H OH metabolites OH Article A Metabolomics Workflow for Analyzing Complex Biological Samples Using a Combined Method of Untargeted and Target-List Based Approaches 1, 2, 1 3 Thomas Züllig y, Martina Zandl-Lang y, Martin Trötzmüller , Jürgen Hartler , Barbara Plecko 2 and Harald C. Köfeler 1,* 1 Core Facility Mass Spectrometry, Medical University of Graz, 8036 Graz, Austria; [email protected] (T.Z.); [email protected] (M.T.) 2 Department of Paediatrics and Adolescent Medicine, Division of General Paediatrics, University Childrens’ Hospital Graz, Medical University of Graz, 8036 Graz, Austria; [email protected] (M.Z.-L.); [email protected] (B.P.) 3 Institute of Pharmaceutical Sciences, University of Graz, 8036 Graz, Austria; [email protected] * Correspondence: [email protected] These authors have contributed equally to this work. y Received: 23 July 2020; Accepted: 20 August 2020; Published: 25 August 2020 Abstract: In the highly dynamic field of metabolomics, we have developed a method for the analysis of hydrophilic metabolites in various biological samples. Therefore, we used hydrophilic interaction chromatography (HILIC) for separation, combined with a high-resolution mass spectrometer (MS) with the aim of separating and analyzing a wide range of compounds. We used 41 reference standards with different chemical properties to develop an optimal chromatographic separation. MS analysis was performed with a set of pooled biological samples human cerebrospinal fluid (CSF), and human plasma. The raw data was processed in a first step with Compound Discoverer 3.1 (CD), a software tool for untargeted metabolomics with the aim to create a list of unknown compounds. In a second step, we combined the results obtained with our internally analyzed reference standard list to process the data along with the Lipid Data Analyzer 2.6 (LDA), a software tool for a targeted approach. In order to demonstrate the advantages of this combined target-list based and untargeted approach, we not only compared the relative standard deviation (%RSD) of the technical replicas of pooled plasma samples (n = 5) and pooled CSF samples (n = 3) with the results from CD, but also with XCMS Online, a well-known software tool for untargeted metabolomics studies. As a result of this study we could demonstrate with our HILIC-MS method that all standards could be either separated by chromatography, including isobaric leucine and isoleucine or with MS by different mass. We also showed that this combined approach benefits from improved precision compared to well-known metabolomics software tools such as CD and XCMS online. Within the pooled plasma samples processed by LDA 68% of the detected compounds had a %RSD of less than 25%, compared to CD and XCMS online (57% and 55%). The improvements of precision in the pooled CSF samples were even more pronounced, 83% had a %RSD of less than 25% compared to CD and XCMS online (28% and 8% compounds detected). Particularly for low concentration samples, this method showed a more precise peak area integration with its 3D algorithm and with the benefits of the LDAs graphical user interface for fast and easy manual curation of peak integration. The here-described method has the advantage that manual curation for larger batch measurements remains minimal due to the target list containing the information obtained by an untargeted approach. Keywords: LC-MS; metabolomics; LDA; XCMS; plasma; CSF Metabolites 2020, 10, 342; doi:10.3390/metabo10090342 www.mdpi.com/journal/metabolites Metabolites 2020, 10, 342 2 of 12 1. Introduction Metabolomics is defined as an “omics” approach in which the global set of small molecules (<1.5 kDa) in a distinct biological sample, e.g., tissue, blood, cerebrospinal fluid (CSF), cells or their compartments, are analyzed with the prospect of a better understanding of their biological function [1]. Metabolomic approaches to clinically relevant data include differential analysis between control groups and diseased groups, which can lead to the identification of novel biomarkers reflecting those abnormalities. Various potential biomarker candidates have been introduced in several clinical fields including respiratory [2,3], neurological [4], and kidney diseases [5,6], diabetes [7,8], cancer [9,10], and metabolic disorders [11]. Depending on the underlying hypothesis, different approaches are applied in metabolomics research. On the one hand there are established targeted workflows where samples are screened for a set of specific and pre-defined, metabolites (e.g., amino acids, biogenic amines, nucleobases, organic acids, sterols, organic phosphates, and vitamins) [12]. Untargeted workflows on the other hand, are non-hypothesis-driven, allowing deeper insights into complex biological samples and, therefore, may also lead to the discovery of new metabolic hypotheses as well as novel biomarkers. The challenge of untargeted metabolomics in larger sample cohorts is the time-consuming data processing, the annotation of unknown compounds and their verification [13–15]. In the field of mass spectrometry (MS) metabolomics, MS devices are usually coupled with an additional separation step—except for direct infusion techniques—to reduce sample complexity and to increase coverage and detection sensitivity [16]. The most used coupling technique is liquid chromatography (LC). This technique benefits from a variety of columns with different stationary phases, which allows separating a large number of distinct polar compounds. Frequently used columns are reversed phased (RP) C18 or C8 [17,18] and hydrophilic interaction chromatography (HILIC) [19]. The variety of these columns offers additional information depending on the composition of the stationary phase and the selected pH range [20]. Gas chromatography (GC) is especially used in breath metabolomics, as shown in several studies [21], benefitting from superior separation and less signal-to-noise ratio compared to liquid chromatography (LC). In addition, GC-MS is used beyond volatile organic compounds by applying different derivatization techniques [22,23]. Capillary electrophoresis coupled to MS (CE-MS) is a beneficial method for highly polar compounds of the metabolome, albeit not as popular as the chromatography techniques [24]. For processing of obtained MS data several software tools are available, e.g., XCMS [25], MZmine [26] or OpenMS [27]. As diverse as the software pool is, results can vary widely and data processing tools have to be adapted to individual biomedical applications [28,29]. Several methods for improving annotation and data quality are available using algorithms with false discovery rate [30], frameworks based on machine learning methods which can improve the quality of automated data processing workflows [31], or a sophisticated graphical interface for customizing parameters to reprocess data [26]. The aim of this work was to demonstrate selective separation of metabolites by performing a HILIC approach within a comprehensive metabolomics workflow. Therefore we used two types of software, Compound Discoverer 3.1 for untargeted metabolomics and Lipid Data Analyzer 2.6 for a target-list based approach to benefit from LDA 3D algorithm for more precise peak border confinement. We also compare the performance of this combined approach with typical untargeted metabolomics software tools (CD and XCMS online), for which we have used the precision of repeatedly measured pooled serum and pooled CSF samples. Since annotation of compounds for metabolomics studies can be complex, we have applied a level of trust that was introduced by the Metabolomics Standards Initiative (MSI). 2. Materials and Methods 2.1. Pooled Serum and Pooled CSF Samples Pooled CSF and pooled plasma samples were obtained from six female patients suffering of Rett Syndrome and 13 (eligible for pooled CSF) and 25 (eligible for pooled plasma) healthy children, Metabolites 2020, 10, 342 3 of 12 respectively. All individuals were aged between 4 and 16 years. The pooled samples were used to develop and evaluate this metabolomics workflow. The study itself is an ongoing project and not the objective of this publication. The project is ethically approved (EK-number 31-162ex18/19). 2.2. Chemicals Acetone (34850-2.5L-M; 99.8%) from Sigma-Aldrich (St. Louis, MO, USA), and acetonitril ≥ chromasolv (34851-2.5L; 99.9%), and methanol chromasolv (34885-2.5L; 99.9%) from Honeywell ≥ ≥ (Charlotte, NC, USA). 2.3. Metabolite Standards Dopamine HCl (56610-5G; 98.5%), l-ornithine monohydrochloride (75469-25G; 99.5%), ≥ ≥ l-prolin (81710-10G; 99%), taurine (86330-25G; 99%), creatinine (C4255-10G; 98%), d-(+)-glucose ≥ ≥ ≥ (G8270-100G; 99.5%), l-alanine (A7627-1g; 98%), l-asparagine (A0884-25G; 98%), l-aspartic acid ≥ ≥ ≥ (A8949-25G; 99%), l-citrulline (C7629-10MG; 98%), l-glutamic acid (G1251-100G; 99%), l-glutamine ≥ ≥ ≥ (G8540-25G; 99%), l-histidine (H8000-5G; 99%), l-isoleucine (I2752-1G; 98%), l-leucine (L8000-25g; ≥ ≥ ≥ 98%), l-lysine (L5501-1G; 98%), l-methionine sulfoxide (M1126-1G; NA), l-phenylalanine ≥ ≥ (P2126-100G; 98%), l-serine (S4500-1G; 99%), l-threonine (T8625-1G; 98%), l-tryptophan (T0254-5G; ≥ ≥ ≥ 98%), l-tyrosine (T3754-50G; 98%), l-valine (V0500-1G; 98%), cholic acid (C1129-25G; 98% ≥ ≥ ≥ ≥ ), d-carnitine (544361-1G; 98%), decanoyl-l-carnitine (50637-10MG; 94%), Folic acid (F7876-1G; ≥ ≥ 97%), hippuric acid (112003-5G; 98%), l-carnosine (C9625-10MG; 99%), Palmitoyl-l-carnitine ≥ ≥ ≥ (91503-10MG; 95%), Riboflavin (R4500-5G; 98%), valeryl-l-carnitine