Abstract
In this age of –omics data-guided big data revolution, metabolomics has received significant attention as compared to genomics, transcriptomics, and proteomics for its proximity to the phenotype, the promises it makes and the challenges it throws. Although metabolomes of entire organisms, organs, biofluids, and tissues are of immense interest, a cell-specific resolution is deemed critical for biomedical applications where a granular understanding of cellular metabolism at cell-type and subcellular resolution is desirable. Mass spectrometry (MS) is a versatile technique that is used to analyze a broad range of compounds from different species and cell-types, with high accuracy, resolution, sensitivity, selectivity, and fast data acquisition speeds. With recent advances in MS and spectroscopy-based platforms, the research community is able to generate high-throughput data sets from single cells. However, it is challenging to handle, store, process, analyze, and interpret data in a routine manner. In this treatise, I present a workflow of metabolomics data generation from single cells and single-cell types to their analysis, visualization, and interpretation for obtaining biological insights.
Key words
- Software
- Tool
- Database
- Mass spectrometry
- Metabolomics
- –Omics
- Web server
- Data
- Pathway
- Network
- Analysis
- Statistical
- Computational
- Single cell
- Single-cell type
- Microbial
- Plant
- Animal
- Cell
This is a preview of subscription content, access via your institution.
Buying options



Abbreviations
- CE:
-
Capillary electrophoresis
- DB:
-
Database
- DESI-MS:
-
Desorption ionization mass spectrometry
- GC:
-
Gas chromatography
- GUI:
-
Graphical user interface
- HRMS:
-
High-resolution mass spectrometry
- HRMS/MS:
-
High-resolution tandem mass spectrometry
- KEGG:
-
Kyoto encyclopedia of genes and genomes
- LAESI-MS:
-
Laser ablation electrospray ionization mass spectrometry
- LC:
-
Liquid chromatography
- MS:
-
Mass spectrometry
- MS/MS:
-
Tandem mass spectrometry
- NMR:
-
Nuclear magnetic resonance
- PCA:
-
Principal component analysis
- PLS-DA:
-
Partial least square -discriminant analysis
- QC:
-
Quality control
- QqQ:
-
Triple quadruple
- Q-ToF:
-
Hybrid quadrupole orthogonal time-of-flight
- R:
-
R-programming language for statistical computing
- ToF-MS:
-
Time-of-flight mass spectrometry
- UPLC:
-
Ultra performance liquid chromatography
- XCMS:
-
Various forms (X) of chromatography mass spectrometry
References
Misra BB, Assmann SM, Chen S (2014) Plant single-cell and single-cell-type metabolomics. Trends Plant Sci 19(10):637–646
Zenobi R (2013) Single-cell metabolomics: analytical and biological perspectives. Science 342(6163):1243259
Uhlén M, Fagerberg L, Hallström BM, Lindskog C, Oksvold P, Mardinoglu A, Sivertsson Å, Kampf C, Sjöstedt E, Asplund A, Olsson I (2015) Tissue-based map of the human proteome. Science 347(6220):1260419
Melé M, Ferreira PG, Reverter F, DeLuca DS, Monlong J, Sammeth M, Young TR, Goldmann JM, Pervouchine DD, Sullivan TJ, Johnson R (2015) The human transcriptome across tissues and individuals. Science 348(6235):660–665
Bock C, Farlik M, Sheffield NC (2016) Multi-omics of single cells: strategies and applications. Trends Biotechnol 34(8):605–608
Shapiro E, Biezuner T, Linnarsson S (2013) Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat Rev Genet 14(9):618–630
Misra BB, der Hooft JJ (2016) Updates in metabolomics tools and resources: 2014–2015. Electrophoresis 37(1):86–110
Misra BB (2016) Quick tips to perform a metabolomics study (No. e2002v1). Peer J Preprints 4:e2002v1
Misra BB (2018) New tools and resources in metabolomics: 2016–2017. Electrophoresis 39(7):909–923
Misra BB, Fahrmann JF, Grapov D (2017) Review of emerging metabolomic tools and resources: 2015–2016. Electrophoresis 38(18):2257–2274
Misra BB, Langefeld CD, Olivier M, Cox LA (2018) Integrated omics: tools, advances, and future approaches. J Mol Endocrinol pii:JME-18-0055
Henry VJ, Bandrowski AE, Pepin AS, Gonzalez BJ, Desfeux A (2014) OMICtools: an informative directory for multi-omic data analysis. Database 2014:bau069
Lo SJ, Yao DJ (2015) Get to understand more from single-cells: current studies of microfluidic-based techniques for single-cell analysis. Int J Mol Sci 16(8):16763–16777
Link H, Fuhrer T, Gerosa L, Zamboni N, Sauer U (2015) Real-time metabolome profiling of the metabolic switch between starvation and growth. Nat Methods 12(11):1091–1097
Broadhurst D, Goodacre R, Reinke SN, Kuligowski J, Wilson ID, Lewis MR, Dunn WB (2018) Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies. Metabolomics 14(6):72
Lisec J, Schauer N, Kopka J, Willmitzer L, Fernie AR (2006) Gas chromatography mass spectrometry-based metabolite profiling in plants. Nat Protoc 1(1):387–396
Zhu ZJ, Schultz AW, Wang J, Johnson CH, Yannone SM, Patti GJ, Siuzdak G (2013) Liquid chromatography quadrupole time-of-flight mass spectrometry characterization of metabolites guided by the METLIN database. Nat Protoc 8(3):451–460
Noack S, Wiechert W (2014) Quantitative metabolomics: a phantom? Trends Biotechnol 32(5):238–244
Palmer A, Trede D, Alexandrov T (2016) Where imaging mass spectrometry stands: here are the numbers. Metabolomics 12(6):1–3
Bartels B, Svatoš A (2015) Spatially resolved in vivo plant metabolomics by laser ablation-based mass spectrometry imaging (MSI) techniques: LDI-MSI and LAESI. Front Plant Sci 6:471
Jacobson RS, Thurston RL, Shrestha B, Vertes A (2015) In situ analysis of small populations of adherent mammalian cells using laser ablation electrospray ionization mass spectrometry in transmission geometry. Anal Chem 87(24):12130–12136
Paglia G, Williams JP, Menikarachchi L, Thompson JW, Tyldesley-Worster R, Halldórsson S, Rolfsson O, Moseley A, Grant D, Langridge J, Palsson BO (2014) Ion mobility derived collision cross sections to support metabolomics applications. Anal Chem 86(8):3985–3993
Perez-Riverol Y, Gatto L, Wang R, Sachsenberg T, Uszkoreit J, Leprevost F, Fufezan C, Ternent T, Eglen SJ, Katz DS, Pollard TJ (2016) Ten simple rules for taking advantage of git and GitHub. bioRxiv 048744
Boekel J, Chilton JM, Cooke IR, Horvatovich PL, Jagtap PD, Käll L, Lehtiö J, Lukasse P, Moerland PD, Griffin TJ (2015) Multi-omic data analysis using galaxy. Nat Biotechnol 33(2):137–139
Haug K, Salek RM, Conesa P, Hastings J, de Matos P, Rijnbeek M, Mahendraker T, Williams M, Neumann S, Rocca-Serra P, Maguire E (2012) MetaboLights—an open-access general-purpose repository for metabolomics studies and associated meta-data. Nucleic Acids Res 41(D1):D781–D786
Sud M, Fahy E, Cotter D, Azam K, Vadivelu I, Burant C, Edison A, Fiehn O, Higashi R, Nair KS, Sumner S (2015) Metabolomics workbench: an international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools. Nucleic Acids Res 44(D1):D463–D470
Wang M, Carver JJ, Phelan VV, Sanchez LM, Garg N, Peng Y, Nguyen DD, Watrous J, Kapono CA, Luzzatto-Knaan T, Porto C (2016) Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nat Biotechnol 34(8):828–837
Wang R, Perez-Riverol Y, Hermjakob H, Vizcaíno JA (2015) Open source libraries and frameworks for biological data visualisation: a guide for developers. Proteomics 15(8):1356–1374
Weiskirchen R, Weiskirchen S, Kim P, Winkler R (2019) Software solutions for evaluation and visualization of laser ablation inductively coupled plasma mass spectrometry imaging (LA-ICP-MSI) data: a short overview. J Cheminform 11(1):16. https://doi.org/10.1186/s13321-019-0338-7
Wang D, Bodovitz S (2010) Single cell analysis: the new frontier in ‘omics’. Trends Biotechnol 28(6):281–290
Vasilevsky N, Johnson T, Corday K et al (2012) Research resources: curating the new Eagle-I Discovery System. Database (Oxford) 2012:bar067
Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten JW, da Silva Santos LB, Bourne PE, Bouwman J (2016) The FAIR guiding principles for scientific data management and stewardship. Sci Data 3:160018
Holman JD, Tabb DL, Mallick P (2014) Employing ProteoWizard to convert raw mass spectrometry data. Curr Protoc Bioinformatics 46:13.24.1–13.24.9
Wenig P, Odermatt J (2010) OpenChrom: a cross-platform open source software for the mass spectrometric analysis of chromatographic data. BMC Bioinformatics 11(1):1
Strohalm M, Kavan D, Novak P, Volny M, Havlicek V (2010) mMass 3: a cross-platform software environment for precise analysis of mass spectrometric data. Anal Chem 82(11):4648–4651
Pluskal T, Castillo S, Villar-Briones A, Orešič M (2010) MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics 11(1):1
Tautenhahn R, Cho K, Uritboonthai W, Zhu Z, Patti GJ, Siuzdak G (2012) An accelerated workflow for untargeted metabolomics using the METLIN database. Nat Biotechnol 30(9):826–828
Tautenhahn R, Patti GJ, Rinehart D, Siuzdak G (2012) XCMS online: a web-based platform to process untargeted metabolomic data. Anal Chem 84(11):5035–5039
Gowda H, Ivanisevic J, Johnson CH, Kurczy ME, Benton HP, Rinehart D, Nguyen T, Ray J, Kuehl J, Arevalo B, Westenskow PD (2014) Interactive XCMS online: simplifying advanced metabolomic data processing and subsequent statistical analyses. Anal Chem 86(14):6931–6939
Smith CA, Want EJ, O'Maille G, Abagyan R, Siuzdak G (2006) XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem 78(3):779–787
Tanaka S, Fujita Y, Parry HE, Yoshizawa AC, Morimoto K, Murase M, Yamada Y, Yao J, Utsunomiya SI, Kajihara S, Fukuda M (2014) Mass++: a visualization and analysis tool for mass spectrometry. J Proteome Res 13(8):3846–3853
Tsugawa H, Cajka T, Kind T, Ma Y, Higgins B, Ikeda K, Kanazawa M, VanderGheynst J, Fiehn O, Arita M (2015) MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat Methods 12(6):523–526
Clasquin MF, Melamud E, Rabinowitz JD (2012) LC-MS data processing with MAVEN: a metabolomic analysis and visualization engine. Curr Protoc Bioinformatics 4:14–11
Davidson RL, Weber RJ, Liu H, Sharma-Oates A, Viant MR (2016) Galaxy-M: a Galaxy workflow for processing and analyzing direct infusion and liquid chromatography mass spectrometry-based metabolomics data. GigaScience 5(1):1
Davies T (1998) The new automated mass spectrometry deconvolution and identification system (AMDIS). Spectrosc Eur 10(3):24–27
Sturm M, Bertsch A, Gröpl C, Hildebrandt A, Hussong R, Lange E, Pfeifer N, Schulz-Trieglaff O, Zerck A, Reinert K, Kohlbacher O (2008) OpenMS—an open-source software framework for mass spectrometry. BMC Bioinformatics 9(1):163
Ma Y, Kind T, Yang D, Leon C, Fiehn O (2014) MS2Analyzer: a software for small molecule substructure annotations from accurate tandem mass spectra. Anal Chem 86(21):10724–10731
Kind T, Liu KH, Lee DY, DeFelice B, Meissen JK, Fiehn O (2013) LipidBlast in silico tandem mass spectrometry database for lipid identification. Nat Methods 10(8):755–758
Lommen A (2012) Data (pre-) processing of nominal and accurate mass LC-MS or GC-MS data using MetAlign. Plant Metabolomics 860:229–253
Jaitly N, Mayampurath A, Littlefield K, Adkins JN, Anderson GA, Smith RD (2009) Decon2LS: an open-source software package for automated processing and visualization of high resolution mass spectrometry data. BMC Bioinformatics 10(1):1
Parry RM, Galhena AS, Gamage CM, Bennett RV, Wang MD, Fernández FM (2013) omniSpect: an open MATLAB-based tool for visualization and analysis of matrix-assisted laser desorption/ionization and desorption electrospray ionization mass spectrometry images. J Am Soc Mass Spectrom 24(4):646–649
O’Connor PB (2002) Boston University data analysis (BUDA). Boston University, Boston, MA. http://www.bumc.bu.edu/ftms/buda
Kopka J, Schauer N, Krueger S, Birkemeyer C, Usadel B, Bergmüller E, Dörmann P, Weckwerth W, Gibon Y, Stitt M, Willmitzer L (2005) GMD@ CSB. DB: the Golm metabolome database. Bioinformatics 21(8):1635–1638
Wishart DS, Jewison T, Guo AC, Wilson M, Knox C, Liu Y, Djoumbou Y, Mandal R, Aziat F, Dong E, Bouatra S (2012) HMDB 3.0—the human metabolome database in 2013. Nucleic Acids Res 41(Database issue):D801–D807
Kanehisa M, Goto S, Sato Y, Kawashima M, Furumichi M, Tanabe M (2014) Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res 42(D1):D199–D205
Horai H, Arita M, Kanaya S, Nihei Y, Ikeda T, Suwa K, Ojima Y, Tanaka K, Tanaka S, Aoshima K, Oda Y (2010) MassBank: a public repository for sharing mass spectral data for life sciences. J Mass Spectrom 45(7):703–714
Mistrik R, Lutisan J, Huang Y, Suchy M, Wang J, Raab M (2013) mzCloud: a key conceptual shift to understand ‘Who’s Who’in untargeted metabolomics. In Metabolomics society 2013 conference, Glasgow, July 2013, pp. 1–4
Sawada Y, Nakabayashi R, Yamada Y, Suzuki M, Sato M, Sakata A, Akiyama K, Sakurai T, Matsuda F, Aoki T, Hirai MY (2012) RIKEN tandem mass spectral database (ReSpect) for phytochemicals: a plant-specific MS/MS-based data resource and database. Phytochemistry 82:38–45
Afendi FM, Okada T, Yamazaki M, Hirai-Morita A, Nakamura Y, Nakamura K, Ikeda S, Takahashi H, Altaf-Ul-Amin M, Darusman LK, Saito K (2012) KNApSAcK family databases: integrated metabolite–plant species databases for multifaceted plant research. Plant Cell Physiol 53(2):e1–e1
Jeffryes JG, Colastani RL, Elbadawi-Sidhu M, Kind T, Niehaus TD, Broadbelt LJ, Hanson AD, Fiehn O, Tyo KE, Henry CS (2015) MINEs: open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics. J Cheminform 7(1):1
Li L, Li R, Zhou J, Zuniga A, Stanislaus AE, Wu Y, Huan T, Zheng J, Shi Y, Wishart DS, Lin G (2013) MyCompoundID: using an evidence-based metabolome library for metabolite identification. Anal Chem 85(6):3401–3408
Allen F, Pon A, Wilson M, Greiner R, Wishart D (2014) CFM-ID: a web server for annotation, spectrum prediction and metabolite identification from tandem mass spectra. Nucleic Acids Res 42(W1):W94–W99
Chawade A, Alexandersson E, Levander F (2014) Normalyzer: a tool for rapid evaluation of normalization methods for omics data sets. J Proteome Res 13(6):3114–3120
Smyth GK, Speed T (2003) Normalization of cDNA microarray data. Methods 31(4):265–273
Husson, F., Josse, J., Le, S., Mazet, J. and Husson, M.F., 2016. Package ‘FactoMineR’
Grapov D (2014) DeviumWeb: dynamic multivariate data analysis and visualization platform. doi:https://doi.org/10.5281/zenodo.17459. https://github.com/dgrapov/DeviumWeb
Chong J, Soufan O, Li C, Caraus I, Li S, Bourque G, Wishart DS, Xia J (2018) MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis. Nucleic Acids Res 46(W1):W486–W494
López-Ibáñez J, Pazos F, Chagoyen M (2016) MBROLE 2.0—functional enrichment of chemical compounds. Nucleic Acids Res 44(W1):W201–W204
Batchelor C, Brenninkmeijer C, Chichester C, Davies M, Digles D, Dunlop I, Evelo CT, Gaulton A, Goble C, Gray AJG, Groth P, Harland L, Karapetyan K, Loizou A, Overington JP, Pettifer S, Steele J, Stevens R, Tkachenko V, Waagmeester A, Williams A, Willighagen EL (2014) Scientific lenses to support multiple views over linked chemistry data. In The semantic Web – ISWC 2014. Lect Notes Comput Sci 8796:98–113
Jewison T, Su Y, Disfany FM, Liang Y, Knox C, Maciejewski A, Poelzer J, Huynh J, Zhou Y, Arndt D, Djoumbou Y (2013) SMPDB 2.0: big improvements to the small molecule pathway database. Nucleic Acids Res 42(Database issue):D478–D484
Caspi R, Billington R, Foerster H, Fulcher CA, Keseler I, Kothari A, Krummenacker M, Latendresse M, Mueller LA, Ong Q, Paley S (2016) BioCyc: online resource for genome and metabolic pathway analysis. FASEB J 30(1 Suppl):lb192
Kutmon M, van Iersel MP, Bohler A, Kelder T, Nunes N, Pico AR, Evelo CT (2015) PathVisio 3: an extendable pathway analysis toolbox. PLoS Comput Biol 11(2):e1004085
Fitzpatrick MA, McGrath CM, Young SP (2014) Pathomx: an interactive workflow-based tool for the analysis of metabolomic data. BMC Bioinformatics 15(1):1
Kaever A, Landesfeind M, Feussner K, Mosblech A, Heilmann I, Morgenstern B, Feussner I, Meinicke P (2015) MarVis-Pathway: integrative and exploratory pathway analysis of non-targeted metabolomics data. Metabolomics 11(3):764–777
Szklarczyk D, Santos A, von Mering C, Jensen LJ, Bork P, Kuhn M (2015) STITCH 5: augmenting protein–chemical interaction networks with tissue and affinity data. Nucleic Acids Res 44:D380–D384
Cottret L, Wildridge D, Vinson F, Barrett MP, Charles H, Sagot MF, Jourdan F (2010) MetExplore: a web server to link metabolomic experiments and genome-scale metabolic networks. Nucleic Acids Res 38(Suppl 2):W132–W137
Narang P, Khan S, Hemrom AJ, Lynn AM (2014) MetaNET-a web-accessible interactive platform for biological metabolic network analysis. BMC Syst Biol 8(1):1
Joshi-Tope G, Gillespie M, Vastrik I, D'Eustachio P, Schmidt E, de Bono B, Jassal B, Gopinath GR, Wu GR, Matthews L, Lewis S (2005) Reactome: a knowledgebase of biological pathways. Nucleic Acids Res 33(Suppl 1):D428–D432
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504
Shannon PT, Reiss DJ, Bonneau R, Baliga NS (2006) The Gaggle: an open-source software system for integrating bioinformatics software and data sources. BMC Bioinformatics 7(1):1
Pathan M, Keerthikumar S, Ang CS, Gangoda L, Quek CY, Williamson NA, Mouradov D, Sieber OM, Simpson RJ, Salim A, Bacic A (2015) FunRich: an open access standalone functional enrichment and interaction network analysis tool. Proteomics 15(15):2597–2601
Xia J, Wishart DS (2016) Using MetaboAnalyst 3.0 for comprehensive metabolomics data analysis. Curr Protoc Bioinformatics 55:14.10.1–14.10.91
Huang SM, Toh W, Benke PI, Tan CS, Ong CN (2014) MetaboNexus: an interactive platform for integrated metabolomics analysis. Metabolomics 10(6):1084–1093
Winkler R (2015) An evolving computational platform for biological mass spectrometry: workflows, statistics and data mining with MASSyPup64. PeerJ 3:e1401
Mak TD, Laiakis EC, Goudarzi M, Fornace AJ Jr (2013) Metabolyzer: a novel statistical workflow for analyzing postprocessed LC–MS metabolomics data. Anal Chem 86(1):506–513
Giacomoni F, Le Corguillé G, Monsoor M, Landi M, Pericard P, Pétéra M, Duperier C, Tremblay-Franco M, Martin JF, Jacob D, Goulitquer S (2015) Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics. Bioinformatics 31(9):1493–1495
Grace SC, Embry S, Luo H (2014) Haystack, a web-based tool for metabolomics research. BMC Bioinformatics 15(11):1
Berthold MR, Cebron N, Dill F, Gabriel TR, Kötter T, Meinl T, Ohl P, Thiel K, Wiswedel B (2009) KNIME-the Konstanz information miner: version 2.0 and beyond. AcM SIGKDD Explor Newsl 11(1):26–31
Beisken S, Earll M, Portwood D, Seymour M, Steinbeck C (2014) MassCascade: visual programming for LC-MS data processing in metabolomics. Mol Inform 33(4):307–310
Ara T, Enomoto M, Arita M, Ikeda C, Kera K, Yamada M, Nishioka T, Ikeda T, Nihei Y, Shibata D, Kanaya S (2015) Metabolonote: a wiki-based database for managing hierarchical metadata of metabolome analyses. Front Bioeng Biotechnol 3:38
Swain MC, Cole JM (2016) ChemDataExtractor: a toolkit for automated extraction of chemical information from the scientific literature. J Chem Inf Model 56(10):1894–1904
Wolstencroft K, Haines R, Fellows D, Williams A, Withers D, Owen S, Soiland-Reyes S, Dunlop I, Nenadic A, Fisher P, Bhagat J (2013) The Taverna workflow suite: designing and executing workflows of Web Services on the desktop, web or in the cloud. Nucleic Acids Res 41(Web Server issue):W557–W561
Warth B, Levin N, Rinehart D, Teijaro J, Benton HP, Siuzdak G (2017) Metabolizing data in the cloud. Trends Biotechnol 35(6):481–483
García-Alcalde F, García-López F, Dopazo J, Conesa A (2011) Paintomics: a web based tool for the joint visualization of transcriptomics and metabolomics data. Bioinformatics 27(1):137–139
Kuo TC, Tian TF, Tseng YJ (2013) 3Omics: a web-based systems biology tool for analysis, integration and visualization of human transcriptomic, proteomic and metabolomic data. BMC Syst Biol 7(1):1
Wägele B, Witting M, Schmitt-Kopplin P, Suhre K (2012) MassTRIX reloaded: combined analysis and visualization of transcriptome and metabolome data. PLoS One 7(7):e39860
Kamburov A, Cavill R, Ebbels TM, Herwig R, Keun HC (2011) Integrated pathway-level analysis of transcriptomics and metabolomics data with IMPaLA. Bioinformatics 27(20):2917–2918
Eichner J, Rosenbaum L, Wrzodek C, Häring HU, Zell A, Lehmann R (2014) Integrated enrichment analysis and pathway-centered visualization of metabolomics, proteomics, transcriptomics, and genomics data by using the InCroMAP software. J Chromatogr B 966:77–82
Wanichthanarak K, Fahrmann JF, Grapov D (2015) Genomic, proteomic, and metabolomic data integration strategies. Biomarker Insights 10(Suppl 4):1
Misra BB, Mohapatra S (2019 Jan) Tools and resources for metabolomics research community: a 2017–2018 update. Electrophoresis 40(2):227–246
Macaulay IC, Ponting CP, Voet T (2017) Single-cell multiomics: multiple measurements from single cells. Trends Genet 33(2):155–168
Fujii T, Matsuda S, Tejedor ML, Esaki T, Sakane I, Mizuno H, Tsuyama N, Masujima T (2015) Direct metabolomics for plant cells by live single-cell mass spectrometry. Nat Protoc 10(9):1445–1456
Rocca-Serra P, Brandizi M, Maguire E, Sklyar N, Taylor C, Begley K, Field D, Harris S, Hide W, Hofmann O, Neumann S (2010) ISA software suite: supporting standards-compliant experimental annotation and enabling curation at the community level. Bioinformatics 26(18):2354–2356
Le Cao KA, Gonzalez I, Dejean S, Rohart F, Gautier B, Monget P, Coquery J, Yao F, Liquet B (2015) Package ‘mixOmics’
Onjiko RM, Moody SA, Nemes P (2015) Single-cell mass spectrometry reveals small molecules that affect cell fates in the 16-cell embryo. Proc Natl Acad Sci 112(21):6545–6550
Acknowledgements
The author thanks numerous pioneers in mass-spectrometry based metabolomics and single-cell and single cell-type -omics research, the developers and inventors of software tools, resources, and databases in metabolomics research who have inspired this compilation. The author also apologizes to the creators of numerous tools, resources, and analytical innovations that could not find a place in this chapter due to limitation in space or inadvertently.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Misra, B.B. (2020). Open-Source Software Tools, Databases, and Resources for Single-Cell and Single-Cell-Type Metabolomics. In: Shrestha, B. (eds) Single Cell Metabolism. Methods in Molecular Biology, vol 2064. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9831-9_15
Download citation
DOI: https://doi.org/10.1007/978-1-4939-9831-9_15
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-4939-9829-6
Online ISBN: 978-1-4939-9831-9
eBook Packages: Springer Protocols