragments of metabolites, ubiquitous matrix interference, and complicated screening and identification procedures for metabolites. In this study, an effective strategy was established to systematically characterize the metabolites, deduce the metabolic pathways, and describe the metabolic profiles of bufadienolides isolated from Venenum Bufonis in vivo. The strategy was divided into five steps. First, the blank and test plasma samples were injected into an ultra-high performance liquid chromatography/linear trap quadrupole-orbitrap-mass spectrometry (MS) system in the full scan mode continuously five times to screen for valid matrix compounds and metabolites. Second, an extension-mass defect filter model was established to obtain the targeted precursor ions of the list of bufadienolide metabolites, which reduced approximately 39% of the interfering ions. Third, an acquisition model was developed and used to trigger more tandem MS (MS/MS) fragments of precursor ions based on the targeted ion list. The acquisition mode enhanced the acquisition capability by approximately four times than that of the regular data-dependent acquisition mode. Fourth, the acquired data were imported into Compound Discoverer software for identification of metabolites with metabolic network prediction. The main in vivo metabolic pathways of bufadienolides were elucidated. A total of 147 metabolites were characterized, and the main biotransformation reactions of bufadienolides were hydroxylation, dihydroxylation, and isomerization. Finally, the main prototype bufadienolides in plasma at different time points were determined using LC-MS/MS, and the metabolic profiles were clearly identified. This strategy could be widely used to elucidate the metabolic profiles of TCM preparations or Chinese patent medicines in vivo and provide critical data for rational drug use.

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Extension-mass defect filter model could reduce about 39% interfering ions.

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The optimized acquisition mode enhanced about 4 times acquisition capability than regular DDA mode.

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147 metabolites were characterized with metabolic network prediction, and the metabolic pathways were deduced in plasmas.

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The quantitative method of 14 prototypes was established by LC-MS/MS for metabolic profiles study.

An integrated data-dependent and data-independent acquisition method for hazardous compounds screening in foods using a single UHPLC-Q-Orbitrap run

Thousands of hazardous compounds that contaminate foods and feeds pose potential risks for human and animal health. However, it remains a challenge to perform a fast monitoring for safety surveillance. Herein we report a novel approach, integrated data-dependent and data-independent acquisition (DDIA) method, to efficiently screen for hundreds of chemicals in a single run using ultra-high-performance liquid chromatography coupled with quadrupole Orbitrap high-resolution mass spectrometry (UHPLC-Q-Orbitrap). This method was successfully applied to analyze 180 veterinary drugs in milk, 220 pesticides in tomato and 50 mycotoxins in maize, respectively. Compared with the widely used approaches of data-dependent acquisition (DDA) or data-independent acquisition (DIA), the obtained results indicate that DDIA-based method combines the advantages of both DDA and DIA, since it achieves higher reproducibility of identification, lower false results for targeted compounds. Notably, the advantage of DDIA approach is that it enables better date retroactivity for untargeted compounds, such as metabolites and decomposition products. With the improvement in high-resolution mass spectrometry (HRMS) as well as data-mining techniques, we believe that DDIA data acquisition approach based on LC-HRMS will be widely applied in various fields in the near future, especially in compound screening and omics field, such as metabolomics and proteomics.

Existing and Emerging Metabolomic Tools for ALS Research

Growing evidence suggests that aberrant energy metabolism could play an important role in the pathogenesis of amyotrophic lateral sclerosis (ALS). Despite this, studies applying advanced technologies to investigate energy metabolism in ALS remain scarce. The rapidly growing field of metabolomics offers exciting new possibilities for ALS research. Here, we review existing and emerging metabolomic tools that could be used to further investigate the role of metabolism in ALS. A better understanding of the metabolic state of motor neurons and their surrounding cells could hopefully result in novel therapeutic strategies.

High-resolution MS/MS metabolomics by data-independent acquisition reveals urinary metabolic alteration in experimental colitis

INTRODUCTION

Traditional high-resolution MS1 based untargeted metabolomics suffers from low sensitivity, while low-resolution MS/MS based multiple reaction monitoring increases sensitivity at the cost of metabolite coverage and the mass accuracy.

OBJECTIVES

To evaluate and apply the high-resolution MS/MS level untargeted metabolomics.

METHODS

SWATH based data-independent acquisition (DIA) was optimized to obtain MS/MS of all precursor ions.

RESULTS

SWATH-MS/MS could rescue MS1 obscured or saturated metabolites and potentially provide diagnostic fragments to differentiate isomers. For SWATH-MS/MS, 4944 out of 21492 (23.0%) and 2289 out of 12831 (17.8%) fragment ion features significantly changed (Fold change > 1.5, P < 0.05) between Normal and experimental acute ulcerative colitis (UC) groups in positive and negative ion mode, respectively. For SWATH-MS1, 1022 out of 4818 (21.2%) and 353 out of 2266 (15.6%) features significantly changed in positive and negative ion mode, respectively. By deciphering the metabolite profiles with high-resolution MS/MS, it allows versatile post-acquisition data mining such as open detection of different sub-metabolome. The method revealed a global urinary metabolic alteration and increased glucuronide and sulfate sub-metabolome in UC. The major limitation of untargeted SWATH-MS/MS is increased interferences derived from wider Q1 isolation window.

CONCLUSIONS

SWATH-MS/MS is a versatile metabolomics strategy, merging the coverage of high-resolution untargeted metabolomics and the sensitivity of MS/MS.

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.

Improving metabolome coverage and data quality: advancing metabolomics and lipidomics for biomarker discovery

This Feature Article highlights some of the key challenges within the field of metabolomics and examines what role separation and analytical sciences can play to improve the use of metabolomics in biomarker discovery and personalized medicine. Recent progress in four key areas is highlighted: (i) improving metabolite coverage, (ii) developing accurate methods for unstable metabolites including in vivo global metabolomics methods, (iii) advancing inter-laboratory studies and reference materials and (iv) improving data quality, standardization and quality control of metabolomics studies.

Exploring the Potential of Data-Independent Acquisition Proteomics Using Untargeted All-Ion Quantitation: Application to Tumor Subtype Diagnosis

Maximizing the recovery of meaningful biological information can facilitate proteomics-guided early detection and precise treatment of diseases. However, the conventional protein and peptide level targeted quantification of untargeted data independent acquisition (DIA) such as sequential window acquisition of all theoretical spectra (SWATH) is not necessarily descriptive of all information. Untargeted all-ion quantification theoretically could retrieve more features in SWATH digital maps by circumventing the initial identification process but is intrinsically susceptible to errors because of the extreme complexity of proteome samples and the poor selectivity of a single ion. In this study, we optimized and applied the untargeted all-ion quantification of SWATH data to differentiate tumor subtypes. Large peptides and low abundant peptides benefited more from untargeted all-ion quantification. Top-ranked significant ions were linked to their corresponding ion envelops, where multiple correlated ions were used for measurement and only ion envelopes containing at least three ions with consistent intensity ratio were kept as refined differentiating features. Multivariate statistical analysis revealed that for the tested data set, the refined markers discovered by untargeted SWATH analysis showed comparable diagnostic power to protein and peptide markers. Limitations and benefits of the approach are further discussed.

Recent advances in the applications of metabolomics in eye research

Metabolomics, the identification and quantitation of metabolites in a system, have been applied to identify new biomarkers or elucidate disease mechanism. In this review, we discussed the application of metabolomics in several ocular diseases and recent developments in metabolomics regarding tear fluids analysis, data acquisition and processing.

Data acquisition workflows in liquid chromatography coupled to high resolution mass spectrometry-based metabolomics: Where do we stand?

Typical mass spectrometry (MS) based untargeted metabolomics protocols are tedious as well as time- and sample-consuming. In particular, they often rely on "full-scan-only" analyses using liquid chromatography (LC) coupled to high resolution mass spectrometry (HRMS) from which metabolites of interest are first highlighted, and then tentatively identified by using targeted MS/MS experiments. However, this situation is evolving with the emergence of integrated HRMS based-data acquisition protocols able to perform multi-event acquisitions. Most of these protocols, referring to as data dependent and data independent acquisition (DDA and DIA, respectively), have been initially developed for proteomic applications and have recently demonstrated their applicability to biomedical studies. In this context, the aim of this article is to take stock of the progress made in the field of DDA- and DIA-based protocols, and evaluate their ability to change conventional metabolomic and lipidomic data acquisition workflows, through a review of HRMS instrumentation, DDA and DIA workflows, and also associated informatics tools.

LipidFrag: Improving reliability of in silico fragmentation of lipids and application to the Caenorhabditis elegans lipidome

Lipid identification is a major bottleneck in high-throughput lipidomics studies. However, tools for the analysis of lipid tandem MS spectra are rather limited. While the comparison against spectra in reference libraries is one of the preferred methods, these libraries are far from being complete. In order to improve identification rates, the in silico fragmentation tool MetFrag was combined with Lipid Maps and lipid-class specific classifiers which calculate probabilities for lipid class assignments. The resulting LipidFrag workflow was trained and evaluated on different commercially available lipid standard materials, measured with data dependent UPLC-Q-ToF-MS/MS acquisition. The automatic analysis was compared against manual MS/MS spectra interpretation. With the lipid class specific models, identification of the true positives was improved especially for cases where candidate lipids from different lipid classes had similar MetFrag scores by removing up to 56% of false positive results. This LipidFrag approach was then applied to MS/MS spectra of lipid extracts of the nematode Caenorhabditis elegans. Fragments explained by LipidFrag match known fragmentation pathways, e.g., neutral losses of lipid headgroups and fatty acid side chain fragments. Based on prediction models trained on standard lipid materials, high probabilities for correct annotations were achieved, which makes LipidFrag a good choice for automated lipid data analysis and reliability testing of lipid identifications.

Enhanced Lipidome Coverage in Shotgun Analyses by using Gas-Phase Fractionation

A high resolving power shotgun lipidomics strategy using gas-phase fractionation and data-dependent acquisition (DDA) was applied toward comprehensive characterization of lipids in a hen ovarian tissue in an untargeted fashion. Using this approach, a total of 822 unique lipids across a diverse range of lipid categories and classes were identified based on their MS/MS fragmentation patterns. Classes of glycerophospholipids and glycerolipids, such as glycerophosphocholines (PC), glycerophosphoethanolamines (PE), and triglycerides (TG), are often the most abundant peaks observed in shotgun lipidomics analyses. These ions suppress the signal from low abundance ions and hinder the chances of characterizing low abundant lipids when DDA is used. These issues were circumvented by utilizing gas-phase fractionation, where DDA was performed on narrow m/z ranges instead of a broad m/z range. Employing gas-phase fractionation resulted in an increase in sensitivity by more than an order of magnitude in both positive- and negative-ion modes. Furthermore, the enhanced sensitivity increased the number of lipids identified by a factor of ≈4, and facilitated identification of low abundant lipids from classes such as cardiolipins that are often difficult to observe in untargeted shotgun analyses and require sample-specific preparation steps prior to analysis. This method serves as a resource for comprehensive profiling of lipids from many different categories and classes in an untargeted manner, as well as for targeted and quantitative analyses of individual lipids. Furthermore, this comprehensive analysis of the lipidome can serve as a species- and tissue-specific database for confident identification of other MS-based datasets, such as mass spectrometry imaging. Graphical Abstract ᅟ.

Improving global feature detectabilities through scan range splitting for untargeted metabolomics by high-performance liquid chromatography-Orbitrap mass spectrometry

Untargeted metabolomics aims at obtaining quantitative information on the highest possible number of low-molecular biomolecules present in a biological sample. Rather small changes in mass spectrometric spectrum acquisition parameters may have a significant influence on the detectabilities of metabolites in untargeted global-scale studies by means of high-performance liquid chromatography-mass spectrometry (HPLC-MS). Employing whole cell lysates of human renal proximal tubule cells, we present a systematic global-scale study of the influence of mass spectrometric scan parameters and post-acquisition data treatment on the number and intensity of metabolites detectable in whole cell lysates. Ion transmission and ion collection efficiencies in an Orbitrap-based mass spectrometer basically depend on the m/z range scanned, which, ideally, requires different instrument settings for the respective mass ranges investigated. Therefore, we split a full scan range of m/z 50-1000 relevant for metabolites into two separate segments (m/z 50-200 and m/z 200-1,000), allowing an independent tuning of the ion transmission parameters for both mass ranges. Three different implementations, involving either scanning from m/z 50-1000 in a single scan, or scanning from m/z 50-200 and from m/z 200-1000 in two alternating scans, or performing two separate HPLC-MS runs with m/z 50-200 and m/z 200-1000 scan ranges were critically assessed. The detected features were subjected to rigorous background filtering and quality control in order to obtain reliable metabolite features for subsequent differential quantification. The most efficient approach in terms of feature number, which forms the basis for statistical analysis, identification, and for generating biological hypotheses, was the separate analysis of two different mass ranges. This lead to an increase in the number of detectable metabolite features, especially in the higher mass range (m/z greater than 400), by 2.5 (negative mode) to 6-fold (positive mode) as compared to analysis involving a single scan range. The total number of features confidently detectable was 560 in positive ion mode, and 436 in negative ion mode.

Tailored sensitivity reduction improves pattern recognition and information recovery with a higher tolerance to varied sample concentration for targeted urinary metabolomics

Variation in total metabolite concentration among different samples has been a major challenge for urinary metabolomics. Here we investigated the potential of tailored sensitivity reduction of high abundance metabolites for improved targeted urinary metabolomics. Two levels of sensitivity reduction of the 21 predominant urinary metabolites were assessed by employing less sensitive transition or collision energy with level 1 (reduced 1) and 2 (reduced 2) exhibiting 30-90% and 2-20% of the optimal sensitivity, respectively. Five postacquisition normalization methods were compared including no normalization, probabilistic quotient normalization, and normalization to sample median, creatinine intensity, and total intensity. Normalization to total intensity with reduced 2 gave the best pattern recognition and information recovery with a higher tolerance to varied sample concentration. Pareto scaling could improve the performance of tailored sensitivity reduction (reduced 2) for targeted urinary metabolomics while data transformation and autoscaling were susceptible to varied sample concentration. Using controlled spike-in experiments, we demonstrated that tailored sensitivity reduction revealed more differentially expressed markers with higher accuracy than did the conventional optimal sensitivity. This was particularly true when the differences between the sample groups are small. This work also served as an introductory guideline for handling targeted metabolomics data using the open-source software MetaboAnalyst.