Metabolomic Method: UPLC-q-ToF Polar and Non-Polar Metabolites in the Healthy Rat Cerebellum Using an In-Vial Dual Extraction

PLOS ONE, Apr 2015

Unbiased metabolomic analysis of biological samples is a powerful and increasingly commonly utilised tool, especially for the analysis of bio-fluids to identify candidate biomarkers. To date however only a small number of metabolomic studies have been applied to studying the metabolite composition of tissue samples, this is due, in part to a number of technical challenges including scarcity of material and difficulty in extracting metabolites. The aim of this study was to develop a method for maximising the biological information obtained from small tissue samples by optimising sample preparation, LC-MS analysis and metabolite identification. Here we describe an in-vial dual extraction (IVDE) method, with reversed phase and hydrophilic liquid interaction chromatography (HILIC) which reproducibly measured over 4,000 metabolite features from as little as 3mg of brain tissue. The aqueous phase was analysed in positive and negative modes following HILIC separation in which 2,838 metabolite features were consistently measured including amino acids, sugars and purine bases. The non-aqueous phase was also analysed in positive and negative modes following reversed phase separation gradients respectively from which 1,183 metabolite features were consistently measured representing metabolites such as phosphatidylcholines, sphingolipids and triacylglycerides. The described metabolomics method includes a database for 200 metabolites, retention time, mass and relative intensity, and presents the basal metabolite composition for brain tissue in the healthy rat cerebellum.

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Metabolomic Method: UPLC-q-ToF Polar and Non-Polar Metabolites in the Healthy Rat Cerebellum Using an In-Vial Dual Extraction

April Metabolomic Method: UPLC-q-ToF Polar and Non-Polar Metabolites in the Healthy Rat Cerebellum Using an In-Vial Dual Extraction Amera A. Ebshiana 0 1 2 Stuart G. Snowden 0 1 2 Madhav Thambisetty 0 1 2 Richard Parsons 0 1 2 Abdul Hye 0 1 2 Cristina Legido-Quigley 0 1 2 0 1 Institute of Pharmaceutical Sciences, King's College London , Franklin-Wilkins Building, 150 Stamford Street, London, SE1 9NH , United Kingdom , 2 Institute of Psychiatry, Department of Old Age Psychiatry, King's College London , De Crespigny Park, London, SE5 8AF , United Kingdom , 3 Clinical and Translational Neuroscience Unit, Laboratory of Behavioural Neuroscience, National Institute on Aging , Baltimore, Maryland , United States of America 1 Funding: This work has been supported by grants from the Butterfield Trust and the Libyan Cultural Bureau London. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript 2 Academic Editor: Damir Janigro, Cleveland Clinic , UNITED STATES Unbiased metabolomic analysis of biological samples is a powerful and increasingly commonly utilised tool, especially for the analysis of bio-fluids to identify candidate biomarkers. To date however only a small number of metabolomic studies have been applied to studying the metabolite composition of tissue samples, this is due, in part to a number of technical challenges including scarcity of material and difficulty in extracting metabolites. The aim of this study was to develop a method for maximising the biological information obtained from small tissue samples by optimising sample preparation, LC-MS analysis and metabolite identification. Here we describe an in-vial dual extraction (IVDE) method, with reversed phase and hydrophilic liquid interaction chromatography (HILIC) which reproducibly measured over 4,000 metabolite features from as little as 3mg of brain tissue. The aqueous phase was analysed in positive and negative modes following HILIC separation in which 2,838 metabolite features were consistently measured including amino acids, sugars and purine bases. The non-aqueous phase was also analysed in positive and negative modes following reversed phase separation gradients respectively from which 1,183 metabolite features were consistently measured representing metabolites such as phosphatidylcholines, sphingolipids and triacylglycerides. The described metabolomics method includes a database for 200 metabolites, retention time, mass and relative intensity, and presents the basal metabolite composition for brain tissue in the healthy rat cerebellum. - Competing Interests: This study was partly supported by a grant from the Butterfield Trust, Bermuda. There are no patents, products in development or marketed products to declare. This Introduction does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials. fragmentation patterns are used for identification, this is an expert field and good quality fragmentation is not always possible. To date there has been a number of metabolomic studies that have looked at the metabolite composition of brain tissue. Salek et al. [28] used 1H-NMR to measure the metabolite composition in the hippocampus, cortex, frontal cortex, midbrain and cerebellum of CRND8 mice identifying 23 metabolites from tissue samples ranging in mass from 1050mg. In humans, brain tissue is in short supply and to date only small numbers (n = 1015) with reversed phase fingerprinting have been profiled. However two groups were able to make important contributions, Graham et al. [29] used 5g of human post mortem brain and UPLC-ToF to develop a method that detected 1,264 metabolic features, with 10 features shown to be correlated to AD. Koichi et al. [30] also used UPLC-ToF metabolomics of human brain and found spermine and spermidine to be increased in AD pathology. Therefore, this study aimed to obtain both polar and non-polar metabolites from a single small sample of brain tissue. For this HILIC together with reversed phase (RP) methods were investigated. Another aim was to provide the means for metabolite identification with the method, the data generated is the basal metabolome in rat cerebellum that can be applied in clinical investigations. Materials and Methods Chemicals and Reagents Experimental Design Tissue homogenisation Prior to homogenisation 20l of methanol and 5l of HILIC internal standard cocktail (2.5mM L-serine13C315N and L-valine13C515N in methanol:water (4:1)) was added per milligram of sample material. The tissue was then homogenised using a Tissuelyzer(Qiagen) in 10 cycles of 30 seconds at 25 Hz, subsequently a 50ul aliquot of homogenate was transferred to a Chromacol HPLC vial (400l fixed insert). In-vial dual extraction of brain tissue LC-MS analysis of IVDE non-aqueous phase LC-MS analysis of IVDE aqueous phase Data processing and metabolite identification addition some peaks in the reversed phase method were annotated by comparing the m/z and retention time of metabolite features to metabolite features previously annotated in Whiley et al. [32]. HILIC Positive HILIC Negative Showing the number of metabolite peaks identified and their relative variability in 100%, 85% and 70% of 7 sample replicates. a percentage of samples a peak is detected in b coefficient of variance of peak intensity between samples. RP Positive HILIC Total Showing the number of metabolite peaks identified and their relative variability in 100%, 85% and 70% of 7 sample replicates. a percentage of samples a peak is detected in b coefficient of variance of peak intensity between samples. RP Negative Assessing the effect of tissue homogenisation and sample mass on method performance and precision (Experiment 2) HILIC Positive HILIC Negative HILIC Total Showing the number of metabolite peaks identified and their relative variability in 100%, 93%, 87%, 80% and 73% of 15 sample replicates. a percentage of samples a peak is detected in b coefficient of variance of peak intensity between samples. RP Positive RP Negative Showing the number of metabolite peaks identified and their relative variability in 100%, 93%, 87%, 80% and 73% of 15 sample replicates. a percentage of samples a peak is detected in b coefficient of variance of peak intensity between samples. Annotated metabolites Molecular Weight (Da) Retention time (Mins) 56.2 28.6 0.4 22.3 39.2 84.5 7.4 69.1 1.4 33.6 1.2 2.2 20.5 1.0 1.2 1.2 6.2 0.8 43.1 61.6 95.2 1.3 4.5 7.9 18.4 5.2 17.4 6.1 0.7 1.6 108.7 388.8 6.1 1.6 16.3 Molecular Weight (Da) Retention time (Mins) 9.5 1.2 0.7 20.3 223.4 5.0 0.6 14.4 30.2 2.2 10.7 0.4 0.4 0.3 0.8 1.9 286.3 0.9 1.6 25.6 1 14.9 6.3 3.8 12.1 0.9 51.7 2.8 Molecular Weight (Da) Retention time (Mins) C47H81O13P C39H73O8P C37H71O8P C42H85O7P C37H69O8P C42H81NO8 C46H89NO8 C46H87NO8 C48H91NO8 C39H74NO8P C44H84NO7P C41H78NO8P C42H74NO8P C41H80NO8P C45H78NO8P C40H76NO8P C46H74NO8P C43H86NO8P C43H74NO8P C43H80NO8P C43H74NO8P 16.8 1.7 3.7 C43H80NO8P C44H82NO8P C46H82NO7P C52H103NO8P C46H80NO7P C52H105NO8P C53H107NO8P C46H92NO8P C48H94NO8P C48H84NO8P C44H76NO8P C48H92NO8P C40H77O10P C44H81O10P C42H79O10P C48H97O9P C40H80NO8P C45H78NO8P C38H74NO8P C40H80NO8P C43H80NO8P C38H72NO8P C39H74NO8P C41H76NO8P C41H78NO8P C41H72NO8P C41H76NO8P C43H74NO8P C43H78NO8P C51H100NO8P C41H86NO6P C45H86NO8P C49H82NO8P C26H50NO7P C28H50NO7P C32H64NO7P C21H43O7P C27H56NO7P C29H58NO7P C36H68NO10P C44H80NO10P C46H80NO10P C43H78NO10P C42H80NO10P C42H82NO10P Molecular Weight (Da) Retention time (Mins) 31.3 36.9 12.4 0.3 Molecular Weight (Da) Retention time (Mins) 781.9955 749.5511 761.5975 565.5435 593.5745 621.6163 649.6372 675.6477 701.5575 Conclusions The method described in this paper is shown to be capable of measuring over 4,000 metabolite features from as little as 3mg of tissue with a high degree of reproducibility of which we were able to annotate 200 metabolites from a variety of metabolite classes across a range of concentrations. It is hoped that the low required sample mass and improved sensitivity of this method will provide a valuable tool to analyse cerebral metabolism, hopefully providing new insights into the functioning of the brain as well as the mechanisms of pathology of neurological disorders. Supporting Information S1 Table. Measured metabolite features in the HILIC method in experiment 2. Showing the number of metabolite peaks identified and their relative variability in 100%, 93%, 87%, 80% and 73% of 15 sample replicates after transformation based on the recovery of both internal standards. a percentage of samples a peak is detected in, b coefficient of variance of peak intensity between samples. (DOCX) This work has been supported by grants from the Butterfield Trust and the Libyan Cultural attach of Libyan embassy. We would also like to thank Dr Chantal Bazenet for critically reading the manuscript and providing constructive feedback, Dr Mathew Arno manager of the genomics centre for permitting us to use his Tissuelyzer and Juzaili Azizi for aiding us with rat brain dissection. Conceived and designed the experiments: CLQ. Performed the experiments: AAE SGS. Analyzed the data: AAE SGS. Contributed reagents/materials/analysis tools: CLQ. Wrote the paper: AAE SGS MT RP AH CLQ. 1. Available: http://www.nlm.nih.gov/medlineplus/neurologicdiseases.html. 2. Rice ML , Warren SF , Betz SK . ( May 2005 ). "Language symptoms of developmental language disorders: An overview of autism , Down syndrome, fragile X, specific language impairment, and Williams syndrome." Applied Psycholinguistics, 26:1 . pp. 7 - 27 . 3. Geschwind DH , Levitt P ( 2007 ) Autism spectrum disorders: developmental disconnection syndromes . Current Opinion in Neurobiology 17 , 103 - 111 . PMID: 17275283 4. Niedermeyer E , Froescher W , Fisher RS ( 1985 ) Epileptic seizure disorders . Journal of Neurology 232 , 1 - 12 . PMID: 3998776 5. Jankovic J ( 2008 ) Parkinson's disease: clinical features and diagnosis . Journal of Neurology, Neurosurgery & Psychiatry 79 , 368 - 376 . 6. McKhann G , Drachman D , Folstein M , Katzman R , Price D , Stadlan EM , et al. ( 1984 ) Clinical diagnosis of Alzheimer's disease: Report of the NINCDS-ADRDA Work Group* under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease . Neurology 34 , 939 . 7. Proitsi P , Kim M , Whiley L , Pritchard M , Leung R , Soininen H , et al. ( 2015 ) Plasma lipidomics analysis finds long chain cholesteryl esters to be associated with Alzheimer/'s disease . Transl Psychiatry 5 , e494. doi: 10.1038/tp.2014.127 PMID: 25585166 8. Whiley L , Sen A , Heaton J , Proitsi P , Garca-Gmez D , Leung R , et al. ( 2014 ) Evidence of altered phosphatidylcholine metabolism in Alzheimer's disease . Neurobiology of Aging 35 , 271 - 278 . doi: 10.1016/j. neurobiolaging. 2013 . 08.001 PMID: 24041970 9. Snowden S , Dahlan S-E , Wheelock CE ( 2012 ) Application of metabolomics approaches to the study of respiratory diseases . Bioanalysis 4, 2265 - 2290 . doi: 10.4155/bio. 12.218 PMID: 23046268 10. Fiehn O ( 2002 ) Metabolomics-the link between genotypes and phenotypes . Plant Molecular Biology 48 , 155 - 171 . PMID: 11860207 11. Psychogios N , Hau DD , Peng J , Guo AC , Mandal R , Bouatra S , et al. ( 2011 ) The Human Serum Metabolome. PLoS ONE 6, e16957 . doi: 10.1371/journal.pone.0016957 PMID: 21359215 12. Pan Z , Raftery D ( 2007 ) Comparing and combining NMR spectroscopy and mass spectrometry in metabolomics . Analytical and Bioanalytical Chemistry 387 , 525 - 527 . PMID: 16955259 13. Zhang T , Creek DJ , Barrett MP , Blackburn G , Watson DG ( 2012 ) Evaluation of Coupling Reversed Phase, Aqueous Normal Phase, and Hydrophilic Interaction Liquid Chromatography with Orbitrap Mass Spectrometry for Metabolomic Studies of Human Urine . Analytical Chemistry 84 , 1994 - 2001 . 14. Kell DB ( 2004 ) Metabolomics and systems biology: making sense of the soup . Current Opinion in Microbiology 7 , 296 - 307 . PMID: 15196499 15. Crockford DJ , Holmes E , Lindon JC , Plumb RS , Zirah S , Bruce SJ , et al. ( 2005 ) Statistical Heterospectroscopy, an Approach to the Integrated Analysis of NMR and UPLC-MS Data Sets: Application in Metabonomic Toxicology Studies . Analytical Chemistry 78 , 363 - 371 . 16. Kleijn RJ , Geertman J-MA , Nfor BK , Ras C , Schipper D , Pronk JT , et al. ( 2007 ) Metabolic flux analysis of a glycerol-overproducing Saccharomyces cerevisiae strain based on GC-MS, LC-MS and NMR-derived 13C-labelling data . FEMS Yeast Research 7 , 216 - 231 . PMID: 17132142 17. Sato Y , Suzuki I , Nakamura T , Bernier F , Aoshima K , Oda Y ( 2012 ) Identification of a new plasma biomarker of Alzheimer's disease using metabolomics technology . Journal of Lipid Research 53 , 567 - 576 . doi: 10.1194/jlr. M022376 PMID: 22203775 18. Llorach R , Urpi-Sarda M , Jauregui O , Monagas M , Andres-Lacueva C ( 2009 ) An LC-MS-Based Metabolomics Approach for Exploring Urinary Metabolome Modifications after Cocoa Consumption . Journal of Proteome Research 8 , 5060 - 5068 . doi: 10.1021/pr900470a PMID: 19754154 19. De Vos RCH , Moco S , Lommen A , Keurentjes JJB , Bino RJ , Hall RD ( 2007 ) Untargeted large-scale plant metabolomics using liquid chromatography coupled to mass spectrometry . Nat. Protocols 2 , 778 - 791 . PMID: 17446877 20. Lanza IR , Zhang S , Ward LE , Karakelides H , Raftery D , Nair KS ( 2010 ) Quantitative Metabolomics by 1H-NMR and LC-MS/MS Confirms Altered Metabolic Pathways in Diabetes. PLoS ONE 5, e10538 . doi: 10.1371/journal.pone.0010538 PMID: 20479934 21. Mallet CR , Lu Z , Mazzeo JR ( 2004 ) A study of ion suppression effects in electrospray ionization from mobile phase additives and solid-phase extracts . Rapid Communications in Mass Spectrometry 18 , 49 - 58 . PMID: 14689559 22. Annesley TM ( 2003 ) Ion Suppression in Mass Spectrometry . Clinical Chemistry 49 , 1041 - 1044 . PMID: 12816898 23. Muller C , Schafer P , Stortzel M , Vogt S , Weinmann W ( 2002 ) Ion suppression effects in liquid chromatography electrospray-ionisation transport-region collision induced dissociation mass spectrometry with different serum extraction methods for systematic toxicological analysis with mass spectra libraries . Journal of Chromatography B 773 , 47 - 52 . PMID: 12015269 24. Antignac J-P , de Wasch K , Monteau F , De Brabander H , Andre F , Le Bizec B ( 2005 ) The ion suppression phenomenon in liquid chromatography-mass spectrometry and its consequences in the field of residue analysis . Analytica Chimica Acta 529 , 129 - 136 . 25. Michopoulos F , Lai L , Gika H , Theodoridis G , Wilson I ( 2009 ) UPLC-MS-Based Analysis of Human Plasma for Metabonomics Using Solvent Precipitation or Solid Phase Extraction . Journal of Proteome Research 8 , 2114 - 2121 . PMID: 19714883 26. Dunn WB , Broadhurst DI , Atherton HJ , Goodacre R , Griffin JL ( 2011 ) Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy . Chemical Society Reviews 40 , 387 - 426 . doi: 10.1039/b906712b PMID: 20717559 27. Wishart DS ( 2011 ) Advances in metabolite identification . Bioanalysis 3, 1769 - 1782 . doi: 10.4155/bio. 11.155 PMID: 21827274 28. Salek RM , Xia J, Innes A , Sweatman BC , Adalbert R , Randle S , et al. ( 2010 ) A metabolomic study of the CRND8 transgenic mouse model of Alzheimer's disease . Neurochemistry International 56 , 937 - 947 . doi: 10.1016/j.neuint. 2010 . 04.001 PMID: 20398713 29. Graham SF , Chevallier OP , Roberts D , Halscher C , Elliott CT , Green BD ( 2013 ) Investigation of the Human Brain Metabolome to Identify Potential Markers for Early Diagnosis and Therapeutic Targets of Alzheimer's Disease . Analytical Chemistry 85 , 1803 - 1811 . doi: 10.1021/ac303163f PMID: 23252551 30. Inoue K , Tsutsui H , Akatsu H , Hashizume Y , Matsukawa N , Yamamoto T , et al. ( 2013 ) Metabolic profiling of Alzheimer's disease brains . Sci. Rep. 3. 31. Spijker S ( 2011 ) Dissection of Rodent Brain Regions In Neuroproteomics Humana Press , pp. 13 - 26 . 32. Whiley L , Godzien J , Ruperez FJ , Legido-Quigley C , Barbas C ( 2012 ) In-Vial Dual Extraction for Direct LC-MS Analysis of Plasma for Comprehensive and Highly Reproducible Metabolic Fingerprinting . Analytical Chemistry 84 , 5992 - 5999 . doi: 10.1021/ac300716u PMID: 22702345 33. US Department of Health and Human Services FaDA , Centre for Drug Evaluation and Research (CDER), Centre for Veterinary Medicine (CVM). (2001) Bioanalytical Method Validation.


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Amera A. Ebshiana, Stuart G. Snowden, Madhav Thambisetty, Richard Parsons, Abdul Hye, Cristina Legido-Quigley. Metabolomic Method: UPLC-q-ToF Polar and Non-Polar Metabolites in the Healthy Rat Cerebellum Using an In-Vial Dual Extraction, PLOS ONE, 2015, DOI: 10.1371/journal.pone.0122883