Untargeted metabolomics suffers from incomplete raw data processing

Introduction

Untargeted metabolomics is a powerful tool for biological discoveries. To analyze the complex raw data, significant advances in computational approaches have been made, yet it is not clear how exhaustive and reliable the data analysis results are.

Objectives

Assessment of the quality of raw data processing in untargeted metabolomics.

Methods

Five published untargeted metabolomics studies, were reanalyzed.

Results

Omissions of at least 50 relevant compounds from the original results as well as examples of representative mistakes were reported for each study.

Conclusion

Incomplete raw data processing shows unexplored potential of current and legacy data.

     

Normalization and integration of large-scale metabolomics data using support vector regression

Introduction

Untargeted metabolomics studies for biomarker discovery often have hundreds to thousands of human samples. Data acquisition of large-scale samples has to be divided into several batches and may span from months to as long as several years. The signal drift of metabolites during data acquisition (intra- and inter-batch) is unavoidable and is a major confounding factor for large-scale metabolomics studies.

Objectives

We aim to develop a data normalization method to reduce unwanted variations and integrate multiple batches in large-scale metabolomics studies prior to statistical analyses.

Methods

We developed a machine learning algorithm-based method, support vector regression (SVR), for large-scale metabolomics data normalization and integration. An R package named MetNormalizer was developed and provided for data processing using SVR normalization.

Results

After SVR normalization, the portion of metabolite ion peaks with relative standard deviations (RSDs) less than 30 % increased to more than 90 % of the total peaks, which is much better than other common normalization methods. The reduction of unwanted analytical variations helps to improve the performance of multivariate statistical analyses, both unsupervised and supervised, in terms of classification and prediction accuracy so that subtle metabolic changes in epidemiological studies can be detected.

Conclusion

SVR normalization can effectively remove the unwanted intra- and inter-batch variations, and is much better than other common normalization methods.

     

Improved batch correction in untargeted MS-based metabolomics

Introduction

Batch effects in large untargeted metabolomics experiments are almost unavoidable, especially when sensitive detection techniques like mass spectrometry (MS) are employed. In order to obtain peak intensities that are comparable across all batches, corrections need to be performed. Since non-detects, i.e., signals with an intensity too low to be detected with certainty, are common in metabolomics studies, the batch correction methods need to take these into account.

Objectives

This paper aims to compare several batch correction methods, and investigates the effect of different strategies for handling non-detects.

Methods

Batch correction methods usually consist of regression models, possibly also accounting for trends within batches. To fit these models quality control samples (QCs), injected at regular intervals, can be used. Also study samples can be used, provided that the injection order is properly randomized. Normalization methods, not using information on batch labels or injection order, can correct for batch effects as well. Introducing two easy-to-use quality criteria, we assess the merits of these batch correction strategies using three large LC–MS and GC–MS data sets of samples from Arabidopsis thaliana.

Results

The three data sets have very different characteristics, leading to clearly distinct behaviour of the batch correction strategies studied. Explicit inclusion of information on batch and injection order in general leads to very good corrections; when enough QCs are available, also general normalization approaches perform well. Several approaches are shown to be able to handle non-detects—replacing them with very small numbers such as zero seems the worst of the approaches considered.

Conclusion

The use of quality control samples for batch correction leads to good results when enough QCs are available. If an experiment is properly set up, batch correction using the study samples usually leads to a similar high-quality correction, but has the advantage that more metabolites are corrected. The strategy for handling non-detects is important: choosing small values like zero can lead to suboptimal batch corrections.

     

A lost opportunity for science: journals promote data sharing in metabolomics but do not enforce it

Introduction

Data sharing is being increasingly required by journals and has been heralded as a solution to the ‘replication crisis’.

Objectives

(i) Review data sharing policies of journals publishing the most metabolomics papers associated with open data and (ii) compare these journals’ policies to those that publish the most metabolomics papers.

Methods

A PubMed search was used to identify metabolomics papers. Metabolomics data repositories were manually searched for linked publications.

Results

Journals that support data sharing are not necessarily those with the most papers associated to open metabolomics data.

Conclusion

Further efforts are required to improve data sharing in metabolomics.

     

Serum metabolites associated with feed efficiency in black angus steers

Introduction

Improving feed utilization in cattle is required to reduce input costs, increase production, and ultimately improve sustainability of the beef cattle industry. Characterizing metabolic differences between efficient and non-efficient animals will allow stakeholders to identify more efficient cattle during backgrounding.

Objectives

This study used an untargeted metabolomics approach to determine differences in serum metabolites between animals of low and high residual feed intake.

Methods

Residual feed intake was determined for 50 purebred Angus steers and 29 steers were selected for the study steers based on low versus high feed efficiency. Blood samples were collected from steers and analyzed using untargeted metabolomics via mass spectrometry. Metabolite data was analyzed using Metaboanalyst, visualized using orthogonal partial least squares discriminant analysis, and p-values derived from permutation testing. Non-esterified fatty acids, urea nitrogen, and glucose were measured using commercially available calorimetric assay kits. Differences in metabolites measured were grouped by residual feed intake was measured using one-way analysis of variance in SAS 9.4.

Results

Four metabolites were found to be associated with differences in feed efficiency. No differences were found in other serum metabolites, including serum urea nitrogen, non-esterified fatty acids, and glucose.

Conclusions

Four metabolites that differed between low and high residual feed intake have important functions related to nutrient utilization, among other functions, in cattle. This information will allow identification of more efficient steers during backgrounding.

     

KniMet: a pipeline for the processing of chromatography–mass spectrometry metabolomics data

Introduction

Data processing is one of the biggest problems in metabolomics, given the high number of samples analyzed and the need of multiple software packages for each step of the processing workflow.

Objectives

Merge in the same platform the steps required for metabolomics data processing.

Methods

KniMet is a workflow for the processing of mass spectrometry-metabolomics data based on the KNIME Analytics platform.

Results

The approach includes key steps to follow in metabolomics data processing: feature filtering, missing value imputation, normalization, batch correction and annotation.

Conclusion

KniMet provides the user with a local, modular and customizable workflow for the processing of both GC–MS and LC–MS open profiling data.

     

Uncovering the metabolic response of abalone (<Emphasis Type="Italic">Haliotis midae)</Emphasis> to environmental hypoxia through metabolomics

Introduction

Oxygen is essential for metabolic processes and in the absence thereof alternative metabolic pathways are required for energy production, as seen in marine invertebrates like abalone. Even though hypoxia has been responsible for significant losses to the aquaculture industry, the overall metabolic adaptations of abalone in response to environmental hypoxia are as yet, not fully elucidated.

Objective

To use a multiplatform metabolomics approach to characterize the metabolic changes associated with energy production in abalone (Haliotis midae) when exposed to environmental hypoxia.

Methods

Metabolomics analysis of abalone adductor and foot muscle, left and right gill, hemolymph, and epipodial tissue samples were conducted using a multiplatform approach, which included untargeted NMR spectroscopy, untargeted and targeted LC–MS spectrometry, and untargeted and semi-targeted GC-MS spectrometric analyses.

Results

Increased levels of anaerobic end-products specific to marine animals were found which include alanopine, strombine, tauropine and octopine. These were accompanied by elevated lactate, succinate and arginine, of which the latter is a product of phosphoarginine breakdown in abalone. Primarily amino acid metabolism was affected, with carbohydrate and lipid metabolism assisting with anaerobic energy production to a lesser extent. Different tissues showed varied metabolic responses to hypoxia, with the largest metabolic changes in the adductor muscle.

Conclusions

From this investigation, it becomes evident that abalone have well-developed (yet understudied) metabolic mechanisms for surviving hypoxic periods. Furthermore, metabolomics serves as a powerful tool for investigating the altered metabolic processes in abalone.

     

Multivariate strategy for the sample selection and integration of multi-batch data in metabolomics

Introduction

Availability of large cohorts of samples with related metadata provides scientists with extensive material for studies. At the same time, recent development of modern high-throughput ‘omics’ technologies, including metabolomics, has resulted in the potential for analysis of large sample sizes. Representative subset selection becomes critical for selection of samples from bigger cohorts and their division into analytical batches. This especially holds true when relative quantification of compound levels is used.

Objectives

We present a multivariate strategy for representative sample selection and integration of results from multi-batch experiments in metabolomics.

Methods

Multivariate characterization was applied for design of experiment based sample selection and subsequent subdivision into four analytical batches which were analyzed on different days by metabolomics profiling using gas-chromatography time-of-flight mass spectrometry (GC–TOF–MS). For each batch OPLS-DA^® was used and its p(corr) vectors were averaged to obtain combined metabolic profile. Jackknifed standard errors were used to calculate confidence intervals for each metabolite in the average p(corr) profile.

Results

A combined, representative metabolic profile describing differences between systemic lupus erythematosus (SLE) patients and controls was obtained and used for elucidation of metabolic pathways that could be disturbed in SLE.

Conclusion

Design of experiment based representative sample selection ensured diversity and minimized bias that could be introduced at this step. Combined metabolic profile enabled unified analysis and interpretation.

     

Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies

Background

Untargeted mass spectrometry (MS)-based metabolomics data often contain missing values that reduce statistical power and can introduce bias in biomedical studies. However, a systematic assessment of the various sources of missing values and strategies to handle these data has received little attention. Missing data can occur systematically, e.g. from run day-dependent effects due to limits of detection (LOD); or it can be random as, for instance, a consequence of sample preparation.

Methods

We investigated patterns of missing data in an MS-based metabolomics experiment of serum samples from the German KORA F4 cohort (n?=?1750). We then evaluated 31 imputation methods in a simulation framework and biologically validated the results by applying all imputation approaches to real metabolomics data. We examined the ability of each method to reconstruct biochemical pathways from data-driven correlation networks, and the ability of the method to increase statistical power while preserving the strength of established metabolic quantitative trait loci.

Results

Run day-dependent LOD-based missing data accounts for most missing values in the metabolomics dataset. Although multiple imputation by chained equations performed well in many scenarios, it is computationally and statistically challenging. K-nearest neighbors (KNN) imputation on observations with variable pre-selection showed robust performance across all evaluation schemes and is computationally more tractable.

Conclusion

Missing data in untargeted MS-based metabolomics data occur for various reasons. Based on our results, we recommend that KNN-based imputation is performed on observations with variable pre-selection since it showed robust results in all evaluation schemes.

     

Urinary antihypertensive drug metabolite screening using molecular networking coupled to high-resolution mass spectrometry fragmentation

Introduction

Mass spectrometry is the current technique of choice in studying drug metabolism. High-resolution mass spectrometry in combination with MS/MS gas-phase experiments has the potential to contribute to rapid advances in this field. However, the data emerging from such fragmentation spectral files pose challenges to downstream analysis, given their complexity and size.

Objectives

This study aims to detect and visualize antihypertensive drug metabolites in untargeted metabolomics experiments based on the spectral similarity of their fragmentation spectra. Furthermore, spectral clusters of endogenous metabolites were also examined.

Methods

Here we apply a molecular networking approach to seek drugs and their metabolites, in fragmentation spectra from urine derived from a cohort of 26 patients on antihypertensive therapy. The mass spectrometry data was collected on a Thermo Q-Exactive coupled to pHILIC chromatography using data dependent analysis (DDA) MS/MS gas-phase experiments.

Results

In total, 165 separate drug metabolites were found and structurally annotated (17 by spectral matching and 122 by classification based on a clustered fragmentation pattern). The clusters could be traced to 13 drugs including the known antihypertensives verapamil, losartan and amlodipine. The molecular networking approach also generated clusters of endogenous metabolites, including carnitine derivatives, and conjugates containing glutamine, glutamate and trigonelline.

Conclusions

The approach offers unprecedented capability in the untargeted identification of drugs and their metabolites at the population level and has great potential to contribute to understanding stratified responses to drugs where differences in drug metabolism may determine treatment outcome.

     

From correlation to causation: analysis of metabolomics data using systems biology approaches

Introduction

Metabolomics is a well-established tool in systems biology, especially in the top–down approach. Metabolomics experiments often results in discovery studies that provide intriguing biological hypotheses but rarely offer mechanistic explanation of such findings. In this light, the interpretation of metabolomics data can be boosted by deploying systems biology approaches.

Objectives

This review aims to provide an overview of systems biology approaches that are relevant to metabolomics and to discuss some successful applications of these methods.

Methods

We review the most recent applications of systems biology tools in the field of metabolomics, such as network inference and analysis, metabolic modelling and pathways analysis.

Results

We offer an ample overview of systems biology tools that can be applied to address metabolomics problems. The characteristics and application results of these tools are discussed also in a comparative manner.

Conclusions

Systems biology-enhanced analysis of metabolomics data can provide insights into the molecular mechanisms originating the observed metabolic profiles and enhance the scientific impact of metabolomics studies.

     

Untargeted metabolomics approach using UPLC-ESI-QTOF-MS to explore the metabolome of fresh-cut iceberg lettuce

Introduction

The shelf-life of fresh-cut lettuce after storage is limited by several factors that affect its quality and lead to consumer rejection. Different metabolic events occur after cutting as an abiotic stress response.

Objectives

This study aims to explore the metabolome of iceberg lettuce and to understand the changes related to storage time and genetics applying an untargeted metabolomics approach.

Methods

Two cultivars with different browning susceptibility, fast-browning (FB) and slow-browning (SB), were analyzed by UPLC-ESI-QTOF-MS just after cutting (d0) and after five days of storage (d5). Extraction, metabolic profiling, and data-pretreatment procedures were optimized to obtain a robust and reliable data set.

Results

Preliminary principal component analysis and hierarchical cluster analysis of the full dataset [around 8551 extracted, aligned and filtered molecular features (MFs)] showed a clear separation between the different samples (FB-d0, FB-d5, SB-d0, and SB-d5), highlighting a clear storage time-dependent effect. After statistical analysis applying Student’s t test, 536 MFs were detected as significantly different between d0 and d5 of storage in FB and 633 in SB. Some of them (221) were common to both cultivars. Out of these significant compounds, 22 were tentatively identified by matching their molecular formulae with those previously reported in the literature. Five families of metabolites were detected: amino acids, phenolic compounds, sesquiterpene lactones, fatty acids, and lysophospholipids. All compounds showed a clear trend to decrease at d5 except phenolic compounds that increased after storage.

Conclusion

The untargeted metabolomics analysis is a powerful tool for characterizing the changes on lettuce metabolome associated with cultivar and especially with storage time. Some families of compounds affected by storage time were reported to be closely related to quality loss.

     

A metabolomics guided exploration of marine natural product chemical space

Introduction

Natural products from culture collections have enormous impact in advancing discovery programs for metabolites of biotechnological importance. These discovery efforts rely on the metabolomic characterization of strain collections.

Objective

Many emerging approaches compare metabolomic profiles of such collections, but few enable the analysis and prioritization of thousands of samples from diverse organisms while delivering chemistry specific read outs.

Method

In this work we utilize untargeted LC–MS/MS based metabolomics together with molecular networking to inventory the chemistries associated with 1000 marine microorganisms.

Result

This approach annotated 76 molecular families (a spectral match rate of 28 %), including clinically and biotechnologically important molecules such as valinomycin, actinomycin D, and desferrioxamine E. Targeting a molecular family produced primarily by one microorganism led to the isolation and structure elucidation of two new molecules designated maridric acids A and B.

Conclusion

Molecular networking guided exploration of large culture collections allows for rapid dereplication of know molecules and can highlight producers of uniques metabolites. These methods, together with large culture collections and growing databases, allow for data driven strain prioritization with a focus on novel chemistries.

     

Crop metabolomics: from diagnostics to assisted breeding

Background

Until recently, plant metabolomics have provided a deep understanding on the metabolic regulation in individual plants as experimental units. The application of these techniques to agricultural systems subjected to more complex interactions is a step towards the implementation of translational metabolomics in crop breeding.

Aim of Review

We present here a review paper discussing advances in the knowledge reached in the last years derived from the application of metabolomic techniques that evolved from biomarker discovery to improve crop yield and quality.

Key Scientific Concepts of Review

Translational metabolomics applied to crop breeding programs.

     

Optimization of fecal sample preparation for untargeted LC-HRMS based metabolomics

Introduction

Collecting feces is easy. It offers direct outcome to endogenous and microbial metabolites.

Objectives

In a context of lack of consensus about fecal sample preparation, especially in animal species, we developed a robust protocol allowing untargeted LC-HRMS fingerprinting.

Methods

The conditions of extraction (quantity, preparation, solvents, dilutions) were investigated in bovine feces.

Results

A rapid and simple protocol involving feces extraction with methanol (1/3, M/V) followed by centrifugation and a step filtration (10 kDa) was developed.

Conclusion

The workflow generated repeatable and informative fingerprints for robust metabolome characterization.

     

Discrimination of pancreatic cancer and pancreatitis by LC-MS metabolomics

Introduction

Pancreatic ductal adenocarcinoma (PDAC) is the fifth most common cause of cancer-related death in Europe with a 5-year survival rate of <5%. Chronic pancreatitis (CP) is a risk factor for PDAC development, but in the majority of cases malignancy is discovered too late for curative treatment. There is at present no reliable diagnostic marker for PDAC available.

Objectives

The aim of the study was to identify single blood-based metabolites or a panel of metabolites discriminating PDAC and CP using liquid chromatography-mass spectrometry (LC-MS).

Methods

A discovery cohort comprising PDAC (n?=?44) and CP (n?=?23) samples was analyzed by LC-MS followed by univariate (Student’s t test) and multivariate (orthogonal partial least squares-discriminant analysis (OPLS-DA)) statistics. Discriminative metabolite features were subject to raw data examination and identification to ensure high feature quality. Their discriminatory power was then confirmed in an independent validation cohort including PDAC (n?=?20) and CP (n?=?31) samples.

Results

Glycocholic acid, N-palmitoyl glutamic acid and hexanoylcarnitine were identified as single markers discriminating PDAC and CP by univariate analysis. OPLS-DA resulted in a panel of five metabolites including the aforementioned three metabolites as well as phenylacetylglutamine (PAGN) and chenodeoxyglycocholate.

Conclusion

Using LC-MS-based metabolomics we identified three single metabolites and a five-metabolite panel discriminating PDAC and CP in two independent cohorts. Although further study is needed in larger cohorts, the metabolites identified are potentially of use in PDAC diagnostics.

     

Metabolic profiling of <Emphasis Type="Italic">Daphnia magna</Emphasis> exposed to environmental stressors by GC–MS and chemometric tools

Introduction

Climate change is a major concern for the scientific community, demanding novel information about the effects of environmental stressors on living organisms. Metabolic profiling is required for achieving the most extensive possible range of compounds and their concentration changes on stressed conditions.

Objectives

Individuals of the crustacean species Daphnia magna were exposed to three different abiotic factors linked to global climate change: high salinity, high temperature levels and hypoxia. Advanced chemometric tools were used to characterize the metabolites affected by the exposure.

Method

An exploratory analysis of gas chromatography-mass spectrometry (GC–MS) data was performed to discriminate between control and exposed daphnid samples. Due to the complexity of these GC–MS data sets, a comprehensive untargeted analysis of the full scan data was performed using multivariate curve resolution-alternating least squares (MCR-ALS) method. This approach enabled to resolve most of the metabolite signals from interference peaks caused by derivatization reactions. Metabolites with significant changes in their peak areas were tentatively identified and the involved metabolic pathways explored.

Results

D. magna metabolic biomarkers are proposed for the considered physical factors. Metabolites related with energy metabolic pathways including some amino acids, carbohydrates, organic acids and nucleosides were identified as potential biomarkers of the investigated treatments.

Conclusions

The proposed untargeted GC–MS metabolomics strategy and multivariate data analysis tools were useful to investigate D. magna metabolome under environmental stressed conditions.

     

Improved experimental data processing for UHPLC–HRMS/MS lipidomics applied to nonalcoholic fatty liver disease

Introduction

Untargeted metabolomics workflows include numerous points where variance and systematic errors can be introduced. Due to the diversity of the lipidome, manual peak picking and quantitation using molecule specific internal standards is unrealistic, and therefore quality peak picking algorithms and further feature processing and normalization algorithms are important. Subsequent normalization, data filtering, statistical analysis, and biological interpretation are simplified when quality data acquisition and feature processing are employed.

Objectives

Metrics for QC are important throughout the workflow. The robust workflow presented here provides techniques to ensure that QC checks are implemented throughout sample preparation, data acquisition, pre-processing, and analysis.

Methods

The untargeted lipidomics workflow includes sample standardization prior to acquisition, blocks of QC standards and blanks run at systematic intervals between randomized blocks of experimental data, blank feature filtering (BFF) to remove features not originating from the sample, and QC analysis of data acquisition and processing.

Results

The workflow was successfully applied to mouse liver samples, which were investigated to discern lipidomic changes throughout the development of nonalcoholic fatty liver disease (NAFLD). The workflow, including a novel filtering method, BFF, allows improved confidence in results and conclusions for lipidomic applications.

Conclusion

Using a mouse model developed for the study of the transition of NAFLD from an early stage known as simple steatosis, to the later stage, nonalcoholic steatohepatitis, in combination with our novel workflow, we have identified phosphatidylcholines, phosphatidylethanolamines, and triacylglycerols that may contribute to disease onset and/or progression.

     

Quality assurance and quality control processes: summary of a metabolomics community questionnaire

Introduction

The Metabolomics Society Data Quality Task Group (DQTG) developed a questionnaire regarding quality assurance (QA) and quality control (QC) to provide baseline information about current QA and QC practices applied in the international metabolomics community.

Objectives

The DQTG has a long-term goal of promoting robust QA and QC in the metabolomics community through increased awareness via communication, outreach and education, and through the promotion of best working practices. An assessment of current QA and QC practices will serve as a foundation for future activities and development of appropriate guidelines.

Method

QA was defined as the set of procedures that are performed in advance of analysis of samples and that are used to improve data quality. QC was defined as the set of activities that a laboratory does during or immediately after analysis that are applied to demonstrate the quality of project data. A questionnaire was developed that included 70 questions covering demographic information, QA approaches and QC approaches and allowed all respondents to answer a subset or all of the questions.

Result

The DQTG questionnaire received 97 individual responses from 84 institutions in all fields of metabolomics covering NMR, LC-MS, GC-MS, and other analytical technologies.

Conclusion

There was a vast range of responses concerning the use of QA and QC approaches that indicated the limited availability of suitable training, lack of Standard Operating Procedures (SOPs) to review and make decisions on quality, and limited use of standard reference materials (SRMs) as QC materials. The DQTG QA/QC questionnaire has for the first time demonstrated that QA and QC usage is not uniform across metabolomics laboratories. Here we present recommendations on how to address the issues concerning QA and QC measurements and reporting in metabolomics.