Mass spectrometry instruments measure the mass to charge ratio of ions, from which we infer the molecular structures. They are key tools for investigating the incredibly diverse chemistry that is associated with biological systems. Typically, when one thinks about the chemistry of biology, one thinks of biochemical pathways, structural lipids or carbohydrates. However, numerous additional chemistries are part of various biological systems. These include molecules that originate from diet, water treatment, personal care, medications, pollutants and environmental exposures including plastics, clothes and furniture. These principles apply not only to people but to all of biology, from the worms at the bottom of the ocean, to the bacteria in our belly buttons and to the birds that fly over Mount Everest. In the past decade, our capacity to inventory the chemistry of biological systems using mass spectrometry at a global level has been revolutionized. In this Review, we discuss the informatics and hardware tools that are available for small-molecule analysis and provide an overview of the tools that could transform how we study the chemistry of biological systems; perhaps in the future this will be as easy as taking a photograph with a smartphone.
This is a preview of subscription content
Subscribe to Nature+
Get immediate online access to the entire Nature family of 50+ journals
Subscribe to Journal
Get full journal access for 1 year
only $9.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
Gohlke, R. S. & McLafferty, F. W. Early gas chromatography/mass spectrometry. J. Am. Soc. Mass Spectrom. 4, 367–371 (1993).
Lesch, M. & Nyhan, W. L. A familial disorder of uric acid metabolism and central nervous system function. Am. J. Med. 36, 561–570 (1964).
Pauling, L., Robinson, A. B., Teranishi, R. & Cary, P. Quantitative analysis of urine vapor and breath by gas–liquid partition chromatography. Proc. Natl Acad. Sci. USA 68, 2374–2376 (1971).
Yamashita, M. & Fenn, J. B. Electrospray ion source. Another variation on the free-jet theme. J. Phys. Chem. 88, 4451–4459 (1984).
Karas, M., Bachmann, D. & Hillenkamp, F. Influence of the wavelength in high-irradiance ultraviolet laser desorption mass spectrometry of organic molecules. Anal. Chem. 57, 2935–2939 (1985).
Tanaka, K. et al. Protein and polymer analyses up to m/z 100 000 by laser ionization time-of-flight mass spectrometry. Rapid Commun. Mass Spectrom. 2, 151–153 (1988).
Takada, Y. et al. High-throughput walkthrough detection portal for counter terrorism: detection of triacetone triperoxide (TATP) vapor by atmospheric-pressure chemical ionization ion trap mass spectrometry. Rapid Commun. Mass Spectrom. 25, 2448–2452 (2011).
Jannetto, P. J. & Fitzgerald, R. L. Effective use of mass spectrometry in the clinical laboratory. Clin. Chem. 62, 92–98 (2016).
Botrè, F. Mass spectrometry and illicit drug testing: analytical challenges of the anti-doping laboratories. Expert Rev. Proteomics 5, 535–539 (2008).
Pico, Y. Advanced Mass Spectrometry for Food Safety and Quality (Elsevier, 2015).
de Lima Moreira, F. et al. Metabolic profile and safety of piperlongumine. Sci. Rep. 6, 33646 (2016).
Rocha, B. A. et al. In vitro metabolism of monensin A: microbial and human liver microsomes models. Xenobiotica 44, 326–335 (2014).
Darst, C. R., Menéndez-Guerrero, P. A., Coloma, L. A. & Cannatella, D. C. Evolution of dietary specialization and chemical defense in poison frogs (Dendrobatidae): a comparative analysis. Am. Nat. 165, 56–69 (2005).
Teuten, E. L. Two abundant bioaccumulated halogenated compounds are natural products. Science 307, 917–920 (2005).
Agarwal, V. et al. Biosynthesis of polybrominated aromatic organic compounds by marine bacteria. Nat. Chem. Biol. 10, 640–647 (2014).
Agarwal, V. et al. Complexity of naturally produced polybrominated diphenyl ethers revealed via mass spectrometry. Environ. Sci. Technol. 49, 1339–1346 (2015).
Rodrigues Hoffmann, A. et al. The skin microbiome in healthy and allergic dogs. PLoS ONE 9, e83197 (2014).
Song, S. J. et al. Cohabiting family members share microbiota with one another and with their dogs. eLife 2, e00458 (2013).
Waldron, A. C. & Naber, E. C. Importance of feed as an unavoidable source of pesticide contamination in poultry meat and eggs. 1. Residues in feedstuff. Poult. Sci. 53, 1359–1371 (1974).
Nair, D. N. & Padmavathy, S. Impact of endophytic microorganisms on plants, environment and humans. ScientificWorldJournal 2014, 250693 (2014).
Seger, C. & Vogeser, M. in LC-MS in Drug Bioanalysis (eds Xu, Q. A. & Madden, T. L. )109–126 (Springer, 2012).
Ganna, A. et al. A workflow for UPLC-MS non-targeted metabolomic profiling in large human population-based studies. Preprint at bioRxivhttp://dx.doi.org/10.1101/002782 (2014).
da Silva, R. R., Dorrestein, P. C. & Quinn, R. A. Illuminating the dark matter in metabolomics. Proc. Natl Acad. Sci. USA 112, 12549–12550 (2015).
Creek, D. J. et al. Metabolite identification: are you sure? And how do your peers gauge your confidence? Metabolomics 10, 350–353 (2014).
Wagner, C., El Omari, M. & König, G. M. Biohalogenation: nature's way to synthesize halogenated metabolites. J. Nat. Prod. 72, 540–553 (2009).
Hatfield, D. L., Berry, M. J. & Gladyshev, V. N. (eds) Selenium: its Molecular Biology and Role in Human Health (Springer Science & Business Media, 2011).
Gaspar, A., Lucio, M., Harir, M. & Schmitt-Kopplin, P. Targeted and non-targeted boron complex formation followed by electrospray Fourier transform ion cyclotron mass spectrometry: a novel approach for identifying boron esters with natural organic matter. Eur. J. Mass Spectrom. 17, 113–123 (2011).
Knight, M. J., Senior, L., Nancolas, B., Ratcliffe, S. & Curnow, P. Direct evidence of the molecular basis for biological silicon transport. Nat. Commun. 7, 11926 (2016).
Kind, T. & Fiehn, O. Seven golden rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry. BMC Bioinformatics 8, 105 (2007).
Kind, T. & Fiehn, O. Advances in structure elucidation of small molecules using mass spectrometry. Bioanal. Rev. 2, 23–60 (2010).
Pence, H. E. & Williams, A. ChemSpider: an online chemical information resource. J. Chem. Educ. 87, 1123–1124 (2010).
Caboche, S. et al. NORINE: a database of nonribosomal peptides. Nucleic Acids Res. 36, D326–D331 (2008).
Wishart, D. S. et al. HMDB 3.0 — The Human Metabolome Database in 2013. Nucleic Acids Res. 41, D801–D807 (2012).
Wishart, D. et al. T3DB: the toxic exposome database. Nucleic Acids Res. 43, D928–D934 (2015).
Psychogios, N. et al. The human serum metabolome. PLoS ONE 6, e16957 (2011).
McEachran, A. D., Sobus, J. R. & Williams, A. J. Identifying known unknowns using the US EPA's CompTox Chemistry Dashboard. Anal. Bioanal. Chem. 409, 1729–1735 (2017).
Ntie-Kang, F. et al. AfroDb: a select highly potent and diverse natural product library from African medicinal plants. PLoS ONE 8, e78085 (2013).
Sanderson, K. Databases aim to bridge the East–West divide of drug discovery. Nat. Med. 17, 1531–1531 (2011).
Jeffryes, J. G. et al. MINEs: open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics. J. Cheminform. 7, 44 (2015).
Huan, T. et al. MyCompoundID MS/MS Search: metabolite identification using a library of predicted fragment-ion-spectra of 383,830 possible human metabolites. Anal. Chem. 87, 10619–10626 (2015).
Villas-Bôas, S. G., Roessner, U., Hansen, M. A. E., Smedsgaard, J. & Nielsen, J. Metabolome Analysis: An Introduction (Wiley, 2007).
Dunn, W. B., Wilson, I. D., Nicholls, A. W. & Broadhurst, D. The importance of experimental design and QC samples in large-scale and MS-driven untargeted metabolomic studies of humans. Bioanalysis 4, 2249–2264 (2012).
Johnson, C. H., Ivanisevic, J., Benton, H. P. & Siuzdak, G. Bioinformatics: the next frontier of metabolomics. Anal. Chem. 87, 147–156 (2015).
Griss, J. et al. The mzTab data exchange format: communicating mass-spectrometry-based proteomics and metabolomics experimental results to a wider audience. Mol. Cell. Proteomics 13, 2765–2775 (2014).
Field, D. et al. The minimum information about a genome sequence (MIGS) specification. Nat. Biotechnol. 26, 541–547 (2008).
Yilmaz, P. et al. Minimum information about a marker gene sequence (MIMARKS) and minimum information about any (x) sequence (MIxS) specifications. Nat. Biotechnol. 29, 415–420 (2011).
Röst, H. L. et al. OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nat. Methods 13, 741–748 (2016).
Misra, B. B. & van der Hooft, J. J. J. Updates in metabolomics tools and resources: 2014–2015. Electrophoresis 37, 86–110 (2016).
Xia, J. & Wishart, D. S. in Current Protocols in Bioinformatics 14.10.1–14.10.91 (Wiley, 2016).
Xia, J. et al. MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucleic Acids Res. 37, W652–W660 (2009).
Tautenhahn, R., Patti, G. J., Rinehart, D. & Siuzdak, G. E. XCMS Online: a web-based platform to process untargeted metabolomic data. Anal. Chem. 84, 5035–5039 (2012).
Barnes, S. et al. Training in metabolomics research. II. Processing and statistical analysis of metabolomics data, metabolite identification, pathway analysis, applications of metabolomics and its future. J. Mass Spectrom. 51, 535–548 (2016).
Chambers, M. C. et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat. Biotechnol. 30, 918–920 (2012).
Vinaixa, M. et al. A guideline to univariate statistical analysis for LC/MS-based untargeted metabolomics-derived data. Metabolites 2, 775–795 (2012).
van den Berg, R. A., Hoefsloot, H. C. J., Westerhuis, J. A., Smilde, A. K. & van Der Werf, M. J. Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics 7, 142 (2006).
Ejigu, B. A. et al. Evaluation of normalization methods to pave the way towards large-scale LC-MS-based metabolomics profiling experiments. OMICS 17, 473–485 (2013).
Di Guida, R. et al. Non-targeted UHPLC-MS metabolomic data processing methods: a comparative investigation of normalisation, missing value imputation, transformation and scaling. Metabolomics 12, 93 (2016).
Armitage, E. G., Godzien, J., Alonso-Herranz, V., López-Gonzálvez, Á. & Barbas, C. Missing value imputation strategies for metabolomics data. Electrophoresis 36, 3050–3060 (2015).
Libbrecht, M. W. & Noble, W. S. Machine learning applications in genetics and genomics. Nat. Rev. Genet. 16, 321–332 (2015).
Wold, S., Sjöström, M. & Eriksson, L. PLS-regression: a basic tool of chemometrics. Chemometr Intell. Lab. Syst. 58, 109–130 (2001).
Hung, J.-H., Yang, T.-H., Hu, Z., Weng, Z. & DeLisi, C. Gene set enrichment analysis: performance evaluation and usage guidelines. Brief. Bioinform. 13, 281–291 (2012).
Chagoyen, M. & Pazos, F. Tools for the functional interpretation of metabolomic experiments. Brief. Bioinform. 14, 737–744 (2012).
Khatri, P., Sirota, M. & Butte, A. J. Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput. Biol. 8, e1002375 (2012).
Bouslimani, A. et al. Molecular cartography of the human skin surface in 3D. Proc. Natl Acad. Sci. USA 112, E2120–E2129 (2015).
Johnson, S. R. & Lange, B. M. Open-access metabolomics databases for natural product research: present capabilities and future potential. Front. Bioeng. Biotechnol. 3, 22 (2015).
Kind, T. et al. Identification of small molecules using accurate mass MS/MS search. Mass Spectrom. Rev.http://dx.doi.org/10.1002/mas.21535 (2017).
Heller, S., McNaught, A., Stein, S., Tchekhovskoi, D. & Pletnev, I. InChI — the worldwide chemical structure identifier standard. J. Cheminform. 5, 7 (2013).
Weininger, D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 28, 31–36 (1988).
Wohlgemuth, G. et al. SPLASH, a hashed identifier for mass spectra. Nat. Biotechnol. 34, 1099–1101 (2016).
Feng, Q. et al. Integrated metabolomics and metagenomics analysis of plasma and urine identified microbial metabolites associated with coronary heart disease. Sci. Rep. 6, 22525 (2016).
Bowen, B. P. & Northen, T. R. Dealing with the unknown: metabolomics and metabolite atlases. J. Am. Soc. Mass Spectrom. 21, 1471–1476 (2010).
Brown, M. et al. Mass spectrometry tools and metabolite-specific databases for molecular identification in metabolomics. Analyst 134, 1322–1332 (2009).
Sumner, L. W. et al. Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics 3, 211–221 (2007).
Dias, D. et al. Current and future perspectives on the structural identification of small molecules in biological systems. Metabolites 6, 46 (2016).
Lawson, T. N. et al. msPurity: automated evaluation of precursor ion purity for mass spectrometry-based fragmentation in metabolomics. Anal. Chem. 89, 2432–2439 (2017).
Wang, M. et al. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nat. Biotechnol. 34, 828–837 (2016).
Dhanasekaran, A. R., Pearson, J. L., Ganesan, B. & Weimer, B. C. Metabolome searcher: a high throughput tool for metabolite identification and metabolic pathway mapping directly from mass spectrometry and using genome restriction. BMC Bioinformatics 16, 62 (2015).
Grapov, D., Wanichthanarak, K. & Fiehn, O. MetaMapR: pathway independent metabolomic network analysis incorporating unknowns. Bioinformatics 31, 2757–2760 (2015).
Hufsky, F., Scheubert, K. & Böcker, S. New kids on the block: novel informatics methods for natural product discovery. Nat. Prod. Rep. 31, 807–817 (2014).
Allwood, J. et al. CASMI — the small molecule identification process from a Birmingham perspective. Metabolites 3, 397–411 (2013).
Schymanski, E. L. et al. Critical assessment of small molecule identification 2016: automated methods. J. Cheminform. 9, 22 (2017).
Dührkop, K., Shen, H., Meusel, M., Rousu, J. & Böcker, S. Searching molecular structure databases with tandem mass spectra using CSI:FingerID. Proc. Natl Acad. Sci. USA 112, 12580–12585 (2015).
Brouard, C. et al. Fast metabolite identification with input output kernel regression. Bioinformatics 32, i28–i36 (2016).
Ruttkies, C., Schymanski, E. L., Wolf, S., Hollender, J. & Neumann, S. MetFrag relaunched: incorporating strategies beyond in silico fragmentation. J. Cheminform. 8, 3 (2016).
Allen, F., Greiner, R. & Wishart, D. Competitive fragmentation modeling of ESI-MS/MS spectra for putative metabolite identification. Metabolomics 11, 98–110 (2014).
van der Hooft, J. J. J., Wandy, J., Barrett, M. P., Burgess, K. E. V. & Rogers, S. Topic modeling for untargeted substructure exploration in metabolomics. Proc. Natl Acad. Sci. USA 113, 13738–13743 (2016).
Kind, T. et al. LipidBlast in silico tandem mass spectrometry database for lipid identification. Nat. Methods 10, 755–758 (2013).
Mohimani, H. et al. Dereplication of peptidic natural products through database search of mass spectra. Nat. Chem. Biol. 13, 30–37 (2016).
Böcker, S. Searching molecular structure databases using tandem MS data: are we there yet? Curr. Opin. Chem. Biol. 36, 1–6 (2017).
Kind, T. & Fiehn, O. Strategies for dereplication of natural compounds using high-resolution tandem mass spectrometry. Phytochem. Lett.http://dx.doi.org/10.1016/j.phytol.2016.11.006 (2016).
Watrous, J. et al. Mass spectral molecular networking of living microbial colonies. Proc. Natl Acad. Sci. USA 109, E1743–E1752 (2012).
Allard, P.-M. et al. Integration of molecular networking and in-silico MS/MS fragmentation for natural products dereplication. Anal. Chem. 88, 3317–3323 (2016).
Bartlett, R. J. & Musiał, M. Coupled-cluster theory in quantum chemistry. Rev. Mod. Phys. 79, 291–352 (2007).
Bauer, C. A. & Grimme, S. How to compute electron ionization mass spectra from first principles. J. Phys. Chem. A 120, 3755–3766 (2016).
Poater, J., Duran, M. & Solà, M. Parametrization of the Becke3-LYP hybrid functional for a series of small molecules using quantum molecular similarity techniques. J. Comput. Chem. 22, 1666–1678 (2001).
Peverati, R. & Truhlar, D. G. Quest for a universal density functional: the accuracy of density functionals across a broad spectrum of databases in chemistry and physics. Phil. Trans. R. Soc. A 372, 20120476 (2014).
Sun, J. et al. Accurate first-principles structures and energies of diversely bonded systems from an efficient density functional. Nat. Chem. 8, 831–836 (2016).
Wang, H. et al. High-efficiency multiphoton boson sampling. Nat. Photonics 11, 361–365 (2017).
Denchev, V. S. et al. What is the computational value of finite-range tunneling? Phys. Rev. X 6, 31015 (2016).
Aguilar-Mogas, A., Sales-Pardo, M., Navarro, M., Guimerà, R. & Yanes, O. iMet: a network-based computational tool to assist in the annotation of metabolites from tandem mass spectra. Anal. Chem. 89, 3474–3482 (2017).
Hastings, J. et al. The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013. Nucleic Acids Res. 41, D456–D463 (2012).
Djoumbou Feunang, Y. et al. ClassyFire: automated chemical classification with a comprehensive, computable taxonomy. J. Cheminform. 8, 61 (2016).
Quinn, R. A. et al. Molecular networking as a drug discovery, drug metabolism, and precision medicine strategy. Trends Pharmacol. Sci. 38, 143–154 (2017).
Ridder, L. & Wagener, M. SyGMa: combining expert knowledge and empirical scoring in the prediction of metabolites. ChemMedChem 3, 821–832 (2008).
Ziemert, N., Alanjary, M. & Weber, T. The evolution of genome mining in microbes — a review. Nat. Prod. Rep. 33, 988–1005 (2016).
Kersten, R. D. et al. A mass spectrometry-guided genome mining approach for natural product peptidogenomics. Nat. Chem. Biol. 7, 794–802 (2011).
Kersten, R. D. et al. Glycogenomics as a mass spectrometry-guided genome-mining method for microbial glycosylated molecules. Proc. Natl Acad. Sci. USA 110, E4407–E4416 (2013).
Duncan, K. R. et al. Molecular networking and pattern-based genome mining improves discovery of biosynthetic gene clusters and their products from Salinispora species. Chem. Biol. 22, 460–471 (2015).
Mohimani, H. et al. NRPquest: coupling mass spectrometry and genome mining for nonribosomal peptide discovery. J. Nat. Prod. 77, 1902–1909 (2014).
Mohimani, H. et al. Automated genome mining of ribosomal peptide natural products. ACS Chem. Biol. 9, 1545–1551 (2014).
Zhang, Q. et al. Structural investigation of ribosomally synthesized natural products by hypothetical structure enumeration and evaluation using tandem MS. Proc. Natl Acad. Sci. USA 111, 12031–12036 (2014).
Ibrahim, A. et al. Dereplicating nonribosomal peptides using an informatic search algorithm for natural products (iSNAP) discovery. Proc. Natl Acad. Sci. USA 109, 19196–19201 (2012).
Johnston, C. W. et al. An automated Genomes-to-Natural Products platform (GNP) for the discovery of modular natural products. Nat. Commun. 6, 8421 (2015).
Steuer, R., Kurths, J., Fiehn, O. & Weckwerth, W. Observing and interpreting correlations in metabolomic networks. Bioinformatics 19, 1019–1026 (2003).
Lai, Z. & Fiehn, O. Mass spectral fragmentation of trimethylsilylated small molecules. Mass Spectrom. Rev.http://dx.doi.org/10.1002/mas.21518 (2016).
Chibucos, M. C. et al. Standardized description of scientific evidence using the Evidence Ontology (ECO). Database (Oxford) 2014, bau075 (2014).
Meringer, M. & Schymanski, E. L. Small molecule identification with MOLGEN and mass spectrometry. Metabolites 3, 440–462 (2013).
Lindsay, R. K., Buchanan, B. G., Feigenbaum, E. A. & Lederberg, J. Applications of Artificial Intelligence for Organic Chemistry: the DENDRAL Project (McGraw-Hill, 1980).
Kerber, A., Laue, R., Meringer, M. & Varmuza, K. MOLGEN-MS: evaluation of low resolution electron impact mass spectra with MS classification and exhaustive structure generation. Adv. Mass Spectrom. 15, 22 (2001).
Böcker, S., Letzel, M. C., Lipták, Z. & Pervukhin, A. SIRIUS: decomposing isotope patterns for metabolite identification. Bioinformatics 25, 218–224 (2009).
Awad, H., Khamis, M. M. & El-Aneed, A. Mass spectrometry, review of the basics: ionization. Appl. Spectrosc. Rev. 50, 158–175 (2015).
El-Aneed, A., Cohen, A. & Banoub, J. Mass spectrometry, review of the basics: electrospray, MALDI, and commonly used mass analyzers. Appl. Spectrosc. Rev. 44, 210–230 (2009).
Marshall, A. G., Hendrickson, C. L. & Jackson, G. S. Fourier transform ion cyclotron resonance mass spectrometry: a primer. Mass Spectrom. Rev. 17, 1–35 (1998).
Nikolaev, E. N., Boldin, I. A., Jertz, R. & Baykut, G. Initial experimental characterization of a new ultra-high resolution FTICR cell with dynamic harmonization. J. Am. Soc. Mass Spectrom. 22, 1125–1133 (2011).
Hoffmann, E.de. & Stroobant, V. Mass Spectrometry: Principles and Applications (Wiley, 2007).
Purves, R. W., Guevremont, R., Day, S., Pipich, C. W. & Matyjaszczyk, M. S. Mass spectrometric characterization of a high-field asymmetric waveform ion mobility spectrometer. Rev. Sci. Instrum. 69, 4094 (1998).
Bicchi, C. et al. Direct resistively heated column gas chromatography (Ultrafast module-GC) for high-speed analysis of essential oils of differing complexities. J. Chromatogr. A 1024, 195–207 (2004).
Cutillas, P. Principles of nanoflow liquid chromatography and applications to proteomics. Curr. Nanosci. 1, 65–71 (2005).
Servick, K. Scientists reveal proposal to build human genome from scratch. Sciencehttp://dx.doi.org/10.1126/science.aag0588 (2016).
Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).
Gilbert, J. A. et al. Meeting report: The Terabase Metagenomics Workshop and the Vision of an Earth Microbiome Project. Stand. Genomic Sci. 3, 243–248 (2010).
Telenti, A. et al. Deep sequencing of 10,000 human genomes. Proc. Natl Acad. Sci. USA 113, 11901–11906 (2016).
White, R. A. III, Callister, S. J., Moore, R. J., Baker, E. S. & Jansson, J. K. The past, present and future of microbiome analyses. Nat. Protoc. 11, 2049–2053 (2016).
Shendure, J. & Ji, H. Next-generation DNA sequencing. Nat. Biotechnol. 26, 1135–1145 (2008).
Makarov, A. Electrostatic axially harmonic orbital trapping: a high-performance technique of mass analysis. Anal. Chem. 72, 1156–1162 (2000).
Comisarow, M. B. & Marshall, A. G. Fourier transform ion cyclotron resonance spectroscopy. Chem. Phys. Lett. 25, 282–283 (1974).
Stein, S. Mass spectral reference libraries: an ever-expanding resource for chemical identification. Anal. Chem. 84, 7274–7282 (2012).
Haug, K. et al. MetaboLights — an open-access general-purpose repository for metabolomics studies and associated meta-data. Nucleic Acids Res. 41, D781–D786 (2013).
Sud, M. et al. Metabolomics Workbench: an international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools. Nucleic Acids Res. 44, D463–D470 (2015).
Schauer, N. et al. GC–MS libraries for the rapid identification of metabolites in complex biological samples. FEBS Lett. 579, 1332–1337 (2005).
Ferry-Dumazet, H. et al. MeRy-B: a web knowledgebase for the storage, visualization, analysis and annotation of plant NMR metabolomic profiles. BMC Plant Biol. 11, 104 (2011).
Scholz, M. & Fiehn, O. SetupX — a public study design database for metabolomic projects. Pac. Symp. Biocomput. 2007, 169–180 (2007).
Skogerson, K., Wohlgemuth, G., Barupal, D. K. & Fiehn, O. The volatile compound BinBase mass spectral database. BMC Bioinformatics 12, 321 (2011).
Myint, L., Kleensang, A., Zhao, L., Hartung, T. & Hansen, K. D. Joint bounding of peaks across samples improves differential analysis in mass spectrometry-based metabolomics. Anal. Chem. 89, 3517–3523 (2017).
Defelice, B. C. et al. Mass Spectral Feature List Optimizer (MS-FLO): a tool to minimize false positive peak reports in untargeted liquid chroamtography–mass spectrometry (LC–MS) data processing. Anal. Chem. 89, 3250–3255 (2017).
David, P. A. Understanding the emergence of ‘open science’ institutions: functionalist economics in historical context. Ind. Corp. Chang. 13, 571–589 (2004).
Томилин, К.А . in Физика XIX-XX вв. в общенаучном и социокультурном контекстах [Russian] Vol. 3 264–304 (Янус, 1997).
Peters, B. How Not to Network a Nation: The Uneasy History of the Soviet Internet (MIT Press, 2016).
[No authors listed.] Where are the data? Nat. Meth. 13, 799 (2016).
Baker, M. 1,500 scientists lift the lid on reproducibility. Nature 533, 452–454 (2016).
Ogungbeni, J. I., Obiamalu, A. R., Ssemambo, S. & Bazibu, C. M. The roles of academic libraries in propagating open science: a qualitative literature review. Inf. Dev.http://dx.doi.org/10.1177/0266666916678444 (2016).
Surowiecki, J. The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economics, Societies and Nations (Doubleday, 2006).
Lozupone, C. A. et al. Meta-analyses of studies of the human microbiota. Genome Res. 23, 1704–1714 (2013).
Debelius, J. et al. Tiny microbes, enormous impacts: what matters in gut microbiome studies? Genome Biol. 17, 217 (2016).
Gilbert, J. A. et al. Microbiome-wide association studies link dynamic microbial consortia to disease. Nature 535, 94–103 (2016).
Bazsó, F. L. et al. Quantitative comparison of tandem mass spectra obtained on various instruments. J. Am. Soc. Mass Spectrom. 27, 1357–1365 (2016).
McDonald, R. S. & Wilks, P. A. JCAMP-DX: a standard form for exchange of infrared spectra in computer readable form. Appl. Spectrosc. 42, 151–162 (1988).
Martens, L. et al. mzML — a community standard for mass spectrometry data. Mol. Cell. Proteomics 10, R110.000133 (2011).
Pedrioli, P. G. A. et al. A common open representation of mass spectrometry data and its application to proteomics research. Nat. Biotechnol. 22, 1459–1466 (2004).
Dougherty, M. T. et al. Unifying biological image formats with HDF5. Commun. ACM 52, 42–47 (2009).
Deutsch, E. W. File formats commonly used in mass spectrometry proteomics. Mol. Cell. Proteomics 11, 1612–1621 (2012).
Alfassi, Z. B. et al. Vector analysis of multi-measurements identification. J. Radioanal. Nucl. Chem. 266, 245–250 (2005).
ASTM International. Standard specification for analytical data interchange protocol for chromatographic data. ASTMhttp://dx.doi.org/10.1520/E1947-98R14 (2014).
Salek, R. M. et al. COordination of Standards in MetabOlomicS (COSMOS): facilitating integrated metabolomics data access. Metabolomics 11, 1587–1597 (2015).
Medema, M. H. et al. Minimum information about a biosynthetic gene cluster. Nat. Chem. Biol. 11, 625–631 (2015).
Diminic, J. et al. Databases of the thiotemplate modular systems (CSDB) and their in silico recombinants (r-CSDB). J. Ind. Microbiol. Biotechnol. 40, 653–659 (2013).
Perkel, J. M. Life science technologies: miniaturizing mass spectrometry. Science 343, 928–930 (2014).
Cacciatore, S. & Loda, M. Innovation in metabolomics to improve personalized healthcare. Ann. NY Acad. Sci. 1346, 57–62 (2015).
Montenegro-Burke, J. R. et al. Data streaming for metabolomics: accelerating data processing and analysis from days to minutes. Anal. Chem. 89, 1254–1259 (2016).
Cartwright, J. Technology: smartphone science. Nature 531, 669–671 (2016).
Warth, B. et al. Metabolizing data in the cloud. Trends Biotechnol. 35, 481–483 (2017).
Rinehart, D. et al. Metabolomic data streaming for biology-dependent data acquisition. Nat. Biotechnol. 32, 524–527 (2014).
Montenegro-Burke, J. R. et al. Smartphone analytics: mobilizing the lab into the cloud for omic-scale analyses. Anal. Chem. 88, 9753–9758 (2016).
Li, D., Heiling, S., Baldwin, I. T. & Gaquerel, E. Illuminating a plant's tissue-specific metabolic diversity using computational metabolomics and information theory. Proc. Natl Acad. Sci. USA 113, E7610–E7618 (2016).
Chong, E. Y. et al. Local false discovery rate estimation using feature reliability in LC/MS metabolomics data. Sci. Rep. 5, 17221 (2015).
Scheubert, K. et al. Significance estimation for large scale untargeted metabolomics annotations. Preprint at bioRxivhttp://dx.doi.org/10.1101/109389 (2017).
Bandodkar, A. J., Jeerapan, I. & Wang, J. Wearable chemical sensors: present challenges and future prospects. ACS Sensors 1, 464–482 (2016).
Azzarelli, J. M., Mirica, K. A., Ravnsbæk, J. B. & Swager, T. M. Wireless gas detection with a smartphone via rf communication. Proc. Natl Acad. Sci. USA 111, 18162–18166 (2014).
Peng, G. et al. Detection of lung, breast, colorectal, and prostate cancers from exhaled breath using a single array of nanosensors. Br. J. Cancer 103, 542–551 (2010).
Clement, R. E. Mass spectral and GC data of drugs, poisons, pesticides, pollutants and their metabolites (Parts 1, 2, 3). Environ. Sci. Pollut. Res. 1, 58–58 (1994).
Hummel, J., Selbig, J., Walther, D. & Kopka, J. in Topics in Current Genetics Vol. 18 75–95 (Springer, 2007).
Horai, H. et al. MassBank: a public repository for sharing mass spectral data for life sciences. J. Mass Spectrom. 45, 703–714 (2010).
Tautenhahn, R. et al. An accelerated workflow for untargeted metabolomics using the METLIN database. Nat. Biotechnol. 30, 826–828 (2012).
El-Elimat, T. et al. High-resolution MS, MS/MS, and UV database of fungal secondary metabolites as a dereplication protocol for bioactive natural products. J. Nat. Prod. 76, 1709–1716 (2013).
Dresen, S., Gergov, M., Politi, L., Halter, C. & Weinmann, W. ESI-MS/MS library of 1,253 compounds for application in forensic and clinical toxicology. Anal. Bioanal. Chem. 395, 2521–2526 (2009).
Shahaf, N. et al. The WEIZMASS spectral library for high-confidence metabolite identification. Nat. Commun. 7, 12423 (2016).
Sawada, Y. et al. RIKEN tandem mass spectral database (ReSpect) for phytochemicals: a plant-specific MS/MS-based data resource and database. Phytochemistry 82, 38–45 (2012).
Oberacher, H., Weinmann, W. & Dresen, S. Quality evaluation of tandem mass spectral libraries. Anal. Bioanal. Chem. 400, 2641–2648 (2011).
Vinaixa, M. et al. Mass spectral databases for LC/MS and GC/MS-based metabolomics: state of the field and future prospects. TrAC Trends Anal. Chem. 78, 23–35 (2015).
The authors' work on mass spectrometry and informatics is supported by the US National Institutes of Health (P41 GM103484, 1U01AI124316-01 and R03 CA211211), the US Office of Naval Research (MURI N00014-15-1-2809) and the Sloan Foundation.
The authors declare no competing interests.
About this article
Cite this article
Aksenov, A., da Silva, R., Knight, R. et al. Global chemical analysis of biology by mass spectrometry. Nat Rev Chem 1, 0054 (2017). https://doi.org/10.1038/s41570-017-0054
Salivary bacterial signatures in depression-obesity comorbidity are associated with neurotransmitters and neuroactive dipeptides
BMC Microbiology (2022)
Comparative metabolomic analysis reveals shared and unique chemical interactions in sponge holobionts
Journal of Cheminformatics (2022)
Nature Reviews Microbiology (2022)
Characterisation of a new online nanoLC-CZE-MS platform and application for the glycosylation profiling of alpha-1-acid glycoprotein
Analytical and Bioanalytical Chemistry (2022)