XCMS Online

Last updated
XCMS nonlinear alignment of liquid chromatography mass spectrometry data sets XCMS alignment.png
XCMS nonlinear alignment of liquid chromatography mass spectrometry data sets
XCMS Online
Initial release25 April 2012;11 years ago (2012-04-25)
Stable release
3.7.1
Platform Web
Type Bioinformatics / Mass spectrometry software
License Freemium / commercial software
Website xcmsonline.scripps.edu

XCMS Online is a cloud version of the original eXtensible Computational Mass Spectrometry (XCMS) [1] [2] [3] technology (a bioinformatics software designed for statistical analysis of mass spectrometry data), created by the Siuzdak Lab at Scripps Research. XCMS introduced the concept of nonlinear retention time alignment that allowed for the statistical assessment of the detected peaks across LCMS and GCMS datasets. [1] XCMS Online [4] was designed to facilitate XCMS analyses through a cloud portal and as a more straightforward [5] (non command driven) way to analyze, visualize and share untargeted metabolomic data. [4] Further to this, the combination of XCMS and METLIN [6] [7] [8] allows for the identification of known molecules using METLIN's tandem mass spectrometry data, and enables the identification of unknown (uncharacterized molecules) via similarity searching of tandem mass spectrometry data. [9] [8] [10] XCMS Online has also become a systems biology tool for integrating different omic data sets. [11] As of January 2021, [12] the XCMSOnline - METLIN platform has over 44,000 registered users. XCMS - METLIN was recognized in 2023 as the year's top analytical innovation. [10]

Contents

XCMS Online works by comparing groups of raw or preprocessed metabolomic data to discover metabolites using methods such as nonlinear retention time alignment and feature detection & matching. Once analysis is complete the data can be viewed several different ways including via bubble plots, heat maps, chromatograms, and box plots. In addition, XCMS Online is integrated with METLIN, a large metabolite database. [1] [13] The following file formats are supported for direct upload to the site. [14]

File TypeVendor
mzXMLOpen Format
mzDataOpen Format
.cdfNetCDF (AIA/ANDI)
.d foldersAgilent, Bruker
.wiffSCIEX
.RAW foldersWaters
.RAW filesThermo

History

In 2005, the Siuzdak Lab created an open-source tool named XCMS [1] in the programming language R. Noticing the need for a more accessible, graphical data processing tool they created the cloud-based XCMS Online in 2012. [4] [15] The ability for users to stream data directly from instruments while being acquired was added in 2014. [16] Also in that year a commercial version named XCMS Plus (owned by Mass Consortium Corporation) was released and, in 2015, SCIEX became a reseller. [17] In 2017 it was shown that XCMS Online could be used in a systems biology workflow. [18] One year later, in the absence of a publicly available alternative, a version of XCMS Online was released with the ability to perform multiple reaction monitoring (MRM). [19]

Related Research Articles

<span class="mw-page-title-main">Mass spectrometry</span> Analytical technique based on determining mass to charge ratio of ions

Mass spectrometry (MS) is an analytical technique that is used to measure the mass-to-charge ratio of ions. The results are presented as a mass spectrum, a plot of intensity as a function of the mass-to-charge ratio. Mass spectrometry is used in many different fields and is applied to pure samples as well as complex mixtures.

<span class="mw-page-title-main">Tandem mass spectrometry</span> Type of mass spectrometry

Tandem mass spectrometry, also known as MS/MS or MS2, is a technique in instrumental analysis where two or more stages of analysis using one or more mass analyzer are performed with an additional reaction step in between these analyses to increase their abilities to analyse chemical samples. A common use of tandem MS is the analysis of biomolecules, such as proteins and peptides.

<span class="mw-page-title-main">Metabolomics</span> Scientific study of chemical processes involving metabolites

Metabolomics is the scientific study of chemical processes involving metabolites, the small molecule substrates, intermediates, and products of cell metabolism. Specifically, metabolomics is the "systematic study of the unique chemical fingerprints that specific cellular processes leave behind", the study of their small-molecule metabolite profiles. The metabolome represents the complete set of metabolites in a biological cell, tissue, organ, or organism, which are the end products of cellular processes. Messenger RNA (mRNA), gene expression data, and proteomic analyses reveal the set of gene products being produced in the cell, data that represents one aspect of cellular function. Conversely, metabolic profiling can give an instantaneous snapshot of the physiology of that cell, and thus, metabolomics provides a direct "functional readout of the physiological state" of an organism. There are indeed quantifiable correlations between the metabolome and the other cellular ensembles, which can be used to predict metabolite abundances in biological samples from, for example mRNA abundances. One of the ultimate challenges of systems biology is to integrate metabolomics with all other -omics information to provide a better understanding of cellular biology.

<span class="mw-page-title-main">Metabolome</span>

The metabolome refers to the complete set of small-molecule chemicals found within a biological sample. The biological sample can be a cell, a cellular organelle, an organ, a tissue, a tissue extract, a biofluid or an entire organism. The small molecule chemicals found in a given metabolome may include both endogenous metabolites that are naturally produced by an organism as well as exogenous chemicals that are not naturally produced by an organism.

<span class="mw-page-title-main">Matrix-assisted laser desorption electrospray ionization</span>

Matrix-assisted laser desorption electrospray ionization (MALDESI) was first introduced in 2006 as a novel ambient ionization technique which combines the benefits of electrospray ionization (ESI) and matrix-assisted laser desorption/ionization (MALDI). An infrared (IR) or ultraviolet (UV) laser can be utilized in MALDESI to resonantly excite an endogenous or exogenous matrix. The term 'matrix' refers to any molecule that is present in large excess and absorbs the energy of the laser, thus facilitating desorption of analyte molecules. The original MALDESI design was implemented using common organic matrices, similar to those used in MALDI, along with a UV laser. The current MALDESI source employs endogenous water or a thin layer of exogenously deposited ice as the energy-absorbing matrix where O-H symmetric and asymmetric stretching bonds are resonantly excited by a mid-IR laser.

<span class="mw-page-title-main">Capillary electrophoresis–mass spectrometry</span>

Capillary electrophoresis–mass spectrometry (CE–MS) is an analytical chemistry technique formed by the combination of the liquid separation process of capillary electrophoresis with mass spectrometry. CE–MS combines advantages of both CE and MS to provide high separation efficiency and molecular mass information in a single analysis. It has high resolving power and sensitivity, requires minimal volume and can analyze at high speed. Ions are typically formed by electrospray ionization, but they can also be formed by matrix-assisted laser desorption/ionization or other ionization techniques. It has applications in basic research in proteomics and quantitative analysis of biomolecules as well as in clinical medicine. Since its introduction in 1987, new developments and applications have made CE-MS a powerful separation and identification technique. Use of CE–MS has increased for protein and peptides analysis and other biomolecules. However, the development of online CE–MS is not without challenges. Understanding of CE, the interface setup, ionization technique and mass detection system is important to tackle problems while coupling capillary electrophoresis to mass spectrometry.

<span class="mw-page-title-main">Fragmentation (mass spectrometry)</span>

In mass spectrometry, fragmentation is the dissociation of energetically unstable molecular ions formed from passing the molecules mass spectrum. These reactions are well documented over the decades and fragmentation patterns are useful to determine the molar weight and structural information of unknown molecules. Fragmentation that occurs in tandem mass spectrometry experiments has been a recent focus of research, because this data helps facilitate the identification of molecules.

The METLIN Metabolite and Chemical Entity Database is the largest repository of experimental tandem mass spectrometry and neutral loss data acquired from standards. The tandem mass spectrometry data on over 930,000 molecular standards is provided to facilitate the identification of chemical entities from tandem mass spectrometry experiments. In addition to the identification of known molecules, it is also useful for identifying unknowns using its similarity searching technology. All tandem mass spectrometry data comes from the experimental analysis of standards at multiple collision energies and in both positive and negative ionization modes.

<span class="mw-page-title-main">Laser ablation electrospray ionization</span>

Laser ablation electrospray ionization (LAESI) is an ambient ionization method for mass spectrometry that combines laser ablation from a mid-infrared (mid-IR) laser with a secondary electrospray ionization (ESI) process. The mid-IR laser is used to generate gas phase particles which are then ionized through interactions with charged droplets from the ESI source. LAESI was developed in Professor Akos Vertes lab by Peter Nemes in 2007 and it was marketed commercially by Protea Biosciences, Inc until 2017. Fiber-LAESI for single-cell analysis approach was developed by Bindesh Shrestha in Professor Vertes lab in 2009. LAESI is a novel ionization source for mass spectrometry (MS) that has been used to perform MS imaging of plants, tissues, cell pellets, and even single cells. In addition, LAESI has been used to analyze historic documents and untreated biofluids such as urine and blood. The technique of LAESI is performed at atmospheric pressure and therefore overcomes many of the obstacles of traditional MS techniques, including extensive and invasive sample preparation steps and the use of high vacuum. Because molecules and aerosols are ionized by interacting with an electrospray plume, LAESI's ionization mechanism is similar to SESI and EESI techniques.

<span class="mw-page-title-main">Surface-assisted laser desorption/ionization</span>

Surface-assisted laser desorption/ionization (SALDI) is a soft laser desorption technique used for mass spectrometry analysis of biomolecules, polymers, and small organic molecules. In its first embodiment Koichi Tanaka used a cobalt/glycerol liquid matrix and subsequent applications included a graphite/glycerol liquid matrix as well as a solid surface of porous silicon. The porous silicon represents the first matrix-free SALDI surface analysis allowing for facile detection of intact molecular ions, these porous silicon surfaces also facilitated the analysis of small molecules at the yoctomole level. At present laser desorption/ionization methods using other inorganic matrices such as nanomaterials are often regarded as SALDI variants. As an example, silicon nanowires as well as Titania nanotube arrays (NTA) have been used as substrates to detect small molecules. SALDI is used to detect proteins and protein-protein complexes. A related method named "ambient SALDI" - which is a combination of conventional SALDI with ambient mass spectrometry incorporating the direct analysis real time (DART) ion source has also been demonstrated. SALDI is considered one of the most important techniques in MS and has many applications.

<span class="mw-page-title-main">Single-cell analysis</span> Testbg biochemical processes and reactions in an individual cell

In the field of cellular biology, single-cell analysis and subcellular analysis is the study of genomics, transcriptomics, proteomics, metabolomics and cell–cell interactions at the single cell level. The concept of single-cell analysis originated in the 1970s. Before the discovery of heterogeneity, single-cell analysis mainly referred to the analysis or manipulation of an individual cell in a bulk population of cells at a particular condition using optical or electronic microscope. To date, due to the heterogeneity seen in both eukaryotic and prokaryotic cell populations, analyzing a single cell makes it possible to discover mechanisms not seen when studying a bulk population of cells. Technologies such as fluorescence-activated cell sorting (FACS) allow the precise isolation of selected single cells from complex samples, while high throughput single cell partitioning technologies, enable the simultaneous molecular analysis of hundreds or thousands of single unsorted cells; this is particularly useful for the analysis of transcriptome variation in genotypically identical cells, allowing the definition of otherwise undetectable cell subtypes. The development of new technologies is increasing our ability to analyze the genome and transcriptome of single cells, as well as to quantify their proteome and metabolome. Mass spectrometry techniques have become important analytical tools for proteomic and metabolomic analysis of single cells. Recent advances have enabled quantifying thousands of protein across hundreds of single cells, and thus make possible new types of analysis. In situ sequencing and fluorescence in situ hybridization (FISH) do not require that cells be isolated and are increasingly being used for analysis of tissues.

<span class="mw-page-title-main">Desorption/ionization on silicon</span> Soft laser desorption method

Desorption/ionization on silicon (DIOS) is a soft laser desorption method used to generate gas-phase ions for mass spectrometry analysis. DIOS is considered the first surface-based surface-assisted laser desorption/ionization (SALDI-MS) approach. Prior approaches were accomplished using nanoparticles in a matrix of glycerol, while DIOS is a matrix-free technique in which a sample is deposited on a nanostructured surface and the sample desorbed directly from the nanostructured surface through the adsorption of laser light energy. DIOS has been used to analyze organic molecules, metabolites, biomolecules and peptides, and, ultimately, to image tissues and cells.

<span class="mw-page-title-main">Exometabolomics</span>

Exometabolomics, also known as 'metabolic footprinting', is the study of extracellular metabolites and is a sub-field of metabolomics.

Phytosphingosine is a sphingoid base, a fundamental building block of more complex sphingolipids. It is abundant in plants and fungi and present in animals. Phytosphingosine has also been found to have interesting T-cell related anti-inflammatory properties in models of inflammatory bowel disease.

<span class="mw-page-title-main">Gary Siuzdak</span> American chemist

Gary Siuzdak is an American chemist best known for his work in the field of metabolomics, activity metabolomics, and mass spectrometry. His lab discovered indole-3-propionic acid as a gut bacteria derived metabolite in 2009. He is currently the Professor and Director of The Center for Metabolomics and Mass Spectrometry at Scripps Research in La Jolla, California. Siuzdak has also made contributions to virus analysis, viral structural dynamics, as well as developing mass spectrometry imaging technology using nanostructured surfaces. The Siuzdak lab is also responsible for creating the research tools eXtensible Computational Mass Spectrometry (XCMS), METLIN, METLIN Neutral Loss and Q-MRM. As of January 2021, the XCMS/METLIN platform has over 50,000 registered users.

<span class="mw-page-title-main">Roy Goodacre</span> British Metabolomic expert and Mass Spectrometrist

Royston "Roy" Goodacre is Chair in Biological Chemistry at the University of Liverpool. With training in both Microbiology and Pyrolysis-Mass Spectrometry, Goodacre runs a multidisciplinary Metabolomics and Raman spectroscopy research group in the Institute of Systems, Molecular and Integrative Biology (ISMIB), and leads ISMIB's Centre for Metabolomics Research and the Laboratory for Bioanalytical Spectroscopy.

María del Coral Barbas Arribas is a professor at the Universidad CEU San Pablo in Madrid, Spain who is known for her research on metabolomics and integration of chemical data.

SIRIUS is a Java-based open-source software for the identification of small molecules from fragmentation mass spectrometry data without the use of spectral libraries. It combines the analysis of isotope patterns in MS1 spectra with the analysis of fragmentation patterns in MS2 spectra. SIRIUS is the umbrella application comprising CSI:FingerID, CANOPUS, COSMIC and ZODIAC.

Gunda Köllensperger is an Austrian chemist and professor of chemistry at the University of Vienna. She investigates metallobiomolecules and drugs using inductively coupled plasma mass spectrometry. She was awarded the 2023 Houska Prize for her prize in Analytical Chemistry.

References

  1. 1 2 3 4 Smith, Colin A.; Want, Elizabeth J.; O'Maille, Grace; Abagyan, Ruben; Siuzdak, Gary (February 2006). "XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification". Analytical Chemistry. 78 (3): 779–787. doi:10.1021/ac051437y. PMID   16448051.
  2. Domingo-Almenara, Xavier; Siuzdak, Gary (2020), Li, Shuzhao (ed.), "Metabolomics Data Processing Using XCMS", Computational Methods and Data Analysis for Metabolomics, Methods in Molecular Biology, New York, NY: Springer US, pp. 11–24, doi:10.1007/978-1-0716-0239-3_2, ISBN   978-1-0716-0239-3 , retrieved 2023-07-19
  3. Heim, Wilasinee; Aisporna, Aries; Hoang, Linh; Benton, H. Paul; Siuzdak, Gary (2023), Ivanisevic, Julijana; Giera, Martin (eds.), "METLIN Tandem Mass Spectrometry and Neutral Loss Databases for the Identification of Microbial Natural Products and Other Chemical Entities", A Practical Guide to Metabolomics Applications in Health and Disease: From Samples to Insights into Metabolism, Learning Materials in Biosciences, Cham: Springer International Publishing, pp. 105–124, doi:10.1007/978-3-031-44256-8_5, ISBN   978-3-031-44256-8 , retrieved 2024-03-01
  4. 1 2 3 Tautenhahn, Ralf; Patti, Gary J.; Rinehart, Duane; Siuzdak, Gary (5 June 2012). "XCMS Online: A Web-Based Platform to Process Untargeted Metabolomic Data". Analytical Chemistry. 84 (11): 5035–5039. doi:10.1021/ac300698c. PMC   3703953 . PMID   22533540.
  5. Gowda, Harsha; Ivanisevic, Julijana; Johnson, Caroline H.; Kurczy, Michael E.; Benton, H. Paul; Rinehart, Duane; Nguyen, Thomas; Ray, Jayashree; Kuehl, Jennifer; Arevalo, Bernardo; Westenskow, Peter D. (2014-07-15). "Interactive XCMS Online: Simplifying Advanced Metabolomic Data Processing and Subsequent Statistical Analyses". Analytical Chemistry. 86 (14): 6931–6939. doi:10.1021/ac500734c. ISSN   0003-2700. PMC   4215863 . PMID   24934772.
  6. Smith, Colin A.; Maille, Grace O'; Want, Elizabeth J.; Qin, Chuan; Trauger, Sunia A.; Brandon, Theodore R.; Custodio, Darlene E.; Abagyan, Ruben; Siuzdak, Gary (December 2005). "METLIN: A Metabolite Mass Spectral Database". Therapeutic Drug Monitoring. 27 (6): 747–751. doi:10.1097/01.ftd.0000179845.53213.39. ISSN   0163-4356. PMID   16404815. S2CID   14774455.
  7. Tautenhahn, Ralf; Cho, Kevin; Uritboonthai, Winnie; Zhu, Zhengjiang; Patti, Gary J.; Siuzdak, Gary (September 2012). "An accelerated workflow for untargeted metabolomics using the METLIN database". Nature Biotechnology. 30 (9): 826–828. doi:10.1038/nbt.2348. ISSN   1546-1696. PMC   3666346 . PMID   22965049.
  8. 1 2 Guijas, Carlos; Montenegro-Burke, J. Rafael; Domingo-Almenara, Xavier; Palermo, Amelia; Warth, Benedikt; Hermann, Gerrit; Koellensperger, Gunda; Huan, Tao; Uritboonthai, Winnie; Aisporna, Aries E.; Wolan, Dennis W. (2018-03-06). "METLIN: A Technology Platform for Identifying Knowns and Unknowns". Analytical Chemistry. 90 (5): 3156–3164. doi:10.1021/acs.analchem.7b04424. ISSN   0003-2700. PMC   5933435 . PMID   29381867.
  9. Benton, H. P.; Wong, D. M.; Trauger, S. A.; Siuzdak, G. (2008-08-01). "XCMS2: Processing Tandem Mass Spectrometry Data for Metabolite Identification and Structural Characterization". Analytical Chemistry. 80 (16): 6382–6389. doi:10.1021/ac800795f. ISSN   0003-2700. PMC   2728033 . PMID   18627180.
  10. 1 2 "The Analytical Scientist Innovation Awards 2023". The Analytical Scientist. 2023-12-12. Retrieved 2023-12-14.
  11. Huan, Tao; Forsberg, Erica M.; Rinehart, Duane; Johnson, Caroline H.; Ivanisevic, Julijana; Benton, H. Paul; Fang, Mingliang; Aisporna, Aries; Hilmers, Brian; Poole, Farris L.; Thorgersen, Michael P. (May 2017). "Systems biology guided by XCMS Online metabolomics". Nature Methods. 14 (5): 461–462. doi:10.1038/nmeth.4260. ISSN   1548-7105. PMC   5933448 . PMID   28448069.
  12. Majumder, Erica L.-W.; Billings, Elizabeth M.; Benton, H. Paul; Martin, Richard L.; Palermo, Amelia; Guijas, Carlos; Rinschen, Markus M.; Domingo-Almenara, Xavier; Montenegro-Burke, J. Rafael; Tagtow, Bradley A.; Plumb, Robert S. (2021-01-22). "Cognitive analysis of metabolomics data for systems biology". Nature Protocols. 16 (3): 1376–1418. doi:10.1038/s41596-020-00455-4. ISSN   1750-2799. OSTI   1774918. PMC   10357461 . PMID   33483720. S2CID   231687415.
  13. Gowda, Harsha; Ivanisevic, Julijana; Johnson, Caroline H.; Kurczy, Michael E.; Benton, H. Paul; Rinehart, Duane; Nguyen, Thomas; Ray, Jayashree; Kuehl, Jennifer; Arevalo, Bernardo; Westenskow, Peter D.; Wang, Junhua; Arkin, Adam P.; Deutschbauer, Adam M.; Patti, Gary J.; Siuzdak, Gary (25 June 2014). "Interactive XCMS Online: Simplifying Advanced Metabolomic Data Processing and Subsequent Statistical Analyses". Analytical Chemistry. 86 (14): 6931–6939. doi:10.1021/ac500734c. PMC   4215863 . PMID   24934772.
  14. "XCMS Online - Documentation". xcmsonline.scripps.edu.
  15. Perkel, Jeffrey M. "Name That Metabolite!". The Scientist Magazine®. The Scientist. Retrieved 25 June 2019.
  16. Rinehart, Duane; Johnson, Caroline H.; Nguyen, Thomas; Ivanisevic, Julijana; Benton, H. Paul; Lloyd, Jessica; Arkin, Adam P.; Deutschbauer, Adam M.; Patti, Gary J.; Siuzdak, Gary (June 2014). "Metabolomic data streaming for biology-dependent data acquisition". Nature Biotechnology. 32 (6): 524–527. doi:10.1038/nbt.2927. ISSN   1546-1696. PMC   4112958 . PMID   24911492.
  17. Evans, Jon. "SCIEX becomes exclusive reseller for XCMS plus - News - spectroscopyNOW.com". www.spectroscopynow.com. Spectroscopy Now. Retrieved 25 June 2019.
  18. Huan, Tao; Forsberg, Erica M; Rinehart, Duane; Johnson, Caroline H; Ivanisevic, Julijana; Benton, H Paul; Fang, Mingliang; Aisporna, Aries; Hilmers, Brian; Poole, Farris L; Thorgersen, Michael P; Adams, Michael W W; Krantz, Gregory; Fields, Matthew W; Robbins, Paul D; Niedernhofer, Laura J; Ideker, Trey; Majumder, Erica L; Wall, Judy D; Rattray, Nicholas J W; Goodacre, Royston; Lairson, Luke L; Siuzdak, Gary (1 May 2017). "Systems biology guided by XCMS Online metabolomics". Nature Methods. 14 (5): 461–462. doi:10.1038/nmeth.4260. PMC   5933448 . PMID   28448069.
  19. Domingo-Almenara, Xavier; Montenegro-Burke, J. Rafael; Ivanisevic, Julijana; Thomas, Aurelien; Sidibé, Jonathan; Teav, Tony; Guijas, Carlos; Aisporna, Aries E.; Rinehart, Duane; Hoang, Linh; Nordström, Anders; Gómez-Romero, María; Whiley, Luke; Lewis, Matthew R.; Nicholson, Jeremy K.; Benton, H. Paul; Siuzdak, Gary (27 August 2018). "XCMS-MRM and METLIN-MRM: a cloud library and public resource for targeted analysis of small molecules". Nature Methods. 15 (9): 681–684. doi:10.1038/s41592-018-0110-3. PMC   6629029 . PMID   30150755.