Two-dimensional mass spectrometry: new perspectives for tandem mass spectrometry

Fragment correlation mass spectrometry: Determining the structures of biopolymers in a complex mixture without isolating individual components

Fragment correlation mass spectrometry correlates ion pairs generated from the same fragmentation pathway, achieved by covariance mapping of tandem mass spectra generated with an unmodified linear ion trap without preseparation. We enable the identification of different precursors at different charge states in a complex mixture from a large isolation window, empowering an analytical approach for data-independent acquisition. The method resolves and matches isobaric fragments, internal ions, and disulfide bond fragments. We suggest that this method represents a major advance for analyzing structures of biopolymers in mixtures.

Unveiling the intricacy of gapmer oligonucleotides through advanced tandem mass spectrometry approaches and scan accumulation for 2DMS

Antisense oligonucleotides (ASOs) are crucial for biological applications as they bind to complementary RNA sequences, modulating protein expression. ASOs undergo synthetic modifications like phosphorothioate (PS) backbone and locked nucleic acid (LNA) to enhance stability and specificity. Tandem mass spectrometry (MS) techniques were employed to study gapmer ASOs, which feature a DNA chain within RNA segments at both termini, revealing enhanced cleavages with ultraviolet photodissociation (UVPD) and complementary fragment ions from collision-induced dissociation (CID) and electron detachment dissociation (EDD). 2DMS, a data-independent analysis technique, allowed for comprehensive coverage and identification of shared fragments across multiple precursor ions. EDD fragmentation efficiency correlated with precursor ion charge states, with higher charges facilitating dissociation due to intramolecular repulsions. An electron energy of 22.8 eV enabled electron capture and radical-based cleavage. Accumulating multiple scans and generating average spectra improved signal intensity, aided by denoising algorithms. Data analysis utilised a custom Python script capable of handling modifications and generating unique mass lists.

Two-Dimensional Fourier Transform Ion Cyclotron Resonance Mass Spectrometry of N-Linked Glycopeptides

Glycosylation is a common modification across living organisms and plays a central role in understanding biological systems and disease. Our ability to probe the gylcome has grown exponentially in the past several decades. However, further improvements to the analytical toolbox available to researchers would allow for increased capabilities to probe structure and function of biological systems and to improve disease treatment. This article applies the developing technique of two-dimensional Fourier transform ion cyclotron resonance mass spectrometry to a glycoproteomic workflow for the standard glycoproteins coral tree lectin (CTL) and bovine ribonuclease B (BRB) to demonstrate its feasibility as a tool for glycoproteomic workflows. 2D infrared multiphoton dissociation and electron capture dissociation spectra of CTL reveal comparable structural information to their 1D counterparts, confirming the site of glycosylation and monosaccharide composition of the glycan. Spectra collected in 2D of BRB reveal correlation lines of fragment ion scans and vertical precursor ion scans for data collected using infrared multiphoton dissociation and diagonal cleavage lines for data collected by electron capture dissociation. The use of similar techniques for glycoproteomic analysis may prove valuable in instances where chromatographic separation is undesirable or quadrupole isolation is insufficient.

Accurate Detection of G-Series Agent Simulants by Pseudo-Multiple Reaction Monitoring on Miniature LIT-MS

Rapid and accurate on-site detection of chemical warfare agents (CWAs) could defend military and civilian populations against current and emerging chemical weapons. With the development of ambient ionization and linear ion trap technology, the rapid and accurate quantitative determination method of CWAs based on direct ionization and multistage mass spectrometry has attracted widespread attention. In this study, a microliter electrospray ionization-miniature linear ion trap mass spectrometry (LIT-MS) instrument was designed and constructed, and the effects of quadrupole enhanced dipole resonance excitation on the resolution and sensitivity were investigated; consequently, the parameters of CWAs detection were optimized. Based on the broad time-frequency ion excitation technology, accurate multiple reaction monitoring (MRM) quantitative analysis of DMMP (G-series agent simulants, m/z 125 → m/z 93) was obtained. The linear correlation coefficient in the concentration range of 1 to 20 μg/mL could reach 99.02%, and the relative standard deviations (RSD) of continuous repeatability, interday repeatability, and intraday repeatability were all less than 10%. The results showed that the accurate pseudo-MRM detection method based on miniature linear ion trap mass spectrometry for CWAs detection was feasible.

Two-Dimensional Fourier Transform Ion Cyclotron Resonance Mass Spectrometry by Matrix-Assisted Laser Desorption Ionization

Two-dimensional Fourier transform ion cyclotron resonance (2D FTICR) mass spectrometry is a developing form of data-independent acquisition that allows for the simultaneous fragmentation and correlation of fragment ions to their precursors across a range of m/z values. The modern usage of 2D FTICR is performed using electrospray ionization (ESI) as the dried droplet preparation for matrix-assisted laser desorption ionization (MALDI) does not produce a consistent packet of ions over a number of scans. This work uses pneumatic spray techniques from mass spectrometry imaging to create a homogeneous surface for use with MALDI as an ionization source for 2D FTICR. A mixture of peptides and matrix was deposited onto a glass slide using an HTX pneumatic sprayer. MALDI was then used to ionize the peptide mixture for use with a standard 2D FTICR pulse sequence. The generated 2D spectrum reveals comparable structural information to spectra collected in a 1D experiment. Artifacts observed in the collected 2D MALDI spectra do not significantly differ from those expected from 2D ESI spectra.

Two-Dimensional Tandem Mass Spectrometry for Biopolymer Structural Analysis

Biopolymer analysis, including proteomics and glycomics, relies heavily on the use of mass spectrometry for structural elucidation, including sequence determination. Novel methods to improve sample workup, instrument performance, and data analysis continue to be developed to address shortcomings associated with sample preparation, analysis time, data quality, and data interpretation. Here, we present a new method that couples in-source collision-induced dissociation (IS-CID) with two-dimensional tandem mass spectrometry (2D MS/MS) as a way to simplify proteomics and glycomics workflows while also providing additional insight into analyte structures over traditional MS/MS experiments. Specifically, IS-CID is employed as a gas-phase digestion method, i.e., to break down intact full-length polysaccharide or peptide ions prior to mass analysis. The resulting mixtures of oligomeric ions are analyzed by 2D-MS/MS, a technique that allows association of product ions with their precursor ions without isolation of the latter. A novel data analysis strategy is introduced to leverage the second dimension of 2D MS/MS spectra, in which stairstep patterns, representing outputs of a molecule's MSn scans, are extracted for structural interconnectivity information on the oligomer. The results demonstrate the potential applicability of 2D MS/MS strategies to the modern omics workflow and structural analysis of various classes of biopolymers.

Advancing PROTAC Characterization: Structural Insights through Adducts and Multimodal Tandem-MS Strategies

Proteolysis targeting chimeras (PROTACs) are specialized molecules that bind to a target protein and a ubiquitin ligase to facilitate protein degradation. Despite their significance, native PROTACs have not undergone tandem mass spectrometry (MS) analysis. To address this gap, we conducted a pioneering investigation on the fragmentation patterns of two PROTACs in development, dBET1 and VZ185. Employing diverse cations (sodium, lithium, and silver) and multiple tandem-MS techniques, we enhanced their structural characterization. Notably, lithium cations facilitated comprehensive positive-mode coverage for dBET1, while negative polarity mode offered richer insights. Employing de novo structure determination on 2DMS data from degradation studies yielded crucial insights. In the case of VZ185, various charge states were observed, with [M + 2H]2+ revealing fewer moieties than [M + H]+ due to charge-related factors. Augmenting structural details through silver adducts suggested both charge-directed and charge-remote fragmentation. This comprehensive investigation identifies frequently dissociated bonds across multiple fragmentation techniques, pinpointing optimal approaches for elucidating PROTAC structures. The findings contribute to advancing our understanding of PROTACs, pivotal for their continued development as promising therapeutic agents.

Towards personalized genome-scale modeling of inborn errors of metabolism for systems medicine applications

Inborn errors of metabolism (IEMs) are a group of more than 1000 inherited diseases that are individually rare but have a cumulative global prevalence of 50 per 100,000 births. Recently, it has been recognized that like common diseases, patients with rare diseases can greatly vary in the manifestation and severity of symptoms. Here, we review omics-driven approaches that enable an integrated, holistic view of metabolic phenotypes in IEM patients. We focus on applications of Constraint-based Reconstruction and Analysis (COBRA), a widely used mechanistic systems biology approach, to model the effects of inherited diseases. Moreover, we review evidence that the gut microbiome is also altered in rare diseases. Finally, we outline an approach using personalized metabolic models of IEM patients for the prediction of biomarkers and tailored therapeutic or dietary interventions. Such applications could pave the way towards personalized medicine not just for common, but also for rare diseases.

Machine-Learning Classification of Bacteria Using Two-Dimensional Tandem Mass Spectrometry

Biothreat detection has continued to gain attention. Samples suspected to fall into any of the CDC's biothreat categories require identification by processes that require specialized expertise and facilities. Recent developments in analytical instrumentation and machine learning algorithms offer rapid and accurate classification of Gram-positive and Gram-negative bacterial species. This is achieved by analyzing the negative ions generated from bacterial cell extracts with a modified linear quadrupole ion-trap mass spectrometer fitted with two-dimensional tandem mass spectrometry capabilities (2D MS/MS). The 2D MS/MS data domain of a bacterial cell extract is recorded within five s using a five-scan average after sample preparation by a simple extraction. Bacteria were classified at the species level by their lipid profiles using the random forest, k-nearest neighbor, and multilayer perceptron machine learning models. 2D MS/MS data can also be treated as image data for use with image recognition algorithms such as convolutional neural networks. The classification accuracy of all models tested was greater than 99%. Adding to previously published work on the 2D MS/MS analysis of bacterial growth and the profiling of sporulating bacteria, this study demonstrates the utility and information-rich nature of 2D MS/MS in the identification of bacterial pathogens at the species level when coupled with machine learning.

Top-Down Proteoform Analysis by 2D MS with Quadrupolar Detection

Two-dimensional mass spectrometry (2D MS) is a multiplexed tandem mass spectrometry method that does not rely on ion isolation to correlate the precursor and fragment ions. On a Fourier transform ion cyclotron resonance mass spectrometer (FT-ICR MS), 2D MS instead uses the modulation of precursor ion radii inside the ICR cell before fragmentation and yields 2D mass spectra that show the fragmentation patterns of all the analytes. In this study, we perform 2D MS for the first time with quadrupolar detection in a dynamically harmonized ICR cell. We discuss the advantages of quadrupolar detection in 2D MS and how we adapted existing data processing techniques for accurate frequency-to-mass conversion. We apply 2D MS with quadrupolar detection to the top-down analysis of covalently labeled ubiquitin with ECD fragmentation, and we develop a workflow for label-free relative quantification of biomolecule isoforms in 2D MS.

Unlocking the Hidden Depths: Multi-Modal Integration of Imaging Mass Spectrometry-Based and Molecular Imaging Techniques

Multimodal imaging (MMI) has emerged as a powerful tool in clinical research, combining different imaging modes to acquire comprehensive information and enabling scientists and surgeons to study tissue identification, localization, metabolic activity, and molecular discovery, thus aiding in disease progression analysis. While multimodal instruments are gaining popularity, challenges such as non-standardized characteristics, custom software, inadequate commercial support, and integration issues with other instruments need to be addressed. The field of multimodal imaging or multiplexed imaging allows for simultaneous signal reproduction from multiple imaging strategies. Intraoperatively, MMI can be integrated into frameless stereotactic surgery. Recent developments in medical imaging modalities such as magnetic resonance imaging (MRI), and Positron Emission Topography (PET) have brought new perspectives to multimodal imaging, enabling early cancer detection, molecular tracking, and real-time progression monitoring. Despite the evidence supporting the role of MMI in surgical decision-making, there is a need for comprehensive studies to validate and perform integration at the intersection of multiple imaging technologies. They were integrating mass spectrometry-based technologies (e.g., imaging mass spectrometry (IMS), imaging mass cytometry (IMC), and Ion mobility mass spectrometry ((IM-IM) with medical imaging modalities, offering promising avenues for molecular discovery and clinical applications. This review emphasizes the potential of multi-omics approaches in tissue mapping using MMI integrated into desorption electrospray ionization (DESI) and matrix-assisted laser desorption ionization (MALDI), allowing for sequential analyses of the same section. By addressing existing knowledge gaps, this review encourages future research endeavors toward multi-omics approaches, providing a roadmap for future research and enhancing the value of MMI in molecular pathology for diagnosis.

Ultra-Accurate Correlation between Precursor and Fragment Ions in Two-Dimensional Mass Spectrometry: Acetylated vs Trimethylated Histone Peptides

Two-dimensional mass spectrometry (2D MS) is a method for tandem mass spectrometry in which precursor and fragment ions are correlated by manipulating ion radii rather than by ion isolation. A 2D mass spectrum contains the fragmentation patterns of all analytes in a sample, acquired in parallel. We report ultrahigh-resolution narrowband 2D mass spectra of a mixture of two histone peptides with the same sequence, one of which carries an acetylation and the other a trimethylation (m/z 0.006 difference). We reduced the distance between data points in the precursor ion dimension and compared the accuracy of the precursor-fragment correlation with the resolving power. We manage to perform label-free quantification on the histone peptide mixture and show that precursor and fragment ions can be accurately correlated even though the precursor ions are not resolved. Finally, we show that increasing the resolution of a 2D mass spectrum in the precursor ion dimension too far can lead to a decline in the signal-to-noise ratio.

In Silico Demonstration of Two-Dimensional Mass Spectrometry Using Spatially Dependent Fragmentation

Two-dimensional mass spectrometry (2DMS) allows for the analysis of complex mixtures of all kinds at high speed and resolution without data loss from isolation or biased acquisition, effectively generating tandem mass spectrometry information for all ions at once. Currently, this technique is limited to instruments utilizing an ion trap such as the Fourier transform ion cyclotron resonance or linear ion traps. To overcome this limitation, new fragmentation waveforms were used in either a temporal or spatial configuration, allowing for the application of 2DMS on a much wider array of instruments. A simulated example of a time-of-flight-based instrument is shown with the new waveforms, which allowed for the correlation of fragment ions to their respective precursors through the processing of the modulation of fragmentation intensity with a Fourier transform. This application indicated that 2D modulation and Fourier precursor/fragment intensity correlation are possible in any case where separation, either temporally or spatially, can be achieved, allowing 2DMS to be applied to almost every type of mass spectrometry instrument.

Advances in Ultra-High-Resolution Mass Spectrometry for Pharmaceutical Analysis

Pharmaceutical analysis refers to an area of analytical chemistry that deals with active compounds either by themselves (drug substance) or when formulated with excipients (drug product). In a less simplistic way, it can be defined as a complex science involving various disciplines, e.g., drug development, pharmacokinetics, drug metabolism, tissue distribution studies, and environmental contamination analyses. As such, the pharmaceutical analysis covers drug development to its impact on health and the environment. Moreover, due to the need for safe and effective medications, the pharmaceutical industry is one of the most heavily regulated sectors of the global economy. For this reason, powerful analytical instrumentation and efficient methods are required. In the last decades, mass spectrometry has been increasingly used in pharmaceutical analysis both for research aims and routine quality controls. Among different instrumental setups, ultra-high-resolution mass spectrometry with Fourier transform instruments, i.e., Fourier transform ion cyclotron resonance (FTICR) and Orbitrap, gives access to valuable molecular information for pharmaceutical analysis. In fact, thanks to their high resolving power, mass accuracy, and dynamic range, reliable molecular formula assignments or trace analysis in complex mixtures can be obtained. This review summarizes the principles of the two main types of Fourier transform mass spectrometers, and it highlights applications, developments, and future perspectives in pharmaceutical analysis.

Next Challenges for the Comprehensive Molecular Characterization of Complex Organic Mixtures in the Field of Sustainable Energy

The conversion of lignocellulosic biomass by pyrolysis or hydrothermal liquefaction gives access to a wide variety of molecules that can be used as fuel or as building blocks in the chemical industry. For such purposes, it is necessary to obtain their detailed chemical composition to adapt the conversion process, including the upgrading steps. Petroleomics has emerged as an integral approach to cover a missing link in the investigation bio-oils and linked products. It relies on ultra-high-resolution mass spectrometry to attempt to unravel the contribution of many compounds in complex samples by a non-targeted approach. The most recent developments in petroleomics partially alter the discriminating nature of the non-targeted analyses. However, a peak referring to one chemical formula possibly hides a forest of isomeric compounds, which may present a large chemical diversity concerning the nature of the chemical functions. This identification of chemical functions is essential in the context of the upgrading of bio-oils. The latest developments dedicated to this analytical challenge will be reviewed and discussed, particularly by integrating ion source features and incorporating new steps in the analytical workflow. The representativeness of the data obtained by the petroleomic approach is still an important issue.

Mass Difference Matching Unfolds Hidden Molecular Structures of Dissolved Organic Matter

Ultrahigh-resolution Fourier transform mass spectrometry (FTMS) has revealed unprecedented details of natural complex mixtures such as dissolved organic matter (DOM) on a molecular formula level, but we lack approaches to access the underlying structural complexity. We here explore the hypothesis that every DOM precursor ion is potentially linked with all emerging product ions in FTMS² experiments. The resulting mass difference (Δm) matrix is deconvoluted to isolate individual precursor ion Δm profiles and matched with structural information, which was derived from 42 Δm features from 14 in-house reference compounds and a global set of 11 477 Δm features with assigned structure specificities, using a dataset of ∼18 000 unique structures. We show that Δm matching is highly sensitive in predicting potential precursor ion identities in terms of molecular and structural composition. Additionally, the approach identified unresolved precursor ions and missing elements in molecular formula annotation (P, Cl, F). Our study provides first results on how Δm matching refines structural annotations in van Krevelen space but simultaneously demonstrates the wide overlap between potential structural classes. We show that this effect is likely driven by chemodiversity and offers an explanation for the observed ubiquitous presence of molecules in the center of the van Krevelen space. Our promising first results suggest that Δm matching can both unfold the structural information encrypted in DOM and assess the quality of FTMS-derived molecular formulas of complex mixtures in general.

FT-ICR Mass Spectrometry Imaging at Extreme Mass Resolving Power Using a Dynamically Harmonized ICR Cell with 1ω or 2ω Detection

MALDI mass spectrometry imaging (MALDI MSI) is a powerful analytical method for achieving 2D localization of compounds from thin sections of typically but not exclusively biological samples. The dynamically harmonized ICR cell (ParaCell) was recently introduced to achieve extreme spectral resolution capable of providing the isotopic fine structure of ions detected in complex samples. The latest improvement in the ICR technology also includes 2ω detection, which significantly reduces the transient time while preserving the nominal mass resolving power of the ICR cell. High-resolution MS images acquired on FT-ICR instruments equipped with 7T and 9.4T superconducting magnets and the dynamically harmonized ICR cell operating at suboptimal parameters suffered severely from the pixel-to-pixel shifting of m/z peaks due to space-charge effects. The resulting profile average mass spectra have depreciated mass measurement accuracy and mass resolving power under the instrument specifications that affect the confidence level of the identified ions. Here, we propose an analytical workflow based on the monitoring of the total ion current to restrain the pixel-to-pixel m/z shift. Adjustment of the laser parameters is proposed to maintain high spectral resolution and mass accuracy measurement within the instrument specifications during MSI analyses. The optimized method has been successfully employed in replicates to perform high-quality MALDI MS images at resolving power (FWHM) above 1,000,000 in the lipid mass range across the whole image for superconducting magnets of 7T and 9.4T using 1 and 2ω detection. Our data also compare favorably with MALDI MSI experiments performed on higher-magnetic-field superconducting magnets, including the 21T MALDI FT-ICR prototype instrument of the NHMFL group at Tallahassee, Florida.

PFAS Analysis with Ultrahigh Resolution 21T FT-ICR MS: Suspect and Nontargeted Screening with Unrivaled Mass Resolving Power and Accuracy

Per- and polyfluoroalkyl substances (PFASs) are a large family of thousands of chemicals, many of which have been identified using nontargeted time-of-flight and Orbitrap mass spectrometry methods. Comprehensive characterization of complex PFAS mixtures is critical to assess their environmental transport, transformation, exposure, and uptake. Because 21 tesla (T) Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS) offers the highest available mass resolving power and sub-ppm mass errors across a wide molecular weight range, we developed a nontargeted 21 T FT-ICR MS method to screen for PFASs in an aqueous film-forming foam (AFFF) using suspect screening, a targeted formula database (C, H, Cl, F, N, O, P, S; ≤865 Da), isotopologues, and Kendrick-analogous mass difference networks (KAMDNs). False-positive PFAS identifications in a natural organic matter (NOM) sample, which served as the negative control, suggested that a minimum length of 3 should be imposed when annotating CF₂-homologous series with positive mass defects. We putatively identified 163 known PFASs during suspect screening, as well as 134 novel PFASs during nontargeted screening, including a suspected polyethoxylated perfluoroalkane sulfonamide series. This study shows that 21 T FT-ICR MS analysis can provide unique insights into complex PFAS composition and expand our understanding of PFAS chemistries in impacted matrices.

Analysis of micropollutants in a marine outfall using network analysis and decision tree

The presence of micropollutants (MPs), including pharmaceutical, industrial, and pesticidal compounds, threatens both human health and the aquatic ecosystem. The development and extensive use of new chemicals have also inevitably led to the accumulation of MPs in aquatic environments. Recreational beaches are especially vulnerable to contamination, affecting humans and aquatic animals via the absorption of MPs in water during marine activities (e.g., swimming, sailing, and windsurfing). Additionally, marine outfalls in an urbanized coastal city can cause serious chemical and microbial pollution on recreational beaches, leading to an increase in adverse effects on public health and the ecological system. Therefore, the aim of this study was to, with the use of network and decision tree analyses, identify the features and factors that influence the change in MP concentrations in a marine outfall. These analyses were conducted to inspect the relationship between each MP and its hierarchical structure as well as hydrometeorological variables. Additionally, a risk analysis was conducted in this study in which the MPs were prioritized based on their optimized risk quotient values. During our monitoring of MP concentrations over time at the marine outfall, high concentrations of pharmaceutical and industrial compounds were detected when the tide level was low after rainfall. Furthermore, results of the risk analysis and the prioritization revealed that a total of 18 substances identified in our study posed a risk to the ecosystem; these include major ecotoxicologically hazardous substances such as telmisartan, mevinphos, and methiocarb. Results of the network analysis demonstrated distinct trends for pharmaceutical and industrial substances, whilst those for pesticide compounds were irregular. Additionally, the hierarchical structures for most MPs consisted of rainfall, tide level, and antecedent dry hours; this implies that these factors influence MP dynamics. These findings will be helpful for establishing chemical contamination management plans for recreational beaches in the future.

AI applications in functional genomics

We review the current applications of artificial intelligence (AI) in functional genomics. The recent explosion of AI follows the remarkable achievements made possible by "deep learning", along with a burst of "big data" that can meet its hunger. Biology is about to overthrow astronomy as the paradigmatic representative of big data producer. This has been made possible by huge advancements in the field of high throughput technologies, applied to determine how the individual components of a biological system work together to accomplish different processes. The disciplines contributing to this bulk of data are collectively known as functional genomics. They consist in studies of: i) the information contained in the DNA (genomics); ii) the modifications that DNA can reversibly undergo (epigenomics); iii) the RNA transcripts originated by a genome (transcriptomics); iv) the ensemble of chemical modifications decorating different types of RNA transcripts (epitranscriptomics); v) the products of protein-coding transcripts (proteomics); and vi) the small molecules produced from cell metabolism (metabolomics) present in an organism or system at a given time, in physiological or pathological conditions. After reviewing main applications of AI in functional genomics, we discuss important accompanying issues, including ethical, legal and economic issues and the importance of explainability.

Applications and Restrictions of Integrated Genomic and Metabolomic Screening: An Accelerator for Drug Discovery from Actinomycetes?

Since the golden age of antibiotics in the 1950s and 1960s actinomycetes have been the most prolific source for bioactive natural products. However, the number of discoveries of new bioactive compounds decreases since decades. New procedures (e.g., activating strategies or innovative fermentation techniques) were developed to enhance the productivity of actinomycetes. Nevertheless, compound identification remains challenging among others due to high rediscovery rates. Rapid and cheap genome sequencing as well as the advent of bioinformatical analysis tools for biosynthetic gene cluster identification in combination with mass spectrometry-based molecular networking facilitated the tedious process of dereplication. In recent years several studies have been dedicated to accessing the biosynthetic potential of Actinomyces species, especially streptomycetes, by using integrated genomic and metabolomic screening in order to boost the discovery rate of new antibiotics. This review aims to present the various possible applications of this approach as well as the newly discovered molecules, covering studies between 2014 and 2021. Finally, the effectiveness of this approach with regard to find new bioactive agents from actinomycetes will be evaluated.

Characterization Across a Dispersity: Polymer Mass Spectrometry in the Second Dimension

Due to the natural dispersity that is present in synthetic polymers, an added complexity is always present in the analysis of polymeric species. Tandem mass spectrometry analysis requires the isolation of individual precursors before a fragmentation event to allow the unambiguous characterization of these species and is not viable at certain levels of complexity due to achievable isolation widths. Two-dimensional mass spectrometry (2DMS) fragments ions and correlates fragments with their corresponding precursors without the need for isolation. In this study, 2DMS electron capture dissociation (ECD) fragmentation of a polyoxazoline and polyacrylamide species was carried out, resulting in the analysis of byproducts and individual polymer species without the use of chromatographic techniques. This study shows that 2DMS ECD is a powerful tool for the analysis of polyacrylamide and polyoxazoline species and offers a new dimension in the characterization of polymers.

Two-Dimensional Partial Covariance Mass Spectrometry for the Top-Down Analysis of Intact Proteins

Two-dimensional partial covariance mass spectrometry (2D-PC-MS) exploits the inherent fluctuations of fragment ion abundances across a series of tandem mass spectra, to identify correlated pairs of fragment ions produced along the same fragmentation pathway of the same parent (e.g., peptide) ion. Here, we apply 2D-PC-MS to the analysis of intact protein ions in a standard linear ion trap mass analyzer, using the fact that the fragment-fragment correlation signals are much more specific to the biomolecular sequence than one-dimensional (1D) tandem mass spectrometry (MS/MS) signals at the same mass accuracy and resolution. We show that from the distribution of signals on a 2D-PC-MS map it is possible to extract the charge state of both parent and fragment ions without resolving the isotopic envelope. Furthermore, the 2D map of fragment-fragment correlations naturally separates the products of the primary decomposition pathways of the molecular ions from those of the secondary ones. We access this spectral information using an adapted version of the Hough transform. We demonstrate the successful identification of highly charged, intact protein molecules bypassing the need for high mass resolution. Using this technique, we also perform the in silico deconvolution of the overlapping fragment ion signals from two co-isolated and co-fragmented intact proteins, demonstrating a viable new method for the concurrent mass spectrometric identification of a mixture of intact protein ions from the same fragment ion spectrum.

Two-Dimensional Mass Spectrometry Analysis of IgG1 Antibodies

Two-dimensional mass spectrometry (2DMS) is a new, and theoretically ideal, data-independent analysis tool, which allows the characterization of a complex mixture and was used in the bottom-up analysis of IgG1 for the identification of post-translational modifications. The new peak picking algorithm allows the distinction between chimeric peaks in proteomics. In this application, the processing of 2DMS data correlates fragments to their corresponding precursors, with fragments from precursors which are <0.1 m/z at m/z 840 easily resolved, without the need for quadrupole or chromatographic separation.

Combining Ultraviolet Photodissociation and Two-Dimensional Mass Spectrometry: A Contemporary Approach for Characterizing Singly Charged Agrochemicals

Ultraviolet photodissociation (UVPD) has been shown to produce extensive structurally informative data for a variety of chemically diverse compounds. Herein, we demonstrate the performance of the 193 nm UVPD fragmentation technique on structural/moiety characterization of 14 singly charged agrochemicals. Two-dimensional mass spectrometry (2DMS) using infrared multiphoton dissociation (IRMPD) and electron-induced dissociation (EID) have previously been applied to a select range of singly charged pesticides. The ≥80% moiety coverage achieved for the majority of the species by the UVPD and 2D-UVPD methods was on par with and, in some cases, superior to the data obtained by other fragmentation techniques in previous studies, demonstrating that UVPD is viable for these types of species. A three-dimensional (3D) peak picking method was implemented to extract the data from the 2DMS spectrum, overcoming the limitations of the line extraction method used in previous studies, successfully separating precursor specific fragments with milli-Dalton accuracy. Whole spectrum internal calibration combined with 3D peak picking obtained sub-part-per-million (ppm) to part-per-billion (ppb) mass accuracies across the entire 2DMS spectrum.

Phase Correction for Absorption Mode Two-Dimensional Mass Spectrometry

Two-dimensional mass spectrometry (2D MS) is a tandem mass spectrometry method that relies on manipulating ion motions to correlate precursor and fragment ion signals. 2D mass spectra are obtained by performing a Fourier transform in both the precursor ion mass-to-charge ratio (m/z) dimension and the fragment ion m/z dimension. The phase of the ion signals evolves linearly in the precursor m/z dimension and quadratically in the fragment m/z dimension. This study demonstrates that phase-corrected absorption mode 2D mass spectrometry improves signal-to-noise ratios by a factor of 2 and resolving power by a factor of 2 in each dimension compared to magnitude mode. Furthermore, phase correction leads to an easier differentiation between ion signals and artefacts, and therefore easier data interpretation.

Application of Optimal Control Theory to Fourier Transform Ion Cyclotron Resonance

We study the application of Optimal Control Theory to Ion Cyclotron Resonance. We test the validity and the efficiency of this approach for the robust excitation of an ensemble of ions with a wide range of cyclotron frequencies. Optimal analytical solutions are derived in the case without any pulse constraint. A gradient-based numerical optimization algorithm is proposed to take into account limitation in the control intensity. The efficiency of optimal pulses is investigated as a function of control time, maximum amplitude and range of excited frequencies. A comparison with adiabatic and SWIFT pulses is done. On the basis of recent results in Nuclear Magnetic Resonance, this study highlights the potential usefulness of optimal control in Ion Cyclotron Resonance.

Identification of influencing factors of A. catenella bloom using machine learning and numerical simulation

Alexandrium catenella (A. catenella) is a notorious algal species known to cause paralytic shellfish poisoning (PSP) in Korean coastal waters. There have been numerous studies on its temporal and spatial blooms in Korea. However, its bloom dynamics have not been fully understood because of the complexity in physical, chemical, and biological environments. This study aims to identify the factors that influence A. catenella blooms by applying a numerical model and machine learning. Intensive monitoring of A. catenella was conducted to investigate temporal variations in its population and its spatial distribution in the area with frequent occurrences of PSP bloom initiation. Moreover, a numerical model was built to analyze the ocean physical factors related to the bloom of A. catenella. Based on the information obtained from the monitored and simulated results, the decision tree (DT) method was applied to identify factors that caused the bloom. The outbreak of A. catenella was observed in the eastern coastal water of Geoje Island in 2017, recording a peak density of 4 × 10⁴ (cell L^-1). Retention time and particle scattering demonstrated that the physical force in 2017 was weaker than that in 2018, as shown by the smaller effects of advection and dispersion in 2017. The decision tree model showed that (1) water temperature below 17.21 °C was ideal for the growth of A. catenella, (2) phosphate influenced the growth of the species, and (3) cell density was accelerated with increasing retention time. The results from DT can contribute to the prediction of A. catenella blooms by determining the conditions that cause bloom initiation. Further, they can be used as a practical approach for mitigating HABs. Thus, machine learning and numerical simulation in this study can be a potential approach for effectively managing the bloom of A. catenella.

Replacing the internal standard to estimate micropollutants using deep and machine learning

Similar to the worldwide proliferation of urbanization, micropollutants have been involved in aquatic and ecological environmental systems. These pollutants have the propensity to wreak havoc on human health and the ecological system; hence, it is important to persistently monitor micropollutants in the environment. Micropollutants are commonly quantified via target analysis using high resolution mass spectrometry and the stable isotope labeled (SIL) standard. However, the cost-intensiveness of this standard presents a major obstacle in measuring micropollutants. This study resolved this problem by developing data-driven models, including deep learning (DL) and machine learning (ML), to estimate the concentration of micropollutants without resorting to the SIL standard. Our study hypothesized that natural organic matter (NOM) could replace internal standards if there was a specific mass spectrum (MS) subset, including NOM information, which correlated with an SIL standard peak. Therefore, we analyzed the MS to find the specific MS subsets for replacing the SIL standard peak. Thirty-five alternative MS subsets were determined for applying DL and ML as input data. Thereafter, we trained four different DL models, namely, ResNet101, GoogLeNet, VGG16, and Inception v3, as well as three different ML models, i.e., random forest (RF), support vector machine (SVM), and artificial neural network (ANN). A total of 680 MS data were used for the model training to estimate five different micropollutants, namely Sulpiride, Metformin, and Benzotriazole. Among the DL models, ResNet 101 exhibited the highest model performance, showing that the average validation R² and MSE were 0.84 and 0.26 ng/L, respectively, while RF was the best in the ML models, manifesting R² and MSE values of 0.69 and 0.58 ng/L. The trained models showed accurate training and validation results for the estimation of the five micropollutant concentrations. Therefore, this study demonstrates that the suggested analysis has a potential for alternative micropollutant measurement that has rapid and economic vantages.

Narrowband Modulation Two-Dimensional Mass Spectrometry and Label-Free Relative Quantification of Histone Peptides

Two-dimensional mass spectrometry (2D MS) on a Fourier transform ion cyclotron resonance (FT-ICR) mass analyzer allows for tandem mass spectrometry without requiring ion isolation. In the ICR cell, the precursor ion radii are modulated before fragmentation, which results in modulation of the abundance of their fragments. The resulting 2D mass spectrum enables a correlation between the precursor and fragment ions. In a standard broadband 2D MS, the range of precursor ion cyclotron frequencies is determined by the lowest mass-to-charge (m/z) ratio to be fragmented in the 2D MS experiment, which leads to precursor ion m/z ranges that are much wider than necessary, thereby limiting the resolving power for precursor ions and the accuracy of the correlation between the precursor and fragment ions. We present narrowband modulation 2D MS, which increases the precursor ion resolving power by reducing the precursor ion m/z range, with the aim of resolving the fragment ion patterns of overlapping isotopic distributions. In this proof-of-concept study, we compare broadband and narrowband modulation 2D mass spectra of an equimolar mixture of histone peptide isoforms. In narrowband modulation 2D MS, we were able to separate the fragment ion patterns of all ¹³C isotopes of the different histone peptide forms. We further demonstrate the potential of narrowband 2D MS for label-free quantification of peptides.

Advantages of Two-Dimensional Electron-Induced Dissociation and Infrared Multiphoton Dissociation Mass Spectrometry for the Analysis of Agrochemicals

Analysis of agrochemicals in an environmental matrix is challenging because these samples contain multiple agrochemicals, their metabolites, degradation products, and endogenous compounds. The analysis of such complex samples is achieved using chromatographic separation techniques coupled to mass spectrometry. Herein, we demonstrate a two-dimensional mass spectrometry (2DMS) technique on a 12 T Fourier transform ion cyclotron resonance mass spectrometer that can analyze a mixture of agrochemicals without using chromatography or quadrupole isolation in a single experiment. The resulting 2DMS contour plot contains abundant tandem MS information for each component in the sample and correlates product ions to their corresponding precursor ions. Two different fragmentation methods are employed, infrared multiphoton dissociation (IRMPD) and electron-induced dissociation (EID), with 2DMS to analyze the mixture of singly charged agrochemicals. The product ions of one of the agrochemicals, pirimiphos-methyl, present in the sample was used to internally calibrate the entire 2DMS spectrum, achieving sub part per million (ppm) to part per billion (ppb) mass accuracies for all species analyzed. The work described in this study will show the advantages of the 2DMS approach, by grouping species with common fragments/core structure and mutual functional groups, using precursor lines and neutral loss lines. In addition, the rich spectral information obtained from IRMPD and EID 2DMS contour plots can accurately identify and characterize agrochemicals.

Facile Determination of Phosphorylation Sites in Peptides Using Two-Dimensional Mass Spectrometry

Detection and characterization of phosphopeptides by infrared multiphoton dissociation two-dimensional mass spectrometry (IRMPD 2DMS) is shown to be particularly effective. A mixture of phosphopeptides was analyzed by 2DMS without any prior separation. 2DMS enables the data independent analysis of the mixture and the correlation of the fragments to their precursor ions. The extraction of neutral loss lines corresponding to the loss of phosphate under IRMPD fragmentation allows the selective identification of phosphopeptides. Resonance of the 10.6 μm infrared radiation with the vibrational modes of the phosphate functional group produced efficient absorption and high cleavage coverage of the phosphopeptides at much lower irradiation fluence than for nonphosphorylated peptides improving discrimination. Additionally, the localization of the phosphate group was determined.

Metallocomplex-Peptide Interactions Studied by Ultrahigh Resolution Mass Spectrometry

The Os^II arene anticancer complex [(η⁶-bip)Os(en)Cl]⁺ (Os1-Cl; where bip = biphenyl and en = ethylenediamine) binds strongly to DNA¹ and biomolecules. Here we investigate the interaction between Os1-Cl and the model protein, BSA, using ultrahigh resolution Fourier transform-ion cyclotron resonance mass spectrometry (FT-ICR MS). The specific binding location of Os1 on BSA was investigated with the use of collisionally activated dissociation (CAD) and electron capture dissociation (ECD). CAD MS/MS was found to dissociate the osmium complex from the metallo-peptide complex readily producing unmodified fragments and losing location information. ECD MS/MS, however, successfully retains the osmium modification on the peptides upon fragmentation allowing localization of metallocomplex binding. This study reveals that lysine is a possible binding location for Os1-Cl, apart from the expected binding sites at methionine, histidine, and cysteine. Using a nano liquid chromatography (nLC)-FT-ICR ECD MS/MS study, multiple binding locations, including the N-terminus and C-terminus of digested peptides, glutamic acid, and lysine were also revealed. These results show the multitargeting binding ability of the organo-osmium compound and can be used as a standard workflow for more complex systems, e.g., metallocomplex-cell MS analysis, to evaluate their behavior toward commonly encountered biomolecules.

Phase relationships in two-dimensional mass spectrometry

Two-dimensional mass spectrometry (2D MS) is a data-independent tandem mass spectrometry technique in which precursor and fragment ion species can be correlated without the need for prior ion isolation. The behavior of phase in 2D Fourier transform mass spectrometry is investigated with respect to the calculation of phase-corrected absorption-mode 2D mass spectra. 2D MS datasets have a phase that is defined differently in each dimension. In both dimensions, the phase behavior of precursor and fragment ions is found to be different. The dependence of the phase for both precursor and fragment ion signals on various parameters (e.g., modulation frequency, shape of the fragmentation zone) is discussed. Experimental data confirms the theoretical calculations of the phase in each dimension. Understanding the phase relationships in a 2D mass spectrum is beneficial to the development of possible algorithms for phase correction, which may improve both the signal-to-noise ratio and the resolving power of peaks in 2D mass spectra.