Comprehensive Metabolomic and Lipidomic Profiling of Human

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Comprehensive metabolomic and lipidomic profiling of human kidney tissue: a platform comparison Patrick Leuthold, Elke Schaeffeler, Stefan Winter, Florian Büttner, Ute Hofmann, Thomas E Mürdter, Steffen Rausch, Denise Sonntag, Judith Wahrheit, Falko Fend, Jörg Hennenlotter, Jens Bedke, Matthias Schwab, and Mathias Haag J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.6b00875 • Publication Date (Web): 19 Dec 2016 Downloaded from http://pubs.acs.org on December 20, 2016

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Journal of Proteome Research

Comprehensive metabolomic and lipidomic profiling of human kidney tissue: a platform comparison Patrick Leuthold1, Elke Schaeffeler1, Stefan Winter1, Florian Büttner1, Ute Hofmann1, Thomas E. Mürdter1, Steffen Rausch1,2, Denise Sonntag3, Judith Wahrheit3, Falko Fend4 ,Jörg Hennenlotter2, Jens Bedke2, Matthias Schwab1, 5, 6, Mathias Haag1* (1) Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany and University of Tübingen, Tübingen, Germany (2) Department of Urology, University Hospital Tübingen, Tübingen, Germany (3) Biocrates Life Sciences AG, Innsbruck, Austria (4) Institute of Pathology and Neuropathology, University Hospital Tübingen, Tübingen, Germany (5) Department of Clinical Pharmacology, University Hospital Tübingen, Tübingen, Germany (6) Department of Pharmacy and Biochemistry, University of Tübingen, Tübingen, Germany

*Corresponding Author: Dr. rer. nat. Mathias Haag Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology Auerbachstr. 112 70376 Stuttgart, Germany Phone +49 (0)711 / 8101-5429 Fax +49 (0)711 / 85 92 95 Mail: [email protected]

1 ACS Paragon Plus Environment


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ABSTRACT Metabolite profiling of tissue samples is a promising approach for the characterization of cancer pathways and tumor classification based on metabolic features. Here, we present an analytical method for non-targeted metabolomics of kidney tissue. Capitalizing on different chemical properties of metabolites allowed us to extract a broad range of molecules covering small polar molecules and less polar lipid classes that were analyzed by LC-QTOF-MS after HILIC and RP chromatographic separation, respectively. More than 1000 features could be reproducibly extracted and analyzed (CV < 30%) in porcine and human kidney tissue which were used as surrogate matrices for method development. To further assess assay performance, cross-validation of the non-targeted metabolomics platform to a targeted metabolomics approach was carried out. Strikingly, from 102 metabolites that could be detected on both platforms the majority (>90%) revealed Spearman’s correlation coefficients ≥ 0.3, indicating that quantitative results from the non-targeted assay are largely comparable to data derived from classical targeted assays. Finally, as proof-of-concept, the method was applied to human kidney tissue where a clear differentiation between kidney cancer and non-tumorous material could be demonstrated based on unsupervised statistical analysis. Keywords metabolomics, lipidomics, kidney cancer, clear cell renal cell carcinoma (ccRCC), Q-TOF, tissue metabolomics, LC-MS, targeted metabolomics, non-targeted metabolomics, platform comparison


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INTRODUCTION Alterations in cellular metabolism are known to be involved in various diseases and cancer.1,2 Determination of these metabolic alterations in body fluids, such as urine and blood, or in tissue samples are used not only for identification of biomarkers, but provide novel insights into pathogenesis and progression of diseases.1,3 In this context, metabolomics, the large-scale analysis of small molecules and lipids, has reached increasing awareness for the molecular characterization of various kidney diseases including renal cancer which is currently recognized as a metabolic disease.4-8 Renal cell carcinoma (RCC) accounts for about 3-4 % of all estimated new cancers9 with more than 100,000 cases of death worldwide. RCC is a heterogeneous disease comprising several histologically different subtypes such as clear cell RCC (ccRCC), papillary RCC and chromophobe RCC.10,11 ccRCC represents the most prevalent histological form accounting for more than 75% of all RCC.11 The poor 5-year survival rates11 and the lack of adequate therapies for advanced, metastatic RCC emphasize the need for novel, tissue-based biomarkers with high prognostic potential.12 Tumor sub-classification based on metabolic features provides a promising approach to improve the prediction of patient outcome. So far tissue metabolomics in RCC has been performed by gas-chromatography mass spectrometry (GC/MS),13 proton nuclear magnetic resonance spectroscopy,14 liquid-chromatography mass spectrometry (LC/MS),15 ambient ionization techniques coupled to mass spectrometry, such as probe electrospray ionization (PESI)16 and desorption electrospray ionization (DESI)17 or by using multi-platform analytical approaches.6,8,18,19 Moreover, metabolic profiling in body fluids such as plasma,20 serum21-23 and urine24-27 from healthy and kidney cancer patients has been applied. Nevertheless, besides all the efforts made, there are still no clinically relevant tissue and 3 ACS Paragon Plus Environment


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biofluid-based biomarkers for proper management of kidney cancer patients available,3 highlighting the importance of a continuous development and refinement of metabolomics strategies. Comprehensive metabolome profiling requires both, monitoring of polar small metabolites involved in classical cancer pathways such as glycolysis or tricarboxylic acid (TCA) cycle and nonpolar lipids which are known to play a crucial role in kidney cancer development.4,15,28 The chemical diversity of these compound classes requires dedicated extraction procedures (e.g. for lipid classes of different polarity29) for their specific and reproducible recovery from human cells, body fluids or tissues.30 Moreover, the combination of complementary chromatographic separation techniques such as reversed-phase (RP) and hydrophilic interaction liquid chromatography (HILIC) have been demonstrated to increase the metabolome coverage.31,32 While targeted metabolomics assays have been the gold standard for biomarker quantification in biological samples within the last years, non-targeted metabolomics is typically applied for discovery-driven research using high-resolution mass spectrometry (HRMS). Given the fact that sensitivity and specificity of HRMS-assays are continuously advancing, targeted biomarker quantification can nowadays be readily performed by accurate mass profiling methods.33 Thus, the ability of conducting biomarker discovery and validation experiments on a single HRMSbased analytical platform represents an attractive strategy that is increasingly considered in metabolomics research.34 As merging of non-targeted and targeted metabolomics methods seems to be a promising scenario in the future,34 careful assessment of results derived from different technologies is of increasing importance.


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Thus, we provide in the present manuscript results of the development, validation and application of a novel non-targeted metabolomics/lipidomics assay for the broad analyses of polar and non-polar metabolites especially in kidney tissue. Besides comprehensive characterization and structural assignment of detected metabolic features in kidney tissue extracts, comparison of the established non-targeted platform to a well-established targeted assay35-37 was conducted for metabolites that overlapped between both platforms. The findings of the platform comparison show that the different technologies largely offer similar results. The assessment of metabolites, for which quantitative data are provided by the different methods, may support biomarker verification by independent analysis. Ultimately, the new established method offers the potential for comprehensive non-targeted metabolomics experiments in kidney tissue, hence enabling the discovery of novel metabolic biomarkers in disease pathophysiology.



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MATERIALS AND METHODS Chemicals and reagents Ultra LC-MS grade acetonitrile (ACN) and methanol (MeOH) was purchased from Carl Roth GmbH & Co KG (Karlsruhe, Germany). LC-MS grade methyl tert-butyl ether (MTBE), isopropanol (IPA), formic acid (FA) and ammonium acetate (AmAc) were purchased from Sigma-Aldrich (Taufkirchen, Germany). Pure water was obtained from a Milli-Q system (Millipore, Billerica, MA, USA) and used for the preparation of aqueous solvents. Kidney tissue samples Human kidney samples were obtained from the University of Tübingen, informed written consent was provided by each subject and the use of the tissue was approved by the ethics committee of the University of Tübingen, Germany. All tissue samples were collected across the kidney cortex and frozen in liquid nitrogen within 30 min after removal and stored at -80 °C until sample preparation. Kidney tissue samples were provided in two different batches in order to independently enable method development (i.e. kidney metabolome characterization and reproducibility assessment [batch A]) as well as method application and analytical platform comparison (batch B). Batch A contained tumor, diseased and benign kidney tissue samples from patients who underwent radical nephrectomy suffering from different forms of kidney disease such as clear cell renal cell carcinoma (ccRCC, tumor area), oncocytoma (OC, tumor area), urothelial cell carcinoma (UCC, benign area), renal trauma (RT, diseased area), interstitial nephritis (IN, diseased area) and dermoid tumor (DT, tumor area). Moreover, porcine kidney samples (fresh food product) were included in batch A to assess reproducibility of sample 6 ACS Paragon Plus Environment

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preparation. Batch B contained ccRCC and matched non-tumor tissue samples from five independent individuals who underwent partial nephrectomy. The histological evaluation of tissue sections, as well as assessment of clinicopathological data was performed at the Department of Pathology, University Hospital Tuebingen, Germany. Patient characteristics for batch B are provided in Supporting Information Table S1. Non-targeted metabolomics Tissue homogenization Frozen kidney samples of approximately 10-35 mg were transferred to homogenization tubes containing Yttria-Stabilized Zirconium Oxide beads (Lysing Matrix Z, MP Biomedicals, Heidelberg, Germany) prefilled with 300 µL of ice-cold (-20 °C) MeOH/water (1:1, v/v). Samples were homogenized in a FastPrep-24TM instrument equipped with a CoolPrepTM adapter (MP Biomedicals, Heidelberg, Germany). Homogenization was achieved within three to five cycles (6.5 m/s for 20 s) followed by determination of the exact sample weight and the addition of icecold (-20 °C) MeOH/water (1:1, v/v) to achieve a final solvent/tissue ratio of 50 µL/mg. In order to obtain a homogeneous suspension, samples were homogenized for one additional cycle (4 m/s for 10 s). Metabolite extraction Recovery of polar to medium-polar metabolites (aqueous extraction) was carried out immediately after homogenization in the same solvent used for the homogenization procedure. To this end, homogenates were shaken at 1400 rpm for 10 min at 4 °C (ThermoMixer®, Eppendorf, Germany) followed by centrifugation for 10 min at 9391 x g and 4 °C (Centrifuge 7 ACS Paragon Plus Environment


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type: 5424R, Eppendorf, Germany). Supernatants were transferred to polypropylene tubes (Eppendorf, Germany) and stored at 4 °C until further processing. Medium-polar to non-polar metabolites (organic extraction) were recovered from the remaining pellet after the addition of 50 µL/mg tissue MTBE:MeOH (3:1, v/v) followed by a single homogenization cycle (4 m/s for 10 s) and subsequent shaking at 1400 rpm for 20 min at 4 °C. Samples were centrifuged (10 min at 9391 x g at 4 °C) and supernatants were transferred into clean polypropylene tubes. Supernatants were subjected to centrifugation (10 min at 21130 x g at 4 °C) and aliquots (100 µl) were transferred to polypropylene tubes, dried at 40 °C under a gentle stream of nitrogen and stored at -20 °C until analysis. LC-QTOF-MS analysis Aqueous extracts were reconstituted in 100 µL of water/ACN (5:95, v/v) and organic extracts were reconstituted in 100 µL IPA:MeOH (3:1, v/v). Quality control (QC) samples were prepared for each batch by pooling equal volumes of reconstituted samples (i.e. one QC sample was prepared from aqueous extracts and one QC sample from organic extracts). Samples were transferred into 2 mL glass vials containing 250 µL glass inserts with polymer feets (Agilent Technologies, Waldbronn, Germany). Vials were covered with pre-slit polytetrafluoroethylene (PTFE)/silicone screw caps (Agilent Technologies, Waldbronn, Germany). Organic extracts were diluted 3-fold with IPA:MeOH (3:1, v/v) prior analysis whereas aqueous extract were measured without dilution. Samples were analyzed in a randomized fashion by using a 1290 Infinity UHPLC System coupled to a 6550 iFunnel quadrupole time-of-flight mass spectrometer (LC-QTOF-MS) from Agilent


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Technologies equipped with a Dual Agilent Jet Stream electrospray source. Following 2-3 blank injections (solvent blanks), 10 QC sample injections were carried out at the beginning of each batch for column conditioning and at least every tenth sample throughout the analytical run to assess analytical reproducibility. The system was operated by the Mass Hunter Data Acquisition Software (version B.05.01). Chromatographic separation of aqueous extracts was performed using a HILIC column (Acquity UPLC BEH Amide Column, 1.7 µm, 2.1 mm x 150 mm; Waters, Eschborn, Germany) whereas separation of organic extracts was achieved by reversed phase (RP) chromatography (Acquity UPLC BEH C8, 1.7 µm, 2.1 mm x 100 mm; Waters, Eschborn, Germany). The autosampler was operated at 6 °C and the column oven at 65 °C. The injection volume was 5 µL and 2 µL for organic and aqueous extracts, respectively. The HILIC column was operated at a flowrate of 0.4 mL/min using mobile phase A (10 mM AmAc and 0.125% FA in water:ACN 1:1, v/v) and mobile phase B (10 mM AmAc and 0.125% FA in water:ACN 5:95, v/v). After sample injection, the column was kept for 2 min at 92% B followed by gradient elution from 2-18 min (92 to 0% B). The column was returned to the initial condition from 18-18.01 min and re-equilibrated for 11.99 minutes at 92% B before injection of the next sample. Needle wash with 95% ACN in water was applied between injections. The RP column was operated at 0.45 mL/min using mobile phase A (5 mM AmAc in water:ACN 8:2, v/v) and mobile phase B (5 mM AmAc in MeOH:ACN:IPA 7.5:2:0.5, v/v/v). After 1 min column flushing with 65% B, gradient elution was performed as follows: 1-4 min, 65-80% B; 4-20 min, 80-100% B; 20-25 min, 100% B; 25-26.5 min, 100-65% B and 26.5-30 min, 65% B. Needle wash with 100% IPA was applied between injections. The total run time was 30 minutes per sample for HILIC and RPLC mode, respectively.



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Analytical batches were analyzed by mass spectrometry in positive and negative ion mode in a subsequent manner with the same solvent mixture to ensure retention time comparability between ionization modes. After finishing the analysis for a particular stationary phase (HILIC or RPLC), the solvent system and the analytical column was manually changed, followed by sample analysis on the remaining stationary phase. Electrospray parameters were as follows for all analytical modes: gas and sheath gas temperature, 175 °C and 200 °C; drying gas and sheath gas flow, 16 L/min and 12 L/min; nebulizer pressure, 35 psig; capillary and nozzle voltage, 3500 V and 100 V; fragmentor and octopole radio frequency peak voltage, 350 V and 750 V. The QTOF was operated in the extended dynamic range mode (~ 2GHz) and low mass range (up to 1700 m/z) resulting in mass resolutions between 20000 and 25000 (m/z-range: 600 - 630) for positive and negative ion mode, respectively. The slicer was set to high resolution. The mass analyzer was calibrated on a daily basis immediately before starting an analytical run. TOF-MS spectra were acquired in centroid mode (intensity threshold 10 counts/0.001%) at an acquisition rate of 4 spectra/s from m/z 50 to 1650. Fragment spectra were acquired in QC samples (batch A) by auto MS/MS analysis (data-dependent) at a rate of 3 spectra/s for MS1 and MS/MS acquisitions, respectively. MS/MS spectra were triggered from precursors that exceeded an absolute threshold of 200 counts and by selecting maximal 3 precursors per cycle. Collision energy (V) was adjusted as a function of m/z (3.5 x m/z x 100-1 + 7) and the quadrupole band pass for precursor isolation was set to medium (~4 m/z). Metabolite identification and annotation For metabolite structural assignment mass spectra (MS1 and MS/MS) acquired in QC samples were investigated with the Mass Hunter Qualitative Analysis Software (Version B.06.00, Agilent 10 ACS Paragon Plus Environment

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Technologies). Depending on available spectral information (accurate mass, fragment ions and/or retention time [RT]) that matched to data derived from online databases (DBs), from the literature or from pure standard compounds, features were classified into different levels of assignment (LoA). LoA 1: accurate mass match (mass tolerance: ± 15 ppm) to data provided in online DBs such as LIPID MAPS,38 HMDB,39 METLIN40 and MassBank,41 LoA 2: match of accurate mass and MS/MS spectrum to data provided in online DBs or the literature,42-47 LoA 3: RT and accurate mass match to pure standard compound, LoA 4: match of accurate mass and RT to standard compound and MS/MS spectrum match to online DBs or literature data and LoA 5: match of accurate mass, RT and MS/MS to standard compound. Spectral information from pure standard compounds was obtained through the analysis of metabolites available in a commercially available library (Mass Spectrometry Metabolite Library of Standards [MSMLS], IRORA Technologies, Bolton, USA). Annotated metabolites and their corresponding LoAs are provided in the Supporting Information Table S2. Note, Table S2 comprises mainly features that could be annotated based on available auto MS/MS data with a LoA of at least 2 in either one of the ionization mode. The total number of metabolites assigned for each analytical mode can be found in the Supporting Information Table S3. Targeted metabolomics For targeted metabolomics, 10-35 mg of ccRCC and adjacent non-tumor tissue samples (Supporting Information Table S1) were used. Targeted metabolomics was performed by Biocrates AG (Innsbruck, Austria) as previously described.36,37 In brief, Biocrates’ commercially available Kit plates (AbsoluteIDQ® p180 Kit assay) were used for the absolute quantification (pmol/mg) of amino acids, biogenic amines, acylcarnitines, (lyso-) phosphatidylcholines, 11 ACS Paragon Plus Environment


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sphingomyelins,

and

hexoses.

The

fully

automated

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assay

was

based

on

PITC

(phenylisothiocyanate) derivatization in the presence of internal standards followed by FIAMS/MS (acylcarnitines, (lyso-) phosphatidylcholines, sphingomyelins, hexoses) and LC-MS/MS (amino acids, biogenic amines) using a 4000 QTRAP® system from SCIEX or a Waters XEVO™ TQMS instrument with electrospray ionization (ESI). For the quantitative analysis of energy metabolism intermediates hydrophilic interaction liquid chromatography (HILIC)-ESI-MS/MS in a highly selective negative multiple reaction monitoring (MRM) mode was used. The MRM detection was performed on a SCIEX 4000 QTRAP® instrument. The sample was protein precipitated and extracted simultaneously with aqueous methanol in a 96-well plate format. Internal standards (ratio external to internal standard) and external calibration were used for quantitation. In total for each sample, a panel comprising 199 metabolites, including 15 sphingolipids, 91 glycerophospholipids, 41 acylcarnitines, 42 amino acids or biogenic amines, monosaccharides, and several intermediates from the energy metabolism (lactate, fumarate, hexosephosphates,

pentosephosphates,

cyclic

adenosine

monophosphate,

pyruvate/oxaloacetate, succinate, 3-phosphoglyceraldehyde/dihydroxyacetonphosphate and alpha-ketoglutaric acid), was analyzed. Data preprocessing and statistical analysis Preprocessing of data derived from the non-targeted approach was carried out by using the Mass Hunter Profinder Software (version B.06.00, Agilent Technologies). For non-targeted feature extraction, “Batch Recursive Feature Extraction (RFE)” was applied with an intensity threshold above 1500 counts. Recursive feature extraction performs in a first step fast data mining by untargeted Molecular Feature Extraction (MFE), followed by a second Find by Ion 12 ACS Paragon Plus Environment

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feature extraction (FbI) step (recursive extraction). MFE finds features in a sample, aligns those features across all the samples on the basis of mass and retention time and summarizes coeluting signals in compound groups. Secondly, on the basis of the median retention time and median mass obtained from the MFE, a targeted feature extraction (FbI) is employed on all samples. For reproducibility assessment specific to annotated metabolites (Supporting Information Table S2) and for analytical platform comparison (Supporting Information Table S4), “Batch Targeted Feature Extraction” was used. To this end metabolite-specific information (molecular formula and retention time) provided in Table S2 were used for feature extraction. For both feature extraction approaches, H+, Na+ and NH4+ adducts were selected for positive mode data whereas for negative mode data H-, CH3COO- and HCOO- adducts were considered. In addition, for targeted extraction of features assigned as ceramides, loss of water was considered. The retention time window was set to 0.2 minutes, the mass window was set to 20 ppm + 2mDa and the extracted ion chromatogram (EIC) range to ±35 ppm. For peak integration, Agile 2 algorithm was selected. TOF-MS spectra were excluded if above 30% of saturation. The list of extracted features was visually inspected in order to ensure correct RT alignment and peak integration throughout the batch. Values were exported as peak area in a comma separated value (csv) file and used for statistical analysis. Multivariate and univariate statistical data analysis was conducted by using Microsoft Excel 2010 and the statistical software R (version 3.3.1).48 Features with a coefficient of variation (CV) ≥ 20% in QC samples were removed from the data prior to analysis except for data used for the evaluation of method reproducibility and for the platform comparison. If not stated otherwise, untargeted metabolomics data was normalized (peak area of each feature divided by the sum of peak areas 13 ACS Paragon Plus Environment


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of all features in one sample) and log2 transformed prior statistical analysis. Paired t-tests were applied for analysis of features differentially regulated between ccRCC and matched adjacent non-tumor tissues. Resulting p-values were adjusted for multiple testing by the BenjaminiHochberg procedure.49 The statistical significance level was defined as 5%.

To determine similarities of the metabolite profiles between analytical platforms (non-targeted and targeted), Spearman’s correlation coefficients were computed between unnormalized and untransformed peak areas from non-targeted metabolomics data and the absolute concentrations (pmol/mg) from the targeted platform (Biocrates). For comparison, quantitative data (absolute concentrations [pmol/mg]) from the targeted assay and semi-quantiative data (relative concentrations [peak areas]) from the non-targeted assays were considered independent of quantification and detection limits. Absolute concentrations (targeted metabolomics) and unnormalized peak areas (non-targeted metabolomics) for ccRCC and nontumor tissue samples are provided in the Supporting Information Table S5. For hierarchical cluster analysis (distance measure: euclidean; agglomeration method: ward.D), quantitative data provided in Table S5 were log2 transformed (generalized log50) and fold changes between ccRCC and non-tumor tissue samples were calculated independently for targeted and nontargeted metabolomics data. For metabolites detectable in both extracts (aqueous and organic) and/or both ionization modes in the non-targeted experiment, EIC signals where chosen on the basis of signal intensity (i.e. higher signals were preferred, saturated signals were avoided). Likewise, lipid species from the non-targeted data set (e.g. PC 16:0/20:4 and PC 18:2/18:2)


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which are monitored as sum of all species by the Biocrates platform (PC 36:4) were considered separately for correlation analysis.



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RESULTS AND DISCUSSION Characterization of the kidney tissue metabolome In order to maximize the coverage of kidney metabolome profiling an analytical pipeline employing two-step extraction (aqueous and organic solvents) coupled to untargeted LC-MS analysis after separation of metabolites by HILIC and RP chromatography was established.32 To further increase the diversity of metabolic features specific to kidney tissue, metabolome characterization was carried out in a pooled quality control sample prepared from extracts derived from various diseased and non-diseased human kidney samples as well as from a porcine kidney sample (see batch A in materials and methods). The largest fraction of metabolic features present in aqueous extracts could be assigned to polar metabolite classes including organic acids, α-amino acids, purine derivatives and nucleosides, monosaccharides, sugar alcohols, acylcarnitines and lysophospholipids (Supporting Information Figure S1 a and b and Table S2). Moreover, less polar lipid classes (e.g. sphingomyelin [SM] and phosphatidylethanolamine

[PE])

belonging

to

the

categories

of

sphingolipids

and

glycerophospholipids, could be annotated in the aqueous extract. Likewise, the same lipid classes could be detected in organic extracts (Supporting Information Figure S1 c and d and Table S2) indicating partitioning of these analytes between both extraction solvents which is in accordance to previous observations.32 Additionally, lipid classes of intermediate and low polarity such as phosphatidylcholines (PC), phosphatidylinositoles (PI), phosphatidylserines (PS), phosphatidylglycerols (PG), ceramides (Cer), glycosphingolipids (GSL), diacylglycerols (DAG) and


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triacylglycerols (TAG) could be assigned in organic extracts demonstrating that the majority of lipids were recovered with the non-polar extraction solvent. Of note, additional structural information about the detected analytes can be obtained through the availability of data from both ionization modes. For example, the lipid species PC 16:0/20:4 could be unambiguously assigned in organic extracts based on complementary fragment ion information from positive and negative ionization mode data, respectively (Supporting Information Figure S2). Whereas positive mode data revealed the PC-specific headgroup ion at 184.07 Da (Supporting Information Figure S2 b), corresponding information about the attached fatty acids C16:0 (255.1 Da) and C20:4 (303.2 Da) can be deduced from fragment ions derived from the same lipid species analyzed as acetate adduct in negative mode data (Supporting Information Figure S2 d). Moreover, an increase in metabolome coverage through the availability of data from both ionization modes is exemplified by the detection of acylcarnitines and fatty acids, lipid classes that preferentially ionize in positive or negative ionization mode, respectively (Supporting Information Figure S1 and Table S2). Besides covering a broad range of endogenous compounds, the non-targeted nature of our assay allows to retrieve information about the status of specific drugs present in kidney tissue. For instance, the type 2 diabetes drug metformin, for which cancer-protective properties are discussed,51 could be detected (Supporting Information Table S2) in a kidney sample from a patient suffering from diabetes and thus provides an important basis for the combined analysis of exo- and endogenous compounds. This aspect facilitates the assessment of drug-induced metabolic effects33 and thus may provide useful information for the investigation of drug toxicity, efficacy and compliance in the future. Moreover, as attempts are currently ongoing to 17 ACS Paragon Plus Environment


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combine non-targeted and targeted metabolomics methods34,52 the analytical platform presented may support the development of novel targeted assays which will likely be required for biomarker validation in future studies. In summary, from 4177 metabolic features that could be detected throughout all analytical modes (Supporting Information Table S3), 268 distinct metabolite species ranging from high to low polarity could be structurally assigned (Supporting Information Table S2) resulting in more than 5% of assigned features per analytical mode (Supporting Information Table S3). The overlap between positive and negative ionization mode for both HILIC and RPLC was below 55% (Supporting Information Figure S3 a and b) indicating a considerable increase in metabolome coverage by collecting data from multiple ion modes. The beneficial effect of “multi-method” approaches on metabolome coverage becomes even more evident when comparing metabolites analyzed in different extracts by the different stationary phases. Here, only 24 assigned compounds (9%) belonging to either phospholipids (e.g. LysoPC 16:0), sphingomyelins (e.g. SM 32:1) or acylcarnitines (e.g. acylcarnitine 16:0) could be detected as overlapping metabolites in HILIC and RPLC mode, respectively (Supporting Information Figure S3 c and Table S2). It is worth mentioning that the annotated and identified metabolites represent only a subset of metabolic features detected in kidney tissue. This limitation can on the one hand be explained by the underlying nature of data-dependent MS/MS spectra which provides fragment information only for a subset of precursor ions detected during an analytical run. On the other hand, a considerable amount of signals (e.g. up to 50% in human plasma53) measured in nontargeted metabolomics experiments still remains unidentified owing to the incompleteness of metabolomics databases. Moreover, as HILIC and RP stationary phases are typically biased 18 ACS Paragon Plus Environment

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towards the detection of specific metabolite classes (e.g. sugar phosphates),54 column choice represents an additional factor that likely affects the number of detected features and hence their structural assignment.



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Reproducibility assessment of sample analysis and metabolite extraction During the development of non-targeted metabolomics methods, reproducibility assessment is a crucial aspect in order to ensure robust analysis of as many metabolic features as possible. Besides analytical reproducibility, which was monitored through pooled QC sample analysis,55 the aspect of reproducible sample preparation was assessed by replicate processing of porcineand human-derived kidney samples. With respect to analytical performance, our assay exhibited excellent analytical reproducibility with average coefficients of variation (CVs ± SD) of all extracted features ranging from 9.9 ± 9.3% to 10.1 ± 8.2% for HILIC and from 6.7 ± 5.1% to 7.4 ± 6.7% for RPLC analysis, respectively. More specifically, > 80% of metabolic features in aqueous and organic extracts exhibited CVs ≤ 20% on both chromatographic systems (HILIC and RP) irrespective of the ionization mode (Figure 1 a-d, blue bars). Remarkably, > 60% of signals showed CVs ≤ 10% indicating high analytical precision of the majority of detected compounds. The observation that most of the features displayed CVs ≤ 30% is in agreement with previous reports demonstrating analytical reproducibility of non-targeted metabolomics assays using either RPLC-MS56 or combined approaches using HILIC-MS and RPLC-MS.32,54,57 For structurally assigned features a similar picture was observed demonstrated by reproducible analysis of all metabolites with CVs < 25% (Supporting Information Table S2). Of note, analytical reproducibility was evaluated based on 6 injections of a QC sample analyzed within a run comprising 58 injections. As the maximum difference between highest and lowest total signal in QC samples was below 15% in all analytical modes (Supporting Information Figure 20 ACS Paragon Plus Environment

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S4), the maximum batch capacity (e.g. deviations up to 25%), has not been reached. Previous reports assume acceptable batch stabilities of about 100 injections for non-targeted metabolomics assays,58 therefore the analysis of larger sample batches on our analytical platform requires careful evaluation. Regarding variability of sample extraction (porcine replicates, n=3), a greater proportion of metabolic features exhibited higher CVs compared to the analytical CVs (Figure 1 and Supporting Information Table S2). This effect is expected since the assessment of sample preparation reproducibility comprises both the variation of the sample preparation process and the analytical variation. Still, a considerable high intraday reproducibility is provided by the twostep extraction protocol as the majority of signals (> 80%) can be monitored with CVs ≤ 30% in both extracts (Figure 1 a-d, green bars). Of note, the proportion of features with extraction reproducibility CV > 30% was higher in aqueous extracts (Figure 1 a and b) compared to features present in the organic extracts (Figure 1 c and d). This finding is in contrast to previous reports where features recovered in aqueous extracts exhibited lower CVs compared to features in organic extracts.56 This discrepancy might be explained by different composition of solvents used for metabolite extraction and resuspension, critical parameters that have been demonstrated to affect reproducibility results of tissue metabolomics experiments.56 In accordance with other studies,54 sample preparation carried out on two different days resulted in moderate interbatch variations as indicated by altered CV distribution profiles between day 1 and day 2 (Supporting Information Figure S5). Sum normalization, as applied here, compensates for batch effects to some extent. However, to further eliminate such variabilities, in particular



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for large-scale metabolomics experiments comprising multiple batches, other normalization strategies (e.g. cyclic-loess or probabilistic quotient) could be considered.59 In addition to validation experiments carried out in porcine samples, reproducibility of sample preparation was assessed in human kidney tissue. To this end, metabolomics data from a set of five replicate kidney samples, each derived from individuals who suffered either from clear cell renal cell carcinoma (ccRCC), oncocytoma (OC), urothelial cell carcinoma (UCC, benign area), renal trauma (RT) or interstitial nephritis (IN), were analyzed by principal component analysis (PCA). Dermoid tumor (DT) samples were removed from the dataset as incomplete tissue homogenization resulted in low ion counts hampering feature extraction (data not shown). Likewise, positive ion mode data acquired in organic extracts after 12 min elution time were excluded from PCA due to the occurrence of highly variable features that were irregularly observed in human sample replicates (Supporting Information Figure S6) which substantially affected the quality of PCA visualization (Supporting Information Figure S7). Taking these inconsistencies into account, moderate variabilities could be observed in aqueous and organic extracts indicated by clustering of sample replicates (Figure 2). In particular, the tumorous samples (ccRCC and OC) exhibited distinct grouping in all analytical modes demonstrating the presence of homogenous donor tissue material. In contrast, non-tumorous samples (UCC, IN and RT) showed higher variability, suggesting the presence of more inhomogeneous material in these cases. Nevertheless, irrespective of the existing differences in tissue homogeneities, a clear distinction between tumorous and non-tumorous samples could be achieved providing a first proof of feasibility that the analytical platform provided here enables discrimination of different types of human kidney samples. 22 ACS Paragon Plus Environment

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Platform comparison on the basis of metabolite profiles derived from targeted and nontargeted metabolomics experiments Next, a comparison of quantitative data derived from targeted and non-targeted analytical platforms was performed in order to evaluate analytical system comparability. Ten ccRCC and adjacent non-tumor kidney tissue samples (Supporting Information Table S1) were analyzed by validated targeted metabolomics methods.36,37 Likewise, aliquots of the same donor tissues were subjected to non-targeted metabolomics according to our protocol (batch B). The experiment was restricted to ten tissue samples due to the availability of tissue material (particularly of non-tumor tissue samples) that had to be present in an amount to divide it into two pieces for sample preparation and analysis on each platform individual. Strikingly, from the 102 metabolites common to both platforms (Supporting Information Table S4 and Figure S8), a mean correlation coefficient (r ± SD) of 0.69 ± 0.25 could be achieved (Figure 3 and Supporting Information Table S4). More precisely, about 90% of these metabolites exhibited Spearman’s correlation coefficients ≥ 0.3 which is in accordance to results from a recently published platform comparison carried out in serum where > 80% of metabolites exhibited at least weak correlations (r ≥ 0.2).60 From the positively correlated metabolites found in our study 64 (62.7%) showed high (r ≥ 0.7), 20 (19.6%) medium (0.5 ≤ r < 0.7) and 10 (9.8%) weak correlations (0.3 ≤ r < 0.5). Remarkably, only a minority of 8 metabolites (7.8%) exhibited low correlations coefficients (r < 0.3). Interestingly, the correlation profile did not reveal any metabolite class-specific grouping effects (e.g. all PC species exhibit correlations < 0.3) demonstrating equal quantification performance throughout all metabolite classes. In addition 23 ACS Paragon Plus Environment


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to platform comparison carried out by correlation analysis (i.e. quantitative data [pmol/mg] correlated to semi-quantitative data [peak areas]) we next asked whether both assays also reveal similar metabolic effects between tumor and non-tumor tissue samples (i.e. fold changes and p-values of selected metabolites). As depicted in Figure 3b, hierarchical cluster analysis based on log2 fold changes of all overlapping metabolites (Supporting Information Table S5) enabled a platform-independent clustering of all patient samples. This observation demonstrates the capability to capture similar metabolic effect between ccRCC and non-tumor tissue by both platforms. The different methods were assessed in more detail on the basis of metabolites that exhibit the most significant effects. Strikingly, from the top ten metabolites that exhibit the lowest p-values on both platforms (Supporting Information Table S5), seven metabolites were in common and showed a similar pattern in terms of p-values and fold changes (Supporting Information Figure S9). Of note, p-values obtained from the targeted analysis were generally lower as those derived from the non-targeted assay which is likely due the higher analytical precision of the targeted assay. Regarding analytical sensitivity the non-targeted platform enables reliable monitoring of metabolite concentrations ranging from low pmol amounts (< 0.5 pmol/mg) up to quantities above 5 nmol/mg (Supporting Information Table S5). This indicates that non-targeted metabolomics is largely comparable to targeted assays in terms of covering similar concentration ranges at least for the selected metabolites. So far, the reasons why some of the metabolites exhibit discrepant effects between the platforms (i.e. low correlation coefficient, differences in fold change or analytical sensitivity) cannot be unambiguously explained. Clarification of this aspect requires further experimental 24 ACS Paragon Plus Environment

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work and is beyond the scope of this project. However, several scenarios, that might explain some of the differences, can be envisaged: (1) tissue heterogeneity; since each tissue sample was separated into two pieces prior to analysis by both approaches (targeted and nontargeted), heterogenic distribution of distinct metabolites across the kidney tissue sample could lead to different metabolite quantities within the two tissue pieces. (2) sample preparation; the non-targeted assay employs bead-based tissue homogenization in 50% methanol at a solvent/tissue ratio of 50µL/mg followed by lipid extraction with MTBE. In contrast, the Biocrates assay is based on homogenization in 100% methanol at a solvent/tissue ratio of 6µL/mg61 and, if required, an extraction step with phosphate buffer is added (e.g. for amino acid analysis). Thus, different efficiencies/reproducibilities of the homogenization or extraction procedure might account for the observed inconsistencies. (3) analytical methods; the platforms compared here are based on analytical instruments that are inherently different regarding dynamic range, detection sensitivity and resolution (e.g. low resolution vs. high resolution). Moreover, different stationary phases are used for the analysis of specific metabolites (e.g. for amino acids). Whereas the Biocrates approach is based on reversed-phase chemistry, amino acid analysis by the non-targeted assay is done after HILIC separation. Altogether, these differences in mass spectrometric instruments as well as chromatographic techniques may in some cases result in ion suppression or signal interferences that affect quantitative results.62 In summary, the platform comparison between non-targeted metabolomics and classical targeted assays was demonstrated here for the first time in human tissue samples. The observations of highly comparable quantitative data between the two platforms provides an



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important finding that will likely promote ongoing efforts towards merging non-targeted and targeted analytical methods in metabolomics research.34,63


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Proof of concept - differentiation of ccRCC and non-tumor tissue based on metabolic profiling In order to demonstrate the applicability of the non-targeted metabolomics assay to discriminate between ccRCC and non-tumor tissue, (Supporting Information Table S1), the data used for platform comparison were further evaluated. Notably, PCA shows a differentiation of ccRCC and adjacent non-tumor tissue in all four analytical modes (Figure 4 a-d). Moreover, analysis of altered features revealed metabolites which were significantly increased or decreased (blue) in ccRCC tissue compared to the adjacent non-tumor tissue region (Figure 5). For example, in aqueous extracts, metabolite levels for L-glutamine, 5-oxoproline, mannitol/sorbitol, N-acetylneuraminate (sialic acid), ergothioneine, trigonellinamide and hexose (e.g. glucose) were increased whereas trigonelline, 1-methyladenosine, hippurate, creatinine and D-pantothenic acid were decreased in ccRCC tissue (Figure 5 a and b). Increased glutamine levels in RCC tissue were previously described in an NMR-study.14 Amongst others, levels of oxoproline and glucose were previously found to be elevated accompanied by a decrease of hippuric acid in RCC tissue based on GC-TOF-MS analysis.13 Moreover in a recent metabolomics study of ccRCC and adjacent non-tumor tissue using GC-MS and LC-MS6, the metabolites ergothioneine, N-acetylneuraminate, glucose, sorbitol, glutamine, creatinine, pantothenic acid, 1-methyladenosine and hippurate could be confirmed to be altered in the same direction as observed with our approach. Whereas glutamine is well known to be involved in various cancer cell metabolism processes such as the biosynthesis of DNA or fatty acids, energy formation or signaling pathways,64 oxoproline is mainly involved in glutathione metabolism which in turn is known for its pivotal role in cancer cell metabolism. Increased glucose in cancerous tissue is a well-known effect and 27 ACS Paragon Plus Environment


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is an explanation for the altered energy metabolism in RCC. In addition, a noticeable finding was the elevated level of sialic acid which might be the result of increased mRNA expression of the sialic acid-producing enzyme sialidase (encoded by NEU3) in RCCs.65 Data derived from organic extracts (Figure 5 c and d) revealed mostly changes within the class of phosphoethanolamine-containing lipids with diacyl lipid species (e.g. PE 16:0/18:1) showing exclusively decreased levels in ccRCC tissue compared to ether-species (e.g. PE P-18:0/20:4) which were all found to be increased. Moreover, diacyl-species of phosphatidylcholines remained

unaltered

between

non-tumor

and

ccRCC

tissue

whereas

ether-linked

phosphatidylcholines (e.g. PC O-38:5) were entirely elevated. The observed alterations within the group of phospholipids are largely in accordance with previous findings were, amongst others, ether- and diacyl PE lipids exhibited strong alterations between normal and RCC tissue samples.15,18,28 An in-depth biological interpretation of altered features revealed here is beyond the scope of this work and would require additional studies on larger cohorts to confirm and validate the preliminary findings. Nevertheless, many effects observed in the proof-of-concept experiment were previously described to be involved in RCC progression or development and therefore confirm the excellent performance of our novel assay with respect to kidney-specific comprehensive metabolite profiling. The presented non-targeted metabolomics method has shown its potential for differentiation of ccRCC and adjacent non-tumor tissue and thus provides a first basis for further application of tissue subtype discrimination based on metabolic signatures.


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As demonstrated already for the platform comparison many metabolic effects were consistent between non-targeted and targeted metabolomics experiments. For example, with respect to ether-linked phosphatidylcholines, the targeted assay also revealed similar species (e.g. PCO38:5 and PC-O32:2) that were altered in the same direction (Supporting Information Figure S9, Figure S10 and Table S5). Nevertheless the strength of non-targeted metabolomics, in particular regarding discovery of novel putative biomarkers, becomes evident when comparing volcano plot data from both platforms (Figure 5 and Figure S10). Here, non-targeted metabolomics revealed many, yet unassigned, features significantly altered thus facilitating the discovery of novel putative biomarkers that were not captured by the targeted metabolomics assay.



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CONCLUSIONS In this study, HILIC and RP chromatography have been coupled with high resolution mass spectrometry for comprehensive metabolic profiling of kidney tissue. The orthogonal separation techniques allowed covering a broad range of metabolites with different physicochemical properties. As little as 10 mg of frozen kidney tissue were sufficient for the combined analysis of small, polar molecules and non-polar lipids in kidney tissue, metabolite classes that are often analyzed in a separate manner.13,15 It was convincingly demonstrated that the vast majority of metabolites could be reproducibly extracted and analyzed in porcine and human kidney tissue. Metabolites analyzed with the novel non-targeted approach could be successfully correlated to a well-established targeted approach, underlining the high quality of the non-targeted analysis workflow. The feasibility of the workflow, to discriminate tumor and healthy tissue samples, was demonstrated on a small cohort of ccRCC tissue and matched non-tumor tissue. A clear discrimination of the two sample groups could be achieved in all four analysis modes using PCA, which indicates valid sample collection, preparation and analysis. This study was designed as proof of concept for metabolite profiling of kidney tissue and offers great potential for the metabolic characterization of kidney tissue with important clinical implications for the future.


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ASSOCIATED CONTENT Supporting Information Table S1: Patient and tumor specific data of kidney tissue samples used for platform comparison and proof-of-concept experiment. Table S2: List of assigned metabolites in kidney tissue extracts by non-targeted metabolomics. Table S3: Number of detected and structurally assigned metabolic features by non-targeted metabolomics. Table S4: Correlation of targeted and nontargeted metabolomics data. Table S5: Comparison of fold changes and p-values (ccRCC and non-tumor tissue) between the non-targeted and the targeted metabolomics assay. Figure S1: Extracted compound chromatograms (ECCs) of main metabolite classes detected by RPLC and HILIC analysis. Figure S2: Extracted ion chromatograms (EIC) and MS/MS spectra of PC (16:0/20:4). Figure S3: Venn diagrams displaying the extend of overlap of assigned metabolic features between the different analytical modes of the non-targeted assay. Figure S4: LeveyJennings chart representing the trend of total areas analyzed in QC samples throughout an analytical batch. Figure S5: Box plots displaying analytical reproducibility and interday sample preparation variability. Figure S6: Overlay of a total compound chromatogram (TCC) of six kidney cortex replicates for IN and OC to illustrate variable features in tissue replicates. Figure S7: PCA of kidney cortex replicates analyzed in organic extracts by ESI(+) to illustrate variable features in tissue replicates. Figure S8: Venn diagram illustrating the overlap of metabolites between the targeted and the non-targeted metabolomics method. Figure S9: Platform comparison on the basis of most significant marker metabolites determined from overlapping metabolites. Figure S10: Results of the analysis of differentially regulated features between



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ccRCC and adjacent non-tumor tissue samples derived from five male donors for the targeted metabolomics analysis. (PDF) AUTHOR INFORMATION Corresponding Author *E-Mail: [email protected]. Phone +49 (0) 711 - 8101-5429. Author Contributions P.L. and M.H. performed the experiments for the non-targeted metabolomics analysis, mainly contributed to the conception and design of the manuscript, performed the statistical analysis and drafted the manuscript. M.S. and E.S. contributed to the conception, design and revision of the manuscript. S.R., J.H. and J.B. revised the manuscript and were involved in the process of data and sample collection. S.W. was involved in the revision of the statistical analysis and the manuscript. T.M.,F.B., and U.H. revised the manuscript. D.S. and J.W. were responsible for the acquisition and evaluation of the targeted metabolomics data. FF was responsible for the histopathological classification of the human tissue samples. All authors have given approval to the final version of the manuscript. Notes D. Sonntag and J. Wahrheit are employees of Biocrates Life Sciences AG. All other authors declare no competing financial interest.


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ACKNOWLEDGMENTS Ursula Waldherr, Markus König and Monika Seiler are gratefully acknowledged for excellent technical assistance. The work was supported by the Robert Bosch Stiftung, Stuttgart and the Bosch-Forschungsstiftung, Stuttgart, as well as the ICEPHA Graduate School Tübingen-Stuttgart, Germany.



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Figures Figure 1 Distribution of the coefficients of variation observed for metabolic features detected in aqueous (a, b) and organic (c, d) extracts. Blue bars represent the analytical reproducibility (n=6) and green bars represent the reproducibility of the sample preparation (n=3). Continuous green and blue lines indicate the numbers of cumulative features. The red dashed line marks the 80% threshold of all features analyzed in the corresponding mode. Figure 2 Principle component analysis of kidney cortex replicates (n=6, technical replicates) of tumorous samples ccRCC (blue) and OC (red) and the non-tumorous samples UCC benign area (green), RT (orange) and IN (black). Data from aqueous (a, 264 features and b, 248 features) and organic (c, 311 features and d, 293 features) extracts is presented. Note: Data analyzed after RT > 12 minutes were excluded in (c) due to variable features present in the sample (see Figure S7). PCA was done based on all extracted features common to all samples.

Figure 3 (a) Spearman correlation coefficients of 102 metabolites analyzed by targeted and nontargeted metabolomics. Dotted lines distinguish metabolites that exhibit correlation coefficients within different ranges (< 0.3, 0.3 ≤ r < 0.5, 0.5 ≤ r < 0.7 and r ≥ 0.7). (b) Hierarchical cluster analysis of log2 fold changes between ccRCC and adjacent non-tumor tissue samples based on metabolites detectable on both platforms.

Figure 4 Principle component analysis of ccRCC (red) and adjacent non-tumor tissue samples (green) of five male donors (see Table S1). (a) and (b) represents data from the aqueous extract analyzed in positive (517 features) and negative mode (451 features), respectively. (c) and (d) represents data derived from the organic extract analyzed in positive (604 features) and 41 ACS Paragon Plus Environment


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negative mode (657 features). Positive mode data from organic extracts (c) were evaluated under exclusion of variable features (RT ≥ 12 min). PCA was done based on all extracted features.

Figure 5 Results of the non-targeted metabolomics analysis of differentially regulated features between ccRCC and adjacent non-tumor tissue samples derived from five male donors (see Table S1). The volcano plots display log2 fold changes versus Benjamini-Hochberg adjusted pvalues (-log10 transformed). Features that exhibited an absolute log2 fold change > 1 and an adjusted p-value < 0.05 are colored in blue. Metabolites which could be structurally annotated are labeled with the corresponding name whereas unassigned metabolites were not labeled. Red dots represent features that were not significantly altered. (a) and (b) represents data from the aqueous extract analyzed in positive (517 features) and negative mode (541 features), respectively. (c) and (d) represents data derived from the organic extract analyzed in positive (604 features) and negative mode (657 features). Positive mode data from organic extracts (c) was evaluated under exclusion of variable features (RT ≥ 12 min).


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Figure 1



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Figure 2


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Figure 3



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Figure 4


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For Table of Contents Only (Graphical Abstract)


Comprehensive Metabolomic and Lipidomic Profiling of Human

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