Untargeted Metabolomics Strategies-Challenges and Emerging Directions

被引:922
作者
Schrimpe-Rutledge, Alexandra C. [1 ,2 ,3 ,4 ]
Codreanu, Simona G. [1 ,2 ,3 ,4 ]
Sherrod, Stacy D. [1 ,2 ,3 ,4 ]
McLean, John A. [1 ,2 ,3 ,4 ]
机构
[1] Vanderbilt Univ, Dept Chem, Nashville, TN 37235 USA
[2] Vanderbilt Univ, Ctr Innovat Technol, Nashville, TN 37235 USA
[3] Vanderbilt Univ, Vanderbilt Inst Chem Biol, Nashville, TN 37235 USA
[4] Vanderbilt Univ, Vanderbilt Inst Integrat Biosyst Res & Educ, Nashville, TN 37235 USA
基金
美国国家卫生研究院;
关键词
Metabolomics; Untargeted; Targeted; Discovery; Global; Validation; Identification; Bioinformatics; CHROMATOGRAPHY-MASS-SPECTROMETRY; METABOLITE IDENTIFICATION; STRUCTURE ELUCIDATION; DATABASE; ANNOTATION; CONFIDENCE; PATHWAYS;
D O I
10.1007/s13361-016-1469-y
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Metabolites are building blocks of cellular function. These species are involved in enzyme-catalyzed chemical reactions and are essential for cellular function. Upstream biological disruptions result in a series of metabolomic changes and, as such, the metabolome holds a wealth of information that is thought to be most predictive of phenotype. Uncovering this knowledge is a work in progress. The field of metabolomics is still maturing; the community has leveraged proteomics experience when applicable and developed a range of sample preparation and instrument methodology along with myriad data processing and analysis approaches. Research focuses have now shifted toward a fundamental understanding of the biology responsible for metabolomic changes. There are several types of metabolomics experiments including both targeted and untargeted analyses. While untargeted, hypothesis generating workflows exhibit many valuable attributes, challenges inherent to the approach remain. This Critical Insight comments on these challenges, focusing on the identification process of LC-MS-based untargeted metabolomics studies-specifically in mammalian systems. Biological interpretation of metabolomics data hinges on the ability to accurately identify metabolites. The range of confidence associated with identifications that is often overlooked is reviewed, and opportunities for advancing the metabolomics field are described.
引用
收藏
页码:1897 / 1905
页数:9
相关论文
共 45 条
[21]   A draft map of the human proteome [J].
Kim, Min-Sik ;
Pinto, Sneha M. ;
Getnet, Derese ;
Nirujogi, Raja Sekhar ;
Manda, Srikanth S. ;
Chaerkady, Raghothama ;
Madugundu, Anil K. ;
Kelkar, Dhanashree S. ;
Isserlin, Ruth ;
Jain, Shobhit ;
Thomas, Joji K. ;
Muthusamy, Babylakshmi ;
Leal-Rojas, Pamela ;
Kumar, Praveen ;
Sahasrabuddhe, Nandini A. ;
Balakrishnan, Lavanya ;
Advani, Jayshree ;
George, Bijesh ;
Renuse, Santosh ;
Selvan, Lakshmi Dhevi N. ;
Patil, Arun H. ;
Nanjappa, Vishalakshi ;
Radhakrishnan, Aneesha ;
Prasad, Samarjeet ;
Subbannayya, Tejaswini ;
Raju, Rajesh ;
Kumar, Manish ;
Sreenivasamurthy, Sreelakshmi K. ;
Marimuthu, Arivusudar ;
Sathe, Gajanan J. ;
Chavan, Sandip ;
Datta, Keshava K. ;
Subbannayya, Yashwanth ;
Sahu, Apeksha ;
Yelamanchi, Soujanya D. ;
Jayaram, Savita ;
Rajagopalan, Pavithra ;
Sharma, Jyoti ;
Murthy, Krishna R. ;
Syed, Nazia ;
Goel, Renu ;
Khan, Aafaque A. ;
Ahmad, Sartaj ;
Dey, Gourav ;
Mudgal, Keshav ;
Chatterjee, Aditi ;
Huang, Tai-Chung ;
Zhong, Jun ;
Wu, Xinyan ;
Shaw, Patrick G. .
NATURE, 2014, 509 (7502) :575-+
[22]   Seven Golden Rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry [J].
Kind, Tobias ;
Fiehn, Oliver .
BMC BIOINFORMATICS, 2007, 8 (1)
[23]   LipidBlast in silico tandem mass spectrometry database for lipid identification [J].
Kind, Tobias ;
Liu, Kwang-Hyeon ;
Lee, Do Yup ;
DeFelice, Brian ;
Meissen, John K. ;
Fiehn, Oliver .
NATURE METHODS, 2013, 10 (08) :755-+
[24]   Metabolomic database annotations via query of elemental compositions:: Mass accuracy is insufficient even at less than 1 ppm [J].
Kind, Tobias ;
Fiehn, Oliver .
BMC BIOINFORMATICS, 2006, 7 (1)
[25]   Greazy: Open-Source Software for Automated Phospholipid Tandem Mass Spectrometry Identification [J].
Kochen, Michael A. ;
Chambers, Matthew C. ;
Holman, Jay D. ;
Nesvizhskii, Alexey I. ;
Weintraub, Susan T. ;
Belisle, John T. ;
Islam, M. Nurul ;
Griss, Johannes ;
Tabb, David L. .
ANALYTICAL CHEMISTRY, 2016, 88 (11) :5733-5741
[26]  
Lanucara F, 2014, NAT CHEM, V6, P281, DOI [10.1038/NCHEM.1889, 10.1038/nchem.1889]
[27]   Predicting Network Activity from High Throughput Metabolomics [J].
Li, Shuzhao ;
Park, Youngja ;
Duraisingham, Sai ;
Strobel, Frederick H. ;
Khan, Nooruddin ;
Soltow, Quinlyn A. ;
Jones, Dean P. ;
Pulendran, Bali .
PLOS COMPUTATIONAL BIOLOGY, 2013, 9 (07)
[28]   MBROLE 2.0-functional enrichment of chemical compounds [J].
Lopez-Ibanez, Javier ;
Pazos, Florencio ;
Chagoyen, Monica .
NUCLEIC ACIDS RESEARCH, 2016, 44 (W1) :W201-W204
[29]   An Integrated Metabolomic and Genomic Mining Workflow To Uncover the Biosynthetic Potential of Bacteria [J].
Maansson, Maria ;
Vynne, Nikolaj G. ;
Klitgaard, Andreas ;
Nybo, Jane L. ;
Melchiorsen, Jette ;
Nguyen, Don D. ;
Sanchez, Laura M. ;
Ziemert, Nadine ;
Dorrestein, Pieter C. ;
Andersen, Mikael R. ;
Gram, Lone .
MSYSTEMS, 2016, 1 (03)
[30]  
Matsuda Fumio, 2014, Mass Spectrom (Tokyo), V3, pS0038, DOI 10.5702/massspectrometry.S0038