New advances in analytical methods for mass spectrometry-based large-scale metabolomics study

Journal Pre-proof New advances in analytical methods for mass spectrometry-based large-scale metabolomics study Xinyu Liu, Lina Zhou, Xianzhe Shi, Guowang Xu PII:

S0165-9936(19)30345-0

DOI:

https://doi.org/10.1016/j.trac.2019.115665

Reference:

TRAC 115665

To appear in:

Trends in Analytical Chemistry

Received Date: 1 June 2019 Revised Date:

7 September 2019

Accepted Date: 11 September 2019

Please cite this article as: X. Liu, L. Zhou, X. Shi, G. Xu, New advances in analytical methods for mass spectrometry-based large-scale metabolomics study, Trends in Analytical Chemistry, https:// doi.org/10.1016/j.trac.2019.115665. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.

New advances in analytical methods for mass spectrometry-based large-scale metabolomics study

Xinyu Liu#, Lina Zhou#, Xianzhe Shi, Guowang Xu* CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China

#

: equal contribution.

*

Corresponding authors:

Prof. Guowang Xu, CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China. Tel./Fax: +86-411-84379530. E-mail: [email protected].

1

Abstract Large-scale metabolomics study based on large population cohorts is increasingly applied to identify important metabolites or critical metabolic alterations related to the metabolite perturbation in disease states or under interventions and to investigate metabolic difference and stimuli response in genetically different individuals. Mass spectrometry (MS) coupled with different chromatographic methods, is suitable for large-scale metabolomics study due to its high sensitivity and selectivity, wide dynamic range, and rich information. However, there are still a series of challenges for really realizing large-scale metabolomics applications. Hence, in this review, we mainly focused on new advances in sample pretreatment methods, nontargeted, targeted and pseudotargeted metabolic data collection techniques, and data correction methods used for MS-based large-scale metabolomics study. Typical applications of MS-based large-scale metabolomics methods in molecular epidemiology, precision medicine, and genome-wide association studies with metabolomics (mGWAS) are also given.

Key words: large-scale metabolomics; mass spectrometry; nontargeted; targeted; pseudotargeted

2

1. Introduction Metabolomics is defined as comprehensively quantitative and qualitative small molecular metabolites in biochemical processes within an organism simultaneously, and further to reveal metabolic response to a series of stimuli from endogenous and exogenous factors [1,2]. Large-scale metabolomics study now is being widely applied in the fields such as molecular epidemiology [3], precision medicine [4], and genome-wide association studies with metabolomics (mGWAS) [5], which is helpful to improve the comprehending of (patho-) physiological mechanism of complex disorders, as well as disease diagnosis, prevention and treatment. Considering the substantial difference among the individual phenotype, large population cohorts are required in the studies mentioned above. Therefore, it is highly desirable to develop high throughput analytical methods with high sensitivity, high information and excellent stability and repeatability, as well as robust data integration methods for large-scale metabolomics analysis. Nontargeted and targeted methods are the two typical metabolomics approaches, the former allows to obtain metabolites information with broad coverage based on high resolution mass spectrometry (HRMS), the latter focuses on quantifying known metabolites by using low resolution triple quadrupole (TQ) MS. While pseudotargeted method [6,7] combines the advantages of nontargeted and targeted methods, in which metabolite ion pairs were generated from nontargeted HRMS method, but subsequently analyzed in targeted TQMS mode. Therefore, the information coverage and quantitative accuracy are markedly improved in pseudotargeted metabolomics method. Currently, mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (NMR) are main analytical tools for metabolomics. Due to high sensitivity, good mass accuracy and wide dynamic range, MS is playing more and 3

more roles

in

metabolomics

study by being hyphenated

with

different

chromatographic separation modes, such as liquid chromatography (LC) [8], gas chromatography (GC) [9,10], and capillary electrophoresis (CE) [11,12]. Furthermore, MS instruments also have a variety of mass analyzers including low resolution mass analyzer such as TQ, and high resolution mass analyzer such as quadrupole-time of flight (Q-TOF), Orbitrap and Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) [13]. The flexibility and advantages make MS technique become a main tool for metabolomics study in the nontargeted, targeted and pseudotargeted modes (Figure 1). Although metabolomics study has achieved great successes in different fields, it is still a challenge to guarantee high data quality when measuring thousands of biological samples in one study due to instrument drift (signal fluctuation and sensitivity reduction), environment fluctuation and analytical variances [14,15]. A series of issues should be covered when large-scale metabolomics is performed (Figure 2). Firstly, high efficiency sample pretreatment method is needed to increase throughput. Secondly, data collection method is critical for ensuring rich metabolic profiling information and high analytical throughput. Thirdly, data repeatability and integration among different batches and different instruments are the key factors for really realizing large-scale metabolomics study. Last but not the least, metabolite identification and biological explanation will decide whether the expected aim can be achieved. In response to these challenges, a lot of new progresses have been made in recent 10 years. Because this journal just accepted a review on metabolite identification [16], to avoid repeatability and save the space, this review will focus on summarizing new advances in analytical methods for MS-based large-scale metabolomics study, mainly focusing on high throughput sample pretreatment 4

methods, nontargeted, targeted and pseudotargeted analytical technology, and data correction methodology. In the meantime, typical applications of metabolomics methods in molecular epidemiology, precision medicine, and mGWAS are also given. On the biological explanation, especially on functional metabolomics, readers can refer to our recent review [17].

2. High throughput sample preparation methods Sample preparation is a time-consuming and tedious step before chromatographic separation and metabolomics fingerprinting acquisition, especially when managing large-scale sample set. Ideal sample pretreatment method for metabolomics needs to have high throughput, good repeatability, broad coverage of metabolites. Currently, commonly used sample preparation methods are mainly based on organic solvent-based deproteinization [18], high throughput 96-well plate [19-21] and solid phase (micro) extraction (SPE/SPME) [22]. Some advanced methods which have the potential to be used for large-scale metabolomics study have been reported. Li et al. developed an efficient sample pretreatment method based on SPE using Ostro 96-well plates, metabolome and lipidome in plasma were extracted by using acetonitrile with 1% formic acid and chloroform/methanol (2/1, v/v), respectively. This method can be used for the rapid and high-throughput extraction [19]. Ostro 96-well plate extraction has been tested for metabolome and for selected classes of lipids, but not all lipids can be extracted, and different groups of lipids require different conditions. [23]. Löfgren et al. developed a novel plasma lipid extraction method using butanol-methanol (BUME) mixture with standard 96-well robots, and lipids of 96 samples can be automatically and efficiently extracted in 60 min [20]. The BUME methods for lipid extraction in biofluids and 5

tissue samples are better than commonly used chloroform-based extraction methods [24,25]. When 96-well plate is not available two-step liquid-liquid extraction protocol is an alternative [26,27]. Chen et al. [28] established a method which enabled simultaneous extraction of metabolome and lipidome with methyl tert-butyl ether (MTBE) from a small amount of tissue sample, lipid and lipophilic compounds were mainly extracted into the MTBE layer, polar and moderate polar metabolites into the methanol/water layer, while amphoteric metabolites are distributed in both upper and lower layers. For the analysis of amphoteric metabolites, mixing non-polar (upper phase) and polar (lower phase) fractions together is necessary to ensure the metabolite coverage [28]. This method is especially useful for simultaneous metabolomics and lipidomics when only a limited amount of tissue or cell is available. Barbas et al. proposed an in-vial dual extraction (IVDE) procedure for a small amount of plasma, all the extraction was performed in one single vial and even recovering proteins for proteomics analysis is possible [29,30]. To satisfy the requirement of time-sensitive clinical applications, particularly in the emergency department, Gehrke et al. developed a rapid liquid-liquid extraction protocol (~4 min) for hydrophilic compounds, which can be applied universally to plasma, whole blood and red blood cells [31]. Besides, commercial metabolomics kits were launched by Biocrates Life Sciences AG (Innsbruck, Austria) in this decade. For example, the AbsoluteIDQ® p400 HR Kit is the standardized “targeted metabolic profiling” solution for 408 metabolites based on HR MS (www.biocrates.com). The design of this kit allows to perform all sample preparation steps directly on the 96-well plate, and provides quantification, reproducibility, and high throughput for various biological samples. At present, although there is no universal method for sample pretreatment 6

applied to all small molecular metabolites, organic solvent-based extraction combined with 96-well plate and 96-well automated handler technology are the most widely used high-throughput sample pretreatment method in large-scale metabolomics study. Automated liquid handler technology is another brilliant choice for high-throughput sample preparation, rigorous standardized operation procedures and automated liquid handler ensure good quality of sample extraction and processing.

3. MS-based metabolic profiling data collection methods 3.1 Nontargeted metabolomics methods Nontargeted method is the most characteristic and most frequently used metabolomics approach. It aims at unbiased detecting as many metabolites as possible in a biological sample in a single run, it can be performed when there is no previous knowledge of sample components [32,33]. Nontargeted metabolomics platforms are predominantly realized by coupling high-efficient

chromatographic

separations

with

the

high-sensitivity

and

high-resolution TOF or Orbitrap-MS detector in the full scan mode. Nevertheless, such coupling techniques take relatively longer analysis time, limiting the throughput, especially when a large cohort is studied. Hence, innovations in chromatographic separation have been made for improving throughput. Applications of shorter or microbore chromatographic column, higher column temperature and faster flow rate would be a good choice to shorten elution gradient, and then shorten the analysis time. Gray et al. developed a microbore UPLC−MS method to provide a high-throughput urine analytical platform by using 1 mm × 50 mm columns, analysis time was shortened to 2.5 min/sample. Compared with the common 2.1 mm × 100 mm columns, 7

detected features were approximately 19 000 and ∼6 000, respectively, the peak capacity and the ions detected were greatly reduced because of a very short analytical time [34]. To compromise the analytical speed and peak capacity, Ouyang et al. developed a 12 min of LC-MS method, which reduced about 60% analysis time and increased analytical throughput significantly. Evaluation result of separation performances and metabolite coverage of this method demonstrated that it was robust for nontargeted metabolic profiling analysis of large-scale metabolomics study [21]. Yin et al. developed a LC-MS-based method for bile acid profiling in a 14 min of analytical cycle, which also disclosed factors affecting bile acids separation and detection by LC-MS in negative mode [35]. Moreover, an on-line heart-cutting 2D-LC-MS method developed by Wang et al. realized simultaneous collection of metabolome and lipidome information in 30 min with a single injection, which is valuable for small amount of samples, as well as large-scale metabolomics studies [36]. Direct injection-mass spectrometry could be another choice for high-throughput metabolomics [37]. HRMS-based direct injection method will provide informative and high-quality data including accurate mass and isotopic distribution. Such approach enabled hundreds to thousands of metabolite fingerprints acquisition per day [38-40]. Chekmeneva et al. showed direct infusion nano-electrospray HRMS method enabled cost-efficient analysis of >2,200 urine samples in <3 weeks and >10,000 urine samples in 12 weeks with the required sensitivity and accuracy. Additionally, method validation was assessed based on linear ranges, inter- and intraday accuracy and precision, method robustness, and long-term stability. And the results were in accordance with the acceptance criteria for analytical quality, which proved that this method was suitable for population based large-scale epidemiological studies [41]. 8

While matrix effects would be a significant impact factor which hampers metabolite detection due to lack of chromatographic separation. Dilution of sample is a helpful manner to lessen matrix effects and improve data repeatability and quality. Apart from matrix effects, direct injection MS also suffers from another limitation to separate structural isomers and isobars. Nowadays, thousands of complex samples per day and matching instrument have been a reality for nontargeted metabolomics analysis, and microfluidics related techniques will likely further advance throughput in the future [42]. Frontiers of high-throughput metabolomics can be found from Fuhrer et al.’ review [42]. MS data generated in nontargeted approach are informative and conducive to structure identification of metabolites due to HRMS, but the limited linear range is a shortcoming for accurate quantification of metabolites with a wide concentration range in biological samples. In the meantime, data repeatability in day-to-day and batch-to-batch and data handling complexity especially in peak matching are also issues of nontargeted methods to be covered.

3.2 Targeted metabolomics methods Targeted analysis methods are generally used to quantify the variation of one or more metabolites in a specific pathway, usually carried out by selective ion monitoring mode (SIM) or multiple reaction monitoring (MRM) mode. Targeted approach is considered as the gold standard for absolute quantitative analysis of metabolites due to it has a wider linear range (four to six orders of magnitude), better repeatability and higher sensitivity [43,6]. Recently, a new approach named as parallel reaction monitoring (PRM), which is carried out on HRMS platforms, contributes more and more to accurate quantification of targeted metabolites (Figure 3). 9

3.2.1 TQ MS-based MRM analysis MRM based on TQ MS or hybrid triple-quadrupole-linear ion trap (QTRAP) MS is most widely used for analyzing targeted known metabolites of interest (Figure 3). Ion pair transitions (precursor ion to product ion) and their corresponding MS parameters (collision energy (CE), etc.) are important, and should be optimized to obtain good sensitivity and selectivity. Many high throughput targeted methods have been developed for metabolomics study. Wei et al. combined three different chromatographic column conditions and two ionization modes to guarantee metabolite coverage, separation and sensitivity of detected metabolites, 205 metabolites including amino acids, nucleic acids, sugar and organic acids were detected in 10 min [44]. Gu et al. described a globally optimized targeted method to analyze 160 biologically important metabolites [45]. And Yuan et al. analyzed 258 metabolites in 15 min with positive/negative ion switching based on HILIC-MS, which are also suitable for different biological sample types [46]. A novel stepwise multiple ion monitoring-enhanced product ion (stepwise MIM-EPI) method has been developed for quantification of 277 metabolites in rice metabolomics, and the construction of MS2 spectral tag library not only facilitated metabolite annotation, but also enlarged metabolites coverage [47]. Compared with classic target strategies, metabolites analyzed in advanced targeted metabolomics approach mentioned above have been enlarged to hundreds in single analysis. While absolute quantification of targeted metabolites needs reference standards and corresponding internal standard compounds, as well as comprehensive method validation procedure. Fast scan speed and fast switching speed between positive and negative modes are the two main advantages of TQ/QTRAP instruments, therefore, it is suitable for developing targeted metabolomics method used for large-scale study. The 10

disadvantages are that transition selection and MS parameter optimization for the targeted metabolites are time-consuming, and the metabolites with the same MRM transition can’t be resolved.

3.2.2 HRMS-based PRM analysis The MRM method has been considered as the gold standard of metabolite quantification for its fast scan speed and stable analytical performance, but it still has un-avoidable drawbacks due to low mass resolution. PRM is a new approach that can be carried out on HRMS platforms. In this approach, metabolite precursor is selected by Q1 and then fragmented at the HCD mode, followed by parallel scan of MS/MS fragments in a HR mass spectrometer (Figure 3). As a new attractive strategy, PRM has shown reliable performance in targeted peptide and metabolite quantification analysis. Peterson and Gallien’s work on targeted peptide PRM method demonstrated such approach had a good measurement accuracy, wide dynamic range and reliable run-to-run reproducibility [48,49], in total 770 tryptic yeast peptides have been analyzed in 60 min [49]. PRM-based targeted strategy for metabolite detection has also been developed on Q-Exactive LC-MS system, which enabled simultaneous quantification of 237 polar metabolites with good quantitative accuracy and data reproducibility. The comparison between PRM and MRM measurements were performed, the results indicated that PRM presented higher mass precision and more comprehensive MS2 information than MRM measurement on QTRAP 6500 [50]. MRM and PRM methods have their own advantages and disadvantages. The most significant advantages of PRM assays are that metabolite identification is verified by higher mass accuracy due to PRM performed based on high resolution instrument, which also facilitates separation of metabolites at MS2 dimension. While traditional 11

MRM measurement carried out on TQ MS is generally unit mass resolution without informative full scan MS1 and MS2. Disadvantages of PRM are relatively low spectra scan speed and slow polarity switching. Hence，PRM and MRM are two excellent complementary tools for accurate quantification of large-scale metabolomics. Targeted detection method is one of the most crucial methods in MS-based metabolomics, especially for absolute quantification. However, major limitation of such approach is the less metabolome coverage, often focusing on a small to medium amounts of known metabolites. Therefore, it is necessary and valuable to develop a new method for broadening metabolome coverage, including unknown metabolite detection.

3.3 Pseudotargeted metabolomics method To integrate the advantages and avoid the disadvantages of targeted and nontargeted methods, pseudotargeted metabolomics methods were launched based on GC-MS and LC-MS in 2012 and 2013, respectively [6,7]. This type of methods has a higher selectivity and sensitivity, wider linear range, better quantitative accuracy, especially broader metabolome coverage. Pseudotargeted method aims to acquire ion pairs from pooled biological samples in the full scan nontargeted mode and to measure as many metabolites as possible in individual studied samples in the targeted MRM mode. The general workflow of pseudotargeted method is as follows. Firstly, comprehensive full scan information of pooled biological sample is acquired using HRMS,

meanwhile

MS2

fragmentation

information

is

collected

at

information-dependent or data-dependent acquisition (IDA or DDA) mode. Different 12

CE voltages covering low, medium and high energy levels (eg. 15 V, 30 V and 45 V or 20 V, 40 V and 60 V) are set in parallel LC-MS injections. In this step, sample preparation takes around 4 hours, and LC-MS analysis for positive and negative modes needs 30 and 25 min, respectively. Secondly, precursor ion and its characteristic product ions are selected as MRM ion pairs of known and unknown metabolites. Utilization of MRM-Ion Pair Finder software significantly speeds up this step (<1 hour). The details of this key procedure will be given below. Then MRM transitions are defined after optimizing collision energy (CE) of metabolites (around 10 hours). Finally, the abundances of metabolites in individual biological samples are collected by using MRM mode of TQ MS [51]. Quantitative performance of the developed pseudotargeted metabolomics method is evaluated by linearity, stability, intra-day repeatability and inter-day repeatability, method evaluation needs extra 3 days. The key, but time-consuming aspect of this approach was to extract the characteristic ion pairs of metabolites from high-complexity raw MS data to generate MRM transitions. To solve this issue, Luo et al. developed a novel strategy for defining MRM ion pairs (Figure 4), IDA based experiment under different CE voltages (20V, 40V and 60V) was firstly carried out by Triple TOF 5600+ MS to obtain comprehensive MS2 information, then home-developed MRM-Ion Pair Finder software was innovatively developed and applied to select the daughter ion with four steps including precursor ions alignment, extraction and reduction of MS2 spectrum, selection of characteristic product ion, and ion fusion. Eventually, the most intensive parent ion and its corresponding CE value would be defined to generate MRM transitions [51]. However, IDA-based method was in favor of acquiring MS2 spectra of ions with relative high intensity, which would limit MRM ion pair 13

construction of metabolites with low abundance. While Sequential Windowed Acquisition of All Theoretical Fragment Ion (SWATH) is an emerging MS acquisition technology to theoretically obtain comprehensive fragmental information on all precursor ions. Wang et al. developed a novel ion pair selection method based on SWATH technology with variable Q1 isolation windows and various CE voltages. Compared with IDA MS mode，additional 253 ion pairs have been identified in SWATH method, which indicated that SWATH could provide richer fragmental information of detected metabolites, especially for metabolites with low abundance [52]. By integrating the detected lipids from nontargeted HRMS lipidomics analysis of multiple matrices (e.g., plasma, cell, and tissue) and the predicted lipids speculated on the basis of the structure and chromatographic retention behavior of the known lipids, a total of 3377 lipid ion pairs covering around 7000 lipid molecular structures across 19 lipid subclasses were involved in the pseudotargeted lipidomics method [53]. Similar to pseudotargeted method, widely targeted metabolomics method was also proposed [54,55]. In the pseudotargeted method, ion pair information of MRM is mainly from MS2 of pooled samples, while in widely targeted method it is mainly from the standards. Pseudotargeted analysis is established based on pooled biological samples, combined with comprehensive acquisition of MS1 and MS2 information, which eliminated the reliance on standards when the method is developed. Such approach retains the advantages of targeted method, meanwhile expands metabolome coverage significantly, and can be used to detect known and unknown metabolites simultaneously. Therefore, pseudotargeted method is the most potential technology for large-scale sample analysis with its excellent quantitation capability [56]. 14

Pseudotargeted method was also used for the sensitive analysis of protein phosphorylation in protein complexes [56].

4. Data correction in large-scale sample analysis Large-scale metabolomics study with the analysis of at least hundreds of samples will take a long continuous analysis time from days to Months. It is only practical if analytical sequences can achieve high analytical stability and repeatability. However, due to limited chromatographic column life and changed mass spectrometry sensitivity as well as environment variances during long-time analysis, systematic and gross errors are frequently observed in the data generated from different analytical sequences and batches [56]. Factors which would affect analytical repeatability in different batches should be seriously considered. There have been a lot of protocols suggested as ‘good practice’ for LC-MS and GC-MS-based large-scale metabolomics [56-59]. In these methods, the pooled quality control (QC) sample was used to evaluate the robustness of the analytical sequence and correct analytical variability [56]. QC sample selection, QC insertion frequency, as well as data calibration and integration method are the successful keys. Generally, pooled real sample was recommended as quality control sample (batchQC) equally inserted in sequence for analytical repeatability and stability evaluation. While for large-scale metabolomics study, it is difficult to pool QC sample from all real samples before analysis start [60]. Consequently, alternative sample generated from laboratory quality control (LabQC) samples belonging to the same type was utilized to monitor analytical sequence. Luo et al. compared the effectiveness of batchQC and LabQC samples in monitoring analysis stability. Data 15

demonstrated that although metabolite concentrations were different in two different types of QCs, majority of detected ion features were steadily measured in both QC samples, and they shared similar CV distribution in detected metabolites, signal drifts calibration results based on LabQC were also as good as those based on batchQC [56]. Hence, LabQC would be a better choice for large-scale metabolomics study since it can be prepared in considerable amount prior to sample analysis. More importantly, LabQC can also be used to monitor the stability and repeatability of the analysis sequence effectively. In theory, QC sample can be used to monitor stability of analytical sequence in real time, and the higher QC insertion frequency perhaps contributed to the better data results after signal drift calibration [14]. But frequent QC insertion reduced analytical throughput, whereas increased instrument deterioration and economic consuming. Hence, compromise should be made between QC insertion frequency associated correction effect and high analytical throughput in large-scale study. Dunn suggested that QC sample inserted at every fifth injection in the nontargeted method-based large-scale study [14]. Our group investigated the influence of different QC insertion frequency on data stability and correction effect in pseudotargeted analytical technology. The result demonstrated that different QC insertion frequencies (every 5, 10 or 15 samples with one QC insertion) made no significant difference in data stability and correction effect. Therefore, pseudotargeted analytical technology is feasible to reduce QC insertion frequency (one QC in every 10-15 sample injections) without compromising data quality and correction effect, and to increase the analytical throughput significantly [56]. In many cases, it is necessary to divide large amount samples set into several batches of small-scale samples to obtain high quality metabolomics data and/or 16

improve the speed by analyzing them in several instruments. Therefore, another important issue in large-scale metabolomics study is the integration of metabolomics data from different analytical batches and different analytical instruments. Total intensity signal (TIS) correction and internal standard (IS) normalization methods are the most widely used methods in metabolomics data correction [10,61]. Good correction result could be achieved when all measured metabolites display similar change tendency with TIS or IS. However, in fact it is impossible for hundreds to thousands of detected metabolite features to show similar change pattern to TIS or IS. Hence, integration of data from different batches couldn’t be achieved by a simple TIS or IS correction. In order to solve this problem, novel strategies of feature-based correction have attracted much more attention and made notable progresses. QC samples could be innovatively used as external standards for calibration of each ion features from all real samples hence to minimize variations between and in the batches [60,62-64]. The local or global mean intensity of QCs (LoMec and GoMec), as well as the local or global linear regression of QC intensity values (LoReg and GoRec) were commonly employed to develop a factor for each feature correction and thus to eliminate systematic bias [56,62]. Zhao et al. provided a novel tactic for removing both gross and systematic errors in large-scale metabolomics study, feature ratio between two adjacent QCs was utilized to construct statistical and fitting models for gross error calibration, while systematic bias was removed by virtual QC of each sample which generated through fitting model based on feature intensities between neighbor QCs [65]. Such method could not only improve data quality significantly, but also make metabolomics data integration from multiple analytical batches and different GC-MS instruments possible. Recently, Thonusin et al. developed an Excel-based tool (MetaboDrift) to evaluate and correct visually for intensity drift in a 17

multi-batch LC-MS dataset [66]. Based on the wavelet transform method with independent component analysis, Deng et al. developed a WaveICA method to find and remove batch effects for large-scale metabolomics data [67]. As a representative work, Vaughan et al. developed calibration transfer models which can be used to merge the data from 2 LC-MS platforms [68]. The development of data correction method enables the integration and comparison of large-scale metabolomics data, and breaks through the bottleneck of required data processing. Figure 5 is a typical workflow for large-scale metabolomics including steps of development, optimization and application [65]. The combination of reliable data collection technique, pooled QC insertion with a suitable frequency, blank-wash of pre- and post-acquisition, can extend analytical time of a single batch and further be applied to multiple batches of samples, the final results are promising for a large-scale analysis.

5. Typical applications of metabolomics in population-based cohorts Metabolomics is often used to identify important metabolites or critical metabolic alterations related to the metabolite perturbation in disease states or under interventions, etc. Due to the diversity in physicochemical properties of the human metabolome, multiple analytical techniques need to be employed to cover relative comprehensive information in the complex metabolism networks. Considering the feasibility and the metabolic targets to be investigated, the most robust and compromise metabolomics approach is often chosen for large-cohort experiments. With the above technical progresses, large-scale population-based metabolomics studies have been carried out (Table 1), which have facilitated identification of potential diagnosis markers and risk factors for metabolic syndrome [69,70], cancer 18

[71,72], exploration of individuality in drug metabolism [73] and nutritional epidemiology [74,75], etc. Here, we focus on the advances in how large-scale metabolomics studies aid to screen risk factors or early clinical diagnosis biomarkers, and identify key metabolic pathways and understand associated mechanisms of disease development at the population level in molecular epidemiology, precision medicine, and mGWAS.

5.1 Identification of clinical diagnosis or risk biomarkers for metabolic diseases and cancers A variety of large-scale metabolomics studies have been performed to identify potential risk markers of metabolic diseases and cancers. Related studies on pre-diabetes and type 2 diabetes (T2D) have been systematically reviewed by Liggi et al. [69] and Park et al.[76]. In the former review each large-scale metabolomics study included the descriptions of research aims, epidemiology cohort for sampling and the number of samples, metabolomics detection method, the derived important findings and related results replicated in other studies, etc. The later carried out a systematic review and meta-analysis on diagnostic indicators from large-scale cohort studies. Large-scale longitudinal metabolomics study of gestational diabetes mellitus (GDM) was also performed, 8 metabolites (leucine/isoleucine, glutamic acid, serine, proline, ornithine, tyrosine, uric acid and lysophosphatidylcholine (LPC (20:4)) might have contributed to the development of GDM [77]. Cardiovascular disease is also a hot field for larger-scale metabolomics [70,78,79]. One shotgun lipidomics research was performed on 685 plasma samples of the prospective population-based Bruneck study by profiling 135 lipid species from 8 different lipid classes, it was found that PE (36:5), CE (16:1) and TAG (54:2) 19

improved the risk prediction and classification of CHD [80]. Based on targeted blood metabolomics measurements on two large German prospective cohorts, alterations in phosphatidylcholine and sphingomyelin metabolism, particularly metabolites of the arachidonic acid pathway were identified independently associated with higher incidence of myocardial infarction in initially healthy adults [81]. To identify at-risk population of CHD among 1,028 individuals (131 CHD events at median 10th year’s follow-up) and 1,670 individuals (282 CHD events at the median 3.9 year’s follow-up), a nontargeted metabolomics study based on UPLC-MS was performed. Four circulating metabolites, lysophosphatidylcholine 18:1 (LPC 18:1), LPC 18:2, sphingomyelin 28:1 and monoglyceride 18:2 (MG 18:2) were independent of main cardiovascular risk factors [70]. Cancer not only is a genetic-related disease, but also is a metabolic disease. Therefore, metabolic marker discovery from the large-scale metabolomics study of cohorts has attracted scientists’ attention [82-84]. Our group collected sera from 1,448 subjects including healthy controls and patients with chronic hepatitis B virus infection, liver cirrhosis, and HCC from 6 centers in China, and used UPLC-MS based metabolomics methods to screen and validate the HCC biomarkers, phenylalanyl-tryptophan and glycocholate were found to exhibit a good diagnostic performance for the early detection of HCC from at-risk populations [82]. Stepien et al. investigated circulating amino acids (AA), biogenic amines and hexoses in a large prospective cohort to see whether they were related to the onset and development of hepatobiliary cancer. Lysine, leucine , glutamine and the ratio of branched chain to aromatic

AA were

inversely,

while

glutamate,

glutamate/glutamine

ratio,

phenylalanine, tyrosine and their ratio, kynurenine and its ratio to tryptophan were positively associated with HCC risk [85]. Chajes et al. investigated the pre-diagnostic 20

plasma phospholipid fatty acid concentrations and risk of breast and gastric cancers on two case-control studies nested in the European prospective investigation on cancer and nutrition (EPIC) cohorts. The differential fatty acids presumably reflecting dietary pattern may be related to both increased risk of gastric and breast cancers [86,87]. More applications on ovarian cancer, breast cancer, bladder cancer and rheumatoid arthritis can be seen in Table 1, the sample size criteria used for inclusion in each study is >400.

5.2 Understand associated mechanisms of metabolic diseases Not only contributing to focus on important risk factors, integrative endeavors on metabolomics

studies

and

genome-wide

association study

(GWAS)

or

epigenome-wide association studies can also provide biological insight and help better understand associated onset and development mechanisms of diseases. Recent studies have integrated GWAS data of large cohorts with metabolomic data to explore the cross-correlations of genetic polymorphisms with metabolic alterations. A nontargeted metabolomics research was performed on 1744 African Americans who were free of heart failure (HF) at their baseline examination in the Atherosclerosis Risk in Communities (ARIC) study [88]. Six known metabolites, including dihydroxy docosatrienoic acid, pyroglutamine, and X-11787 (being either hydroxy-leucine or hydroxy-isoleucine) were identified as risk factors for incident HF. To further identify novel genetic factors that are associated with the above three metabolites, GWAS data from 1260 African-Americans free of HF at the baseline were analyzed and three SNPs were identified associated with the three reported HF-related metabolites [89]. By integrating genotyping and targeted plasma betaine detection, genetic risk 21

variants related to coronary artery disease (CAD) and their influences on plasma betaine levels were investigated in two-stage human cohort studies [90], two locus on chromosomes 2q34 and 5q14.1 were significantly associated with betaine levels, and the variant on 2q24 (rs715) also exhibited a very significant female-specific decreased risk of CAD. A genome-wide genotyping and plasma metabolic profiling were also performed in 2076 participants of the Framingham Heart Study (FHS). This cohort was characteristic of family-based structure and rich in cardiometabolic phenotyping, which finally helped demonstrate the higher contributions of inherited factors on the metabolome than that of clinical covariates. Thirty-one genetic locus were identified associated with plasma metabolites, including 8 locus-metabolite associations previously reported. Especially, a new role of AGXT2 has been found in regulating cholesterol ester and triacylglycerol metabolism [91]. In

addition

to

the

integrated

studies

of

GWAS

and

metabolomics,

methylome-metabotype associations were also investigated by analyzing metabolic profile and DNA methylation in human blood from 1814 participants of the Kooperative Gesundheitsforschung in der Region Augsburg (KORA) population study [92]. Though DNA methylation was found playing a critical role in regulating metabolism, the effects of DNA methylation on metabotypes were weaker than what the authors previously reported for associations of genotype with metabotypes. And it is not easy to interpret the causality between methylation and metabolic traits.

6. Future perspectives In the future, with the significant progresses of MS and chromatography, large-scale metabolomics study will increasingly be applied in molecular 22

epidemiology, precision medicine, and mGWAS. From the analytical point of view, advanced sample preparation based on high throughput 96-well plate as well as automated robot combined with rapid chromatographic separation conditions will save sample pretreatment time and significantly increase analytical throughput. Different kinds of liquid handlers with robotization and automatization are helpful to provide better reproducible and reliable results. Micro separation techniques including microfluidics will likely further advance throughput. Combined with derivatization [93] and stable isotope labeling, the quantitation sensitivity for trace metabolites will be greatly improved. In the meantime, the improvement in sensitivity and response stability of MS is one of key factors, MS and chromatography should further increase the antipollution ability to reduce matrix influences from the biological samples. It can also be expected that as the second generation of metabolomics technique, pseudotargeted metabolomics is helpful to fill the gap between nontargeted and traditional targeted metabolomics methods, and will be more applied in clinical biomarker discovery, basic analysis of targeted gene function, as well as quantitative systems biology. On the other hand, artificial intelligence (deep learning) needs to be considered for handling measured metabolic profiling data, it will play very important roles in identifying MS features and increasing the data comparability and repeatability from different batches and instruments in large-scale metabolomics. To integrate GWAS and metabolomics data and expand our knowledge on the genetic effects on circulating metabolites, the next challenge will still be to harmonize and standardize some of metabolome detection methods so that large-cohort metabolomics data can be compared across different research projects and different laboratories in the same way as the current GWAS studies do. 23

Acknowledgements This work was funded by the National Key Research and Development Program of China (2017YFC0906900), the foundation projects (No. 21705147 and No. 21876169) from the National Natural Science Foundation of China and the innovation program (DICP TMSR201601) of science and research from the DICP, CAS.

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42

Figure legends

Figure 1. The properties of the common analytical strategies used in large-scale metabolomics illustrated by information amount, data complexity and variability, stability and repeatability, and quantitative accuracy.

Figure 2. A series of important issues which should be taken into consideration when performing large-scale metabolomics study. Firstly, high-throughput sample pretreatment method is needed to ensure high efficiency sample preparation. Secondly, data acquisition methods should not only achieve rich metabolic profiling information, but also have a high analytical throughput. Thirdly, data repeatability and integration among different batches and different instruments are the key factors for truly realizing large-scale metabolomics study.

Figure 3. Schematic representation of PRM (upper) and MRM (lower) as performed on Q Exactive and TQ MS instruments, respectively. Generally, the precursor ions are selected in Q1 and fragmented in Q2(MRM) or HCD (PRM). In MRM, predesigned product ion transition is monitored at a time, while all possible product ion transitions are simultaneously analyzed in high resolution and high mass accuracy mass analysis in PRM. The figure is reproduced from [50] with permission.

Figure 4. General workflow of MRM ion pairs selection for LC-MS basedpseudotargeted metabolomics. Firstly, IDA based experiment under different CE voltages was carried out by HRMS to obtain comprehensive MS2 information, home-developed MRM-Ion Pair Finder software was innovatively applied to select 43

the daughter ion with most intensive parent ion and its corresponding CE value to generate MRM transitions. Then detection of these MRM transitions was carried out on TQ MS. The figure is reproduced from [51] with permission.

Figure 5. Workflow for strategy development and optimization based on pseudotargeted analysis for stability and robustness improvement in large-scale metabolomics. QC sample selection, frequency of QC insertion and data calibration methods should be taken into consideration to improve stability and robustness for large-scale metabolomics. The figure is reproduced from [56] with permission.

44

Table 1. Typical applications of large-scale metabolomics Analytical strategy

Analytical

Sample size

tool-platform Targeted approach

two commercial kits

Sample

Number of targets

Disease

Main results

Ref.

105 metabolites

Myocardial

Metabolites of the arachidonic acid

[81]

infarction and

pathway are independently associated

type 3684

serum

(BIOCRATES)

ischemic stroke (MI) Targeted methods

LC-MS

510

plasma

Branched-chain

Pancreatic cancer

amino acid

NanoMate

coupled

685

plasma

to TQ-MS

Increased plasma BCAA level is

[72]

associated with increased risk of

(BCAA) Shotgun lipidomics

with risk of MI

pancreatic cancer

135 lipid species

Cardiovascular

from 8 different

disease

lipid classes

Levels

of

individual

species

of

[80]

cholesterol esters, LPCs, PCs, PEs, sphingomyelins,

and

TAGs

were

related to cardiovascular disease Global metabolomic profiling & targeted validation

LC-MS

discovery: 579 validation:425660

serum

>300 metabolites

Lung cancer

Low

levels

of

serum

bilirubin

[71]

contributed higher risk for lung cancer incidence and mortality in male smokers 45

Nontargeted method

UHPLC-MS

428

serum

Metabolomics

Gestational diabetes

profiling (93

mellitus

differential

Trimester-specific alterations

in

metabolite the

amino

[77]

acid

metabolism, lipid metabolism, and

metabolites were

other pathways.

identified)

Nontargeted method

UPLC - Xevo G2 Q-TOFMS

discovery:1028

plasma

validation:

Metabolomics

Coronary heart

Four lipid-related metabolites had

profiling

disease (CHD)

clinical

UPLC-Q-TOF/MS

potential,

and

contributed to CHD development.

1670 Nontargeted method

utility

[70]

1072

urine

Metabolomics

Coronary Heart

15 CHD- blood stasis syndrome

profiling

disease

biomarkers and 12 CHD-Qi and Yin

[79]

deficiency syndrome biomarkers Nontargeted methods

GC

1065

serum

Fatty acids

Breast cancer

A high serum level of

[86]

trans-monounsaturated fatty acids is probably related to increased risk of invasive breast cancer. Nontargeted method

GC

864

plasma

Phospholipid fatty

Gastric

Plasma phospholipid fatty acid profile

[87]

46

acids

adenocarcinomas

may be related to increased gastric cancer risk.

Nontargeted method

UPLC-MS

448

plasma

53 differential

Ovarian cancer

Piperine, 3-indolepropionic acid,

[94]

5-hydroxyindoleacetaldehyde and

metabolites were

hydroxyphenyllactate were defined as

identified

metabolic biomarkers of epithelial ovarian cancer. Pseudotargeted method

LC-MS

1448

serum

776 (239

Hepatocellular

A serum metabolite biomarker panel

identified

carcinoma

consisting of phenylalanyl-tryptophan

metabolites)

[88]

and glycocholate was defined

47

Highlights ●Large-scale metabolomics study is increasingly applied in diseases-related fields. ●Mass spectrometry is a main platform for large-scale metabolomics due to its unique advantages.

●New advances in sample pretreatment methods, metabolic data collection techniques, and data correction methods were summarized.

●Typical applications of large-scale metabolomics in precision medicine, molecular epidemiology and mGWAS are given.