AMDORAP: Non-targeted metabolic profiling based on high-resolution LC-MS
© Takahashi et al; licensee BioMed Central Ltd. 2011
Received: 15 February 2011
Accepted: 24 June 2011
Published: 24 June 2011
Liquid chromatography-mass spectrometry (LC-MS) utilizing the high-resolution power of an orbitrap is an important analytical technique for both metabolomics and proteomics. Most important feature of the orbitrap is excellent mass accuracy. Thus, it is necessary to convert raw data to accurate and reliable m/z values for metabolic fingerprinting by high-resolution LC-MS.
In the present study, we developed a novel, easy-to-use and straightforward m/z detection method, AMDORAP. For assessing the performance, we used real biological samples, Bacillus subtilis strains 168 and MGB874, in the positive mode by LC-orbitrap. For 14 identified compounds by measuring the authentic compounds, we compared obtained m/z values with other LC-MS processing tools. The errors by AMDORAP were distributed within ±3 ppm and showed the best performance in m/z value accuracy.
Our method can detect m/z values of biological samples much more accurately than other LC-MS analysis tools. AMDORAP allows us to address the relationships between biological effects and cellular metabolites based on accurate m/z values. Obtaining the accurate m/z values from raw data should be indispensable as a starting point for comparative LC-orbitrap analysis. AMDORAP is freely available under an open-source license at http://amdorap.sourceforge.net/.
Metabolomics is defined as technology designed to give us the broadest, least biased insight into the richly diverse population of small molecules present in living things . Understanding cells at the levels of the transcriptome and metabolome provides insight into the network of complex biological regulations [2–5]. Metabolites within cells have the diverse range of chemical and physical properties and the wide range of those concentrations . To achieve metabolomics, two analytical platforms, i.e., mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (NMR), have been widely used [7, 8]. Chromatography-MS technologies play a central role in measuring the complex biological samples. Out of these, liquid chromatography-MS (LC-MS) is capable of detecting a broader range of metabolites than other MS technologies such as gas chromatography-MS and capillary electrophoresis-MS . Therefore, LC-MS has become more widely used in metabolomics analysis. An orbitrap mass analyzer is the most recent addition to the set of tools that can be applied to identification, characterization and quantitation of components in biological systems since its commercial introduction in 2005 . Orbitrap-based MSs have been proven to be a powerful tool in proteomics because they have ≈100 000 resolving power at a mass-to-charge ratio (m/z) 400 [11, 12]. The most important feature of the orbitrap is that it can stably maintain excellent mass accuracy without re-calibration, and does not require the use of calibration standards . Accurate m/z values can be used to define molecular formulae in putative identification of metabolites [7, 14]. Consequently, in the field of non-targeted metabolomics, those instruments make it possible to identify candidate molecular formulae from mass differences in measured m/z values [15, 16].
Public databases of chemical compounds such as ChEBI , HMDB , KEGG , KNApSAcK  and PubChem  provide suitable compounds for each molecular formula without measuring reference samples in advance. The species-metabolite relationship database KNApSAcK, for example, can easily narrow down candidates from accurate masses according to the species information or the type of ion adducts [22, 23]. Several molecular ion adducts should be considered especially when the ionization of molecules in samples is performed by electrospray ionization [24, 25]. Once given, the accurate m/z values can lead to the information of molecular formulae and candidate compounds by considering the mass differences, the appropriate ion adduct and the species together. However, it should be noted that structural isomers and stereoisomers with the same mass require the complicated chromatographic separation before mass analyzing .
Allen et al.  analyzed several "silent mutants" of yeasts (viable mutants with no obvious phenotype) by comparing extracellular metabolites using LC-MS data collected in a non-targeted approach. In preprocessing the LC-MS data, they skipped peak detection and annotation schemes typically used for such data; instead, they reduced data into a single aggregate MS vector and applied clustering and machine learning methods. Their study demonstrated the effectiveness of metabolic fingerprinting of extracellular extracts by non-complicated preprocessed data. Metabolic fingerprinting with the exclusion of m/z resolution, however, is impossible to get more insight from same data sets. The high-resolution of the orbitrap can be exploited in metabolic fingerprinting. In NMR or Fourier transform ion cyclotron resonance based MS (FT-ICR-MS), valid information about metabolic regulation in biological samples can be obtained by resolving power alone, even without any chromatographic separation .
An easy-to-use, flexible and automated tool is a key to success in metabolomics studies. This is particularly the case in high-resolution MS analyses mainly because of the data size. Our aim is to estimate more accurate m/z values and extract interesting m/z values from raw data in comparative LC-orbitrap analysis. In the present study, we describe a novel straightforward m/z detection method, "AMDORAP" (A ccurate m/z d etection method for LC-o rbitrap) for high-resolution MS (e.g., the orbitrap) by taking advantage of its stable mass accuracy.
Several freely available frameworks for analyzing LC-MS data sets have been developed . The typical MS data processing workflow comprises multiple stages, including filtering, feature detection, alignment and normalization. In MZmine 2 [29, 30], peak alignment across samples, for example, follows peak detection for individual samples. The Bioconductor package XCMS [31, 32] mainly consists of peak detection, peak matching and retention time alignment. A common concept shared by widely used methods, including MZmine 2 and XCMS, is that peak detection step for both m/z and retention time dimensions is executed for an individual sample, or scan, followed by an alignment (or merging) step across samples. The most important reason for using high-resolution MS is to obtain more accurate m/z values from biological samples. That makes it possible to identify correct candidate molecular formulae from mass differences alone. Since the orbitrap can determine m/z values extremely accurately, we assumed that m/z values derived from compounds with the same compositional formula, including structural isomers and stereoisomers, should be robust with respect to retention time and differences between samples.
Collect data points with intensities larger than a threshold for all samples.
Group collected data points by m/z closeness, and estimate representative m/z values for individual m/z groups.
Extract ion chromatograms for the m/z list.
The main idea motivating this procedure is that peak picking and alignment steps of m/z values should be performed in a single step. In the following section, the AMDORAP performance was assessed using data sets in the positive mode from two Bacillus subtilis strains 168 and MGB874 .
Results and Discussion
Sample preparation and experimental conditions
In order to assess the AMDORAP performance, we performed the experiments and then prepared the biological data sets. Two Bacillus subtilis strains, wild-type 168 and the genome reduced strain MGB874 , were used for metabolome analysis. The cells were cultured at 37°C to an OD600 value of 4.0 in the early stationary phase of growth, in Spizizen's minimal medium (SMM)  supplemented with 0.5% glucose, 5 μ g/ml tryptophan, 20 μ g/ml methionine and trace elements . Metabolite extraction was performed according to Takahashi et al. . The culture media were passed through a 0.4 μ m HTTP filter (Millipore). Residual cells on the filter were washed twice with HPLC grade water and then immersed in 2 ml of methanol. After incubation at 4°C overnight, the extracts were centrifuged at 9000 × g at 4°C for 10 min, filtered through 0.2 μ m PTFE membrane (Advantec), evaporated at room temperature and stored at -80°C. The extracts were dissolved in 200 μ l of 80% methanol before analysis in the LC-orbitrap.
Mass analysis was performed using a Paradigm MS4 system (Michrome BioResources) coupled to an LTQ-orbitrap XL-HTC-PAL system (Thermo Fisher Scientific). All experimental events were controlled by Xcalibur software version 2.0.7 (Thermo Fisher Scientific). HPLC was performed under the conditions as described by Iijima et al. . Samples were injected into to a TSKgel column ODS-100V (4.6 × 250 mm, 5 μ m; TOSOH). Water (HPLC grade; solvent A) and acetonitrile (HPLC grade; solvent B) were used as the mobile phase with 0.1% v/v formic acid. The gradient program was as follows: 3% B to 97% B (45 min), 97% B (5 min) and 10% B (10 min). The flow rate was set to 0.5 ml/min. The ESI setting was as follows: spray voltage 4.5 kV and capillary temperature 350°C for the positive ionization mode. Nitrogen sheath gas and auxiliary gas were set at 60 and 20 arbitrary units, respectively. A full MS scan was performed in the m/z range 70-1500 at a resolution of 60 000. Simultaneously, top three MS2 spectra within each full MS scan were gained by the linear ion trap at a collision energy of 35 eV. Thermo Fisher mass spectrometry RAW files were converted from profile mode into centroid mode using the ReAdW program .
Collection of data points
Grouping collected data points and estimation of representative m/z values for individual groups
As the second step, all collected m/z values are grouped by closeness, i.e., if differences between the neighboring m/z values are within 5 ppm (default setting), they are grouped together. There is no limit of data points within one m/z group as long as this constrain is fulfilled. Out of the m/z alignment methods, Kazmi et al.  developed the method to create bins and then combine consecutive bins together according to the constrains, similar to complete linkage hierarchical clustering. While they must consider the origins of m/z values, our method is to collect all data points with relatively higher intensities and then deal with collected data as one spectrum. Consequently, the grouping of m/z values is feasible in one step.
Comparison of detected m/z values for fourteen compounds by AMDORAP, MZmine 2 and XCMS
[M + H] +
Extraction of ion chromatograms for the m/z list
In metabolic profiling by the high-resolution mass technologies, it is important to convert raw data to reliable m/z values in order to quickly get the information of correct candidate metabolites in biological samples. With respect to obtained m/z accuracy, comparison study was performed using only 14 identified compounds. Clearly, the m/z errors by AMDORAP are smallest, although the number of compared compounds might be not enough. In the range of tested parameters, we couldn't get better results for 14 compounds by MZmine 2 and XCMS. This suggests that parameter optimization of those tools is time consuming process and difficult to find out best settings for both dimensions, i.e., m/z and retention time. Furthermore, it would suggest that both mass and retention time alignment processes introduce the larger errors for obtained m/z values, while AMDORAP uses only the ions with relatively higher intensities for estimating the m/z values. In addition, a signal-to-noise ratio cutoff by Gaussian filtering could allow us to achieve a reliable comparison of the ion abundances between samples, even when there are peaks with noisy baseline. Thus, AMDORAP can detect more accurate m/z values from raw data and provide the platform for metabolic fingerprinting. Information of MS n , retention time and behaviors of the authentic compounds has the essential roles to finally verify the ions as particular metabolites. However, the extraction of interesting accurate m/z values by AMDORAP should be indispensable as a starting point for comparative LC-orbitrap analysis, because of the limitations of available authentic compounds and simultaneously obtained MS2 spectra with a full MS scan per sample.
Availability and requirements
Project name: AMDORAP
Project home page: http://amdorap.sourceforge.net/
Operating systems: Platform independent
Programming language: R
License: GPL v2
Any restrictions to use by non-academics: No
This work has been partly supported by the Japan Science and Technology Agency, CREST (Elucidation of Amino Acid Metabolism in Plants Based on Integrated Omics Analyses). B. subtilis strain MGB874 was created by the New Energy and Industrial Technology Development Organization, NEDO (Development of a Technology for the Creation of a Host Cell). We thank the Plant Global Education Program of Nara Institute of Science and Technology for the use of the LC-orbitrap. We also would like to acknowledge Dr. Kazuki Saito of RIKEN Plant Science Center for offering the authentic compounds.
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