Next Article in Journal
Application of Machine Learning Techniques to Assess Alpha-Fetoprotein at Diagnosis of Hepatocellular Carcinoma
Previous Article in Journal
MicroRNAs: The Missing Link between Hypertension and Periodontitis?
Previous Article in Special Issue
Mass Spectrometric Identification of Metabolites after Magnetic-Pulse Treatment of Infected Pyrus communis L. Microplants
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Editorial

Special Issue with Research Topics on “Recent Analysis and Applications of Mass Spectra on Biochemistry”

by
Bojidarka Ivanova
Lehrstuhl für Analytische Chemie, Institut für Umweltforschung, Fakultät für Chemie und Chemische Biologie, Universität Dortmund, Otto-Hahn-Straße 6, 44221 Dortmund, Germany
Int. J. Mol. Sci. 2024, 25(4), 1995; https://doi.org/10.3390/ijms25041995
Submission received: 20 January 2024 / Accepted: 4 February 2024 / Published: 7 February 2024
(This article belongs to the Special Issue Recent Analysis and Applications of Mass Spectrum on Biochemistry)
Analytical mass spectrometry applies irreplaceable mass spectrometric (MS) methods to analytical chemistry and chemical analysis, among other areas of analytical science. There are ongoing debates [1,2,3] on the definitions of analytical chemistry. These definitions by Zolotov (2021) [1] and Adams and Adriaens (2021) [3], labeled (1) and (2) below, respectively, shaped the research tasks of the latter field:
(1) “Analytical Chemistry is the science creating and developing the general methodology, methods, and means of the determination of the chemical composition and chemical structure of substances and developing methods of chemical analysis of particular material samples [1]”.
(2) “Analytical chemistry is the autonomous and fundamental scientific field involved with the development of methods for the complete or partial compositional and structural description in space and/or time of specific, natural, or man-made material objects or representative portions thereof, in order to relate this information with properties or functional or other characteristics of the objects. While its name historically refers to its origins in chemistry, analytical chemistry now applies any chemical, physical, biological, or other principles and methods to pursue its specific objectives. The discipline feeds and is connected to chemical analysis, a related applied scientific field, which is involved with various applications of analytical chemistry for either fundamental research in various scientific disciplines, or for technological or societal applications [3]”.
Therefore, analytical chemistry uses measurands of chemical analysis, thus detailing the analyte amount, its molecular properties, 3D molecular conformation, and electronic structure via instrumental methods. From the perspective of the themes of the Special Issue, analytical mass spectrometry uses measurable variables of the mass spectrum of chemicals. Methodologically, the field elaborates not only MS instrumentation and techniques but also methods for the data processing of measurands. The latter approaches are implemented into so-called omics methods, thus gaining crucial knowledge of biological systems. Fields of bioinformatics utilize these bioanalytical tools [3,4,5].
Among the various MS methods, soft ionization methods have become the gold standard in analytical practice [6,7]. They exhibit superior instrumental features and performances, thus showing (a) ultra-high accuracy, precision, reproducibility, sensitivity, reliability, selectivity, and specificity; (ii) capability of low- and high-molecular weight analyte (10–100 kDa) analyses; (iii) low concentration limits chemical of detection and quantitation within the framework of attomole to fmol levels [8,9]; and (iv) ultra-high resolving power, determining the error of mass-to-charge (m/z) measurement ~1 ppm [10,11,12,13], respectively. These performances are achieved via high-resolution mass analyzers, in particular Orbitrap [11,12,13,14] and Fourier transform (FT) ion cyclotron resonance (ICR) [14,15,16,17]. The former analyzer was developed by Makarov [18], while the latter was developed by Comisarow and Marshall, respectively [19]. The orbital ion trapping phenomenon established by Kingdon [20] is also used in designing the Orbitrap analyzer. These industrial-scale implemented innovations have resulted in crucial methodological developments in analytical mass spectrometric instrumentation [2,14,21,22,23,24], respectively, in a large number of multidisciplinary research fields utilizing mass spectrometric approaches.
The dynamically harmonized measurement cell of FTICR-MS, designed by Nikolaev and Boldin [25,26], refines isotopomers of peptides and proteins [27]. The newcomer solves problems with the FT-ICR-MS phenomena of inhomogeneity of trapping electric fields [28] and ion coalescence [29] because FTICR-MS is often limited by space-charge effects, thus shifting and broadening MS peaks. There is systematic spectral error in proteomics and metabolomics due to the merging of two close MS peaks in FT-ICR-MS experiments. Explanatory and predictive theories of these phenomena have been developed, respectively, by Boldin and Nikolaev [29] and Naito and Inoue [30,31]. There is a decrease in the error contribution to MS measurands from ion sources, analyzers, and detectors, thus obtaining the mass spectrum of the analyte and exhibiting its fine isotope distribution. The task is challenging even when utilizing the latest generation of FT-ICR or Orbitrap analyzers. There is also theory detailing the space charge shift of ICR frequency by Jeffries and co-workers [32]. The same phenomenon has also been described theoretically by Gorshkov, Marshall, and Nikolaev [33]. The molecular isotopologies yield a relative isotopic abundance of stable natural isotopes of atoms in analytes, thus increasing the crucial reliability of their identification and annotation. The task is important for omics protocols used in clinical precision medicine due to the compulsory request for high analytical standard method performance of omics analyses.
However, so-called fluctuations in elemental composition are due to isotope fractionation obtained by (bio)chemical and geochemical processes [27]. They perturb the value of the isotope ratio among isotopes of the same atom and also yield errors in proteomics [34] or isotomics [27,35]. Highly precise measurands of isotopomers contribute crucially not only to the fields of medicine and clinical diagnostics but also to ecology, geology, history, forensic anthropology, and more [36].
In highlighting the ultra-high resolution of MS measurands, it should be mentioned that the already achieved single-sample analysis of 126,264 species using 9.4T FT-ICR-MS, in addition to the highest broadband accuracy and resolving power obtained via MS methods at 21 T [16,17,37], is 3.105 resolution at m/z 400. A resolving power of 2.106 has been detected when studying proteins over a measurement span time t = 12 s. The Orbitrap analyzer accurately determines the m/z value of heterogeneous viral specials and oligomers of immunoglobulin with high charges [38,39].
Mass spectrometric methods also exhibit (v) (automated) direct analysis and assay without employment in sample pre-treatment [40,41,42,43,44]; (vi) flexible (and portable) instrumentation coupled to methods of chromatography, electrochemistry, and more; (vii) lab-on-chip technologies; and (viii) miniaturized instrumentation devices and techniques [45,46], respectively. A miniature mass spectrometer achieves fast monitoring (t~4 min) of therapeutics in a whole blood sample (r2 = 0.9962) [46,47]. The methods (ix) adopt imaging techniques [27]. The so-called imaging mass spectrometry assesses living cells, organs, microorganisms [48,49], and whole bodies [50,51,52,53,54,55,56,57], as well as determines, free of isotope labeling, hundreds to thousands of chemicals, metabolites, lipids, proteins, and more in tissue within the framework of a single experiment [50]. Applications of the technique to the biochemistry of lipids in tissues have been highlighted comprehensively in the review article [53]. Monitoring of bacterial growth has been illustrated (2022) [58].
Mass spectrometric approaches (x) monitor continuous flow chemical reactions; (xi) examine complex analyte mixtures in biological tissues and fluids [37,40,59], environmental [60,61,62], and foodstuff samples; (xii) are used for in vivo diagnostics; and (xiii) experimentally determine kinetics, thermodynamics, diffusion, and ion mobility parameters of chemicals and their reactions [63,64,65,66,67,68].
The kinetic method developed by Cooks and co-workers shows many advantages, among others [69]. In addition, a linear correlation between the energetics of MS reactions based on Hammett free energy and kinetic parameters has been established by McLafferty and co-workers [70]. Data on the intensity of peaks of the mass spectra of parent and product ions of analytes examining reaction kinetics show a linear relation between intensity peak ratio and time of chemical reaction (r2 = 0.99) [64,65,66,67,68]. Experimental mass spectrometric, ion-mobility spectrometric, and diffusion parameters provide potentials of ion-molecule interaction, thus allowing for the calculation of ion-ion recombination coefficients, average ionic energy, rate of dispersion of ions, electric discharges, different atmospheric phenomena, and more [63].
Complementary employment in ion mobility spectrometry, mass spectrometry, and diffusion extracts the so-called collision cross-section of the analyte [63,71,72,73,74,75,76,77,78]. The parameter can be obtained theoretically via high-accuracy methods of computational quantum chemistry using static approaches and molecular dynamics [71,72,73,74,75,76,77,78]. Since data on collision cross-sections provide 3D molecular structures of analytes, experimental MS and ion mobility 3D molecular structures and properties of molecules are correlated with theoretical ones [61,62,71,72,73,74,75,76,77,78,79,80].
Looking at soft-ionization MS methods, electrospray ionization (ESI) and matrix-assisted laser/desorption ionization (MALDI) ones (ESI- and MALDI-MS) are used in many subfields of chemistry, biochemistry, and biology [81,82,83,84,85,86,87,88,89,90,91,92,93,94]. The former method generates analyte molecular-radical and protomer from solution without perturbing specific noncovalent interactions of molecular complexes, if any. For this reason, ESI-MS is a well-suited method for the 3D structural analysis of biologically active molecules. It provides elemental composition and stoichiometry of analytes and their molecular complexes during transfer from solution into gas phase [85,86,87]. ESI-MS is characterized by significant reproducibility and ionization efficiency~100% examining biomacromolecules. The transmission efficiency is 96%. Quantification of peptides yields r2 = 0.98991–0.98747 [81]. Singly charged lipid cations generated by MALDI-MS have been first reported herein [83].
There are MS applications to many subfields of analytical and environmental chemistry [61,95,96,97], clinical diagnostics [37], petroleum chemistry, laboratory medicine [98], biochemistry [98], medicinal chemistry, drug design and development of new efficacious therapeutics, forensic chemistry [99], investigations for forensic medico-legal purposes [100], pharmacy [98], toxicology [97,101], nuclear forensics [102], food technology [62], agricultural science, geology, archaeology, etc. MS methods for molecular identification, annotation, and quantification are implemented in metabolomics (m/z 50–1500) [62], proteomics, (neuro)-proteomics, lipidomics; food-omics, steroid-omics [6,7], glycomics [103], pesticide analysis and control [104], genomics, DNA adduct-omics, transcriptomics, lignomics [10], interactomics [105,106], doping control, petrol-omics, isotomics [27,35], and more.
Clinical trans-omics is an innovative field integrating clinical phenomes with multi-omics approaches [107]. The precision and reliability of omics methods determine their use in clinical precision medicine. Omics-method performances should be traceable to very high-order analytical standards. Therefore, analytical protocols should have defined uncertainty based on quantitative criteria in statistics and chemometrics [40,61,62,108].
Proteomics provide in-depth knowledge of processes in living cells [16,56,57,109]. The first algorithm elaborated for the purposes of automatic assignment of analyte charge states of ions as well as data-processing methods for deconvolution mass spectra of multiply charged proteins has been developed by Mann and co-workers [5]. Due to limitations in space for this Editorial, it is unable to highlight all contributions devoted to developing algorithms and software for the data processing of MS measurands [110].
Metabolomics uses omics methods based on hyphenated instrumentation of chromatography coupled to mass spectrometry [111,112,113] and examines small-molecular metabolites of cellular metabolism [105,106]. It provides insight into biochemical reactions and a comprehensive understanding of the real-time (mechanistic) processes of cells/tissues at the moment of sampling. Due to the high complexity of biological samples, metabolomics methods performances are relative quantitative ones (r2 = 0.99) [4,114,115].
Genetics and transcriptomics answer the following question: What is a cell or tissue capable of doing [105,116]?
Mass spectrometry determines molecular sequences and modifications, thus addressing many questions about biological processes in vivo. Therefore, it provides crucial knowledge of the relationship between molecular structure and biological function both in vitro and in vivo [117,118]. It also allows us to understand the in-depth neurobiological reactions of neural circuits and cells. First, MALDI-MSI application to clinical diagnostics has been proposed by Caprioli [119,120].
The implementation of biochemical methods and mass spectrometry in forensic medico-legal investigations highlights the crucial advantages of analytical mass spectrometry as an objective approach [100].
Lignomics applies MS omics-methods to quantify oligomers of lignin and its derivatives [10].
In addition, the molecules have many molecular isotopologies [27,34,35], showing variation of number of isotopomers. In analyte sample there is concentration of the isotopologically different atoms, perspective, molecular structures of single analyte. It is called sample’s isotome [35]. It encodes data on sample physical and chemical history. Mass spectrometric collisional fragmentation reactions and FTICR or Orbitrap MS analysers detail on sample’s isotome.
Beyond omics methods, mass spectrometric applications to biochemistry and biology expand dramatically to (macro)molecular structural analysis or the field of structural biology [14]. The reader of this Editorial, perhaps, may not be aware of fundamental issues regarding the utilization of analytical mass spectrometry for analyzing 3D molecular and electronic structure. It, however, methodologically develops the fields of analytical mass spectrometry and structural analysis.
In the context of the preceding paragraph, single-crystal X-ray diffraction comprises a major method for determining the 3D structures of biological (macro)molecules [121]. However, for purposes of structural biology, it also requires high amounts of pure analytes, good sample crystal growth, and good scattering properties. Frequently, these requirements are major drawbacks of single-crystal X-ray diffraction and its implementation in research on structural biology.
Computational quantum chemical methods provide high-accuracy data on analyte 3D molecular and electronic structures as well as geometry parameters. Molecular dynamics yields time-dependent results from molecular structure and properties under designed experimental conditions. Theoretical thermodynamics, kinetics, ion mobility, binding affinity, diffusion, catalytic activity, and more parameters allow us to determine the 3D molecular conformation of molecules. The enzyme inactivation reaction step of a biochemical reaction is also obtained.
Therefore, computational quantum chemistry often overcomes the need to crystallize high-quality single crystals of biologically active (macro)molecules.
Instrumental methods detailing biochemical reactions and molecular structure include nuclear magnetic resonance, Raman spectroscopy, circular dichroism, and more. However, they often show drawbacks to a broad implementation into biochemistry and structural biology. For instance, nuclear magnetic resonance experiments can be limited to describing the structural differences of biologically active compounds in a small number of sequences [122].
Enormous contributions to developing mass spectrometry as a robust instrumental method for chemical analysis have positioned it in the 21st century as analytical instrumentation, having high versatility to identify and quantify biological (macro)molecules and biochemical reactions in vitro and in vivo [61,62,71,72,73,74,75,76,77,78,79,80].
Despite this, little attention is focused on the mass spectrometric capability of obtaining exact 3D molecular and electronic structural data using complementary MS measurands and quantum chemical data [61,62,71,72,73,74,75,76,77,78,79,80]. However, proposed candidate structures are often isomeric and have complex electronic effects: tautomeris, isotopomers, protomers, and more. Due to these reasons, accurately determining molecular structure among a set of candidate structures still represents a challenging research task of both the experimental instrumental and theoretical computational methods, even when employing MS instrumentation showing superior method performances or high-accuracy computational quantum chemistry tools [61,62,71,72,73,74,75,76,77,78,79,80].
Moreover, mass spectrometric methods utilizing techniques of isotope labeling analytes and H/D exchange yield complete analytical data on the structural consequences of biomacromolecules and the activation biochemical reactions of enzymes. Knowledge develops crucially in the field of biochemistry [122].
Methodological developments in exact mass spectrometric methods for 3D structural analysis based on stochastic dynamics approaches to data processing of measurands are also highlighted in the Special Issue [40,61,62].
As previously mentioned, the theme of this Special Issue lies in multi-disciplinary research fields encompassing areas of analytical mass spectrometry, analytical chemistry, and chemometrics, among others, and their application to a broad spectrum of research fields in analytical science. Looking at the content of this Special Issue, it is immediately clear to the reader that among all the theoretical and experimental approaches to mass spectrometry and their applications to numerous multi-disciplinary research fields, it addresses only a few of them.
However, it provides innovative developments in the fields sketched above for purposes of mass spectrometric-based quantitation and structural analysis of biologically active analytes and samples both in vitro and in vivo, thus highlighting the field of biochemistry. The guest editor, editors, reviewers, and authors were motivated to provide novelty to these scientific fields for researchers and academics working in different disciplines who place their research efforts and innovations into a broader application perspective for fundamental science and industry.
As the guest editor of the Special Issue, I would like to thank all authors who contributed their research and review articles, were devoted to the high quality of their innovative and exciting scientific developments and achievements, and collaborated in publishing them.
I would like to thank the editors and reviewers for their invaluable and creative recommendations, comments, and remarks, who contributed significantly to the quality of papers and the larger-than-usual review task.

Funding

This research received no external funding.

Conflicts of Interest

The author declares no conflict of interest.

References

  1. Zolotov, Y. Methodological aspects of analytical chemistry. J. Anal. Chem. 2021, 76, 1–14. [Google Scholar] [CrossRef]
  2. Adams, F.; Adriaens, M. The metamorphosis of analytical chemistry. Anal. Bioanal. Chem. 2020, 412, 3525–3537. [Google Scholar] [CrossRef]
  3. Adams, F.; Adriaens, M. Methodological aspects of analytical chemistry. J. Anal. Chem. 2021, 76, 671–673. [Google Scholar] [CrossRef]
  4. Hormann, F.; Sommer, S.; Heiles, S. Formation and tandem mass spectrometry of doubly charged lipid-metal ion complexes. J. Am. Soc. Mass Spectrom. 2023, 34, 1436–1446. [Google Scholar] [CrossRef]
  5. Mann, M.; Meng, C.; Fenn, J. Interpreting mass spectra of multiply charged ions. Anal. Chem. 1989, 61, 1702–1708. [Google Scholar] [CrossRef]
  6. Chen, R.; Brown, H.; Cooks, C. Metabolic profiles of human brain parenchyma and glioma for rapid tissue diagnosis by targeted desorption electrospray ionization mass spectrometry. Anal. Bioanal. Chem. 2021, 413, 6213–6224. [Google Scholar] [CrossRef] [PubMed]
  7. Liere, P.; Schumacher, M. Mass spectrometric analysis of steroids: All that glitters is not gold. Expert Rev. Endocrinol. Metabol. 2015, 10, 463–465. [Google Scholar] [CrossRef] [PubMed]
  8. Chiva, C.; Pastor, O.; Trilla-Fuertes, L.; Gamez-Pozo, A.; Angel, J.; Sabido, F. Isotopologue multipoint calibration for roteomics biomarker quantification in clinical practice. Anal. Chem. 2019, 91, 4934–4938. [Google Scholar] [CrossRef] [PubMed]
  9. Eisenberg, S.; Knizner, K.; Muddiman, D. Development of an object-based image analysis tool for mass spectrometry imaging ion classification. Anal. Bioanal. Chem. 2023, 415, 4725–4730. [Google Scholar] [CrossRef]
  10. Pikovskoi, I.; Kosyakov, D. Kendrick mass defect analysis—A tool for high-resolution Orbitrap mass spectrometry of native lignin. Anal. Bioanal. Chem. 2023, 415, 3525–3534. [Google Scholar] [CrossRef]
  11. Gorshkov, M.; Fornelli, L.; Tsybin, Y. Observation of ion coalescence in Orbitrap Fourier transform mass spectrometry. Rapid Commun. Mass Spectrom. 2012, 26, 1711–1717. [Google Scholar] [CrossRef]
  12. Blake, S.; Walker, S.; Muddiman, D.; Hinks, D.; Beck, K. Spectral accuracy and sulfur counting capabilities of the LTQ-FT-ICR and the LTQ-Orbitrap XL for small molecule analysis. J. Am. Soc. Mass Spectrom. 2011, 22, 2269–2275. [Google Scholar] [CrossRef] [PubMed]
  13. Gorshkov, M.; Good, D.; Lyutvinskiy, Y.; Yang, H.; Zubarev, R. Calibration function for the Orbitrap FTMS accounting for the space charge effect. J. Am. Soc. Mass Spectrom. 2010, 21, 1846–1851. [Google Scholar] [CrossRef] [PubMed]
  14. Tamara, S.; Den Boer, M.; Heck, A. High-resolution native mass spectrometry. Chem. Rev. 2022, 122, 7269–7326. [Google Scholar] [CrossRef] [PubMed]
  15. Kharchenko, A.; Vladimirov, G.; Heeren, R.; Nikolaev, E. Performance of Orbitrap mass analyzer at various space charge and non-ideal field conditions: Simulation approach. J. Am. Soc. Mass Spectrom. 2012, 23, 977–987. [Google Scholar] [CrossRef]
  16. Weisbrod, C.; Kaiser, N.; Syka, J.; Early, L.; Mullen, C.; Dunyach, J.; English, A.; Anderson, L.; Blakney, G.; Shabanowitz, J.; et al. Front-end electron transfer dissociation coupled to a 21 Tesla FT-ICR mass spectrometer for intact protein sequence analysis. J. Am. Soc. Mass Spectrom. 2017, 28, 1787–1795. [Google Scholar] [CrossRef]
  17. Hendrickson, C.; Quinn, J.; Kaiser, N.; Smith, D.; Blakney, G.; Chen, T.; Marshall, A.; Weisbrod, C.; Beu, S. 21 Tesla Fourier transform ion cyclotron resonance mass spectrometer: A National resource for ultrahigh resolution mass analysis. J. Am. Soc. Mass Spectrom. 2015, 26, 1626–1632. [Google Scholar] [CrossRef]
  18. Makarov, A. Electrostatic axially harmonic Orbital trapping: A high-performance technique of mass analysis. Anal. Chem. 2000, 72, 1156–1162. [Google Scholar] [CrossRef]
  19. Comisarow, M.; Marshall, A. Fourier transform ion cyclotron resonance spectroscopy. Chem. Phys. Lett. 1974, 25, 282–283. [Google Scholar] [CrossRef]
  20. Kingdon, K. Method for the neutralization of electron space charge by positive ionization at very low gas pressures. Phys. Rev. 1923, 21, 408–418. [Google Scholar] [CrossRef]
  21. Lange, O.; Damoc, E.; Wieghaus, A.; Makarov, A. Enhanced Fourier transform for Orbitrap mass spectrometry. Int. J. Mass Spectrom. 2014, 369, 16–22. [Google Scholar] [CrossRef]
  22. Makarov, A.; Denisov, E.; Lange, O. Performance evaluation of a high-field Orbitrap mass analyser. J. Am. Soc. Mass Spectrom. 2009, 20, 1391–1396. [Google Scholar] [CrossRef] [PubMed]
  23. Rose, R.; Damoc, E.; Denisov, E.; Makarov, A.; Heck, A. High-sensitivity Orbitrap mass analysis of intact macromolecular assemblies. Nat. Methods 2012, 9, 1084–1086. [Google Scholar] [CrossRef] [PubMed]
  24. Makarov, A. Method of Generating a Mass Spectrum Having Improved Resolving Power. U.S. Patent 9,043,164. B2, 26 May 2015. [Google Scholar]
  25. Boldin, I.; Nikolaev, E. Fourier transform ion cyclotron resonance cell with dynamic harmonization of the electric field in the whole volume by shaping of the excitation and detection electrode assembly. Rapid Commun. Mass Spectrom. 2011, 25, 122–126. [Google Scholar] [CrossRef]
  26. Nikolaev, E.; Boldin, I.; Jertz, R.; Baykut, G. Initial experimental characterization of a new ultra-high resolution FTICR cell with dynamic harmonization. J. Am. Soc. Mass Spectrom. 2011, 22, 1125–1133. [Google Scholar] [CrossRef] [PubMed]
  27. Claesen, J.; Rockwood, A.; Gorshkov, M.; Valkenborg, D. The isotope distribution: A rose with thorns. Mass Spec. Rev. 2023, 2023, 1–21. [Google Scholar] [CrossRef] [PubMed]
  28. Nagornov, K.; Gorshkov, M.; Kozhinov, A.; Tsybin, Y. High-resolution Fourier transform ion cyclotron resonance mass spectrometry with increased throughput for biomolecular analysis. Anal. Chem. 2014, 86, 9020–9028. [Google Scholar] [CrossRef]
  29. Boldin, I.; Nikolaev, E. Theory of peak coalescence in Fourier transform ion cyclotron resonance mass spectrometry. Rapid Commun. Mass Spectrom. 2009, 23, 3213–3219. [Google Scholar] [CrossRef]
  30. Naito, Y.; Inoue, M. Peak confluence phenomenon in Fourier transform ion cyclotron resonance mass spectrometry. J. Mass Spectrom. Soc. Jpn. 1994, 42, 1–9. [Google Scholar] [CrossRef]
  31. Naito, Y.; Inoue, M. Collective motion of ions in an ion trap for Fourier transform ion cyclotron resonance mass spectrometry. Int. J. Mass Spectrom. 1996, 157/158, 85–96. [Google Scholar] [CrossRef]
  32. Jeffries, J.; Barlow, S.; Dunn, G. Theory of space-charge shift of ion cyclotron resonance frequencies. Int. J. Mass Spectrom. 1983, 54, 169–187. [Google Scholar] [CrossRef]
  33. Gorshkov, M.; Marshall, A.; Nikolaev, E. Analysis and elimination of systematic errors originating from coulomb mutual interaction and image charge in fourier transform ion cyclotron resonance precise mass difference measurements. J. Am. Soc. Mass Spectrom. 1993, 4, 855–868. [Google Scholar] [CrossRef]
  34. Claesen, J.; Lermyte, F.; Sobott, F.; Burzykowski, T.; Valkenborg, D. Differences in the elemental isotope definition may lead to errors in modern mass-spectrometry-based proteomics. Anal. Chem. 2015, 87, 10747–10754. [Google Scholar] [CrossRef]
  35. Csernica, T.; Eiler, J. High-dimensional isotomics, part 1: A mathematical framework for isotomics. Chem. Geol. 2023, 617, 121235. [Google Scholar] [CrossRef]
  36. Bartelink, E.; Chesson, L. Recent applications of isotope analysis to forensic anthropology. Forens. Sci. Res. 2019, 4, 29–44. [Google Scholar] [CrossRef]
  37. He, L.; Rockwood, A.; Agarwa, A.; Anderson, L.; Weisbrod, C.; Hendrickson, C.; Marshall, A. Diagnosis of hemoglobinopathy and thalassemia by 21 tesla Fourier transform ion cyclotron resonance mass spectrometry and tandem mass spectrometry of hemoglobin from blood. Clin. Chem. 2019, 65, 986–994. [Google Scholar] [CrossRef] [PubMed]
  38. Woerner, T.; Snijder, J.; Bennett, A.; Agbandje-McKenna, M.; Makarov, A.; Heck, A. Resolving heterogeneous macromolecular assemblies by Orbitrap-based single-particle charge detection mass spectrometry. Nat. Met. 2020, 17, 395–398. [Google Scholar] [CrossRef] [PubMed]
  39. Kafader, J.; Melani, R.; Durbin, K.; Ikwuagwu, B.; Early, B.; Fellers, R.; Beu, S.; Zabrouskov, V.; Makarov, A.; Maze, J.; et al. Multiplexed mass spectrometry of individual ions improves measurement of proteoforms and their complexes. Nat. Met. 2020, 17, 391–394. [Google Scholar] [CrossRef]
  40. Ivanova, B.; Spiteller, M. Stochastic dynamic electrospray ionization mass spectrometric quantitative analysis of metronidazole in human urine. Anal. Chem. Lett. 2022, 12, 322–348. [Google Scholar] [CrossRef]
  41. Carlsson, J.; Åstroem, T.; Oestman, C.; Nilsson, U. Solvent-free automated thermal desorption-gas chromatography/mass spectrometry for direct screening of hazardous compounds in consumer textiles. Anal. Bioanal. Chem. 2023, 415, 4675–4687. [Google Scholar] [CrossRef] [PubMed]
  42. Wang, D.; Baudys, J.; Osman, S.; Barr, J. Analysis of the N-glycosylation profiles of the spike proteins from the alpha, beta, gamma, and delta variants of SARS-CoV-2. Anal. Bioanal. Chem. 2023, 415, 4779–4793. [Google Scholar] [CrossRef]
  43. Solovyeva, E.; Bubis, J.; Tarasova, I.; Lobas, A.; Ivanov, M.; Nazarov, A.; Shutkov, I.; Gorshkov, M. On the feasibility of using an ultra-fast DirectMS1 method of proteome-wide analysis for searching drug targets in chemical proteomics. Biochemistry 2022, 87, 1342–1353. [Google Scholar] [CrossRef] [PubMed]
  44. Ivanov, M.; Bubis, J.; Gorshkov, V.; Tarasova, I.; Levitsky, L.; Solovyeva, E.; Lipatova, A.; Kjeldsen, F.; Gorshkov, F. DirectMS1Quant: Ultrafast quantitative proteomics with MS/MS free mass spectrometry. Anal. Chem. 2022, 94, 13068–13075. [Google Scholar] [CrossRef] [PubMed]
  45. De Oro-Carretero, P.; Sanz-Landaluze, J. Miniaturized method for the quantification of persistent organic pollutants and their metabolites in HepG2 cells: Assessment of their biotransformation. Anal. Bioanal. Chem. 2023, 415, 4813–4825. [Google Scholar] [CrossRef]
  46. Szalwinski, L.; Cooks, R. Complex mixture analysis by two-dimensional mass spectrometry using a miniature ion trap. Talanta Open 2021, 3, 100028. [Google Scholar] [CrossRef]
  47. Pu, F.; Pandey, S.; Bushman, L.; Anderson, P.; Ouyang, Z.; Cooks, R. Direct quantitation of tenofovir diphosphate in human blood with mass spectrometry for adherence monitoring. Anal. Bioanal. Chem. 2020, 412, 1243–1249. [Google Scholar] [CrossRef] [PubMed]
  48. Gonzalez, L.; Szalwinski, L.; Sams, T.; Dziekonski, E.; Cooks, R. Metabolomic and lipidomic profiling of bacillus using two-dimensional tandem mass spectrometry. Anal. Chem. 2022, 94, 16838–16846. [Google Scholar] [CrossRef]
  49. Siuzdak, G.; Bothnet, B.; Yeager, M.; Brugidou, C.; Fauquet, C.; Hoey, K.; Chang, C. Mass spectrometry and viral analysis. Chem. Biol. 1996, 3, 45–48. [Google Scholar] [CrossRef]
  50. Sharman, K.; Patterson, N.; Migas, L.; Neumann, E.; Allen, J.; Gibson-Corley, K.; Spraggins, J.; Van de Plas, R.; Skaar, E.; Caprioli, R. MALDI IMS-derived molecular contour maps: Augmenting histology whole-slide images. J. Am. Soc. Mass Spectrom. 2023, 34, 905–912. [Google Scholar] [CrossRef]
  51. Fincher, J.; Djambazova, K.; Klein, D.; Dufresne, M.; Migas, L.; Van de Plas, R.; Caprioli, R.; Spraggins, J. Molecular mapping of neutral lipids using silicon nanopost arrays and TIMS imaging mass spectrometry. J. Am. Soc. Mass Spectrom. 2021, 32, 2519–2527. [Google Scholar] [CrossRef]
  52. Neumann, E.; Djambazova, K.; Caprioli, R.; Spraggins, J. Multimodal imaging mass spectrometry: Next generation molecular mapping in biology and medicine. J. Am. Soc. Mass Spectrom. 2020, 31, 2401–2415. [Google Scholar] [CrossRef] [PubMed]
  53. Berry, K.; Hankin, J.; Barkley, R.; Spraggins, J.; Caprioli, R.; Murphy, R. MALDI imaging of lipid biochemistry in tissues by mass spectrometry. Chem. Rev. 2011, 111, 6491–6512. [Google Scholar] [CrossRef] [PubMed]
  54. Djambazova, K.; Dufresne, M.; Migas, L.; Kruse, A.; Van de Plas, R.; Caprioli, R.; Spraggins, R. MALDI TIMS IMS of disialoganglioside isomers GD1a and GD1b in murine brain tissue. Anal. Chem. 2023, 95, 1176–1183. [Google Scholar] [CrossRef] [PubMed]
  55. Rivera, E.; Weiss, A.; Migas, L.; Freiberg, J.; Djambazova, K.; Neumann, E.; Van de Plas, R.; Spraggins, J.; Skaar, E.; Caprioli, R. Imaging mass spectrometry reveals complex lipid distributions across Staphylococcus aureus biofilm layers. J. Mass Spectrom. Adv. Clin. Lab. 2022, 26, 36–46. [Google Scholar] [CrossRef] [PubMed]
  56. Sharman, K.; Patterson, P.; Weiss, A.; Neumann, E.; Guiberson, E.; Ryan, D.; Gutierrez, D.; Spraggins, J.; Van de Plas, R.; Skaar, E.; et al. Rapid multivariate analysis approach to explore differential spatial protein profiles in tissue. J. Proteome Res. 2023, 22, 1394–1405. [Google Scholar] [CrossRef] [PubMed]
  57. Takemura, H.; Choi, J.; Fushimi, K.; Narikawa, R.; Wu, J.; Kondo, M.; Nelson, D.; Suzuki, T.; Ouchi, H.; Inai, M.; et al. Role of hypoxanthine-guanine phosphoribosyltransferase in the metabolism of fairy chemicals in rice. Org. Biomol. Chem. 2023, 21, 2556–2561. [Google Scholar] [CrossRef]
  58. Szalwinski, L.; Gonzalez, L.; Morato, N.; Marsh, B.; Cooks, R. Bacterial growth monitored by two-dimensional tandem mass spectrometry. Analyst 2022, 147, 940–946. [Google Scholar] [CrossRef]
  59. Artymowicz, M.; Struck-Lewicka, W.; Wiczling, P.; Markuszewski, M.; Markuszewski, M.; Siluk, D. Targeted quantitative metabolomics with a linear mixed-effect model for analysis of urinary nucleosides and deoxynucleosides from bladder cancer patients before and after tumor resection. Anal. Bioanal. Chem. 2023, 415, 5511–5528. [Google Scholar] [CrossRef]
  60. Borowska, M.; Jankowski, K. Basic and advanced spectrometric methods for complete nanoparticles characterization in bio/eco systems: Current status and future prospects. Anal. Bioanal. Chem. 2023, 415, 4023–4038. [Google Scholar] [CrossRef]
  61. Ivanova, B. Stochastic dynamic mass spectrometric quantitative and structural analyses of pharmaceutics and biocides in biota and sewage sludge. Int. J. Mol. Sci. 2023, 24, 6306. [Google Scholar] [CrossRef] [PubMed]
  62. Upadyshev, M.; Ivanova, B.; Motyleva, S. Mass spectrometric identification of metabolites after magnetic-pulse treatment of infected Pyrus communis L. microplants. Int. J. Mol. Sci. 2023, 24, 16776. [Google Scholar] [CrossRef]
  63. Mason, E.; McDaniel, E. Transport Properties of Ions in Gases; John Wiley & Sons Inc.: New York, NY, USA, 1988; pp. 1–560. [Google Scholar]
  64. Li, Y.; Mehari, T.; Wei, Z.; Liu, Y.; Cooks, R. Reaction acceleration at air-solution interfaces: Anisotropic rate constants for Katritzky transamination. J. Mass Spectrom. 2021, 56, e4585. [Google Scholar] [CrossRef]
  65. Wei, Z.; Li, Y.; Cooks, R.; Yan, X. Accelerated reaction kinetics in microdroplets: Overview and recent developments. Annu. Rev. Phys. Chem. 2020, 71, 31–51. [Google Scholar] [CrossRef]
  66. Mehnert, S.; Fischer, J.; McDaniel, M.; Fabijanczuk, K.; McLuckey, S. Dissociation kinetics in quadrupole ion traps: Effective temperatures under dipolar DC collisional activation conditions. J. Am. Soc. Mass Spectrom. 2023, 34, 1166–1174. [Google Scholar] [CrossRef] [PubMed]
  67. Fabijanczuk, K.; Chao, H.; Fischer, J.; McLuckey, S. Structural elucidation and isomeric differentiation/ quantitation of monophosphorylated phosphoinositides using gas-phase ion/ion reactions and dissociation kinetics. Analyst 2022, 147, 5000–5010. [Google Scholar] [CrossRef] [PubMed]
  68. Kulathunga, S.; Morato, N.; Zhou, Q.; Cooks, R.; Mesecar, A. Desorption electrospray ionization mass spectrometry assay for label-free characterization of SULT2B1b enzyme kinetics. ChemMedChem 2022, 17, e202200043. [Google Scholar] [CrossRef] [PubMed]
  69. Cooks, R.; Wong, P. Kinetic method of making thermochemical determinations:  Advances and applications. Accts. Chem. Res. 1998, 31, 379–386. [Google Scholar] [CrossRef]
  70. Augusti, R.; Turowski, M.; Chen, H.; Cooks, R. Dissociation of ionized benzophenones investigated by the kinetic method: Effective temperature, steric effects and gas-phase CO+• affinities of phenyl radicals. J. Mass Spectrom. 2004, 39, 558–564. [Google Scholar] [CrossRef] [PubMed]
  71. Christofi, E.; Barran, P. Ion mobility mass spectrometry (IM–MS) for structural biology: Insights gained by measuring mass, charge, and collision cross section. Chem. Rev. 2023, 123, 2902–2949. [Google Scholar] [CrossRef] [PubMed]
  72. Jiang, T.; Chen, Y.; Mao, L.; Marshall, A.; Xu, W. Extracting biomolecule collision cross sections from the high-resolution FT-ICR mass spectral linewidths. Phys. Chem. Chem. Phys. 2016, 18, 713–717. [Google Scholar] [CrossRef] [PubMed]
  73. Mao, L.; Chen, Y.; Xin, Y.; Chen, Y.; Zheng, L.; Kaiser, N.; Marshall, A.; Xu, W. Collision cross section measurements for biomolecules within a high-resolution Fourier transform ion cyclotron resonance cell. Anal. Chem. 2015, 87, 4072–4075. [Google Scholar] [CrossRef]
  74. Geue, N.; Bennett, T.; Ramakers, L.; Timco, G.; McInnes, E.; Burton, N.; Armentrout, P.; Winpenny, R.; Barran, P. Adduct ions as diagnostic probes of metallosupramolecular complexes using ion mobility mass spectrometry. Inorg. Chem. 2023, 62, 2672–2679. [Google Scholar] [CrossRef] [PubMed]
  75. Haler, J.; Far, J.; De la Rosa, V.; Kune, C.; Hoogenboom, R.; De Pauw, E. Using ion mobility–mass spectrometry to extract physicochemical enthalpic and entropic contributions from synthetic polymers. J. Am. Soc. Mass Spectrom. 2021, 32, 330–339. [Google Scholar] [CrossRef] [PubMed]
  76. Zhu, F.; Lee, S.; Valentine, S.; Reilly, J.; Clemmer, D. Mannose7 glycan isomer characterization by IMS-MS/MS analysis. J. Am. Soc. Mass Spectrom. 2012, 23, 2158–2166. [Google Scholar] [CrossRef] [PubMed]
  77. Kwantwi-Barima, P.; Sandilya, V.; Garimella, B.; Attah, I.; Zheng, X.; Ibrahim, Y.; Smith, R. Accumulation of large ion populations with high ion densities and effects due to space charge in traveling wave-based structures for lossless ion manipulations (SLIM) IMS–MS. J. Am. Soc. Mass Spectrom. 2024. [CrossRef] [PubMed]
  78. Wyttenbach, T.; Pierson, N.; Clemmer, D.; Bowers, M. Ion mobility analysis of molecular dynamics. Annu. Rev. Phys. Chem. 2014, 65, 175–196. [Google Scholar] [CrossRef] [PubMed]
  79. Trimpin, S.; Inutan, E.; Karki, S.; Elia, E.; Zhang, W.; Weidner, S.; Marshall, D.; Hoang, K.; Lee, C.; Davis, E.; et al. Fundamental studies of new ionization technologies and insights from IMS–MS. J. Am. Soc. Mass Spectrom. 2019, 30, 1133–1147. [Google Scholar] [CrossRef] [PubMed]
  80. Snyder, D.; Haarvey, S.; Wysocki, V. Surface-induced dissociation mass spectrometry as a structural biology tool. Chem. Rev. 2022, 122, 7442–7487. [Google Scholar] [CrossRef] [PubMed]
  81. Wang, W.; Qiu, C.; Xu, F.; Ding, L.; Ding, C. Genetic algorithm optimized printed circuit board ion funnel tandem subambient pressure ionization with nanoelectrospray (SPIN) for high sensitivity mass spectrometry. J. Am. Soc. Mass Spectrom. 2023, 34, 1805–1812. [Google Scholar] [CrossRef]
  82. Mathew, A.; Giskes, F.; Lekkas, A.; Greisch, J.; Eijkel, G.; Anthony, I.; Fort, K.; Heck, A.; Papanastasiou, D.; Makarov, A.; et al. An Orbitrap/time-of-flight mass spectrometer for photofragment ion imaging and high-resolution mass analysis of native macromolecular assemblies. J. Am. Soc. Mass Spectrom. 2023, 34, 1359–1371. [Google Scholar] [CrossRef]
  83. Specker, J.; Prentice, B. Separation of isobaric lipids in imaging mass spectrometry using gas-phase charge inversion ion/ion reactions. J. Am. Soc. Mass Spectrom. 2023, 34, 1868–1878. [Google Scholar] [CrossRef]
  84. Pitts-McCoy, A.; Abdillahi, A.; Lee, K.; McLuckey, S. Multiply charged cation attachment to facilitate mass measurement in negative-mode native mass spectrometry. Anal. Chem. 2022, 94, 2220–2226. [Google Scholar] [CrossRef]
  85. Lawler, J.; Harrilal, C.; DeBlase, A.; Sibert III, E.; McLuckey, S.; Zwier, T. Single-conformation spectroscopy of cold, protonated DPG-containing peptides: Switching β-turn types and formation of a sequential type II/II0 double β-turn. Phys. Chem. Chem. Phys. 2022, 24, 2095–2109. [Google Scholar] [CrossRef]
  86. Rolland, A.; Prell, J. Approaches to heterogeneity in native mass spectrometry. Chem. Rev. 2022, 122, 7909–7951. [Google Scholar] [CrossRef] [PubMed]
  87. Abdillahi, A.; Lee, K.; McLuckey, S. Mass analysis of macro-molecular analytes via multiply-charged ion attachment. Anal. Chem. 2020, 92, 16301–16306. [Google Scholar] [CrossRef] [PubMed]
  88. Brown, H.; Garcia, D.; Middlebrooks, E.; Morato, M.; Chaichana, K.; Quinones-Hinojosa, A.; Cooks, R. High-throughput analysis of tissue microarrays using automated desorption electrospray ionization mass spectrometry. Sci. Rep. 2022, 12, 18851. [Google Scholar]
  89. Chao, H.; McLuckey, S. Manipulation of ion types via gas-phase ion/ion chemistry for the structural characterization of the glycan moiety on gangliosides. Anal. Chem. 2021, 93, 15752–15760. [Google Scholar] [CrossRef]
  90. Alexandrov, M.; Gall, L.; Krasnov, N.; Nikolaev, V.; Pavlenko, V.; Shkurov, V. Extraction of ions from solutions under atmospheric pressure as a method for mass spectrometric analysis of bioorganic compounds. Rapid Commun. Mass Spectrom. 2008, 22, 267–270. [Google Scholar] [CrossRef]
  91. Mann, M. The ever expanding scope of electrospray mass spectrometry—A 30 year journey. Nat. Commun. 2019, 10, 3744. [Google Scholar] [CrossRef] [PubMed]
  92. Konermann, L.; Rodriguez, A.; Liu, J. On the formation of highly charged gaseous ions from unfolded proteins by electrospray ionization. Anal. Chem. 2012, 84, 6798–6804. [Google Scholar] [CrossRef]
  93. Chen, H.; Venter, A.; Cooks, R. Extractive electrospray ionization for direct analysis of undiluted urine, milk and other complex mixtures without sample preparation. Chem. Commun. 2006, 2006, 2042–2044. [Google Scholar] [CrossRef] [PubMed]
  94. Yamashita, M.; Fenn, J. Electrospray ion source. Another variation on the free-jet theme. J. Phys. Chem. 1984, 88, 4451–4459. [Google Scholar] [CrossRef]
  95. Mosher, J.; Kaplan, L.; Podgorski, D.; McKenna, A.; Marshall, A. Longitudinal shifts in dissolved organic matter chemogeography and chemodiversity within headwater streams: A river continuum reprise. Biogeochem 2015, 124, 371–385. [Google Scholar] [CrossRef]
  96. Akhlaqi, M.; Wang, W.; Moeckel, C.; Kruve, A. Complementary methods for structural assignment of isomeric candidate structures in non-target liquid chromatography ion mobility high-resolution mass spectrometric analysis. Anal. Bioanal. Chem. 2023, 415, 5247–5259. [Google Scholar] [CrossRef] [PubMed]
  97. Lazofsky, A.; Brinker, A.; Rivera-Nunez, Z.; Buckley, B. A comparison of four liquid chromatography-mass spectrometry platforms for the analysis of zeranols in urine. Anal. Bioanal. Chem. 2023, 415, 4885–4899. [Google Scholar] [CrossRef] [PubMed]
  98. Setou, M.; Oka, H. Biomedical mass spectrometry. Anal. Bioanal. Chem. 2011, 400, 1827. [Google Scholar] [CrossRef]
  99. Lesne, E.; Munoz-Bartual, M.; Esteve-Turrillas, F. Determination of synthetic hallucinogens in oral fluids by microextraction by packed sorbent and liquid chromatography-tandem mass spectrometry. Anal. Bioanal. Chem. 2023, 415, 3607–3617. [Google Scholar] [CrossRef]
  100. Brockbals, L.; Garrett-Rickman, S.; Fu, S.; Ueland, M.; McNevin, D.; Padula, M. Estimating the time of human decomposition based on skeletal muscle biopsy samples utilizing an untargeted LC–MS/MS-based proteomics approach. Anal. Bioanal. Chem. 2023, 415, 5487–5498. [Google Scholar] [CrossRef]
  101. Peng, T.; Rao, J.; Zhang, T.; Wang, Y.; Li, N.; Gao, Q.; Feng, X.; Song, Z.; Wang, K.; Qiu, F. Elucidation of the relationship between evodiamine-induced liver injury and CYP3A4-mediated metabolic activation by UPLC–MS/MS analysis. Anal. Bioanal. Chem. 2023, 415, 5619–5635. [Google Scholar] [CrossRef]
  102. Goodwin, J.; Manard, B.; Ticknor, B.; Cable-Dunlap, P.; Marcus, R. Initial characterization and optimization of the liquid sampling-atmospheric pressure glow discharge ionization source coupled to an orbitrap mass spectrometer for the determination of plutonium. Anal. Chem. 2023, 95, 12131–12138. [Google Scholar] [CrossRef]
  103. Helm, J.; Gruenwald-Gruber, C.; Thader, A.; Urteil, J.; Fuehrer, J.; Stenitzer, D.; Maresch, D.; Neumann, L.; Pabst, M.; Altmann, F. Bisecting Lewis X in hybrid-type N-glycans of human brain revealed by deep structural glycomics. Anal. Chem. 2021, 93, 15175–15182. [Google Scholar] [CrossRef]
  104. Krajewski, L.; Lobodin, V.; Johansen, C.; Bartges, T.; Maksimova, E.; MacDonald, I.; Marshall, A. Linking natural oil seeps from the gulf of Mexico to their origin by use of Fourier transform ion cyclotron resonance mass spectrometry. Environ. Sci. Technol. 2018, 52, 1365–1374. [Google Scholar] [CrossRef] [PubMed]
  105. Reinke, S.; Chaleckis, R.; Wheelock, C. Metabolomics in pulmonary medicine: Extracting the most from your data. Eur. Respir. J. 2022, 60, 2200102. [Google Scholar] [CrossRef] [PubMed]
  106. Dewez, F.; Oejten, J.; Henkel, C.; Hebeler, R.; Neuweger, H.; De Pauw, E.; Heeren, R.; Balluff, B. MS imaging-guided microproteomics for spatial omics on a single instrument. Proteomics 2020, 20, 1900369. [Google Scholar] [CrossRef] [PubMed]
  107. Shi, L.; Dai, X.; Yan, F.; Lin, Y.; Lin, L.; Zhang, Y.; Zeng, Y.; Chen, X. Novel lipidomes profile and clinical phenotype identified in pneumoconiosis patients. J. Health Popul. Nutr. 2023, 42, 55. [Google Scholar] [CrossRef]
  108. Beasley-Green, A.; Heckert, N. Estimation of measurement uncertainty for the quantification of protein by ID-LC-MS/MS. Anal. Bioanal. Chem. 2023, 415, 3265–3274. [Google Scholar] [CrossRef]
  109. Fedorov, I.; Lineva, V.; Tarasova, I.; Gorshkov, M. Mass spectrometry-based chemical proteomics for drug target discoveries. Biochemistry 2022, 87, 983–994. [Google Scholar] [CrossRef] [PubMed]
  110. Zhang, Z.; Marshall, A. A universal algorithm for fast and automated charge state deconvolution of electrospray mass-to-charge ratio spectra. J. Am. Soc. Mass Spectrom. 1998, 9, 225–233. [Google Scholar] [CrossRef] [PubMed]
  111. Krueger, C.; Moran, E.; Tessier, K.; Tretyakova, N. Isotope labeling mass spectrometry to quantify endogenous and exogenous DNA adducts and metabolites of 1,3-butadiene In Vivo. Chem. Res. Toxicol. 2023, 36, 1409–1418. [Google Scholar]
  112. Zulfiqar, M.; Gadelha, L.; Steinbeck, C.; Sorokina, M.; Peters, K. The reproducible metabolome annotation workflow for untargeted tandem mass spectrometry. J. Cheminform. 2023, 15, 32. [Google Scholar] [CrossRef]
  113. Samples, R.; Puckett, S.; Balunas, M. Metabolomics peak analysis computational tool (MPACT): An advanced informatics tool for metabolomics and data visualization of molecules from complex biological samples. Anal. Chem. 2023, 95, 8770–8779. [Google Scholar] [CrossRef]
  114. Calderon-Celis, F.; Encinar, J.; Sanz-Medel, A. Standardization approaches in absolute quantitative proteomics with mass spectrometry. Mass Spec Rev. 2018, 37, 715–737. [Google Scholar] [CrossRef]
  115. Kazakova, E.; Solovyeva, E.; Levitsky, L.; Bubis, J.; Emekeeva, D.; Antonets, A.; Nazarov, A.; Gorshkov, M.; Tarasova, I. Proteomics-based scoring of cellular response to stimuli for improved characterization of signaling pathway activity. Proteomics 2023, 23, 2200275. [Google Scholar] [CrossRef] [PubMed]
  116. Rea, M.; Jiang, T.; Eleazer, R.; Eckstein, M.; Marshall, A.; Fondufe-Mittendorf, Y. Quantitative mass spectrometry reveals changes in histone H2B variants as cells undergo inorganic arsenic-mediated cellular transformation. Mol. Cell. Proteom. 2016, 15, 2411–2422. [Google Scholar] [CrossRef]
  117. Sanders, J.; Grinfeld, D.; Aizikov, K.; Makarov, A.; Holden, D.; Brodbelt, J. Determination of collision cross sections of protein ions in an Orbitrap mass analyser. Anal. Chem. 2018, 90, 5896–5902. [Google Scholar] [CrossRef]
  118. Zubarev, R.; Makarov, A. Orbitrap mass spectrometry. Anal. Chem. 2013, 85, 5288–5296. [Google Scholar] [CrossRef] [PubMed]
  119. Choi, S.; Munoz-Lancao, P.; Manzini, M.; Nemes, P. Data-dependent acquisition ladder for capillary electrophoresis mass spectrometry-based ultrasensitive (neuro)proteomics. Anal. Chem. 2021, 93, 15964–15972. [Google Scholar] [CrossRef] [PubMed]
  120. Seeley, E.; Caprioli, R. MALDI imaging mass spectrometry of human tissue: Method challenges and clinical perspectives. Trends Biotechnol. 2011, 29, 136–143. [Google Scholar] [CrossRef]
  121. Ferrer-Sueta, G.; Campolo, N.; Trujillo, M.; Bartesaghi, S.; Carballal, S.; Romero, N.; Alvarez, B.; Radi, R. Biochemistry of peroxynitrite and protein tyrosine nitration. Chem. Rev. 2018, 118, 1338–1408. [Google Scholar] [CrossRef]
  122. Sternisha, S.; Liu, P.; Marshall, A.; Miller, B. Mechanistic origins of enzyme activation in human glucokinase variants associated with congenital hyperinsulinism. Biochemistry 2018, 57, 1632–1639. [Google Scholar] [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Ivanova, B. Special Issue with Research Topics on “Recent Analysis and Applications of Mass Spectra on Biochemistry”. Int. J. Mol. Sci. 2024, 25, 1995. https://doi.org/10.3390/ijms25041995

AMA Style

Ivanova B. Special Issue with Research Topics on “Recent Analysis and Applications of Mass Spectra on Biochemistry”. International Journal of Molecular Sciences. 2024; 25(4):1995. https://doi.org/10.3390/ijms25041995

Chicago/Turabian Style

Ivanova, Bojidarka. 2024. "Special Issue with Research Topics on “Recent Analysis and Applications of Mass Spectra on Biochemistry”" International Journal of Molecular Sciences 25, no. 4: 1995. https://doi.org/10.3390/ijms25041995

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop