Ion mobility-mass spectrometry: time-dispersive instrumentation

Evaluating Ion Mobility Data Acquisition, Calibration, and Processing for Small Molecules: A Cross-Platform Assessment of Drift Tube and Traveling Wave Methodologies

As ion mobility spectrometry (IMS) separations continue to be added to analytical workflows due to their power in environmental and biological sample analyses, harmonization and capability understanding between existing and newly released instruments are desperately needed. Developments in IMS platforms often exhibit focus on increasing resolving power (Rp) to better separate molecules of similar structure. While the additional separation capacity is advantageous, ensuring these developments coincide with appropriate data extraction and analysis methods is imperative to ensure routine adoption. Herein, we assess the performance of the MOBILion MOBIE in relation to a commercially available drift tube IMS-MS, the Agilent 6560, and evaluate feature extraction and analysis pipelines. Both instruments were operated using matched conditions when possible, and performance metrics of scan speed, Rp, limits of detection (LOD), and propensity for isomer separation via LC-IMS-MS were evaluated. Similar scan speeds pertaining to IMS-MS frame generation were noted for both platforms, and collision cross section (CCS) values for the MOBIE were generally within ≤ 1% difference from previously reported drift tube values. Both platforms were also able to generate quantitative data (comparable limits of detection) in experiments with perfluoroalkyl substances (PFAS) mixtures in a cell-based model (both medium and cell lysates), as demonstrated in Skyline with adjusted mobility filtering parameters. Higher Rp was, however, noted on the MOBIE in comparison to the 6560 (200-300 vs 45-60 CCS/ΔCCS without data processing), allowing the detection of more PFAS isomers and indicating promise toward future applications in chemical exposomics studies and biomarker discovery when molecules exhibit similar structures.

Bromine-Based Derivatization of Carboxyl-Containing Metabolites for Liquid Chromatography-Trapped Ion Mobility Spectrometry-Mass Spectrometry

The analysis of small carboxyl-containing metabolites (CCMs), such as tricarboxylic acid (TCA) cycle intermediates, provides highly useful information about the metabolic state of cells. However, their detection using liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS) methods can face sensitivity and specificity challenges given their low ionization efficiency and the presence of isomers. Ion mobility spectrometry (IMS), such as trapped ion mobility spectrometry (TIMS), provides additional specificity, but further signal loss can occur during the mobility separation process. We, therefore, developed a solution to boost CCM ionization and chromatographic separation as well as leverage specificity of IMS. Inspired by carbodiimide-mediated coupling of carboxylic acids with 4-bromo-N-methylbenzylamine (4-BNMA) for quantitative analysis, we newly report the benefits of this reagent for TIMS-based measurement. We observed a pronounced (orders of magnitude) increase in signal and enhanced isomer separations, particularly by LC. We found that utilization of a brominated reagent, such as 4-BNMA, offered unique benefits for untargeted CCM measurement. Derivatized CCMs displayed shifted mobility out of the metabolite and lipid region of the TIMS-MS space as well as characteristic isotope patterns, which were leveraged for data mining with Mass Spectrometry Query Language (MassQL) and indication of the number of carboxyl groups. The utility of our LC-ESI-TIMS-MS/MS method with 4-BMA derivatization was demonstrated via the characterization of alterations in CCM expression in bone marrow-derived macrophages upon activation with lipopolysaccharide. While metabolic reprogramming in activated macrophages has been characterized previously, especially with respect to TCA cycle intermediates, we report a novel finding that isomeric itaconic, mesaconic, and citraconic acid increase after 24 h, indicating possible roles in the inflammatory response.

The bacterial chaperone CsgC inhibits functional amyloid CsgA formation by promoting the intrinsically disordered pre-nuclear state

E. coli secretes a functional amyloid called curli during biofilm formation. Curli fibers are composed of polymers of the CsgA protein, which adopts a beta-sheet rich fold upon fibrillization. A chaperone-like protein called CsgC inhibits CsgA amyloid formation. Like other amyloidogenic proteins, CsgA undergoes a 3-stage aggregation process: an initial lag phase where a beta-rich nucleus forms, an exponential elongation phase, and a plateau phase. It is currently not known if CsgC inhibits amyloid formation by inhibiting formation of the pre-fibril nucleus, or rather, if CsgC inhibits a later stage of amyloid formation by blocking monomer addition to a growing fiber. Here, CsgC homologs from C. youngae , C. davisae , and H. alvei were purified and characterized for their ability to interrogate CsgA amyloid formation. Each of the CsgC homologs prolonged the lag phase of E. coli CsgA amyloid formation in a similar fashion as E. coli CsgC. Additionally, we found that E. coli CsgC interacted transiently and weakly with a monomeric, pre-nucleus species of CsgA and that this interaction delayed amyloid formation. A transient CsgC-CsgA heterodimer was observed using ion mobility-mass spectrometry. When CsgC was added to actively polymerizing CsgA, exponential growth commonly associated with nucleation-dependent amyloid formation was lost. However, the addition of preformed CsgA seeds did not rescue exponential growth indicating that CsgC also has inhibitory activity during fibril elongation. Indeed, CsgC interacted strongly with CsgA fibers, suggesting that the interaction between CsgC and CsgA fibers can slow new fiber growth. CsgC displays a unique inhibitory activity at multiple stages of amyloid formation. CsgC acts as an energy-independent chaperone that transiently interacts with prefibrillar CsgA as well as an amyloid fiber.

Surface-Induced Unfolding Reveals Unique Structural Features and Enhances Machine Learning Classification Models

Native ion mobility-mass spectrometry combined with collision-induced unfolding (CIU) is a powerful analytical method for protein characterization, offering insights into structural stability and enabling the differentiation of analytes with similar mass and mobility. A surface-induced dissociation (SID) device was recently commercialized, enabling broader adoption of SID measurements and surface-induced unfolding (SIU). This study evaluates SIU, benchmarking its reproducibility and performance against CIU on a Waters CyclicIMS ion mobility-mass spectrometer. Reproducibility studies were conducted on model proteins, including β-lactoglobulin (β-lac), bovine serum albumin (BSA), and immunoglobulin G1 kappa (IgG1κ). SIU and CIU exhibited comparable reproducibility, with root-mean-square deviation (RMSD) values averaging less than 4% across multiple charge states. Notably, SIU achieved unfolding transitions at lower lab-frame energies, enhancing sensitivity to subtle structural differences and providing additional analytical information, such as unique high arrival time unfolding features and additional unfolding transitions. Furthermore, the differentiation of closely related protein subclasses, such as IgG1κ and IgG4κ, was improved with SIU, as evidenced by the enhancement of supervised machine learning models for IgG subclass classifications. SIU-trained models outperformed or matched CIU-trained models, achieving high cross-validation accuracies (>90%) and robust classifications of biotherapeutics Adalimumab and Nivolumab. This work establishes SIU as a complementary and efficient alternative to CIU, offering improved sensitivity and analytical depth without loss in reproducibility. This work highlights the benefits of including SIU in protein characterization workflows, particularly in high-throughput and machine learning-guided applications.

Reference Library for Suspect Non-targeted Screening of Environmental Toxicants Using Ion Mobility Spectrometry-Mass Spectrometry

As our health is affected by the xenobiotic chemicals we are exposed to, it is important to rapidly assess these molecules both in the environment and our bodies. Targeted analytical methods coupling either gas or liquid chromatography with mass spectrometry (GC-MS or LC-MS) are commonly utilized in current exposure assessments. While these methods are accepted as the gold standard for exposure analyses, they often require multiple sample preparation steps and more than 30 minutes per sample. This throughput limitation is a critical gap for exposure assessments and has resulted in an evolving interest in using ion mobility spectrometry and MS (IMS-MS) for non-targeted studies. IMS-MS is a unique technique due to its rapid analytical capabilities (millisecond scanning) and detection of a wide range of chemicals based on unique collision cross section (CCS) and mass-to-charge (m/z) values. To increase the availability of IMS-MS information for exposure studies, here we utilized drift tube IMS-MS to evaluate 4,685 xenobiotic chemical standards from the Environmental Protection Agency Toxicity Forecaster (ToxCast) program including pesticides, industrial chemicals, pharmaceuticals, consumer products, and per- and polyfluoroalkyl substances (PFAS). In the analyses, 3,993 [M+H]+, [M+Na]+, [M-H]- and [M+]+ ion types were observed with high confidence and reproducibility (≤1% error intra-laboratory and ≤2% inter-laboratory) from 2,140 unique chemicals. These values were then assembled into an openly available multidimensional database and uploaded to PubChem to enable rapid IMS-MS suspect screening for a wide range of environmental contaminants, faster response time in environmental exposure assessments, and assessments of xenobiotic-disease connections.

Development of an Oxylipin Library Using Liquid Chromatography-Ion Mobility Quadrupole Time-of-Flight: Application to Mouse Brain Tissue

Oxylipins are bioactive lipid mediators derived from polyunsaturated fatty acids (PUFAs) that play crucial roles in physiological and pathological processes. The analysis and identification of oxylipins are challenging due to the numerous isomeric forms. Ion mobility (IM), which separates ions based on their spatial configuration, combined with liquid chromatography (LC) and mass spectrometry (MS), has been proven effective for separating isomeric compounds. In this study, we developed an extensive oxylipin library containing information on retention time (RT), m/z, and CCS values for 74 oxylipin standards using LC-IM-QTOF-MS in positive and negative ionization modes. The oxylipins in the library were grouped into 15 isomer categories to evaluate the efficacy of IM in isomeric separation. Various adducts were investigated, including protonated, deprotonated, and sodiated forms. The ΔCCS% for more than 1000 isomeric pairs was calculated, revealing that 30% of these exhibited a ΔCCS% greater than 2%. Positive ionization mode demonstrated superior separation capabilities, with 274 isomer pairs achieving baseline separation (ΔCCS% >  4%). Sodium adducts significantly improved isomer separation. With the inclusion of LC separation, only nine oxylipins coeluted, forming six different isomeric pairs. CCS values for the adducts [M+Na]+ and [M+2Na-H]+ separated three of these isomeric pairs. The CCS values were compared to experimental libraries, confirming the high reproducibility of CCS measurements, with average errors below 2%. Applying this library to mouse brain samples, 19 different oxylipins were identified by matching RT, m/z, and CCS values. Coeluting isomers, 9- and 13-HODE, 8- and 12-HETE, and 15-oxo-ETE and 14(15)-EpETrE, were successfully separated and identified using drift time separation.

Structural Changes in Atomically Precise Ag29 Nanoclusters upon Sequential Attachment and Detachment of Secondary Ligands

Elucidating the structural dynamics of ligand-stabilized noble metal nanoclusters (NCs) is critical for understanding their properties and for developing applications. Ligand rearrangement at NC surfaces is an important contributor to structural change. In this study, we investigate the dynamic behavior of ligand-protected [Ag29(L)12]3- NC's (L = 1,3-benzenedithiol) interacting with secondary ligand 2,2'-[1,4-phenylenebis (methylidynenitrilo)] bis[benzenethiol] (referred to as L'). We specifically focus on the structural characteristics of NC-based adducts [Ag29(L)12]L'n3- where n ranges from 1 to 4. This is probed experimentally by using a combination of ultraviolet-visible (UV-vis) spectroscopy, high-resolution electrospray ionization mass spectrometry (ESI MS), and ion mobility mass spectrometry (IM MS) coupled with collision-induced dissociation (MS2 IMS). Density functional theory (DFT) calculations infer comparatively weak noncovalent interactions between the NCs and attached secondary ligands consistent with the fragmentation behavior and experimentally determined collision cross-sections (CCSs), which show a monotonic CCS increase of [Ag29(L)12]L'n3- with an increasing number of L' (n = 1-4). From a detailed analysis of the predicted structures, we infer progressive expansion of the Ag-S staple framework of the precursor NC as secondary ligands are added. Interestingly, detachment of L' from gas-phase [Ag29(L)12]L'n3- by collisional heating yields structures which retain "footprints" of the detached secondary ligands on a millisecond time scale.

diaTracer enables spectrum-centric analysis of diaPASEF proteomics data

Data-independent acquisition has become a widely used strategy for peptide and protein quantification in liquid chromatography-tandem mass spectrometry-based proteomics studies. The integration of ion mobility separation into data-independent acquisition analysis, such as the diaPASEF technology available on Bruker's timsTOF platform, further improves the quantification accuracy and protein depth achievable using data-independent acquisition. We introduce diaTracer, a spectrum-centric computational tool optimized for diaPASEF data. diaTracer performs three-dimensional (mass to charge ratio, retention time, ion mobility) peak tracing and feature detection to generate precursor-resolved "pseudo-tandem mass spectra", facilitating direct ("spectral-library free") peptide identification and quantification from diaPASEF data. diaTracer is available as a stand-alone tool and is fully integrated into the widely used FragPipe computational platform. We demonstrate the performance of diaTracer and FragPipe using diaPASEF data from triple-negative breast cancer, cerebrospinal fluid, and plasma samples, data from phosphoproteomics and human leukocyte antigens immunopeptidomics experiments, and low-input data from a spatial proteomics study. We also show that diaTracer enables unrestricted identification of post-translational modifications from diaPASEF data using open/mass-offset searches.

Synergy of native mass spectrometry and other biophysical techniques in studies of iron‑sulfur cluster proteins and their assembly

The application of mass spectrometric methodologies has revolutionised biological chemistry, from identification through to structural and conformational studies of proteins and other macromolecules. Native mass spectrometry (MS), in which proteins retain their native structure, is a rapidly growing field. This is particularly the case for studies of metalloproteins, where non-covalently bound cofactors remain bound following ionisation. Such metalloproteins include those that contain an iron‑sulfur (FeS) cluster and, despite their fragility and O2 sensitivity, they have been a particular focus for applications of native MS because of its capacity to accurately monitor mass changes that reveal chemical changes at the cluster. Here we review recent advances in these applications of native MS, which, together with data from more traditionally applied biophysical methods, have yielded a remarkable breadth of information about the FeS species present, and provided key mechanistic insight not only for FeS cluster proteins themselves, but also their assembly.

Advanced LC-IMS-MS Protocol for Holistic Metabolite Analysis in Wine and Grape Samples

The final aim of metabolomics is the comprehensive and holistic study of the metabolome in biological samples. Therefore, the use of instruments that enable the analysis of metabolites belonging to various chemical classes in a wide range of concentrations is essential, without compromising on robustness, resolution, sensitivity, specificity, and metabolite annotation. These characteristics are crucial for the analysis of very complex samples, such as wine, whose metabolome is the result of the sum of metabolites derived from grapes, yeast(s), bacteria(s), and chemical or physical modification during winemaking. In recent years, a big advantage, in this direction, was the hardware developments on hyphenated instruments that enable the integration of liquid chromatography (LC), ion mobility spectrometry (IMS), and mass spectrometry (MS). This chapter describes an LC-IMS-MS protocol for the analysis of wine and grape samples as well as the use of IMS data in metabolite annotation.

Identification and Structural Elucidation of Acylsugars in Tomato Leaves Using Liquid Chromatography-Ion Mobility-Tandem Mass Spectrometry (LC-IM-MS/MS)

Leaves of tomato plants contain various glandular trichomes that produce a wide range of metabolic products including acylsugars, which may serve as a defense mechanism against various insect pests. Acylsugars exhibit significant structural diversity, differing in their sugar cores, acylated positions, and type of acyl chains. This work demonstrated a comprehensive approach using multidimensional separation techniques, specifically liquid chromatography-ion mobility-tandem mass spectrometry (LC-IM-MS/MS), for structural characterization, and the discrimination of different tomato plants (one cultivar and five accessions) was demonstrated using tomato leaf extracts; six genotypes from five species of Solanum were represented. As a result, we identified 16 acylsugars through their molecular formulas and annotations using LC and MS analyses. The incorporation of ion mobility (IM) analysis revealed an additional 9 isomeric forms, resulting in a comprehensive total of 25 isomeric acylsugars identified. Furthermore, the experimental collision cross section (CCSexp) values agreed reasonably well with the corresponding predicted values (CCSpred), with an overall estimated error of less than 2%. These findings pave the way for research into how the different structural isomers of acylsugars might influence the self-defense mechanism in plants. Moreover, this work demonstrated that the investigated cultivar and accessions of tomatoes can be distinguished from each other based on their metabolite profile, e.g., acylsugars, with principal component analysis (PCA) and linear discriminant analysis (LDA) statistical models, yielding a prediction rate of 98.3%.

Advances in mass spectrometry for metabolomics: Strategies, challenges, and innovations in disease biomarker discovery

Mass spectrometry (MS) plays a crucial role in metabolomics, especially in the discovery of disease biomarkers. This review outlines strategies for identifying metabolites, emphasizing precise and detailed use of MS techniques. It explores various methods for quantification, discusses challenges encountered, and examines recent breakthroughs in biomarker discovery. In the field of diagnostics, MS has revolutionized approaches by enabling a deeper understanding of tissue-specific metabolic changes associated with disease. The reliability of results is ensured through robust experimental design and stringent system suitability criteria. In the past, data quality, standardization, and reproducibility were often overlooked despite their significant impact on MS-based metabolomics. Progress in this field heavily depends on continuous training and education. The review also highlights the emergence of innovative MS technologies and methodologies. MS has the potential to transform our understanding of metabolic landscapes, which is crucial for disease biomarker discovery. This article serves as an invaluable resource for researchers in metabolomics, presenting fresh perspectives and advancements that propels the field forward.

Ion mobility spectrometry and ion mobility-mass spectrometry in clinical chemistry

Advancements in clinical chemistry have major implications in terms of public health, prompting many clinicians to seek out chemical information to aid in diagnoses and treatments. While mass spectrometry (MS) and hyphenated-MS techniques such as LC-MS or tandem MS/MS have long been the analytical methods of choice for many clinical applications, these methods routinely demonstrate difficulty in differentiating between isomeric forms in complex matrices. Consequently, ion mobility spectrometry (IM), which differentiates molecules on the basis of size, shape, and charge, has demonstrated unique advantages in the broad application of stand-alone IM and hyphenated IM instruments towards clinical challenges. Here, we highlight representative IM applications and approaches and describe contemporary commercial offerings of IM technology and how these can be, or are currently being, applied to the field of clinical chemistry.

Metagenomic, metabolomic, and lipidomic shifts associated with fecal microbiota transplantation for recurrent Clostridioides difficile infection

Recurrent C. difficile infection (rCDI) is an urgent public health threat, for which the last resort and lifesaving treatment is a fecal microbiota transplant (FMT). However, the exact mechanisms that mediate a successful FMT are not well-understood. Here, we use longitudinal stool samples collected from patients undergoing FMT to evaluate intra-individual changes in the microbiome, metabolome, and lipidome after successful FMTs relative to their baselines pre-FMT. We show changes in the abundance of many lipids, specifically a decrease in acylcarnitines post-FMT, and a shift from conjugated bile acids pre-FMT to deconjugated secondary bile acids post-FMT. These changes correlate with a decrease in Enterobacteriaceae, which encode carnitine metabolism genes, and an increase in Lachnospiraceae, which encode bile acid altering genes such as bile salt hydrolases (BSHs) and the bile acid-inducible (bai) operon, post-FMT. We also show changes in gut microbe-encoded amino acid biosynthesis genes, of which Enterobacteriaceae was the primary contributor to amino acids C. difficile is auxotrophic for. Liquid chromatography, ion mobility spectrometry, and mass spectrometry (LC-IMS-MS) revealed a shift from microbial conjugation of primary bile acids pre-FMT to secondary bile acids post-FMT. Here, we define the structural and functional changes associated with a successful FMT and generate hypotheses that require further experimental validation. This information is meant to help guide the development of new microbiota-focused therapeutics to treat rCDI.IMPORTANCERecurrent C. difficile infection is an urgent public health threat, for which the last resort and lifesaving treatment is a fecal microbiota transplant. However, the exact mechanisms that mediate a successful FMT are not well-understood. Here, we show changes in the abundance of many lipids, specifically acylcarnitines and bile acids, in response to FMT. These changes correlate with Enterobacteriaceae pre-FMT, which encodes carnitine metabolism genes, and Lachnospiraceae post-FMT, which encodes bile salt hydrolases and baiA genes. There was also a shift from microbial conjugation of primary bile acids pre-FMT to secondary bile acids post-FMT. Here, we define the structural and functional changes associated with a successful FMT, which we hope will help aid in the development of new microbiota-focused therapeutics to treat rCDI.

diaTracer enables spectrum-centric analysis of diaPASEF proteomics data

Data-independent acquisition (DIA) has become a widely used strategy for peptide and protein quantification in mass spectrometry-based proteomics studies. The integration of ion mobility separation into DIA analysis, such as the diaPASEF technology available on Bruker's timsTOF platform, further improves the quantification accuracy and protein depth achievable using DIA. We introduce diaTracer, a new spectrum-centric computational tool optimized for diaPASEF data. diaTracer performs three-dimensional (m/z, retention time, ion mobility) peak tracing and feature detection to generate precursor-resolved "pseudo-MS/MS" spectra, facilitating direct ("spectral-library free") peptide identification and quantification from diaPASEF data. diaTracer is available as a stand-alone tool and is fully integrated into the widely used FragPipe computational platform. We demonstrate the performance of diaTracer and FragPipe using diaPASEF data from triple-negative breast cancer (TNBC), cerebrospinal fluid (CSF), and plasma samples, data from phosphoproteomics and HLA immunopeptidomics experiments, and low-input data from a spatial proteomics study. We also show that diaTracer enables unrestricted identification of post-translational modifications from diaPASEF data using open/mass-offset searches.

Streamlining Phenotype Classification and Highlighting Feature Candidates: A Screening Method for Non-Targeted Ion Mobility Spectrometry-Mass Spectrometry (IMS-MS) Data

Nontargeted analysis (NTA) is increasingly utilized for its ability to identify key molecular features beyond known targets in complex samples. NTA is particularly advantageous in exploratory studies aimed at identifying phenotype-associated features or molecules able to classify various sample types. However, implementing NTA involves extensive data analyses and labor-intensive annotations. To address these limitations, we developed a rapid data screening capability compatible with NTA data collected on a liquid chromatography, ion mobility spectrometry, and mass spectrometry (LC-IMS-MS) platform that allows for sample classification while highlighting potential features of interest. Specifically, this method aggregates the thousands of IMS-MS spectra collected across the LC space for each sample and collapses the LC dimension, resulting in a single summed IMS-MS spectrum for screening. The summed IMS-MS spectra are then analyzed with a bootstrapped Lasso technique to identify key regions or coordinates for phenotype classification via support vector machines. Molecular annotations are then performed by examining the features present in the selected coordinates, highlighting potential molecular candidates. To demonstrate this summed IMS-MS screening approach, we applied it to clinical plasma lipidomic NTA data and exposomic NTA data from water sites with varying contaminant levels. Distinguishing coordinates were observed in both studies, enabling the evaluation of phenotypic molecular annotations and resulting in screening models capable of classifying samples with up to a 25% increase in accuracy compared to models using annotated data.

Fe-S cluster biosynthesis and maturation: Mass spectrometry-based methods advancing the field

Iron‑sulfur (FeS) clusters are inorganic protein cofactors that perform essential functions in many physiological processes. Spectroscopic techniques have historically been used to elucidate details of FeS cluster type, their assembly and transfer, and changes in redox and ligand binding properties. Structural probes of protein topology, complex formation, and conformational dynamics are also necessary to fully understand these FeS protein systems. Recent developments in mass spectrometry (MS) instrumentation and methods provide new tools to investigate FeS cluster and structural properties. With the unique advantage of sampling all species in a mixture, MS-based methods can be utilized as a powerful complementary approach to probe native dynamic heterogeneity, interrogate protein folding and unfolding equilibria, and provide extensive insight into protein binding partners within an entire proteome. Here, we highlight key advances in FeS protein studies made possible by MS methodology and contribute an outlook for its role in the field.

A deep learning-guided automated workflow in LipidOz for detailed characterization of fungal fatty acid unsaturation by ozonolysis

Understanding fungal lipid biology and metabolism is critical for antifungal target discovery as lipids play central roles in cellular processes. Nuances in lipid structural differences can significantly impact their functions, making it necessary to characterize lipids in detail to understand their roles in these complex systems. In particular, lipid double bond (DB) locations are an important component of lipid structure that can only be determined using a few specialized analytical techniques. Ozone-induced dissociation mass spectrometry (OzID-MS) is one such technique that uses ozone to break lipid DBs, producing pairs of characteristic fragments that allow the determination of DB positions. In this work, we apply OzID-MS and LipidOz software to analyze the complex lipids of Saccharomyces cerevisiae yeast strains transformed with different fatty acid desaturases from Histoplasma capsulatum to determine the specific unsaturated lipids produced. The automated data analysis in LipidOz made the determination of DB positions from this large dataset more practical, but manual verification for all targets was still time-consuming. The DL model reduces manual involvement in data analysis, but since it was trained using mammalian lipid extracts, the prediction accuracy on yeast-derived data was reduced. We addressed both shortcomings by retraining the DL model to act as a pre-filter to prioritize targets for automated analysis, providing confident manually verified results but requiring less computational time and manual effort. Our workflow resulted in the determination of detailed DB positions and enzymatic specificity.

Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry

Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this Review will serve as a handbook for researchers who are new to the field of bottom-up proteomics.

Exploring Ion Mobility Mass Spectrometry Data File Conversions to Leverage Existing Tools and Enable New Workflows

Ion mobility (IM) is often combined with LC-MS experiments to provide an additional dimension of separation for complex sample analysis. While highly complex samples are better characterized by the full dimensionality of LC-IM-MS experiments to uncover new information, downstream data analysis workflows are often not equipped to properly mine the additional IM dimension. For many samples the data acquisition benefits of including IM separations are all that is necessary to uncover sample information and the full dimensionality of the data is not required for data analysis. Postacquisition reduction and adaptation of the dimensions of LC-IM-MS and IM-MS experiments into an LC-MS format opens the possibility to use a plethora of existing software tools. In this work, we developed data file conversion tools to reduce the complexity of IM data analysis. Three data file transformations are introduced in the PNNL PreProcessor software: (1) mapping the IM axis to the LC axis for IM-MS data, (2) converting the drift time vs m/z space to CCS/z vs m/z space, and (3) transforming All Ions IM/MS mobility aligned fragmentation data to a standard LC-MS DDA data file format. These new data file conversions are demonstrated with corresponding lipidomics and proteomics workflows that leverage existing LC-MS data analysis software to highlight the benefits of the data transformations.

Direct Enantiomer Differentiation of Drugs and Drug-Like Compounds via Noncovalent Copper-Amino Acid Complexation and Ion Mobility-Mass Spectrometry

Drug enantiomers can possess vastly different pharmacological properties, yet they are identical in their chemical composition and structural connectivity. Thus, resolving enantiomers poses a great challenge in the field of separation science. Enantiomer separations necessitate interaction of the analyte with a chiral environment─in mass spectrometry-based analysis, a common approach is through a three-point interaction with a chiral selector commonly introduced during sample preparation. In select cases, the structural difference imparted through noncovalent complexation results in enantiomer-specific structural differences, facilitating measurement using a structurally selective analytical technique such as ion mobility-mass spectrometry (IM-MS). In this work, we investigate the direct IM-MS differentiation of chiral drug compounds using mononuclear copper complexes incorporating an amino acid chiral selector. A panel of 20 chiral drugs and drug-like compounds were investigated for separation, and four l-amino acids (l-histidine, l-tryptophan, l-proline, and l-tyrosine) were evaluated as chiral selectors (CS) to provide the chiral environment necessary for differentiation. Enantiomer differentiation was achieved for four chiral molecule pairs (R/S-thalidomide, R/S-baclofen, R/S-metoprolol, and d/l-panthenol) with two-peak resolution (Rp-p) values ranging from 0.7 (>10% valley) to 1.5 (baseline separation). Calibration curves relating IM peak areas to enantiomeric concentrations enabled enantiomeric excess quantitation of racemic thalidomide and metoprolol with residuals of 5.7 and 2.5%, respectively. Theoretical models suggest that CuII and l-histidine complexation around the analyte chiral center is important for gas-phase stereoselectivity. This study demonstrates the potential of combining enantioselective noncovalent copper complexation with structurally selective IM-MS for differentiating chiral drugs and drug-like compounds.

Single ion mobility monitoring (SIM2) stitching method for high-throughput and high ion mobility resolution chiral analysis

Chiral analysis is of high interest in many fields such as chemistry, pharmaceuticals and metabolomics. Mass spectrometry and ion mobility spectrometry are useful analytical tools, although they cannot be used as stand-alone methods. Here, we propose an efficient strategy for the enantiomer characterization of amino acids (AAs) using non-covalent copper complexes. A single ion mobility monitoring (SIM2) method was applied on a TIMS-ToF mass spectrometer to maximize the detection and mobility separation of isomers. Almost all of the 19 pairs of proteinogenic AA enantiomers could be separated with at least one combination with the chiral references L-Phe and L-Pro. Furthermore, we extended the targeted SIM2 method by stitching several mobility ranges, in order to be able to analyze complex mixtures in a single acquisition while maintaining high mobility resolution. Most of the enantiomeric pairs of AAs separated with the SIM2 method were also detected with this approach. The SIM2 stitching method thus opens the way to a more comprehensive chiral analysis with TIMS-ToF instruments.

Mapping the Multiscale Proteomic Organization of Cellular and Disease Phenotypes

While the primary sequences of human proteins have been cataloged for over a decade, determining how these are organized into a dynamic collection of multiprotein assemblies, with structures and functions spanning biological scales, is an ongoing venture. Systematic and data-driven analyses of these higher-order structures are emerging, facilitating the discovery and understanding of cellular phenotypes. At present, knowledge of protein localization and function has been primarily derived from manual annotation and curation in resources such as the Gene Ontology, which are biased toward richly annotated genes in the literature. Here, we envision a future powered by data-driven mapping of protein assemblies. These maps can capture and decode cellular functions through the integration of protein expression, localization, and interaction data across length scales and timescales. In this review, we focus on progress toward constructing integrated cell maps that accelerate the life sciences and translational research.

Spatially and temporally probing distinctive glycerophospholipid alterations in Alzheimer's disease mouse brain via high-resolution ion mobility-enabled sn-position resolved lipidomics

Dysregulated glycerophospholipid (GP) metabolism in the brain is associated with the progression of neurodegenerative diseases including Alzheimer's disease (AD). Routine liquid chromatography-mass spectrometry (LC-MS)-based large-scale lipidomic methods often fail to elucidate subtle yet important structural features such as sn-position, hindering the precise interrogation of GP molecules. Leveraging high-resolution demultiplexing (HRdm) ion mobility spectrometry (IMS), we develop a four-dimensional (4D) lipidomic strategy to resolve GP sn-position isomers. We further construct a comprehensive experimental 4D GP database of 498 GPs identified from the mouse brain and an in-depth extended 4D library of 2500 GPs predicted by machine learning, enabling automated profiling of GPs with detailed acyl chain sn-position assignment. Analyzing three mouse brain regions (hippocampus, cerebellum, and cortex), we successfully identify a total of 592 GPs including 130 pairs of sn-position isomers. Further temporal GPs analysis in the three functional brain regions illustrates their metabolic alterations in AD progression.

High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage

In the modern "omics" era, measurement of the human exposome is a critical missing link between genetic drivers and disease outcomes. High-resolution mass spectrometry (HRMS), routinely used in proteomics and metabolomics, has emerged as a leading technology to broadly profile chemical exposure agents and related biomolecules for accurate mass measurement, high sensitivity, rapid data acquisition, and increased resolution of chemical space. Non-targeted approaches are increasingly accessible, supporting a shift from conventional hypothesis-driven, quantitation-centric targeted analyses toward data-driven, hypothesis-generating chemical exposome-wide profiling. However, HRMS-based exposomics encounters unique challenges. New analytical and computational infrastructures are needed to expand the analysis coverage through streamlined, scalable, and harmonized workflows and data pipelines that permit longitudinal chemical exposome tracking, retrospective validation, and multi-omics integration for meaningful health-oriented inferences. In this article, we survey the literature on state-of-the-art HRMS-based technologies, review current analytical workflows and informatic pipelines, and provide an up-to-date reference on exposomic approaches for chemists, toxicologists, epidemiologists, care providers, and stakeholders in health sciences and medicine. We propose efforts to benchmark fit-for-purpose platforms for expanding coverage of chemical space, including gas/liquid chromatography-HRMS (GC-HRMS and LC-HRMS), and discuss opportunities, challenges, and strategies to advance the burgeoning field of the exposome.

Radiation-Induced Molecular Processes in DNA: A Perspective on Gas-Phase Interaction Studies

Studying the direct effects of DNA irradiation is essential for understanding the impact of radiation on biological systems. Gas-phase interactions are especially well suited to uncover the molecular mechanisms underlying these direct effects. Only relatively recently, isolated DNA oligonucleotides were irradiated by ionizing particles such as VUV or X-ray photons or ion beams, and ionic products were analyzed by mass spectrometry. This article provides a comprehensive review of primarily experimental investigations in this field over the past decade, emphasizing the description of processes such as ionization, fragmentation, charge and hydrogen transfer triggered by photoabsorption or ion collision, and the recent progress made thanks to specific atomic photoabsorption. Then, we outline ongoing experimental developments notably involving ion-mobility spectrometry, crossed beams or time-resolved measurements. The discussion extends to potential research directions for the future.

High-Specificity Imaging Mass Spectrometry

Imaging mass spectrometry (IMS) enables highly multiplexed, untargeted tissue mapping for a broad range of molecular classes, facilitating in situ biological discovery. Yet, challenges persist in molecular specificity, which is the ability to discern one molecule from another, and spatial specificity, which is the ability to link untargeted imaging data to specific tissue features. Instrumental developments have dramatically improved IMS spatial resolution, allowing molecular observations to be more readily associated with distinct tissue features across spatial scales, ranging from larger anatomical regions to single cells. High-performance mass analyzers and systems integrating ion mobility technologies are also becoming more prevalent, further improving molecular coverage and the ability to discern chemical identity. This review provides an overview of recent advancements in high-specificity IMS that are providing critical biological context to untargeted molecular imaging, enabling integrated analyses, and addressing advanced biomedical research applications.

Recent advances in gas phase unfolding: Instrumentation and applications

Broader adoption of native mass spectrometry (MS) and ion mobility-mass spectrometry (IM-MS) has propelled the development of several techniques which take advantage of the selectivity, sensitivity, and speed of MS to make measurements of complex biological molecules in the gas phase. One such method, collision induced unfolding (CIU), has risen to prominence in recent years, due to its well documented capability to detect shifts in structural stability of biological molecules in response to external stimuli (e.g., mutations, stress, non-covalent interactions, sample conditions etc.). This increase in reported CIU measurements is enabled partly due to advances in IM-MS instrumentation by vendors, and also innovative method development by researchers. This perspective highlights a few of these advances and concludes with a look forward toward the future of the gas phase unfolding field.

Advances of Ion Mobility Platform for Plant Metabolomics

Metabolomics aims to profile the extensive array of metabolites that exists in different types of matrices using modern analytical techniques. These techniques help to separate, identify, and quantify the plethora of chemical compounds at various analytical platforms. Hence, ion mobility spectrometry (IMS) has emerged as an advanced analytical approach, exclusively owing to the 3D separation of metabolites and their isomers. Furthermore, separated metabolites are identified based on their mass fragmentation pattern and CCS (collision cross-section) values. The IMS provides an advanced alternative dimension to separate the isomeric metabolites with enhanced throughput with lesser chemical noise. Thus, the present review highlights the types, factors affecting the resolution, and applications of IMMS (Ion mobility mass spectrometry) for isomeric separations, and ionic contaminants in the plant samples. Furthermore, an overview of IMS-based applications for the identification of plant metabolites (volatile and non-volatile) over the last few decades has been discussed, followed by future assumptions for creating IM-based databases. Such approaches could be significant to accelerate and improve our knowledge of the vast chemical diversity found in plants.

Serum untargeted lipidomic characterization in a general Chinese cohort with residual per-/polyfluoroalkyl substances by liquid chromatography-drift tube ion mobility-mass spectrometry

Per- and polyfluoroalkyl substances (PFAS) remain controversial due to their high persistency and potential human toxicity. Although occupational exposure to PFAS has been widely investigated, the implications of PFAS occurrence in the general population remain to be unraveled. Considering that serum from most people contains PFAS, the aim of this study was to characterize the lipidomic profile in human serum from a general cohort (n = 40) with residual PFAS levels. The geometric means of ∑PFAS (11.8 and 4.4 ng/mL) showed significant differences (p < 0.05) for the samples with the highest (n = 20) and lowest (n = 20) concentrations from the general population respectively. Reverse-phase liquid chromatography coupled to drift tube ion mobility and high-resolution mass spectrometry using dual polarity ionization was used to characterize the lipid profile in both groups. The structural elucidation involved the integration of various parameters, such as retention time, mass-to-charge ratio, tandem mass spectra and collision cross section values. This approach yielded a total of 20 potential biomarkers linked to the perturbed glycerophospholipid metabolism, energy metabolism and sphingolipid metabolism. Among these alterations, most lipids were down-regulated and some specific lipids (PC 36:5, PC 37:4 and PI O-34:2) exhibited a relatively strong Spearman correlation and predictive capacity for PFAS contamination. This study could support further toxicological assessments and mechanistic investigations into the effects of PFAS exposure on the lipidome.

CCSfind: A tool for chemically informed LC-IM-MS database building

In addition to providing critical knowledge of the accurate mass of ions, ion mobility-mass spectrometry (IM-MS) delivers complementary data relating to the conformation and size of ions in the form of an ion mobility spectrum and derived parameters, namely, the ion's mobility (K) and the IM-derived collision cross section (CCS). However, the maximum amount of information obtained in IM-MS measurements is not currently transferred into analytical databases including the full mobility spectra (CCS distributions) as well as capturing of additional ion species (e.g., adducts) into the same compound entry. We introduce CCSfind, a new tool for building comprehensive databases from experimental IM-MS measurements of small molecules. CCSfind allows predicted ion species to be chosen for input chemical formulae, which are then targeted by CCSfind after parsing open source mzML input files to provide a unified set of results within a single data processing step. CCSfind can handle both chromatographically separated isomers and IM separation of isomeric ions (e.g., "protomers" or conformers of the same ion species) with simple user control over the output for new database entries in SQL format. Files of up to 1 GB can be processed in less than 2 min on a desktop computer with 32 GB RAM with computational time scaling linearly with the size of the input mzML file or the number of input molecular formulae. Results are manually reviewed, annotated with experimental settings, before committing the database where the full dataset can be retrieved.

Interplatform comparison between three ion mobility techniques for human plasma lipid collision cross sections

The implementation of ion mobility spectrometry (IMS) in liquid chromatography-high-resolution mass spectrometry (LC-HRMS) workflows has become a valuable tool for improving compound annotation in metabolomics analyses by increasing peak capacity and by adding a new molecular descriptor, the collision cross section (CCS). Although some studies reported high repeatability and reproducibility of CCS determination and only few studies reported good interplatform agreement for small molecules, standardized protocols are still missing due to the lack of reference CCS values and reference materials. We present a comparison of CCS values of approximatively one hundred lipid species either commercially available or extracted from human plasma. We used three different commercial ion mobility technologies from different laboratories, drift tube IMS (DTIMS), travelling wave IMS (TWIMS) and trapped IMS (TIMS), to evaluate both instrument repeatability and interlaboratory reproducibility. We showed that CCS discrepancies of 0.3% (average) could occur depending on the data processing software tools. Moreover, eleven CCS calibrants were evaluated yielding mean RSD below 2% for eight calibrants, ESI Low concentration tuning mix (Tune Mix) showing the lowest RSD (< 0.5%) in both ion modes. Tune Mix calibrated CCS from the three different IMS instruments proved to be well correlated and highly reproducible (R2 > 0.995 and mean RSD ≤ 1%). More than 90% of the lipid CCS had deviations of less than 1%, demonstrating high comparability between techniques, and the possibility to use the CCS as molecular descriptor. We highlighted the need of standardized procedures for calibration, data acquisition, and data processing. This work demonstrates that using harmonized analytical conditions are required for interplatform reproducibility for CCS determination of human plasma lipids.

High-resolution ion mobility separations coupled to mass spectrometry: What's next?

Herein, I provide a personal perspective on high-resolution multipass ion mobility spectrometry-mass spectrometry (IMS-MS), with a specific emphasis on cyclic (cIMS) and structures for lossless ion manipulations (SLIM IMS)-based separations. My overarching goal for this perspective was to detail what I believe will be the key important areas in which IMS-MS will help shape the bioanalytical community and especially omics-based research.

Mass spectrometry of polymers: A tutorial review

Ever since the inception of synthetic polymeric materials in the late 19th century, the number of studies on polymers as well as the complexity of their structures have only increased. The development and commercialization of new polymers with properties fine-tuned for specific technological, environmental, consumer, or biomedical applications requires powerful analytical techniques that permit the in-depth characterization of these materials. One such method with the ability to provide chemical composition and structure information with high sensitivity, selectivity, specificity, and speed is mass spectrometry (MS). This tutorial review presents and exemplifies the various MS techniques available for the elucidation of specific structural features in a synthetic polymer, including compositional complexity, primary structure, architecture, topology, and surface properties. Key to every MS analysis is sample conversion to gas-phase ions. This review describes the fundamentals of the most suitable ionization methods for synthetic materials and provides relevant sample preparation protocols. Most importantly, structural characterizations via one-step as well as hyphenated or multidimensional approaches are introduced and demonstrated with specific applications, including surface sensitive and imaging techniques. The aim of this tutorial review is to illustrate the capabilities of MS for the characterization of large, complex polymers and emphasize its potential as a powerful compositional and structural elucidation tool in polymer chemistry.

Development of a Modular, Open-Source, Reduced-Pressure, Drift Tube Ion Mobility Spectrometer

Toward the goal of minimizing construction costs while maintaining high performance, a new, reduced-pressure, drift tube ion mobility system is coupled with an ion trap mass analyzer through a custom ion shuttle. The availability of reduced-pressure ion mobility systems remains limited due to comparatively expensive commercial options and limited shared design features in the open literature. This report details the complete design and benchmarking characteristics of a reduced-pressure ion mobility system. The system is constructed from FR4 PCB electrodes and encased in a PTFE vacuum enclosure with custom torque-tightened couplers to utilize standard KF40 bulkheads. The PTFE enclosure directly minimizes the overall system expenses, and the implementation of threaded brass inserts allows for facile attachments to the vacuum enclosure without damaging the thermoplastic housing. Front and rear ion funnels maximize ion transmission and help mitigate the effects of radial ion diffusion. A custom planar ion shuttle transports ions from the exit of the rear ion funnel into the ion optics of an ion trap mass analyzer. The planar ion shuttle can couple the IM system to any contemporary Thermo Scientific ion trap mass analyzer. Signal stability and ion intensity remain unchanging following the implementation of the planar ion shuttle when compared to the original stacked ring ion guide. The constructed IM system showed resolving powers up to 85 for various small molecules and proteins using the Fourier transform from a ∼1 m drift tube. Recorded mobilities derived from first principles agree with published literature results with an average error of 1.1% and an average error toward literature values using single field calibration of <1.3%.

Accurate Clinical Detection of Vitamin D by Mass Spectrometry: A Review

Vitamin D deficiency is thought to be associated with a wide range of diseases, including diabetes, cancer, depression, neurodegenerative diseases, and cardiovascular and cerebrovascular diseases. This vitamin D deficiency is a global epidemic affecting both developing and developed countries and therefore qualitative and quantitative analysis of vitamin D in a clinical context is essential. Mass spectrometry has played an increasingly important role in the clinical analysis of vitamin D because of its accuracy, sensitivity, specificity, and the ability to detect multiple substances at the same time. Despite their many advantages, mass spectrometry-based methods are not without analytical challenges. Front-end and back-end challenges such as protein precipitation, analyte extraction, derivatization, mass spectrometer functionality, must be carefully considered to provide accurate and robust analysis of vitamin D through a well-designed approach with continuous control by internal and external quality control. Therefore, the aim of this review is to provide a comprehensive overview of the development of mass spectrometry methods for vitamin D accurate analysis, including emphasis on status markers, deleterious effects of biological matrices, derivatization reactions, effects of ionization sources, contribution of epimers, standardization of assays between laboratories.

Association of Blood Metabolomics Biomarkers with Brain Metabolites and Patient-Reported Outcomes as a New Approach in Individualized Diagnosis of Schizophrenia

Given its polygenic nature, there is a need for a personalized approach to schizophrenia. The aim of the study was to select laboratory biomarkers from blood, brain imaging, and clinical assessment, with an emphasis on patients' self-report questionnaires. Metabolomics studies of serum samples from 51 patients and 45 healthy volunteers, based on the liquid chromatography-electrospray ionization-mass spectrometry (LC-ESI-MS/MS), led to the identification of 3 biochemical indicators (cortisol, glutamate, lactate) of schizophrenia. These metabolites were sequentially correlated with laboratory tests results, imaging results, and clinical assessment outcomes, including patient self-report outcomes. The hierarchical cluster analysis on the principal components (HCPC) was performed to identify the most homogeneous clinical groups. Significant correlations were noted between blood lactates and 11 clinical and 10 neuroimaging parameters. The increase in lactate and cortisol were significantly associated with a decrease in immunological parameters, especially with the level of reactive lymphocytes. The strongest correlations with the level of blood lactate and cortisol were demonstrated by brain glutamate, N-acetylaspartate and the concentrations of glutamate and glutamine, creatine and phosphocreatine in the prefrontal cortex. Metabolomics studies and the search for associations with brain parameters and self-reported outcomes may provide new diagnostic evidence to specific schizophrenia phenotypes.

Molecular Gas-Phase Conformational Ensembles

Accurately determining the global minima of a molecular structure is important in diverse scientific fields, including drug design, materials science, and chemical synthesis. Conformational search engines serve as valuable tools for exploring the extensive conformational space of molecules and for identifying energetically favorable conformations. In this study, we present a comparison of Auto3D, CREST, Balloon, and ETKDG (from RDKit), which are freely available conformational search engines, to evaluate their effectiveness in locating global minima. These engines employ distinct methodologies, including machine learning (ML) potential-based, semiempirical, and force field-based approaches. To validate these methods, we propose the use of collisional cross-section (CCS) values obtained from ion mobility-mass spectrometry studies. We hypothesize that experimental gas-phase CCS values can provide experimental evidence that we likely have the global minimum for a given molecule. To facilitate this effort, we used our gas-phase conformation library (GPCL) which currently consists of the full ensembles of 20 small molecules and can be used by the community to validate any conformational search engine. Further members of the GPCL can be readily created for any molecule of interest using our standard workflow used to compute CCS values, expanding the ability of the GPCL in validation exercises. These innovative validation techniques enhance our understanding of the conformational landscape and provide valuable insights into the performance of conformational generation engines. Our findings shed light on the strengths and limitations of each search engine, enabling informed decisions for their utilization in various scientific fields, where accurate molecular structure determination is crucial for understanding biological activity and designing targeted interventions. By facilitating the identification of reliable conformations, this study significantly contributes to enhancing the efficiency and accuracy of molecular structure determination, with particular focus on metabolite structure elucidation. The findings of this research also provide valuable insights for developing effective workflows for predicting the structures of unknown compounds with high precision.

Metagenomic, metabolomic, and lipidomic shifts associated with fecal microbiota transplantation for recurrent Clostridioides difficile infection

Recurrent C. difficile infection (rCDI) is an urgent public health threat for which the last resort and lifesaving treatment is a fecal microbiota transplant (FMT). However, the exact mechanisms which mediate a successful FMT are not well understood. Here we use longitudinal stool samples collected from patients undergoing FMT to evaluate changes in the microbiome, metabolome, and lipidome after successful FMTs. We show changes in the abundance of many lipids, specifically acylcarnitines and bile acids, in response to FMT. These changes correlate with Enterobacteriaceae, which encode carnitine metabolism genes, and Lachnospiraceae, which encode bile salt hydrolases and baiA genes. LC-IMS-MS revealed a shift from microbial conjugation of primary bile acids pre-FMT to secondary bile acids post-FMT. Here we define the structural and functional changes in successful FMTs. This information will help guide targeted Live Biotherapeutic Product development for the treatment of rCDI and other intestinal diseases.

Transition Metal Ion FRET-Based Probe to Study Cu(II)-Mediated Amyloid- Ligand Binding

Recent therapeutic strategies suggest that small peptides can act as aggregation inhibitors of monomeric amyloid-β (Αβ) by inducing structural rearrangements upon complexation. However, characterizing the binding events in such dynamic and transient noncovalent complexes, especially in the presence of natively occurring metal ions, remains a challenge. Here, we deploy a combined transition metal ion Förster resonance energy transfer (tmFRET) and native ion mobility-mass spectrometry (IM-MS) approach to characterize the structure of mass- and charge-selected Aβ complexes with Cu(II) ions (a quencher) and a potential aggregation inhibitor, a small neuropeptide named leucine enkephalin (LE). We show conformational changes of monomeric Αβ species upon Cu(II)-binding, indicating an uncoiled N-terminus and a close interaction between the C-terminus and the central hydrophobic region. Furthermore, we introduce LE labeled at the N-terminus with a metal-chelating agent, nitrilotriacetic acid (NTA). This allows us to employ tmFRET to probe the binding even in low-abundance and transient Aβ-inhibitor-metal ion complexes. Complementary intramolecular distance and global shape information from tmFRET and native IM-MS, respectively, confirmed Cu(II) displacement toward the N-terminus of Αβ, which discloses the binding region and the inhibitor's orientation.

Suspect and nontarget screening of per- and polyfluoroalkyl substances based on ion mobility mass spectrometry and machine learning techniques

High-resolution mass spectrometry (HRMS)-based suspect and nontarget screening techniques are powerful tools for the comprehensive identification of per- and polyfluoroalkyl substances (PFASs), but the interference of complex matrices (especially for wastewater) pose an analytical challenge. This study explored the potential of combining ion mobility spectrometry (IMS) with HRMS and machine learning techniques to achieve the rapid and accurate suspect and nontarget screening of PFAS in wastewater. There were fewer interfering peaks and a clearer spectrum in the data acquired by IMS-HRMS than conventional HRMS. The introduction of collision cross section (CCS) in PFAS homologous series search could filter out 63% of false positive results. Retention time and CCS prediction models were helpful in improving the confidence for PFAS qualitative identification and the random forest algorithm combined with RDKit descriptor performed best for CCS prediction. With the inclusion of extra dimensional information, this study also proposed a comprehensive and concise confidence assignment criterion to better convey the certainty of the qualitative identification of PFAS. Finally, a total of 56 potential PFASs were identified in the wastewater sample using the newly developed method and 45 of them were identified outside reference standards, emphasizing the importance of suspect and nontarget screening for PFAS.

Towards IMn with Electrostatic Drift Fields: Resetting the Potential of Trapped Ions Between Dimensions of Ion Mobility

Increasing the dimensionality of ion mobility (IM) presents an enticing opportunity to increase the information content and selectivity of many analyses. However, for implementations of IM that use constant electrostatic gradients to separate ions in a buffer gas, technical challenges have limited the adoption of the technique and number of dimensions within individual experiments. Here, we introduce a strategy to "reset" the potentials of ions between IM dimensions. To achieve this, mobility-selected ions are trapped between dimensions of IM, using a combination of RF and electrostatic fields, while the subsequent dimension of IM is devoid of any drift field. By applying an incremental voltage ramp, the potential of the trapping region is elevated, simultaneously establishing the drift field in the subsequent dimension of IM. The trapped ions are then released and separated. We measured similar arrival-time distributions of protein ions using this strategy and a method without potential resetting, suggesting that potential resetting can be performed without additional losses or activation of ions. The findings of those experiments were corroborated by ion trajectory simulations, which exhibited a very small changes in ion position and no significant changes in effective temperatures during potential resetting. Finally, we demonstrate that IM information can be preserved during potential resetting by selecting subpopulations of 9+ cytochrome c ions, resetting their potential, subjecting them to a second-dimension IM separation, and observing the retention of conformers within each subpopulation. We anticipate that this strategy will be useful for advancing flexible, multidimensional experiments on electrostatic IM instruments.

Application of Ion Mobility Spectrometry and the Derived Collision Cross Section in the Analysis of Environmental Organic Micropollutants

Ion mobility spectrometry (IMS) is a rapid gas-phase separation technique, which can distinguish ions on the basis of their size, shape, and charge. The IMS-derived collision cross section (CCS) can serve as additional identification evidence for the screening of environmental organic micropollutants (OMPs). In this work, we summarize the published experimental CCS values of environmental OMPs, introduce the current CCS prediction tools, summarize the use of IMS and CCS in the analysis of environmental OMPs, and finally discussed the benefits of IMS and CCS in environmental analysis. An up-to-date CCS compendium for environmental contaminants was produced by combining CCS databases and data sets of particular types of environmental OMPs, including pesticides, drugs, mycotoxins, steroids, plastic additives, per- and polyfluoroalkyl substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and polybrominated diphenyl ethers (PBDEs), as well as their well-known transformation products. A total of 9407 experimental CCS values from 4170 OMPs were retrieved from 23 publications, which contain both drift tube CCS in nitrogen (DTCCSN2) and traveling wave CCS in nitrogen (TWCCSN2). A selection of publicly accessible and in-house CCS prediction tools were also investigated; the chemical space covered by the training set and the quality of CCS measurements seem to be vital factors affecting the CCS prediction accuracy. Then, the applications of IMS and the derived CCS in the screening of various OMPs were summarized, and the benefits of IMS and CCS, including increased peak capacity, the elimination of interfering ions, the separation of isomers, and the reduction of false positives and false negatives, were discussed in detail. With the improvement of the resolving power of IMS and enhancements of experimental CCS databases, the practicability of IMS in the analysis of environmental OMPs will continue to improve.

Comprehensive Overview of Bottom-Up Proteomics using Mass Spectrometry

Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods to aid the novice and experienced researcher. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this work to serve as a basic resource for new practitioners in the field of shotgun or bottom-up proteomics.

Improved analysis of derivatized steroid hormone isomers using ion mobility-mass spectrometry (IM-MS)

Over the last decade, applications of ion mobility-mass spectrometry (IM-MS) have exploded due primarily to the widespread commercialization of robust instrumentation from several vendors. Unfortunately, the modest resolving power of many of these platforms (~40-60) has precluded routine separation of constitutional and stereochemical isomers. While instrumentation advances have pushed resolving power to >150 in some cases, chemical approaches offer an alternative for increasing resolution with existing IM-MS instrumentation. Herein we explore the utility of two reactions, derivatization by Girard's reagents and 1,1-carbonyldiimidazole (CDI), for improving IM separation of steroid hormone isomers. These reactions are fast (≤30 min), simple (requiring only basic lab equipment/expertise), and low-cost. Notably, these reactions are structurally selective in that they target carbonyl and hydroxyl groups, respectively, which are found in all naturally occurring steroids. Many steroid hormone isomers differ only in the number, location, and/or stereochemistry of these functional groups, allowing these reactions to "amplify" subtle structural differences and improve IM resolution. Our results show that resolution was significantly improved amongst CDI-derivatized isomer groups of hydroxyprogesterone (two-peak resolution of Rpp = 1.10 between 21-OHP and 11B-OHP), deoxycortisone (Rpp = 1.47 between 11-DHC and 21-DOC), and desoximetasone (Rpp = 1.98 between desoximetasone and fluocortolone). Moreover, characteristic collision cross section (DTCCSN2) measurements can be used to increase confidence in the identification of these compounds in complex biological mixtures. To demonstrate the feasibility of analyzing the derivatized steroids in complex biological matrixes, the reactions were performed following steroid extraction from urine and yielded similar results. Additionally, we applied a software-based approach (high-resolution demultiplexing) that further improved the resolving power (>150). Overall, our results suggest that targeted derivatization reactions coupled with IM-MS can significantly improve the resolution of challenging isomer groups, allowing for more accurate and efficient analysis of complex mixtures.

Uncovering per- and polyfluoroalkyl substances (PFAS) with nontargeted ion mobility spectrometry-mass spectrometry analyses

Because of environmental and health concerns, legacy per- and polyfluoroalkyl substances (PFAS) have been voluntarily phased out, and thousands of emerging PFAS introduced as replacements. Traditional analytical methods target a limited number of mainly legacy PFAS; therefore, many species are not routinely assessed in the environment. Nontargeted approaches using high-resolution mass spectrometry methods have therefore been used to detect and characterize unknown PFAS. However, their ability to elucidate chemical structures relies on generation of informative fragments, and many low concentration species are not fragmented in typical data-dependent acquisition approaches. Here, a data-independent method leveraging ion mobility spectrometry (IMS) and size-dependent fragmentation was developed and applied to characterize aquatic passive samplers deployed near a North Carolina fluorochemical manufacturer. From the study, 11 PFAS structures for various per- and polyfluorinated ether sulfonic acids and multiheaded perfluorinated ether acids were elucidated in addition to 36 known PFAS. Eight of these species were previously unreported in environmental media, and three suspected species were validated.

High-throughput analysis of lipidomic phenotypes of methicillin-resistant Staphylococcus aureus by coupling in situ 96-well cultivation and HILIC-ion mobility-mass spectrometry

Antimicrobial resistance is a major threat to human health as resistant pathogens spread globally, and the development of new antimicrobials is slow. Since many antimicrobials function by targeting cell wall and membrane components, high-throughput lipidomics for bacterial phenotyping is of high interest for researchers to unveil lipid-mediated pathways when dealing with a large number of lab-selected or clinical strains. However, current practice for lipidomic analysis requires the cultivation of bacteria on a large scale, which does not replicate the growth conditions for high-throughput bioassays that are normally carried out in 96-well plates, such as susceptibility tests, growth curve measurements, and biofilm quantitation. Analysis of bacteria grown under the same condition as other bioassays would better inform the differences in susceptibility and other biological metrics. In this work, a high-throughput method for cultivation and lipidomic analysis of antimicrobial-resistant bacteria was developed for standard 96-well plates exemplified by methicillin-resistant Staphylococcus aureus (MRSA). By combining a 30-mm liquid chromatography (LC) column with ion mobility (IM) separation, elution time could be dramatically shortened to 3.6 min for a single LC run without losing major lipid features. Peak capacity was largely rescued by the addition of the IM dimension. Through multi-linear calibration, the deviation of retention time can be limited to within 5%, making database-based automatic lipid identification feasible. This high-throughput method was further validated by characterizing the lipidomic phenotypes of antimicrobial-resistant mutants derived from the MRSA strain, W308, grown in a 96-well plate.

AllCCS2: Curation of Ion Mobility Collision Cross-Section Atlas for Small Molecules Using Comprehensive Molecular Representations

The development of ion mobility-mass spectrometry (IM-MS) has revolutionized the analysis of small molecules, such as metabolomics, lipidomics, and exposome studies. The curation of comprehensive reference collision cross-section (CCS) databases plays a pivotal role in the successful application of IM-MS for small-molecule analysis. In this study, we presented AllCCS2, an enhanced version of AllCCS, designed for the universal prediction of the ion mobility CCS values of small molecules. AllCCS2 incorporated newly available experimental CCS data, including 10,384 records and 7713 unified values, as training data. By leveraging a neural network trained on diverse molecular representations encompassing mass spectrometry features, molecular descriptors, and graph features extracted using a graph convolutional network, AllCCS2 achieved exceptional prediction accuracy. AllCCS2 achieved median relative error (MedRE) values of 0.31, 0.72, and 1.64% in the training, validation, and testing sets, respectively, surpassing existing CCS prediction tools in terms of accuracy and coverage. Furthermore, AllCCS2 exhibited excellent compatibility with different instrument platforms (DTIMS, TWIMS, and TIMS). The prediction uncertainties in AllCCS2 from the training data and the prediction model were comprehensively investigated by using representative structure similarity and model prediction variation. Notably, small molecules with high structural similarities to the training set and lower model prediction variation exhibited improved accuracy and lower relative errors. In summary, AllCCS2 serves as a valuable resource to support applications of IM-MS technologies. The AllCCS2 database and tools are freely accessible at http://allccs.zhulab.cn/.

Permethylation and Metal Adduction: A Toolbox for the Improved Characterization of Glycolipids with Cyclic Ion Mobility Separations Coupled to Mass Spectrometry

Lipids are an important class of molecules involved in various biological functions but remain difficult to characterize through mass-spectrometry-based methods because of their many possible isomers. Glycolipids, specifically, play important roles in cell signaling but display an even greater level of isomeric heterogeneity as compared to other lipid classes stemming from the introduction of a carbohydrate and its corresponding linkage position and α/β anomericity at the headgroup. While liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) remains the gold standard technique in lipidomics, it is still unable to characterize all isomeric species, thus presenting the need for new, orthogonal, methodologies. Ion mobility spectrometry-mass spectrometry (IMS-MS) can provide an additional dimension of information that supplements LC-MS/MS workflows, but has seen little use for glycolipid analyses. Herein, we present an analytical toolbox that enables the characterization of various glycolipid isomer sets using high-resolution cyclic ion mobility separations coupled with mass spectrometry (cIMS-MS). Specifically, we utilized a combination of both permethylation and metal adduction to fully resolve isomeric sphingolipids and ceramides with our cIMS-MS platform. We also introduce a new metric that can enable comparing peak-to-peak resolution across varying cIMS-MS pathlengths. Overall, we envision that our presented methodologies are highly amenable to existing LC-MS/MS-based workflows and can also have broad utility toward other omics-based analyses.

Collision cross section measurement and prediction methods in omics

Omics studies such as metabolomics, lipidomics, and proteomics have become important for understanding the mechanisms in living organisms. However, the compounds detected are structurally different and contain isomers, with each structure or isomer leading to a different result in terms of the role they play in the cell or tissue in the organism. Therefore, it is important to detect, characterize, and elucidate the structures of these compounds. Liquid chromatography and mass spectrometry have been utilized for decades in the structure elucidation of key compounds. While prediction models of parameters (such as retention time and fragmentation pattern) have also been developed for these separation techniques, they have some limitations. Moreover, ion mobility has become one of the most promising techniques to give a fingerprint to these compounds by determining their collision cross section (CCS) values, which reflect their shape and size. Obtaining accurate CCS enables its use as a filter for potential analyte structures. These CCS values can be measured experimentally using calibrant-independent and calibrant-dependent approaches. Identification of compounds based on experimental CCS values in untargeted analysis typically requires CCS references from standards, which are currently limited and, if available, would require a large amount of time for experimental measurements. Therefore, researchers use theoretical tools to predict CCS values for untargeted and targeted analysis. In this review, an overview of the different methods for the experimental and theoretical estimation of CCS values is given where theoretical prediction tools include computational and machine modeling type approaches. Moreover, the limitations of the current experimental and theoretical approaches and their potential mitigation methods were discussed.