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Single Cell Metabolism pp 191–217Cite as

Open-Source Software Tools, Databases, and Resources for Single-Cell and Single-Cell-Type Metabolomics

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 2064))

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.

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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

  1. Misra BB, Assmann SM, Chen S (2014) Plant single-cell and single-cell-type metabolomics. Trends Plant Sci 19(10):637–646

    Article  CAS  PubMed  Google Scholar 

  2. Zenobi R (2013) Single-cell metabolomics: analytical and biological perspectives. Science 342(6163):1243259

    Article  CAS  PubMed  Google Scholar 

  3. 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

    Article  PubMed  CAS  Google Scholar 

  4. 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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Bock C, Farlik M, Sheffield NC (2016) Multi-omics of single cells: strategies and applications. Trends Biotechnol 34(8):605–608

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Shapiro E, Biezuner T, Linnarsson S (2013) Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat Rev Genet 14(9):618–630

    Article  CAS  PubMed  Google Scholar 

  7. Misra BB, der Hooft JJ (2016) Updates in metabolomics tools and resources: 2014–2015. Electrophoresis 37(1):86–110

    Article  CAS  PubMed  Google Scholar 

  8. Misra BB (2016) Quick tips to perform a metabolomics study (No. e2002v1). Peer J Preprints 4:e2002v1

    Google Scholar 

  9. Misra BB (2018) New tools and resources in metabolomics: 2016–2017. Electrophoresis 39(7):909–923

    Article  CAS  PubMed  Google Scholar 

  10. Misra BB, Fahrmann JF, Grapov D (2017) Review of emerging metabolomic tools and resources: 2015–2016. Electrophoresis 38(18):2257–2274

    Article  CAS  PubMed  Google Scholar 

  11. Misra BB, Langefeld CD, Olivier M, Cox LA (2018) Integrated omics: tools, advances, and future approaches. J Mol Endocrinol pii:JME-18-0055

    Google Scholar 

  12. Henry VJ, Bandrowski AE, Pepin AS, Gonzalez BJ, Desfeux A (2014) OMICtools: an informative directory for multi-omic data analysis. Database 2014:bau069

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. 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

    Article  CAS  PubMed  Google Scholar 

  15. 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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. 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

    Article  CAS  PubMed  Google Scholar 

  17. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Noack S, Wiechert W (2014) Quantitative metabolomics: a phantom? Trends Biotechnol 32(5):238–244

    Article  CAS  PubMed  Google Scholar 

  19. Palmer A, Trede D, Alexandrov T (2016) Where imaging mass spectrometry stands: here are the numbers. Metabolomics 12(6):1–3

    Article  CAS  Google Scholar 

  20. 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

    Article  PubMed  PubMed Central  Google Scholar 

  21. 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

    Article  CAS  PubMed  Google Scholar 

  22. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. 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

    Google Scholar 

  24. 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

    Article  CAS  PubMed  Google Scholar 

  25. 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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. 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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. 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

    Article  PubMed  PubMed Central  Google Scholar 

  30. Wang D, Bodovitz S (2010) Single cell analysis: the new frontier in ‘omics’. Trends Biotechnol 28(6):281–290

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Vasilevsky N, Johnson T, Corday K et al (2012) Research resources: curating the new Eagle-I Discovery System. Database (Oxford) 2012:bar067

    Article  CAS  Google Scholar 

  32. 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

    Article  PubMed  PubMed Central  Google Scholar 

  33. 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

    Google Scholar 

  34. 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

    Article  CAS  Google Scholar 

  35. 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

    Article  CAS  PubMed  Google Scholar 

  36. 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

    Article  CAS  Google Scholar 

  37. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. 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

    Article  CAS  PubMed  Google Scholar 

  41. 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

    Article  CAS  PubMed  Google Scholar 

  42. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. 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

    Google Scholar 

  44. 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

    Article  CAS  Google Scholar 

  45. Davies T (1998) The new automated mass spectrometry deconvolution and identification system (AMDIS). Spectrosc Eur 10(3):24–27

    CAS  Google Scholar 

  46. 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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Lommen A (2012) Data (pre-) processing of nominal and accurate mass LC-MS or GC-MS data using MetAlign. Plant Metabolomics 860:229–253

    Article  CAS  Google Scholar 

  50. 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

    Article  CAS  Google Scholar 

  51. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. O’Connor PB (2002) Boston University data analysis (BUDA). Boston University, Boston, MA. http://www.bumc.bu.edu/ftms/buda

    Google Scholar 

  53. 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

    Article  CAS  PubMed  Google Scholar 

  54. 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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. 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

    Article  CAS  PubMed  Google Scholar 

  56. 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

    Article  CAS  PubMed  Google Scholar 

  57. 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

    Google Scholar 

  58. 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

    Article  CAS  PubMed  Google Scholar 

  59. 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

    Article  CAS  PubMed  Google Scholar 

  60. 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

    Article  CAS  Google Scholar 

  61. 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

    Article  CAS  PubMed  Google Scholar 

  62. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Smyth GK, Speed T (2003) Normalization of cDNA microarray data. Methods 31(4):265–273

    Article  CAS  PubMed  Google Scholar 

  65. Husson, F., Josse, J., Le, S., Mazet, J. and Husson, M.F., 2016. Package ‘FactoMineR’

    Google Scholar 

  66. Grapov D (2014) DeviumWeb: dynamic multivariate data analysis and visualization platform. doi:https://doi.org/10.5281/zenodo.17459. https://github.com/dgrapov/DeviumWeb

  67. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. López-Ibáñez J, Pazos F, Chagoyen M (2016) MBROLE 2.0—functional enrichment of chemical compounds. Nucleic Acids Res 44(W1):W201–W204

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  69. 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

    Article  Google Scholar 

  70. 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

    PubMed  PubMed Central  Google Scholar 

  71. 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

    Google Scholar 

  72. 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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  73. Fitzpatrick MA, McGrath CM, Young SP (2014) Pathomx: an interactive workflow-based tool for the analysis of metabolomic data. BMC Bioinformatics 15(1):1

    Article  Google Scholar 

  74. 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

    Article  CAS  PubMed  Google Scholar 

  75. 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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  76. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. 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

    Article  CAS  Google Scholar 

  78. 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

    CAS  PubMed  Google Scholar 

  79. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. 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

    Article  CAS  Google Scholar 

  81. 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

    Article  CAS  PubMed  Google Scholar 

  82. Xia J, Wishart DS (2016) Using MetaboAnalyst 3.0 for comprehensive metabolomics data analysis. Curr Protoc Bioinformatics 55:14.10.1–14.10.91

    Article  Google Scholar 

  83. Huang SM, Toh W, Benke PI, Tan CS, Ong CN (2014) MetaboNexus: an interactive platform for integrated metabolomics analysis. Metabolomics 10(6):1084–1093

    Article  CAS  Google Scholar 

  84. Winkler R (2015) An evolving computational platform for biological mass spectrometry: workflows, statistics and data mining with MASSyPup64. PeerJ 3:e1401

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  85. 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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  86. 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

    Article  CAS  PubMed  Google Scholar 

  87. Grace SC, Embry S, Luo H (2014) Haystack, a web-based tool for metabolomics research. BMC Bioinformatics 15(11):1

    Google Scholar 

  88. 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

    Article  Google Scholar 

  89. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. 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

    Article  PubMed  PubMed Central  Google Scholar 

  91. 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

    Article  CAS  PubMed  Google Scholar 

  92. 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

    Article  PubMed  PubMed Central  Google Scholar 

  93. Warth B, Levin N, Rinehart D, Teijaro J, Benton HP, Siuzdak G (2017) Metabolizing data in the cloud. Trends Biotechnol 35(6):481–483

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. 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

    Article  PubMed  CAS  Google Scholar 

  95. 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

    Article  Google Scholar 

  96. 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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  97. 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

    Article  CAS  PubMed  Google Scholar 

  98. 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

    Article  CAS  Google Scholar 

  99. Wanichthanarak K, Fahrmann JF, Grapov D (2015) Genomic, proteomic, and metabolomic data integration strategies. Biomarker Insights 10(Suppl 4):1

    CAS  PubMed  PubMed Central  Google Scholar 

  100. Misra BB, Mohapatra S (2019 Jan) Tools and resources for metabolomics research community: a 2017–2018 update. Electrophoresis 40(2):227–246

    Article  CAS  PubMed  Google Scholar 

  101. Macaulay IC, Ponting CP, Voet T (2017) Single-cell multiomics: multiple measurements from single cells. Trends Genet 33(2):155–168

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. 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

    Article  CAS  PubMed  Google Scholar 

  103. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Le Cao KA, Gonzalez I, Dejean S, Rohart F, Gautier B, Monget P, Coquery J, Yao F, Liquet B (2015) Package ‘mixOmics’

    Google Scholar 

  105. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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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.

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Authors and Affiliations

  1. Center for Precision Medicine, Section of Molecular Medicine, Department of Internal Medicine, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC, USA

    Biswapriya B. Misra

  2. Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, USA

    Biswapriya B. Misra

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  1. Biswapriya B. Misra
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  1. Waters Corporation, Milford, MA, USA

    Bindesh Shrestha

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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

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  • DOI: https://doi.org/10.1007/978-1-4939-9831-9_15

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-4939-9829-6

  • Online ISBN: 978-1-4939-9831-9

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