Gary Siuzdak

Last updated
Gary Siuzdak
SizudakDC7.jpg
BornDecember 29, 1961
Scientific career
FieldsAnalytical Chemistry, Metabolomics

Gary Siuzdak is an American chemist best known for his work in the field of metabolomics, [1] [2] activity metabolomics [3] [4] [5] [6] [7] (a termed coined in 2019 [8] ), and mass spectrometry. [9] [10] [11] [12] [13] [14] His lab discovered indole-3-propionic acid as a gut bacteria derived metabolite in 2009. [15] He is currently the Professor and Director of The Center for Metabolomics and Mass Spectrometry at Scripps Research in La Jolla, California. [16] Siuzdak has also made contributions to virus analysis, [17] [18] viral structural dynamics, [19] [20] [21] as well as developing mass spectrometry imaging technology using nanostructured surfaces. [11] [22] The Siuzdak lab is also responsible for creating the research tools eXtensible Computational Mass Spectrometry (XCMS), [9] [23] METLIN, [13] METLIN Neutral Loss [24] and Q-MRM. [25] [26] [27] As of January 2021, [28] the XCMS/METLIN platform has over 50,000 registered users.

Contents

Siuzdak studied chemistry (B.S.) and applied mathematics (B.A.) at Rhode Island College. He then went to Dartmouth College for his graduate work where he built his first mass spectrometer [29] to perform multi-photon ionization mass spectrometry experiments and occasionally competed in powerlifting. [30] At Dartmouth he received his Ph.D. in Physical Chemistry (March 29, 1990) and on April 1, 1990, started at Scripps Research. [16] In 2017 Siuzdak received an honorary doctorate (with Emmanuelle Charpentier) from Umeå University [31] for his work in metabolomics. Siuzdak has hundreds of papers and has authored two books: Mass Spectrometry for Biotechnology (1996) and The Expanding Role of Mass Spectrometry in Biotechnology (2003) as well as The Expanding Role of Mass Spectrometry in Biotechnology 2nd Ed. (2006). [32]

Notable research

From 1994 to the present the Siuzdak lab has been working on activity metabolomics. [1] [3] [4] [5] [9] [12] [33] [15] [34] [35] using liquid chromatography mass spectrometry-based metabolomics to identify metabolites that alter phenotype. [1] [3] [4] [5] [33] [15] [34] [35] [12] His initial efforts with Richard Lerner, [4] used liquid chromatography mass spectrometry to perform metabolomic experiments on the cerebral spinal fluid of sleep deprived animals. cis-9,10-octadecenoamide , a novel lipid hormone (also known as oleamide), [4] was observed and shown to have sleep inducing properties. This work is one of the earliest such experiments combining liquid chromatography mass spectrometry and metabolomics to identify active metabolites. [4] [1] [3] Another notable activity metabolomics effort with Oscar Yanes (Spain) identified [5] neuroprotectin D1 as a metabolite that promotes stem cell differentiation.

In 1996 whole virus analysis was performed with an electrospray ionization mass spectrometer where the virus was collected and successfully tested for viability. [17] Later, he and his collaborators provided the first example of a whole intact virus (tobacco mosaic virus) being mass measured using a charge detection mass spectrometer, an instrument designed by Henry Benner and Stephen Fuerstenau at Lawrence Berkeley National Labs. [18]

In 1999, the Siuzdak lab described the use of nanostructures to enhance desorption/ionization on porous silicon of small molecules (DIOS), [10] this is also known as the first surface-based example of surface-assisted laser desorption/ionization mass spectrometry (SALDI-MS). This technology went on to be expanded using fluorinated initiator molecules used within the porous silicon and was described as Nanostructure Initiator Mass Spectrometry (NIMS), [11] it is also known as Nanostructure Imaging Mass Spectrometry (NIMS) because of its expanded application to imaging. [11] [22]

In 2005, the Siuzdak lab was engaged in identifying dysregulated metabolic peaks from liquid chromatography mass spectrometry data sets, to address the issue retention time alignment they developed the first algorithm that allowed for the nonlinear alignment of metabolomics data called XCMS. [9] [36]

From the early 2000s [37] [12] to the present, the Siuzdak lab created and has been expanding the tandem mass spectrometry database known as METLIN. METLIN is made up solely of experimental data generated from high resolution tandem mass spectrometry instrumentation, all of the data is derived from molecular standards. METLIN (as of August 2022) has over 870,000 molecular standards with experimental tandem mass spectrometry data. [14] [13] [38] METLIN is unique with respect to its size, as other databases are over an order of magnitude smaller, [13] and it is also unique because all of METLIN's tandem mass spectrometry data has been systematically generated at multiple collision energies and in positive and negative ionization modes.

In 2020, the Siuzdak lab building off their work with Xavi Domingo [39] and METLIN, [12] [37] developed Enhanced In-Source Fragmentation/Annotation (EISA) [25] to facilitate the fragmentation, identification, and quantification (via Q-MRM) [26] [40] of molecules without the use of tandem mass spectrometry.

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 mass analyzers are coupled together using an additional reaction step 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">Lipidomics</span>

Lipidomics is the large-scale study of pathways and networks of cellular lipids in biological systems The word "lipidome" is used to describe the complete lipid profile within a cell, tissue, organism, or ecosystem and is a subset of the "metabolome" which also includes other major classes of biological molecules. Lipidomics is a relatively recent research field that has been driven by rapid advances in technologies such as mass spectrometry (MS), nuclear magnetic resonance (NMR) spectroscopy, fluorescence spectroscopy, dual polarisation interferometry and computational methods, coupled with the recognition of the role of lipids in many metabolic diseases such as obesity, atherosclerosis, stroke, hypertension and diabetes. This rapidly expanding field complements the huge progress made in genomics and proteomics, all of which constitute the family of systems biology.

<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.

Soft laser desorption (SLD) is laser desorption of large molecules that results in ionization without fragmentation. "Soft" in the context of ion formation means forming ions without breaking chemical bonds. "Hard" ionization is the formation of ions with the breaking of bonds and the formation of fragment ions.

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

MALDI mass spectrometry imaging (MALDI-MSI) is the use of matrix-assisted laser desorption ionization as a mass spectrometry imaging technique in which the sample, often a thin tissue section, is moved in two dimensions while the mass spectrum is recorded. Advantages, like measuring the distribution of a large amount of analytes at one time without destroying the sample, make it a useful method in tissue-based study.

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

Desorption electrospray ionization (DESI) is an ambient ionization technique that can be coupled to mass spectrometry (MS) for chemical analysis of samples at atmospheric conditions. Coupled ionization sources-MS systems are popular in chemical analysis because the individual capabilities of various sources combined with different MS systems allow for chemical determinations of samples. DESI employs a fast-moving charged solvent stream, at an angle relative to the sample surface, to extract analytes from the surfaces and propel the secondary ions toward the mass analyzer. This tandem technique can be used to analyze forensics analyses, pharmaceuticals, plant tissues, fruits, intact biological tissues, enzyme-substrate complexes, metabolites and polymers. Therefore, DESI-MS may be applied in a wide variety of sectors including food and drug administration, pharmaceuticals, environmental monitoring, and biotechnology.

<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">Ambient ionization</span>

Ambient ionization is a form of ionization in which ions are formed in an ion source outside the mass spectrometer without sample preparation or separation. Ions can be formed by extraction into charged electrospray droplets, thermally desorbed and ionized by chemical ionization, or laser desorbed or ablated and post-ionized before they enter the mass spectrometer.

<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 870,000 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 Dr. 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 Dr. 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 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 ant-inflammatory properties in models of inflammatory bowel disease.

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

Secondary electro-spray ionization (SESI) is an ambient ionization technique for the analysis of trace concentrations of vapors, where a nano-electrospray produces charging agents that collide with the analyte molecules directly in gas-phase. In the subsequent reaction, the charge is transferred and vapors get ionized, most molecules get protonated and deprotonated. SESI works in combination with mass spectrometry or ion-mobility spectrometry.

<span class="mw-page-title-main">XCMS Online</span> Bioinformatics software

XCMS Online is a cloud version of the original eXtensible Computational Mass Spectrometry (XCMS) technology, 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. XCMS Online was designed to facilitate XCMS analyses through a cloud portal and as a more straightforward way to analyze, visualize and share untargeted metabolomic data. Further to this, the combination of XCMS and METLIN allows for the identification of known molecules using METLIN's tandem mass spectrometry data, and enables the identification of unknown via similarity searching of tandem mass spectrometry data. XCMS Online has also become a systems biology tool for integrating different omic data sets. As of January 2021, the XCMSOnline /METLIN platform has over 44,000 registered users.

References

  1. 1 2 3 4 Guijas, Carlos; Montenegro-Burke, J. Rafael; Warth, Benedikt; Spilker, Mary E.; Siuzdak, Gary (April 2018). "Metabolomics activity screening for identifying metabolites that modulate phenotype". Nature Biotechnology. 36 (4): 316–320. doi:10.1038/nbt.4101. ISSN   1546-1696. PMC   5937131 . PMID   29621222.
  2. ↑ Johnson, Caroline H.; Ivanisevic, Julijana; Siuzdak, Gary (July 2016). "Metabolomics: beyond biomarkers and towards mechanisms". Nature Reviews Molecular Cell Biology. 17 (7): 451–459. doi:10.1038/nrm.2016.25. ISSN   1471-0080. PMC   5729912 . PMID   26979502.
  3. 1 2 3 4 Rinschen, Markus M.; Ivanisevic, Julijana; Giera, Martin; Siuzdak, Gary (June 2019). "Identification of bioactive metabolites using activity metabolomics". Nature Reviews Molecular Cell Biology. 20 (6): 353–367. doi:10.1038/s41580-019-0108-4. ISSN   1471-0080. PMC   6613555 . PMID   30814649.
  4. 1 2 3 4 5 6 Cravatt, BF; Prospero-Garcia, O; Siuzdak, G; Gilula, NB; Henriksen, SJ; Boger, DL; Lerner, RA (9 June 1995). "Chemical characterization of a family of brain lipids that induce sleep". Science. 268 (5216): 1506–9. Bibcode:1995Sci...268.1506C. doi:10.1126/science.7770779. PMID   7770779.
  5. 1 2 3 4 Yanes, Oscar; Clark, Julie; Wong, Diana M.; Patti, Gary J.; Sánchez-Ruiz, Antonio; Benton, H. Paul; Trauger, Sunia A.; Desponts, Caroline; Ding, Sheng; Siuzdak, Gary (June 2010). "Metabolic oxidation regulates embryonic stem cell differentiation". Nature Chemical Biology. 6 (6): 411–417. doi:10.1038/nchembio.364. ISSN   1552-4469. PMC   2873061 . PMID   20436487.
  6. ↑ Lerner, R. A.; Siuzdak, G.; Prospero-Garcia, O.; Henriksen, S. J.; Boger, D. L.; Cravatt, B. F. (1994-09-27). "Cerebrodiene: a brain lipid isolated from sleep-deprived cats". Proceedings of the National Academy of Sciences. 91 (20): 9505–9508. Bibcode:1994PNAS...91.9505L. doi: 10.1073/pnas.91.20.9505 . ISSN   0027-8424. PMC   44841 . PMID   7937797.
  7. ↑ Montenegro-Burke, J. Rafael; Kok, Bernard P.; Guijas, Carlos; Domingo-Almenara, Xavier; Moon, Clara; Galmozzi, Andrea; Kitamura, Seiya; Eckmann, Lars; Saez, Enrique; Siuzdak, Gary E.; Wolan, Dennis W. (2021-09-28). "Metabolomics activity screening of T cell–induced colitis reveals anti-inflammatory metabolites". Science Signaling. 14 (702): eabf6584. doi:10.1126/scisignal.abf6584. PMC   8757460 . PMID   34582249.
  8. ↑ Rinschen, Markus M.; Ivanisevic, Julijana; Giera, Martin; Siuzdak, Gary (June 2019). "Identification of bioactive metabolites using activity metabolomics". Nature Reviews Molecular Cell Biology. 20 (6): 353–367. doi:10.1038/s41580-019-0108-4. ISSN   1471-0080. PMC   6613555 . PMID   30814649.
  9. 1 2 3 4 Smith, Colin A.; Want, Elizabeth J.; O'Maille, Grace; Abagyan, Ruben; Siuzdak, Gary (2006-02-01). "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. ISSN   0003-2700. PMID   16448051.
  10. 1 2 Wei, Jing; Buriak, Jillian M.; Siuzdak, Gary (May 1999). "Desorption–ionization mass spectrometry on porous silicon". Nature. 399 (6733): 243–246. Bibcode:1999Natur.399..243W. doi:10.1038/20400. ISSN   1476-4687. PMID   10353246. S2CID   4314372.
  11. 1 2 3 4 Northen, Trent R.; Yanes, Oscar; Northen, Michael T.; Marrinucci, Dena; Uritboonthai, Winnie; Apon, Junefredo; Golledge, Stephen L.; Nordström, Anders; Siuzdak, Gary (October 2007). "Clathrate nanostructures for mass spectrometry". Nature. 449 (7165): 1033–1036. Bibcode:2007Natur.449.1033N. doi:10.1038/nature06195. ISSN   1476-4687. PMID   17960240. S2CID   4404703.
  12. 1 2 3 4 5 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.
  13. 1 2 3 4 Xue, Jingchuan; Guijas, Carlos; Benton, H. Paul; Warth, Benedikt; Siuzdak, Gary (2020-08-24). "METLIN MS 2 molecular standards database: a broad chemical and biological resource". Nature Methods. 17 (10): 953–954. doi:10.1038/s41592-020-0942-5. ISSN   1548-7105. PMC   8802982 . PMID   32839599.
  14. 1 2 Giera, Martin; Yanes, Oscar; Siuzdak, Gary (2022-01-04). "Metabolite discovery: Biochemistry's scientific driver". Cell Metabolism. 34 (1): 21–34. doi: 10.1016/j.cmet.2021.11.005 . ISSN   1550-4131. PMC   10131248 . PMID   34986335. S2CID   245729571.
  15. 1 2 3 Wikoff, William R.; Anfora, Andrew T.; Liu, Jun; Schultz, Peter G.; Lesley, Scott A.; Peters, Eric C.; Siuzdak, Gary (2009-03-10). "Metabolomics analysis reveals large effects of gut microflora on mammalian blood metabolites". Proceedings of the National Academy of Sciences. 106 (10): 3698–3703. Bibcode:2009PNAS..106.3698W. doi: 10.1073/pnas.0812874106 . ISSN   0027-8424. PMC   2656143 . PMID   19234110.
  16. 1 2 "Faculty Page, Gary Siuzdak". scripps.edu. Retrieved 22 May 2019.
  17. 1 2 Siuzdak, Gary; Bothner, Brian; Yeager, Mark; Brugidou, Christophe; Fauquet, Claude M.; Hoey, Kenway; Change, Cheng-Ming (1996-01-01). "Mass spectrometry and viral analysis". Chemistry & Biology. 3 (1): 45–48. doi: 10.1016/S1074-5521(96)90083-6 . ISSN   1074-5521. PMID   8807827.
  18. 1 2 Fuerstenau, Stephen D.; Benner, W. Henry; Thomas, John J.; Brugidou, Christophe; Bothner, Brian; Siuzdak, Gary (2001). "Mass Spectrometry of an Intact Virus". Angewandte Chemie International Edition. 40 (3): 541–544. doi:10.1002/1521-3773(20010202)40:3<541::AID-ANIE541>3.0.CO;2-K. ISSN   1521-3773.
  19. ↑ Bothner, Brian; Dong, X. Fan; Bibbs, Lisa; Johnson, John E.; Siuzdak, Gary (1998-01-09). "Evidence of Viral Capsid Dynamics Using Limited Proteolysis and Mass Spectrometry". Journal of Biological Chemistry. 273 (2): 673–676. doi: 10.1074/jbc.273.2.673 . ISSN   0021-9258. PMID   9422714.
  20. ↑ Lewis, J. Kathleen; Bothner, Brian; Smith, Thomas J.; Siuzdak, Gary (1998-06-09). "Antiviral agent blocks breathing of the common cold virus". Proceedings of the National Academy of Sciences. 95 (12): 6774–6778. Bibcode:1998PNAS...95.6774L. doi: 10.1073/pnas.95.12.6774 . ISSN   0027-8424. PMC   22631 . PMID   9618488.
  21. ↑ Bothner, Brian; Schneemann, Anette; Marshall, Dawn; Reddy, Vijay; Johnson, John E.; Siuzdak, Gary (February 1999). "Crystallographically identical virus capsids display different properties in solution". Nature Structural Biology. 6 (2): 114–116. doi:10.1038/5799. ISSN   1545-9985. PMID   10048920. S2CID   1756648.
  22. 1 2 Kurczy, ME; Northen, TR; Trauger, SA; Siuzdak, G (2015). Nanostructure imaging mass spectrometry: the role of fluorocarbons in metabolite analysis and yoctomole level sensitivity. Methods in Molecular Biology. Vol. 1203. pp. 141–9. doi:10.1007/978-1-4939-1357-2_14. ISBN   978-1-4939-1356-5. PMC   4755109 . PMID   25361674.
  23. ↑ 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, vol. 2104, pp. 11–24, doi:10.1007/978-1-0716-0239-3_2, ISBN   978-1-0716-0239-3, PMID   31953810, S2CID   210709390 , retrieved 2023-07-13
  24. ↑ Aisporna, Aries; Benton, H. Paul; Galano, Jean Marie; Giera, Martin; Siuzdak, Gary (2021-04-04). "METLIN Neutral Loss Database Enhances Similarity Analysis": 2021.04.02.438066. doi:10.1101/2021.04.02.438066. S2CID   233175525.{{cite journal}}: Cite journal requires |journal= (help)
  25. 1 2 Xue, Jingchuan; Domingo-Almenara, Xavier; Guijas, Carlos; Palermo, Amelia; Rinschen, Markus M.; Isbell, John; Benton, H. Paul; Siuzdak, Gary (2020-04-21). "Enhanced in-Source Fragmentation Annotation Enables Novel Data Independent Acquisition and Autonomous METLIN Molecular Identification". Analytical Chemistry. 92 (8): 6051–6059. doi:10.1021/acs.analchem.0c00409. ISSN   0003-2700. PMC   8966047 . PMID   32242660.
  26. 1 2 Xue, Jingchuan; Derks, Rico J. E.; Webb, Bill; Billings, Elizabeth M.; Aisporna, Aries; Giera, Martin; Siuzdak, Gary (2021-08-10). "Single Quadrupole Multiple Fragment Ion Monitoring Quantitative Mass Spectrometry". Analytical Chemistry. 93 (31): 10879–10889. doi:10.1021/acs.analchem.1c01246. hdl:1887/3243130. ISSN   0003-2700. PMC   8762722 . PMID   34313111.
  27. ↑ Xue, Jingchuan; Derks, Rico J. E.; Hoang, Linh; Giera, Martin; Siuzdak, Gary (2021-10-11). "Proteomics with Enhanced In-Source Fragmentation/Annotation: Applying XCMS-EISA Informatics and Q-MRM High-Sensitivity Quantification". Journal of the American Society for Mass Spectrometry. 32 (11): 2644–2654. doi:10.1021/jasms.1c00188. ISSN   1044-0305. PMC   10245389 . PMID   34633184. S2CID   238581609.
  28. ↑ 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.
  29. ↑ Siuzdak, Gary.; BelBruno, Joseph J. (May 1990). "Laser multiphoton dissociation/ionization of butylamines: competitive processes in radical cations". The Journal of Physical Chemistry. 94 (11): 4559–4565. doi:10.1021/j100374a038. ISSN   0022-3654.
  30. ↑ Scandura, Mike (30 Dec 1987). "Weightlifting the right way good for Siuzdak". The Evening Times (Pawtucket, R.I.).
  31. ↑ "Biochemist and metabolomics researcher take honorary roles at Umeå University". www.umu.se. Retrieved 2020-10-21.
  32. ↑ "Interview with Dr. Gary Siuzdak". MetaboNews, Issue 13 - September 2012. Retrieved 22 May 2019.
  33. 1 2 Patti, Gary J.; Yanes, Oscar; Shriver, Leah P.; Courade, Jean-Phillipe; Tautenhahn, Ralf; Manchester, Marianne; Siuzdak, Gary (March 2012). "Metabolomics implicates altered sphingolipids in chronic pain of neuropathic origin". Nature Chemical Biology. 8 (3): 232–234. doi:10.1038/nchembio.767. ISSN   1552-4469. PMC   3567618 . PMID   22267119.
  34. 1 2 Beyer, Brittney A.; Fang, Mingliang; Sadrian, Benjamin; Montenegro-Burke, J. Rafael; Plaisted, Warren C.; Kok, Bernard P. C.; Saez, Enrique; Kondo, Toru; Siuzdak, Gary; Lairson, Luke L. (January 2018). "Metabolomics-based discovery of a metabolite that enhances oligodendrocyte maturation". Nature Chemical Biology. 14 (1): 22–28. doi:10.1038/nchembio.2517. ISSN   1552-4469. PMC   5928791 . PMID   29131145.
  35. 1 2 Guijas, Carlos; Montenegro-Burke, J. Rafael; Cintron-Colon, Rigo; Domingo-Almenara, Xavier; Sanchez-Alavez, Manuel; Aguirre, Carlos A.; Shankar, Kokila; Majumder, Erica L.-W.; Billings, Elizabeth; Conti, Bruno; Siuzdak, Gary (2020-09-08). "Metabolic adaptation to calorie restriction". Science Signaling. 13 (648). doi: 10.1126/scisignal.abb2490 . ISSN   1945-0877. PMC   7580877 . PMID   32900879.
  36. ↑ Tautenhahn, Ralf; Patti, Gary J.; Rinehart, Duane; Siuzdak, Gary (2012-06-05). "XCMS Online: A Web-Based Platform to Process Untargeted Metabolomic Data". Analytical Chemistry. 84 (11): 5035–5039. doi:10.1021/ac300698c. ISSN   0003-2700. PMC   3703953 . PMID   22533540.
  37. 1 2 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.
  38. ↑ Guijas, Carlos; To, Andrew; Montenegro-Burke, J. Rafael; Domingo-Almenara, Xavier; Alipio-Gloria, Zaida; Kok, Bernard P.; Saez, Enrique; Alvarez, Nicole H.; Johnson, Kristen A.; Siuzdak, Gary (August 2022). "Drug-Initiated Activity Metabolomics Identifies Myristoylglycine as a Potent Endogenous Metabolite for Human Brown Fat Differentiation". Metabolites. 12 (8): 749. doi: 10.3390/metabo12080749 . ISSN   2218-1989. PMC   9415469 . PMID   36005620.
  39. ↑ Domingo-Almenara, Xavier; Montenegro-Burke, J. Rafael; Guijas, Carlos; Majumder, Erica L.-W.; Benton, H. Paul; Siuzdak, Gary (2019-03-05). "Autonomous METLIN-Guided In-source Fragment Annotation for Untargeted Metabolomics". Analytical Chemistry. 91 (5): 3246–3253. doi:10.1021/acs.analchem.8b03126. ISSN   0003-2700. PMC   6637741 . PMID   30681830.
  40. ↑ Xue, Jingchuan; Derks, Rico J. E.; Hoang, Linh; Giera, Martin; Siuzdak, Gary (2021-10-11). "Proteomics with Enhanced In-Source Fragmentation/Annotation: Applying XCMS-EISA Informatics and Q-MRM High-Sensitivity Quantification". Journal of the American Society for Mass Spectrometry. 32 (11): 2644–2654. doi:10.1021/jasms.1c00188. ISSN   1044-0305. PMC   10245389 . PMID   34633184. S2CID   238581609.
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