Multidimensional Mass Spectrometry Research Articles

Segmented Template-Directed Self-Assembly of Giant Truncated Triangular Supramolecules.

The template-directed strategy has been extensively employed for the construction of supramolecular architectures. However, with the increase in the size and complexity of these structures, the synthesis difficulty of the templates escalates exponentially, thereby impeding the widespread application of this strategy. In this study, two truncated triangles T1 and T2 were successfully self-assembled through a novel segmented template strategy by segmenting the core triangular template into portions. Two metallo-organic ligands L2 and L3 were designed and synthesized by dividing the central stable triangle into three separate parts and incorporating them into the precursor ligands, which served as templates to guide the self-assembly process with ligands L1 and L4, respectively. The assembled structures were unambiguously characterized by multidimensional and multinuclear NMR (1H, COSY, NOESY), multidimensional mass spectrometry analysis (ESI-MS and TWIM-MS), and transmission electron microscopy (TEM). Moreover, we observed the formation of fiberlike nanotubes from single-molecule triangles by hierarchical self-assembly.

Differentiation of oligosaccharide isomers by direct infusion multidimensional mass spectrometry.

Oligosaccharides demonstrate many bioactivities with applications in the pharmaceutical, cosmetic, and food industries. They also serve as biomarkers for various diseases including cancer and glycogen storage disorders. These make the structural characterization of oligosaccharides very important. Unfortunately, the structural diversity found in saccharides make their characterization challenging, necessitating the development of sophisticated instrumentation to enable isomer differentiation. Herein, we report the ability of halide (Cl- and Br-) adducts to enable direct differentiation of oligosaccharide isomers using conventional collision-induced dissociation (CID) tandem MS (MS/MS). The halide adducts were generated by direct infusion nano-electrospray ionization (nESI). For the first time, this traditional nESI CID MS/MS platform was used to differentiate stereoisomers of trisaccharides (cellotriose β(1 → 4) and maltotriose α(1 → 4), tetrasaccharides (cellotetraose and maltotetraose), and pentasaccharides (cellopentaose and maltopentaose)). In addition, the MS/MS of halide adducts enabled the differentiation of positional, structural, and linkage isomers from a total of 14 oligosaccharides. The isomer differentiation was realized by the generation of distinct diagnostic fragment ions in CID. We also performed principal component analysis using the entire range of MS/MS fragment ion profiles and found that negative-ion mode halide adduction provided more effective isomer differentiation compared with positive-ion mode sodium adduction. Finally, we demonstrated complex mixture analysis by spiking all 14 oligosaccharides into raw urine, of which we successfully distinguished species based on molecular weight (first dimension) and CID MS/MS fragmentation patterns as the second dimension separation. This work effectively showcases the potential to use direct infusion nESI-MS/MS to characterize synthetic oligosaccharide isomers in unpurified reaction mixture as well as from biofluids for diagnostic purposes.

Adjusting the Architecture of Heptagonal Metallo-Macrocycles by Embedding Metal Nodes into the Backbone.

Coordination-driven self-assembly has been extensively employed for the bottom-up construction of discrete metallo-macrocycles. However, the prevalent use of benzene rings as the backbone limits the formation of large metallo-macrocycles with more than six edges. Herein, by embedding metal nodes into the ligand backbone, we successfully regulated the ligand arm angle and assembled two giant heptagonal metallo-macrocycles with precise control. The angle between two arms at position 4 of the central terpyridine (tpy) extended after complexation with metal ions, leading to ring expansion of the metallo-macrocycle. The assembled structures were straightforwardly identified through multi-dimensional NMR spectroscopy (1 H, COSY, NOESY), multidimensional mass spectrometry analysis (ESI-MS and TWIM-MS), transmission electron microscopy (TEM), as well as scanning tunneling microscopy (STM). In addition, the catalytic performances of metallo-macrocycles in the oxidation of thioanisole were studied, with both supramolecules exhibiting good conversion rates. Furthermore, fiber-like nanostructures were observed from single-molecule heptagons by hierarchical self-assembly.

Adjusting the Architecture of Heptagonal Metallo‐Macrocycles by Embedding Metal Nodes into the Backbone

AbstractCoordination‐driven self‐assembly has been extensively employed for the bottom‐up construction of discrete metallo‐macrocycles. However, the prevalent use of benzene rings as the backbone limits the formation of large metallo‐macrocycles with more than six edges. Herein, by embedding metal nodes into the ligand backbone, we successfully regulated the ligand arm angle and assembled two giant heptagonal metallo‐macrocycles with precise control. The angle between two arms at position 4 of the central terpyridine (tpy) extended after complexation with metal ions, leading to ring expansion of the metallo‐macrocycle. The assembled structures were straightforwardly identified through multi‐dimensional NMR spectroscopy (1H, COSY, NOESY), multidimensional mass spectrometry analysis (ESI‐MS and TWIM‐MS), transmission electron microscopy (TEM), as well as scanning tunneling microscopy (STM). In addition, the catalytic performances of metallo‐macrocycles in the oxidation of thioanisole were studied, with both supramolecules exhibiting good conversion rates. Furthermore, fiber‐like nanostructures were observed from single‐molecule heptagons by hierarchical self‐assembly.

PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements

Multidimensional measurements using state-of-the-art separations and mass spectrometry provide advantages in untargeted metabolomics analyses for studying biological and environmental bio-chemical processes. However, the lack of rapid analytical methods and robust algorithms for these heterogeneous data has limited its application. Here, we develop and evaluate a sensitive and high-throughput analytical and computational workflow to enable accurate metabolite profiling. Our workflow combines liquid chromatography, ion mobility spectrometry and data-independent acquisition mass spectrometry with PeakDecoder, a machine learning-based algorithm that learns to distinguish true co-elution and co-mobility from raw data and calculates metabolite identification error rates. We apply PeakDecoder for metabolite profiling of various engineered strains of Aspergillus pseudoterreus, Aspergillus niger, Pseudomonas putida and Rhodosporidium toruloides. Results, validated manually and against selected reaction monitoring and gas-chromatography platforms, show that 2683 features could be confidently annotated and quantified across 116 microbial sample runs using a library built from 64 standards.

Discovery of novel cage compounds of diamondoids using multi-dimensional mass spectrometry

Diamondoids and their analogs, as an important class of cage compounds, are not only measuring sticks for crustal evolution but also archetypal macroscopic molecules that are valuable for material science. Unfortunately, the synthesis of higher diamondoids (C22-tetra-cage and higher) is extremely difficult, and the discovery of naturally existing members has ground to a standstill. Here, two categories of diamondoid compounds, namely thiaethanodiamondoids (1–7 cages) and higher ethanodiamondoids (4–7 cages), were discovered and identified for the first time in the crude oil. Thiaethanodiamondoid was introduced as a new member of diamondoid family. The fresh discovery relied on a multi-dimensional mass spectrometry (MS) strategy. Ultra-high resolution MS, tandem MS, and trapped ion mobility spectrometry MS (TIMS-MS) were combined to achieve structural characterization. Development of solvent-assisted atmospheric pressure chemical ionization (APCI) for clean and soft ionization of higher hydrocarbons, introduction of ion mobility resolution to distinguish isomers, and establishment of correlations between compound structures and collision cross section (CCS) values provided insights into the configurational complexity resulting from cage-fusion. The proposed methodology can drive the discovery of natural products existing in complex matrices at low concentrations (sub-ppm).

Biological mass spectrometry enables spatiotemporal 'omics: From tissues to cells to organelles.

Biological processes unfold across broad spatial and temporal dimensions, and measurement of the underlying molecular world is essential to their understanding. Interdisciplinary efforts advanced mass spectrometry (MS) into a tour de force for assessing virtually all levels of the molecular architecture, some in exquisite detection sensitivity and scalability in space-time. In this review, we offer vignettes of milestones in technology innovations that ushered sample collection and processing, chemical separation, ionization, and 'omics analyses to progressively finer resolutions in the realms of tissue biopsies and limited cell populations, single cells, and subcellular organelles. Also highlighted are methodologies that empowered the acquisition and analysis of multidimensional MS data sets to reveal proteomes, peptidomes, and metabolomes in ever-deepening coverage in these limited and dynamic specimens. In pursuit of richer knowledge of biological processes, we discuss efforts pioneering the integration of orthogonal approaches from molecular and functional studies, both within and beyond MS. With established and emerging community-wide efforts ensuring scientific rigor and reproducibility, spatiotemporal MS emerged as an exciting and powerful resource to study biological systems in space-time.

Integrating ion mobility into comprehensive multidimensional metabolomics workflows: critical considerations.

Ion mobility (IM) separation capabilities are now widely available to researchers through several commercial vendors and are now being adopted into many metabolomics workflows. The added peak capacity that ion mobility offers with minimal compromise to other analytical figures-of-merit has provided real benefits to sensitivity and structural selectivity and have allowed more specific metabolite annotations to be assigned in untargeted workflows. One of the greatest promises of contemporary IM-enabled instrumentation is the capability of operating multiple analytical dimensions inline with minimal sample volumes, which has the potential to address many grand challenges currently faced in the omics fields. However, comprehensive operation of multidimensional mass spectrometry comes with its own inherent challenges that, beyond operational complexity, may not be immediately obvious to practitioners of these techniques. In this review, we outline the strengths and considerations for incorporating IM analysis in metabolomics workflows and provide a critical but forward-looking perspective on the contemporary challenges and prospects associated with interpreting IM data into chemical knowledge. We outline a strategy for unifying IM-derived collision cross section (CCS) measurements obtained from different IM techniques and discuss the emerging field of high resolution ion mobility (HRIM) that is poised to address many of the contemporary challenges associated with ion mobility metabolomics. Whereas the LC step limits the throughput of comprehensive LC-IM-MS, the higher peak capacity of HRIM can allow fast LC gradients or rapid sample cleanup via solid-phase extraction (SPE) to be utilized, significantly improving the sample throughput.

MZA: A Data Conversion Tool to Facilitate Software Development and Artificial Intelligence Research in Multidimensional Mass Spectrometry.

Modern mass spectrometry-based workflows employing hybrid instrumentation and orthogonal separations collect multidimensional data, potentially allowing deeper understanding in omics studies through adoption of artificial intelligence methods. However, the large volume of these rich spectra challenges existing data storage and access technologies, therefore precluding informatics advancements. We present MZA (pronounced m-za), the mass-to-charge (m/z) generic data storage and access tool designed to facilitate software development and artificial intelligence research in multidimensional mass spectrometry measurements. Composed of a data conversion tool and a simple file structure based on the HDF5 format, MZA provides easy, cross-platform and cross-programming language access to raw MS-data, enabling fast development of new tools in data science programming languages such as Python and R. The software executable, example MS-data and example Python and R scripts are freely available at https://github.com/PNNL-m-q/mza.

Tripterygium wilfordii glycosides ameliorates collagen-induced arthritis and aberrant lipid metabolism in rats.

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease, and the dysregulation of lipid metabolism has been found to play an important role in the pathogenesis of RA and is related to the severity and prognosis of patients. Tripterygium wilfordii glycosides (TWG) is extracted from the roots of Tripterygium wilfordii Hook F. with anti-inflammatory and immunosuppressive effects, and numerous clinical trials have supported its efficacy in the treatment of RA. Some evidence suggested that TWG can modulate the formation of lipid mediators in various innate immune cells; however whether it can improve RA-related lipid disorders has not been systematically studied. In the study, type Ⅱ collagen-induced arthritis (CIA) model was used to investigate the efficacy of TWG in the treatment of RA and its effect on lipid metabolism. Paw volume, arthritis score, pathological changes of ankle joint, serum autoantibodies and inflammatory cytokines were detected to assess the therapeutic effect on arthritis in CIA rats. Then, shotgun lipidomics based on multi-dimensional mass spectrometry platform was performed to explore the alterations in serum lipidome caused by TWG. The study showed that TWG could effectively ameliorate arthritis in CIA rats, such as reducing paw volume and arthritis score, alleviating the pathological damages of joint, and preventing the production of anti-CII autoantibodies and IL-1β cytokine. Significant increase in ceramide and decrease in lysophosphatidylcholine were observed in CIA rats, and were highly correlated with arthritis score and IL-1β level. After TWG treatment, these lipid abnormalities can be corrected to a great extent. These data demonstrate that TWG exerts a beneficial therapeutic effect on aberrant lipid metabolism which may provide new insights for further exploring the role and mechanism of TWG in the treatment of RA.

Shape-Dependent Complementary Ditopic Terpyridine Pair with Two Levels of Self-Recognition for Coordination-Driven Self-Assembly.

Molecular recognition in biological systems plays a vital role in the precise construction of biomacromolecules and the corresponding biological activities. Such recognition mainly relies on the highly specific binding of complementary molecular pairs with complementary sizes, shapes, and intermolecular forces. It still remains challenging to develop artificial complementary motif pairs for coordination-driven self-assembly. Herein, a series of shape-dependent complementary motif pairs, based on ditopic 2,2':6',2″-terpyridine (TPY) backbone, are designed and synthesized. The fidelity degrees of self-assemblies from these motifs are carefully evaluated by multi-dimensional mass spectrometry, nuclear magnetic resonance spectroscopy, and molecular modeling. In addition, two levels of self-recognition in both homoleptic and heteroleptic assembly are discovered in the assembled system. Through finely tuning the shape and size of the ligands, a complementary pair is developed with error-free narcissistically self-sorting at two levels of self-recognition, and the intrinsic principle is carefully investigated.

Multidimensional Mass Spectrometry Assisted Metallo-Supramolecular Chemistry

Multidimensional Mass Spectrometry Assisted Metallo-Supramolecular Chemistry

Flying High with Sensitivity and Selectivity: GC–MS to GC–MS/MS

Mass spectrometry (MS), often termed mass selective detection, is the most powerful detector available for gas chromatography (GC). Multidimensional mass spectrometry (MS/MS) takes mass selective detection to another level on benchtop systems, offering both universal and selective detection along with low detection limits. In this installment of “GC Connections,” we review the fundamentals of MS/MS and how they relate to MS as a detector for GC. We see how using full-scan analyses can make the detector universal, and how, by using selected ion monitoring and multiple reaction monitoring, the detector can be so selective and noise-free that femtogram quantitative analysis is commonplace. We then examine some scenarios that should lead analysts to consider using GC–MS/MS to solve complex problems.

Elucidation of Low Molecular Weight Polymers in Vehicular Engine Deposits by Multidimensional Mass Spectrometry

Engine oil is mainly comprised of base oils and various additives, which are low molecular weight polymers mixed into the oil resulting in a polymer blend. Dispersants and detergents, the two most ...

A Lipidomics View of Circadian Biology.

Lipidomics approaches provide quantitative characterization of hundreds of lipid species from biological samples. Recent studies highlight the value of these methods in studying circadian biology, and their potential goes far beyond studying lipid metabolism per se. For example, lipidomics analyses of subcellular compartments can be used to determine daily rhythmicity of different organelles and their intracellular dynamics. In this chapter we describe in detail the procedure for around the clock shotgun lipidomics, from sample preparation to bioinformatics analyses. Sample preparation includes biochemical fractionation of nuclei and mitochondria from mouse liver harvested throughout the day. Lipid content is determined and quantified, in unbiased manner and with wide coverage, using multidimensional mass spectrometry shotgun lipidomics (MDMS-SL). Circadian parameters are then determined with nonparametric statistical tests. Overall, the approach described herein is applicable for various animal models, tissues, and organelles, and is expected to yield new insight on various aspects of circadian biology and lipid metabolism.

Detection and Structural Characterization of Ether Glycerophosphoethanolamine from Cortical Lysosomes Following Traumatic Brain Injury Using UPLC-HDMSE.

The use of ultra performance liquid chromatography coupled to data independent tandem mass spectrometry with traveling wave ion mobility for detection and structural identification of ether-linked glycerophosphoethanolamine is described. The experimental design generates 4D data (chromatographic retention time, precursor accurate mass, drift time with associated calculated collisional cross-section, and time-aligned accurate mass diagnostic product ions) for each ionization mode. Confident structure identification depends on satisfying 4D data confirmation in both positive and negative ion mode. Using this methodology, a number of ether-linked glycerophosphoethanolamine lipids are structurally elucidated from mouse brain lysosomes. It is further determined that several ether-linked glycerophosphoethanolamine structures are differentially abundant between lysosomes isolated from mouse cortex following traumatic brain injury as compared to that of sham animals. The combined effort of aligning multi-dimensional mass spectrometry data with a well-defined traumatic brain injury model lays the foundation for gaining mechanistic insight in the role lysosomal membrane damage plays in neuronal cell death following brain injury.

Self-assembly of polycyclic supramolecules using linear metal-organic ligands

Coordination-driven self-assembly as a bottom-up approach has witnessed a rapid growth in building giant structures in the past few decades. Challenges still remain, however, within the construction of giant architectures in terms of high efficiency and complexity from simple building blocks. Inspired by the features of DNA and protein, which both have specific sequences, we herein design a series of linear building blocks with specific sequences through the coordination between terpyridine ligands and Ru(II). Different generations of polycyclic supramolecules (C1 to C5) with increasing complexity are obtained through the self-assembly with Cd(II), Fe(II) or Zn(II). The assembled structures are characterized via multi-dimensional mass spectrometry analysis as well as multi-dimensional and multinuclear NMR (1H, COSY, NOESY) analysis. Moreover, the largest two cycles C4 and C5 hierarchically assemble into ordered nanoscale structures on a graphite based on their precisely-controlled shapes and sizes with high shape-persistence.

Stepwise Self-Assembly and Dynamic Exchange of Supramolecular Snowflakes.

Snowflake, a highly symmetrical hexagram figure, is challenging to be expressed by chemistry/supramolecular chemistry due to the complex structure. Herein, we have constructed super snowflake supramolecules using terpyridine (tpy)-based metal-organic building blocks with <tpy-Ru(II)-tpy> and <tpy-Zn(II)-tpy> connectivities through stepwise strategies in high yield. The structures were characterized by multi-dimensional mass spectrometry and multi-dimensional NMR spectrometry. In order to address the stability/tolerance of our designed super snowflake structures, ligand exchange behaviors between different supramolecules with various arm length were fully investigated by mass spectrometry. The study revealed that three modes could exist in such binary systems, including full exchange, partial exchange and self-sorting (no exchange) depending on the length difference of ligands.

A targeted strategy for analyzing untargeted mass spectral data to identify lanostane–type triterpene acids in Poria cocos by integrating a scientific information system and liquid chromatography–tandem mass spectrometry combined with ion mobility spectrometry

Rapid structural identification of natural compounds in the crude extract of traditional Chinese medicine by conventional liquid chromatography–mass spectrometry is complex and challenging. In particular, it is difficult to distinguish and identify structural isomers. In this work, we proposed a novel strategy that combines a typical ultrahigh–performance liquid chromatography–multidimensional mass spectrometry approach and the post–processing UNIFI scientific information system to rapidly identify lanostane analogs and isomers in Poria cocos. First, this strategy requires setting up a high–resolution key MS database and an in–house compound library. Then, ultrahigh–performance liquid chromatography coupled with high–resolution tandem data–independent mass spectrometry and ion mobility mass spectrometry was used to acquire untargeted multidimensional mass spectral data. Finally, a new and reliable multidimensional MS analytical workflow was developed to targeted filter the acquired data based on an in–house compound library via the UNIFI™ software. As result, a total of 121 lanostane–type triterpene acids were identified by high–resolution molecular mass, fragment ions, and collision cross–section values. Eight triterpene acids were unambiguously identified by comparing the retention time and MS/MS data with those of reference compounds. Three compounds were detected and reported for the first time based on their neutral losses, characteristic ions, and fragmentation pathways compared with those of known compounds. We anticipate that such an analytical approach can be extended to rapidly screen and characterize other herbal medicine compounds with multiple isomers.

Abstract 367: Quantitative Phosphoproteomic Profiling and Causal Pathway Analysis Reveal Novel Mediators and Mechanisms Supporting Glycoprotein VI (GPVI) Signaling and Platelet Function

Intracellular signaling systems associated with the platelet glycoprotein VI receptor (GPVI) are essential to hemostasis and also initiate atherothrombotic events in cardiovascular disease contexts. While many separable molecular entities linked to GPVI-mediated platelet activation have been characterized, a systemic understanding of GPVI signaling over the context of the entire platelet proteome remains far from complete. To test the hypothesis that specific, intact signaling systems downstream of GPVI regulate platelet function, we used a combination of tandem mass tag (TMT) labeling and high-resolution, multidimensional mass spectrometry and informatics tools to quantify and model protein phosphorylation events reproducibly measurable in platelets from healthy human subjects (n=5) in response to the GPVI specific agonist collagen-related peptide (CRP). Following CRP treatment, platelets rapidly regulated the phosphorylation state of 1004 serine, threonine and tyrosine residues on 542 proteins (fold change &gt; 1.5; false discovery rate &lt; 0.05). In addition to regulatory sites on kinases, phospholipases and other well-known targets, the majority of phosphorylation events following CRP stimulation occurred on proteins with uncharacterized roles in platelet function. CausalPath analysis of this data mapped 46 high confidence pathway relations, suggesting that Syk, PKCs and MAPKs ultimately drive the phosphorylation of several novel effectors specifically downstream of GPVI in platelet regulation, including CRMP-2, RAD23B and caldesmon. In addition to comprehensively mapping out known signaling relations while illuminating previously unrecognized regulatory mechanisms of platelet function around GPVI, our work provides a platform to generate novel, mechanistic and testable hypotheses from omics data sets for subsequent discovery-driven efforts to identify platelet-based therapeutic and biomarker targets relevant to cardiovascular disease risk, progression and mortality.