Trapped ion mobility spectrometry and PASEF enable in-depth lipidomics from minimal sample amounts

Altering metabolism programs cell identity via NAD+-dependent deacetylation

Cells change their metabolic profiles in response to underlying gene regulatory networks, but how can alterations in metabolism encode specific transcriptional instructions? Here, we show that forcing a metabolic change in embryonic stem cells (ESCs) promotes a developmental identity that better approximates the inner cell mass (ICM) of the early mammalian blastocyst in cultures. This shift in cellular identity depends on the inhibition of glycolysis and stimulation of oxidative phosphorylation (OXPHOS) triggered by the replacement of D-glucose by D-galactose in ESC media. Enhanced OXPHOS in turn activates NAD + -dependent deacetylases of the Sirtuin family, resulting in the deacetylation of histones and key transcription factors to focus enhancer activity while reducing transcriptional noise, which results in a robustly enhanced ESC phenotype. This exploitation of a NAD + /NADH coenzyme coupled to OXPHOS as a means of programming lineage-specific transcription suggests new paradigms for how cells respond to alterations in their environment, and implies cellular rejuvenation exploits enzymatic activities for simultaneous activation of a discrete enhancer set alongside silencing genome-wide transcriptional noise.

Quality evaluation of metabolite annotation based on comprehensive simulation of MS/MS data from high-resolution mass spectrometry (HRMS) and similarity scoring

Metabolite annotation is a critical step in discovery metabolomics, but remains a significant challenge. In this study, the accuracy of metabolite annotation was systematically evaluated by leveraging the proposed strategies for simulation of tandem mass spectrometry (MS/MS) data from high-resolution mass spectrometry (HRMS) and then construction of a large-scale virtual database. Furthermore, various similarity scoring methods were comprehensively compared to assess the performance for annotation. First, three key characteristics that are essential for simulating MS/MS spectra to closely resemble experimental data were identified: (i) the number of mass-to-charge ratio (m/z) features, (ii) the differences between neighboring m/z values, and (iii) the intensity distribution of MS/MS features. These factors were employed to generate representative MS/MS spectra for subsequent study. A meticulously designed virtual MS/MS database was constructed to facilitate accurate annotation assessment, which covered over 100,000 metabolites with diverse structural similarities and differences. To evaluate annotation quality, two simulation strategies on the basis of strong and weak data inference were respectively proposed to replicate MS/MS spectra for unknown metabolites. These simulated spectra were then compared with the virtual database, which provided insights into the expected variations in experimental MS/MS data. Furthermore, eight similarity evaluation methods, including entropy similarity (ES) and weighted dot product (W/DP) algorithms, were rigorously evaluated for their effectiveness in metabolite annotation. The results revealed that some methods, such as ES, exhibited strong resistance to interference and broad adaptability across different MS/MS patterns, whereas others selectively yielded reliable outcomes under specific conditions. This study provided a systematic framework for quality evaluation in metabolite annotation and offered strategies to mitigate false-positive identifications. The findings held great significance for advancing metabolomics research and further improving annotation reliability in complex biological samples.

Advances in Tandem Mass Spectrometry Imaging for Next-Generation Spatial Metabolomics

Spatial metabolomics based on mass spectrometry imaging (MSI) is a promising approach for fundamental biological research and disease biomarker discovery. It simultaneously reveals the spatial distributions of hundreds of metabolites across tissue sections. While previous MSI experiments predominantly rely on high-resolution mass analysis for metabolite annotation, the high specificity in resolving molecular structures is essential to distinguish isomers or isobars to obtain ultimate identities of the metabolites. This is also critical for correlating their biological functions with spatial distribution patterns. Tandem mass spectrometry (MS/MS) is effectively used to obtain molecular structural information and has been integrated into MSI for spatial mapping of structurally distinct biomolecules, though typically with low coverage. The main technical challenge in achieving high-coverage, high-structure-resolving spatial mapping of biomolecules lies in the limited amount of sample available from each tissue pixel in conventional MS/MS analysis, which restricts the number of MS/MS scans that can be conducted on the metabolite precursors of interest. In this Perspective, we highlight recent developments in advanced MS/MS imaging strategies aimed at achieving high-coverage spatial metabolomics.

Single Cell Untargeted Lipidomics Using Liquid Chromatography Ion Mobility-Mass Spectrometry

Advancements in technology over the years have propelled omics analysis to the level of single cell resolution. Following the breakthroughs in single cell transcriptomics and genomics, single cell proteomics has recently rapidly progressed, aided by highly sensitive mass spectrometry instrumentation. However, there is currently a paucity of studies and methodologies for single cell lipidomics, aside from imaging-based approaches. Profiling lipids at the single cell level holds promise for providing novel insights into the complex heterogeneity of cells in various human disorders. Further, by integrating single cell lipidomics with other single cell omics including proteomics, it becomes possible to achieve single cell multiomics, enabling the discovery of novel molecular signatures. We developed untargeted single cell lipidomics using nanoflow liquid chromatography-ion mobility spectrometry-mass spectrometry. To enhance lipid coverage at the single cell level, the method was conducted in both positive and negative ion modes. We identified an average of 161 lipids spanning phospholipids, sphingolipids, cholesteryl esters, and glycerides in positive ion mode from single cells of human cholangiocarcinoma cells based on a rule-based lipid annotation. Additionally, an average of 20 species of phospholipids was identified in the negative ion mode. These preliminary data demonstrate a new methodology to profile lipids at a single or low input of cells.

Recent Developments in Single-Cell Metabolomics by Mass Spectrometry─A Perspective

Recent advancements in single-cell (sc) resolution analyses, particularly in sc transcriptomics and sc proteomics, have revolutionized our ability to probe and understand cellular heterogeneity. The study of metabolism through small molecules, metabolomics, provides an additional level of information otherwise unattainable by transcriptomics or proteomics by shedding light on the metabolic pathways that translate gene expression into functional outcomes. Metabolic heterogeneity, critical in health and disease, impacts developmental outcomes, disease progression, and treatment responses. However, dedicated approaches probing the sc metabolome have not reached the maturity of other sc omics technologies. Over the past decade, innovations in sc metabolomics have addressed some of the practical limitations, including cell isolation, signal sensitivity, and throughput. To fully exploit their potential in biological research, however, remaining challenges must be thoroughly addressed. Additionally, integrating sc metabolomics with orthogonal sc techniques will be required to validate relevant results and gain systems-level understanding. This perspective offers a broad-stroke overview of recent mass spectrometry (MS)-based sc metabolomics advancements, focusing on ongoing challenges from a biologist's viewpoint, aimed at addressing pertinent and innovative biological questions. Additionally, we emphasize the use of orthogonal approaches and showcase biological systems that these sophisticated methodologies are apt to explore.

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.

Microflow Liquid Chromatography Coupled to Multinozzle Electrospray Ionization for Improved Lipidomics Coverage of 3D Clear Cell Renal Cell Carcinoma

In most bioanalytical laboratories, high-resolution mass spectrometry (HRMS) systems with electrospray ionization (ESI) are hyphenated to liquid chromatography platforms. The latter typically operate under analytical flow (AF; 0.2-1 mL/min) regimes. Hence, AF/ESI-HRMS methods prioritize the detection of analytes of higher abundances or ionizability and tend to suffer from matrix effects or ion suppression. A far higher sensitivity can be obtained with electrospray at nanoflow (10-1000 nL/min) thanks to a better ionization efficiency and significant decrease in matrix effects. Both advantages are crucial to reliably accessing low-abundance compounds or weakly ionizable analytes. This work presents a microflow (μF) chromatographic setup coupled to a novel microfabricated multinozzle electrospray (mnESI) emitter with five nozzles spraying at 600 nL/min per nozzle for untargeted HRMS lipidomic profiling. With a runtime of 19 min, similar to our established analytical flow (AF/ESI) lipidomics platform, μF/mnESI produced a 16-fold median increase across 69 deuterated lipid standards. The performance of this new configuration was also evaluated in the context of the profiling of a 3D clear cell renal cell carcinoma (ccRCC) model exposed to a multidrug combination therapy. The processing of the acquired data resulted in 1270 (μF/mnESI) vs 752 (AF/ESI) MS2-annotated lipids. Among those, 762 achieved <10% variation on pooled QC samples for μF/mnESI compared to only 361 for the AF method. In addition, the measurements of ccRCC samples confirmed the improvements in ionization efficiency and adduct patterns observed with standards, enabling to annotate 79 oxidized triglycerides, 38 cholesterol esters (only five and four detected in AF/ESI, respectively), and 12 sitosterol esters, not yet reported in mammalian cell cultures.

Tailoring Super-Performed Chemo-Sensor via Simulation-Modeling and MEMS-Screening

Chemo-sensor designing involves a time-consuming trial-and-error screening process, which commonly cannot lead to optimal S4R features (Sensitivity, Selectivity, Speed, Stability, and Reversibility). Due to strong path dependence on reported groups/mechanisms, conventional chemo-sensors often fail to meet critical application demands, especially in achieving high reversibility without compromising other features. Here, a three-step screen and design strategy is developed for gaining customized chemo-sensors, through Structure modeling; MEMS (Micro Electro Mechanical Systems) analysis, and Performance verification. With such a strategy, the coordination hanging anion mechanism is screened out for reversible nerve agent detection and shows reversible emission enhancement by 25.8 times with DCP, ultrasensitive vapor phase detection (5.7 ppb), and rapid response(10 s) and recovery speed (20 s). Such tailored designing strategy for new organic chemo-sensors will probably play an important role in developing high-performance sensing system in the future.

An accessible workflow for high-sensitivity proteomics using parallel accumulation-serial fragmentation (PASEF)

Deep and accurate proteome analysis is crucial for understanding cellular processes and disease mechanisms; however, it is challenging to implement in routine settings. In this protocol, we combine a robust chromatographic platform with a high-performance mass spectrometric setup to enable routine yet in-depth proteome coverage for a broad community. This entails tip-based sample preparation and pre-formed gradients (Evosep One) combined with a trapped ion mobility time-of-flight mass spectrometer (timsTOF, Bruker). The timsTOF enables parallel accumulation-serial fragmentation (PASEF), in which ions are accumulated and separated by their ion mobility, maximizing ion usage and simplifying spectra. Combined with data-independent acquisition (DIA), it offers high peak sampling rates and near-complete ion coverage. Here, we explain how to balance quantitative accuracy, specificity, proteome coverage and sensitivity by choosing the best PASEF and DIA method parameters. The protocol describes how to set up the liquid chromatography-mass spectrometry system and enables PASEF method generation and evaluation for varied samples by using the py_diAID tool to optimally position isolation windows in the mass-to-charge and ion mobility space. Biological projects (e.g., triplicate proteome analysis in two conditions) can be performed in 3 d with ~3 h of hands-on time and minimal marginal cost. This results in reproducible quantification of 7,000 proteins in a human cancer cell line in quadruplicate 21-min injections and 29,000 phosphosites for phospho-enriched quadruplicates. Synchro-PASEF, a highly efficient, specific and novel scan mode, can be analyzed by Spectronaut or AlphaDIA, resulting in superior quantitative reproducibility because of its high sampling efficiency.

Efficient Green Extraction of Nutraceutical Compounds from Nannochloropsis gaditana: A Comparative Electrospray Ionization LC-MS and GC-MS Analysis for Lipid Profiling

Microalgae have been described as a potential alternative source of a wide range of bioactive compounds, including polar lipids and carotenoids. Specifically, Nannochloropsis gaditana is described as producing large amounts of polar lipids, such as glycolipids and phospholipids. These natural active compounds serve as key ingredients for food, cosmetic, or nutraceutical applications. However, microalgae usually possess a rigid cell wall that complicates the extraction of these compounds. Thus, an ultrasound-assisted enzymatic pretreatment is necessary to efficiently extract bioactives from microalgae, and it was studied in this article. Pretreated biomass was extracted using different advanced and green methodologies and compared to traditional extraction. Furthermore, the analysis, characterization, and identification of valuable compounds using GC-MS and LC-MS analytical methods were also investigated. Interestingly, major results demonstrated the efficiency of the pretreatment, enriching polar lipids' distribution in all extracts produced no matter the extraction technique, although they presented differences in their concentration. Pressurized liquid extraction and microwave-assisted extraction were found to be the techniques with the highest yields, whereas ultrasound-assisted extraction achieved the highest percentage of glycolipids. In summary, green extraction techniques showed their effectiveness compared to traditional extraction.

A Perspective of Multi-Reflecting TOF MS

Time-of-flight mass spectrometry (TOF MS) excels in rapid and high-sensitivity analysis, making it a cornerstone of analytical chemistry. But as sample complexity explodes in omics studies, so does the need for higher resolving power to ensure accurate results. Traditional TOF instruments face a challenge: achieving high resolution often requires a very large instrument. To overcome this limitation, scientists developed alternative designs for TOF analyzers called multi-pass TOF analyzers (MPT). These MPT analyzers come in two main configurations: multi-turn (MTT) and multi-reflecting (MRT). Drawing on the authors' extensive experience, this review describes two decades of MPT advancements. It highlights the critical development of optimized analyzer designs, tracing the evolution towards mirror-based MRT instruments, generally providing superior resolution and spatial acceptance compared to MTT. While the manuscript attempts to overview MTT advances, it primarily focuses on MRT technology. Additionally, the review explores the role of orthogonal accelerators and trap pulse converters, comparing their efficiency and the dynamic range limits imposed by space charge effects. By comparing various MRT configurations and commercially available instruments, the review sets out to inform and empower researchers so they can make informed decisions about MRT mass spectrometers.

Development of LC-FAIMS-MS and its application to lipidomics study of Acinetobacter baumannii infection

The recent advances in mass spectrometry (MS) technologies have enabled comprehensive lipid profiling in biological samples. However, the robustness and efficiency of MS-based lipidomics is compromised by the complexity of biological samples. High-field asymmetric waveform ion mobility spectrometry (FAIMS) is a technology that can continuously transmit one type of ion, independent of the mass-to-charge ratio. Here we present the development and application of LC-FAIMS-MS/MS-based platform for untargeted lipidomics. We used 3 optimally balanced compensation voltages, i.e., 29 V, 34 V and 39 V, to analyze all subclasses of glycerophospholipids. The reproducibility of the method was evaluated using reference standards. The reproducibility of retention times ranged from 0.9% to 1.5% RSD; whereas RSD values of 5%-10% were observed for peak areas. More importantly, the coupling of a FAIMS device can significantly improve the robustness and efficiency. We exploited this NPLC-FAIMS-HRMS to analyze the serum lipid profiles in mice infected intranasally with Acinetobacter baumannii. The temporal profiles of serum lipids after A. baumannii inoculation were obtained for 4 h, 8 h, and 24 h. We found that nearly all ether PC and ether PE lipids were significantly decreased 8 h after inoculation. The resultant volcano plot illustrated the distribution of 28 increased and 28 decreased lipid species in mouse sera 24 h after inoculation. We also found that a single ether PE composition can comprise multiple isomeric structures, and the relative abundance of each isomer could be quantified using the newly developed NPLC-FAIMS-PRM method. We have demonstrated that the proposed LC-FAIMS-MS is a valuable platform for lipidomics.

Uncovering lipid dynamics in Staphylococcus aureus osteomyelitis using multimodal imaging mass spectrometry

Osteomyelitis occurs when Staphylococcus aureus invades the bone microenvironment, resulting in a bone marrow abscess with a spatially defined architecture of cells and biomolecules. Imaging mass spectrometry and microscopy are tools that can be employed to interrogate the lipidome of S. aureus-infected murine femurs and reveal metabolic and signaling consequences of infection. Here, nearly 250 lipids were spatially mapped to healthy and infection-associated morphological features throughout the femur, establishing composition profiles for tissue types. Ether lipids and arachidonoyl lipids were altered between cells and tissue structures in abscesses, suggesting their roles in abscess formation and inflammatory signaling. Sterols, triglycerides, bis(monoacylglycero)phosphates, and gangliosides possessed ring-like distributions throughout the abscess, suggesting a hypothesized dysregulation of lipid metabolism in a population of cells that cannot be discerned with traditional microscopy. These data provide insight into the signaling function and metabolism of cells in the fibrotic border of abscesses, likely characteristic of lipid-laden macrophages.

Fast and broad-coverage lipidomics enabled by ion mobility-mass spectrometry

Aberrant lipid metabolism has been widely recognized as a hallmark of various diseases. However, the comprehensive analysis of distinct lipids is challenging due to the complexity of lipid molecular structures, wide concentration ranges, and numerous isobaric and isomeric lipids. Usually, liquid chromatography-mass spectrometry (LC-MS)-based lipidomics requires a long time for chromatographic separation to achieve optimal separation and selectivity. Ion mobility (IM) adds a new separation dimension to LC-MS, significantly enhancing the coverage, sensitivity, and resolving power. We took advantage of the rapid separation provided by ion mobility and optimized a fast and broad-coverage lipidomics method using the LC-IM-MS technology. The method required only 8 minutes for separation and detected over 1000 lipid molecules in a single analysis of common biological samples. The high reproducibility and accurate quantification of this high-throughput lipidomics method were systematically characterized. We then applied the method to comprehensively measure dysregulated lipid metabolism in patients with colorectal cancer (CRC). Our results revealed 115 significantly changed lipid species between preoperative and postoperative plasma of patients with CRC and also disclosed associated differences in lipid classes such as phosphatidylcholines (PC), sphingomyelins (SM), and triglycerides (TG) regarding carbon number and double bond. Together, a fast and broad-coverage lipidomics method was developed using ion mobility-mass spectrometry. This method is feasible for large-scale clinical lipidomic studies, as it effectively balances the requirements of high-throughput and broad-coverage in clinical studies.

Proteomics Studies on Extracellular Vesicles Derived from Glioblastoma: Where Do We Stand?

Like most tumors, glioblastoma multiforme (GBM), the deadliest brain tumor in human adulthood, releases extracellular vesicles (EVs). Their content, reflecting that of the tumor of origin, can be donated to nearby and distant cells which, by acquiring it, become more aggressive. Therefore, the study of EV-transported molecules has become very important. Particular attention has been paid to EV proteins to uncover new GBM biomarkers and potential druggable targets. Proteomic studies have mainly been performed by "bottom-up" mass spectrometry (MS) analysis of EVs isolated by different procedures from conditioned media of cultured GBM cells and biological fluids from GBM patients. Although a great number of dysregulated proteins have been identified, the translation of these findings into clinics remains elusive, probably due to multiple factors, including the lack of standardized procedures for isolation/characterization of EVs and analysis of their proteome. Thus, it is time to change research strategies by adopting, in addition to harmonized EV selection techniques, different MS methods aimed at identifying selected tumoral protein mutations and/or isoforms due to post-translational modifications, which more deeply influence the tumor behavior. Hopefully, these data integrated with those from other "omics" disciplines will lead to the discovery of druggable pathways for novel GBM therapies.

Triple three-dimensional MS/MS spectrum facilitates quantitative ginsenosides-targeted sub-metabolome characterization in notoginseng

Although serving as the workhorse, MS/MS cannot fully satisfy the analytical requirements of quantitative sub-metabolome characterization. Because more information intrinsically correlates to more structural and concentration clues, here, efforts were devoted to comprehensively tracing and deciphering MS/MS behaviors through constructing triple three-dimensional (3×3D)-MS/MS spectrum. Ginsenosides-targeted metabolomics of notoginseng, one of the most famous edible medicinal plants, was employed as a proof-of-concept. Serial authentic ginsenosides were deployed to build the correlations between 3×3D-MS/MS spectra and structure/concentration features. Through assaying ginsenosides with progressive concentrations using QTOF-MS to configure 1st 3D spectrum, the generations of MS1 spectral signals, particularly multi-charged multimer anions, e.g., [2M-2H]2- and [2M+2HCOO]2- ions, relied on both concentration and the amount of sugar chains. By programming progressive collision energies to the front collision cell of Qtrap-MS device to gain 2nd 3D spectrum, optimal collision energy (OCE) corresponding to the glycosidic bond fission was primarily correlated with the masses of precursor and fragment ions and partially governed by the glycosidation site. The quantitative relationships between OCEs and masses of precursor and fragment ions were utilized to build large-scale quantitative program for ginsenosides. After applying progressive exciting energies to the back collision chamber to build 3rd 3D spectrum, the fragment ion and the decomposition product anion exhibited identical dissociation trajectories when they shared the same molecular geometry. After ginsenosides-focused quantitative metabolomics, significant differences occurred for sub-metabolome amongst different parts of notoginseng. The differential ginsenosides were confirmatively identified by applying the correlations between 3×3D-MS/MS spectra and structures. Together, 3×3D-MS/MS spectrum covers all MS/MS behaviors and dramatically facilitates sub-metabolome characterization from both quantitative program development and structural identification.

Reproducible mass spectrometry data processing and compound annotation in MZmine 3

Untargeted mass spectrometry (MS) experiments produce complex, multidimensional data that are practically impossible to investigate manually. For this reason, computational pipelines are needed to extract relevant information from raw spectral data and convert it into a more comprehensible format. Depending on the sample type and/or goal of the study, a variety of MS platforms can be used for such analysis. MZmine is an open-source software for the processing of raw spectral data generated by different MS platforms. Examples include liquid chromatography-MS, gas chromatography-MS and MS-imaging. These data might typically be associated with various applications including metabolomics and lipidomics. Moreover, the third version of the software, described herein, supports the processing of ion mobility spectrometry (IMS) data. The present protocol provides three distinct procedures to perform feature detection and annotation of untargeted MS data produced by different instrumental setups: liquid chromatography-(IMS-)MS, gas chromatography-MS and (IMS-)MS imaging. For training purposes, example datasets are provided together with configuration batch files (i.e., list of processing steps and parameters) to allow new users to easily replicate the described workflows. Depending on the number of data files and available computing resources, we anticipate this to take between 2 and 24 h for new MZmine users and nonexperts. Within each procedure, we provide a detailed description for all processing parameters together with instructions/recommendations for their optimization. The main generated outputs are represented by aligned feature tables and fragmentation spectra lists that can be used by other third-party tools for further downstream analysis.

Hyphenation of supercritical fluid chromatography and trapped ion mobility-mass spectrometry for quantitative lipidomics

BACKGROUND

Lipidomics studies require rapid separations with accurate and reliable quantification results to further elucidate the role of lipids in biological processes and their biological functions. Supercritical fluid chromatography (SFC), in particular, can provide this rapid and high-resolution separation. The combination with trapped ion mobility spectrometry (TIMS) has not yet been applied, although the post-ionization separation method in combination with liquid chromatography or imaging techniques has already proven itself in resolving isomeric and isobaric lipids and preventing false identifications. However, a multidimensional separation method should not only allow confident identification but also provide quantitative results to substantiate studies with absolute concentrations.

RESULTS

A SFC method was developed and the hyphenation of SFC and TIMS was further explored towards the separation of different isobaric overlaps. Furthermore, lipid identification was performed using mass spectrometry (MS) and parallel accumulation serial fragmentation (PASEF) MS/MS experiments in addition to retention time and collision cross section (CCS). Quantification was further investigated with short TIMS ramps and performed based on the ion mobility signal of lipids, since TIMS increases the sensitivity by noise filtering. The final method was, as an exemplary study, applied to investigate the function of different ceramide synthases (CerS) in the nematode and model organism Caenorhabditis elegans (C. elegans). Loss of three known CerS hyl-1, hyl-2 and lagr-1 demonstrated different influences on and alterations in the sphingolipidome.

SIGNIFICANCE

This method describes for the first time the combination of SFC and TIMS-MS/MS, which enables a fast and sensitive quantification of lipids. The results of the application to C. elegans samples prove the functionality of the method and support research on the metabolism of sphingolipids in nematodes.

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.

Ion Mobility Separations Using Cocentric Architecture

Ion mobility separations, especially using drift tube ion mobility spectrometers, are usually performed in linear channels, which can have a large footprint when extended to achieve higher resolving powers. In this work, we explored the performance of an ion mobility device with a curved architecture, which can have a more compact form. The cocentric ion mobility spectrometer (CoCIMS) manipulates ions between two cocentric surfaces containing a serpentine track. The mobility separation inside the CoCIMS is achieved using traveling waveforms (TWs). We initially evaluated the device using ion trajectory simulations using SIMION, which indicated that when ions traveled circularly inside the CoCIMS they resulted in similar resolving powers and transmitted m/z range as traveling in a straight path. We then performed experimental validation of the CoCIMS in conjunction with a TOF MS. The CoCIMS was made of two flexible printed circuit board materials folded into cocentric cylinders separated by a gap of 2.8 mm. The device was about 50 mm diameter ×152 mm long and provided 1.846 m of serpentine path length. Three sets of mixtures (Agilent tune mixture, tetraalkylammonium salts, and an eight-peptide mixture) and four traveling waveform profiles (square, sine, triangle, and sawtooth) were used. The sawtooth TW profile produced a slightly higher resolving power for the Agilent tuning mixture and tetraalkylammonium ions. The average resolving power for Agilent tune mixture ions ranged from 37 (using sawtooth TW) to 27 (using square TW). The average resolving powers ranged from 45 (sawtooth TW) to 31 (square TW) for tetraalkylammonium ions. The resolving power of the peptide mixture ions was similar among the four TW profiles and ranged from 51 to 56. The average percent error in TWCCS for the peptide mixture ions was about 0.4%. The new device showed promising results, but improvements are needed to further increase the resolving power.

Advances in the Clinical Application of High-throughput Proteomics

High-throughput proteomics has become an exciting field and a potential frontier of modern medicine since the early 2000s. While significant progress has been made in the technical aspects of the field, translating proteomics to clinical applications has been challenging. This review summarizes recent advances in clinical applications of high-throughput proteomics and discusses the associated challenges, advantages, and future directions. We focus on research progress and clinical applications of high-throughput proteomics in breast cancer, bladder cancer, laryngeal squamous cell carcinoma, gastric cancer, colorectal cancer, and coronavirus disease 2019. The future application of high-throughput proteomics will face challenges such as varying protein properties, limitations of statistical modeling, technical and logistical difficulties in data deposition, integration, and harmonization, as well as regulatory requirements for clinical validation and considerations. However, there are several noteworthy advantages of high-throughput proteomics, including the identification of novel global protein networks, the discovery of new proteins, and the synergistic incorporation with other omic data. We look forward to participating in and embracing future advances in high-throughput proteomics, such as proteomics-based single-cell biology and its clinical applications, individualized proteomics, pathology informatics, digital pathology, and deep learning models for high-throughput proteomics. Several new proteomic technologies are noteworthy, including data-independent acquisition mass spectrometry, nanopore-based proteomics, 4-D proteomics, and secondary ion mass spectrometry. In summary, we believe high-throughput proteomics will drastically shift the paradigm of translational research, clinical practice, and public health in the near future.

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.

MobiLipid: A Tool for Enhancing CCS Quality Control of Ion Mobility-Mass Spectrometry Lipidomics by Internal Standardization

Ion mobility-mass spectrometry (IM-MS) offers benefits for lipidomics by obtaining IM-derived collision cross sections (CCS), a conditional property of an ion that can enhance lipid identification. While drift tube (DT) IM-MS retains a direct link to the primary experimental method to derive CCS values, other IM technologies rely solely on external CCS calibration, posing challenges due to dissimilar chemical properties between lipids and calibrants. To address this, we introduce MobiLipid, a novel tool facilitating the CCS quality control of IM-MS lipidomics workflows by internal standardization. MobiLipid utilizes a newly established DTCCSN2 library for uniformly (U)13C-labeled lipids, derived from a U13C-labeled yeast extract, containing 377 DTCCSN2 values. This automated open-source R Markdown tool enables internal monitoring and straightforward compensation for CCSN2 biases. It supports lipid class- and adduct-specific CCS corrections, requiring only three U13C-labeled lipids per lipid class-adduct combination across 10 lipid classes without requiring additional external measurements. The applicability of MobiLipid is demonstrated for trapped IM (TIM)-MS measurements of an unlabeled yeast extract spiked with U13C-labeled lipids. Monitoring the CCSN2 biases of TIMCCSN2 values compared to DTCCSN2 library entries utilizing MobiLipid resulted in mean absolute biases of 0.78% and 0.33% in positive and negative ionization mode, respectively. By applying the CCS correction integrated into the tool for the exemplary data set, the mean absolute CCSN2 biases of 10 lipid classes could be reduced to approximately 0%.

Untargeted 4D-metabolomics using Trapped Ion Mobility combined with LC-HRMS in extra virgin olive oil adulteration study with lower-quality olive oils

Metabolomics is widely established in the field of food authenticity to address demanding issues, such as adulteration cases. Trapped ion mobility spectrometry (TIMS) coupled to liquid chromatography (LC) and high-resolution mass spectrometry (HRMS) provides an additional analytical dimension, introducing mobility-enhanced metabolomics in four dimensions (4D). In the present work, the potential of LC-TIMS-HRMS as a reliable analytical platform for authenticity studies is being explored, applied in extra virgin olive oil (EVOO) adulteration study. An integrated untargeted 4D-metabolomics approach is being implemented to investigate adulteration, with refined olive oils (ROOs) and olive pomace oils (OPOs) set as adulterants. Robust prediction models are built, successfully discriminating authentic EVOOs from adulterated ones and highlighting markers in each group. Noteworthy outcomes are retrieved regarding TIMS added value in LC-HRMS workflows, resulting in a significant increase of metabolic coverage, while, thanks to platform's enhanced sensitivity, detection of adulteration is being achieved down to 1%.

Recent Analytical Methodologies in Lipid Analysis

Lipids represent a large group of biomolecules that are responsible for various functions in organisms. Diseases such as diabetes, chronic inflammation, neurological disorders, or neurodegenerative and cardiovascular diseases can be caused by lipid imbalance. Due to the different stereochemical properties and composition of fatty acyl groups of molecules in most lipid classes, quantification of lipids and development of lipidomic analytical techniques are problematic. Identification of different lipid species from complex matrices is difficult, and therefore individual analytical steps, which include extraction, separation, and detection of lipids, must be chosen properly. This review critically documents recent strategies for lipid analysis from sample pretreatment to instrumental analysis and data interpretation published in the last five years (2019 to 2023). The advantages and disadvantages of various extraction methods are covered. The instrumental analysis step comprises methods for lipid identification and quantification. Mass spectrometry (MS) is the most used technique in lipid analysis, which can be performed by direct infusion MS approach or in combination with suitable separation techniques such as liquid chromatography or gas chromatography. Special attention is also given to the correct evaluation and interpretation of the data obtained from the lipid analyses. Only accurate, precise, robust and reliable analytical strategies are able to bring complex and useful lipidomic information, which may contribute to clarification of some diseases at the molecular level, and may be used as putative biomarkers and/or therapeutic targets.

Proteomics revealed an association between ribosome-associated proteins and amyloid beta deposition in Alzheimer's disease

Most scholars believe that amyloid-beta (Aβ) has the potential to induce apoptosis, stimulate an inflammatory cascade, promote oxidative stress and exacerbate the pathological progression of Alzheimer's disease (AD). Therefore, it is crucial to investigate the deposition of Aβ in AD. At approximately 6 months of age, APP/PS1 double transgenic mice gradually exhibit the development of plaques, as well as spatial and learning impairment. Notably, the hippocampus is specifically affected in the course of AD. Herein, 6-month-old APP/PS1 double transgenic mice were utilized, and the differentially expressed (DE) proteins in the hippocampus were identified and analyzed using 4D label-free quantitative proteomics technology and parallel reaction monitoring (PRM). Compared to wild-type mice, 29 proteins were upregulated and 25 proteins were downregulated in the AD group. Gene Ontology (GO) enrichment analysis of biological processes (BP) indicated that the DE proteins were mainly involved in 'ribosomal large subunit biogenesis'. Molecular function (MF) analysis results were primarily associated with '5.8S rRNA binding' and 'structural constituent of ribosome'. In terms of cellular components (CC), the DE proteins were mainly found in 'polysomal ribosome', 'cytosolic large ribosomal subunit', 'cytosolic ribosome', and 'large ribosomal subunit', among others. Furthermore, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis demonstrated that the results were mainly enriched in the 'Ribosome signaling pathway'. The key target proteins identified were ribosomal protein (Rp)l18, Rpl17, Rpl19, Rpl24, Rpl35, and Rpl6. The PRM verification results were consistent with the findings of the 4D label-free quantitative proteomics analysis. Overall, these findings suggest that Rpl18, Rpl17, Rpl19, Rpl24, Rpl35, and Rpl6 may have potential therapeutic value for the treatment of AD by targeting Aβ.

Sodium doping and trapped ion mobility spectrometry improve lipid detection for novel MALDI-MSI analysis of oats

Oat (Avena sativa L.) is an important cereal grain with a unique nutritional profile including a high proportion of lipids. Understanding lipid composition and distribution in oats is valuable for plant, food and nutritional research, and can be achieved using MALDI mass spectrometry imaging (MALDI-MSI). However, this approach presents several challenges for sample preparation (hardness of grains) and analysis (isobaric and isomeric properties of lipids). Here, oat sections were successfully mounted onto gelatin-coated indium tin oxide slides with minimal tearing. Poor detection of triacylglycerols was resolved by applying sodium chloride during mounting, increasing signal intensity. In combination with trapped ion mobility spectrometry (TIMS), lipid identification significantly improved, and we report the separation of several isobaric and isomeric lipids with visualisation of their "true" spatial distributions. This study describes a novel MALDI-TIMS-MSI analytical technique for oat lipids, which may be used to improve the discovery of biomarkers for grain quality.

Sub-5-min RP-UHPLC-TIMS for high-throughput untargeted lipidomics and its application to multiple matrices

Untargeted lipidomics, with its ability to take a snapshot of the lipidome landscape, is an important tool to highlight lipid changes in pathology or drug treatment models. One of the shortcomings of most untargeted lipidomics based on UHPLC-HRMS is the low throughput, which is not compatible with large-scale screening. In this contribution, we evaluate the application of a sub-5-min high-throughput four-dimensional trapped ion mobility mass spectrometry (HT-4D-TIMS) platform for the fast profiling of multiple complex biological matrices. Human AC-16 cells and mouse brain, liver, sclera, and feces were used as samples. By using a fast 4-min RP gradient, the implementation of TIMS allows us to differentiate coeluting isomeric and isobaric lipids, with correct precursor ion isolation, avoiding co-fragmentation and chimeric MS/MS spectra. Globally, the HT-4D-TIMS allowed us to annotate 1910 different lipid species, 1308 at the molecular level and 602 at the sum composition level, covering 58 lipid subclasses, together with quantitation capability covering more than three orders of magnitude. Notably, TIMS values were highly comparable with respect to longer LC gradients (CV% = 0.39%). These results highlight how HT-4D-TIMS-based untargeted lipidomics possess high coverage and accuracy, halving the analysis time with respect to conventional UHPLC methods, and can be used for fast and accurate untargeted analysis of complex matrices to rapidly evaluate changes of lipid metabolism in disease models or drug discovery campaigns.

Comprehensive phytochemical profiles and antioxidant activity of Korean local cultivars of red chili pepper (Capsicum annuum L.)

Red chili pepper (Capsicum annuum L.), which belongs to the Solanaceae family, contains a variety of phytochemicals with health-promoting properties including capsaicinoids, phenolics and fatty acids. Red chili pepper is one of the most consumed vegetables in Korea and occupies the largest cultivated area among spices. In this study, the ethanolic extracts from two Korean local cultivars, namely Subicho and Eumseong, were analyzed using a hybrid trapped ion mobility Q-TOF mass spectrometer equipped with a UPLC system, and their phytochemical profiles were then compared with those of a common phytophthora disease-resistant cultivar called Dokbulwang, which is extensively used for red chili pepper powder in public spaces across Korea. Utilizing high-resolution ion-mobility Q-TOF MS analysis, 458 and 192 compounds were identified from the three different red chili peppers in positive and negative ion modes, respectively, by matching with a reference spectral library. Principal component analysis revealed clear distinctions among the three cultivars, allowing us to identify key phytochemical components responsible for discriminating the local cultivars from the public cultivar. Furthermore, the assessment of total flavonoid, phenolic, and antioxidant activity in the red pepper extracts, highlighted their diverse molecular and chemical profiles. Despite the higher total flavonoid and phenolic content values observed in the public cultivar, the radical scavenging rate was higher in the local cultivars, particularly in Subicho. This suggest the presence of stronger antioxidant compounds in the local cultivar, indicating their potential health benefits due to their rich content of bioactive compounds. Notably, the local cultivars exhibited significantly higher proportions of organic compounds (more than four times) and terpenoids (more than two times) compared to the public cultivar. Specifically, higher levels of five major capsaicinoid compounds were found in the local cultivars when compared to the public cultivar. The observed disparities in phytochemical composition and antioxidant activities indicate the molecular diversity present among these cultivars. Further exploration of the bioactive compounds in these local cultivars could prove invaluable for the development of native crops, potentially leading to the discovery of novel sources of bioactive molecules for various applications in health and agriculture.

IDSL.GOA: gene ontology analysis for interpreting metabolomic datasets

Biological interpretation of metabolomic datasets often ends at a pathway analysis step to find the over-represented metabolic pathways in the list of statistically significant metabolites. However, definitions of biochemical pathways and metabolite coverage vary among different curated databases, leading to missed interpretations. For the lists of genes, transcripts and proteins, Gene Ontology (GO) terms over-presentation analysis has become a standardized approach for biological interpretation. But, GO analysis has not been achieved for metabolomic datasets. We present a new knowledgebase (KB) and the online tool, Gene Ontology Analysis by the Integrated Data Science Laboratory for Metabolomics and Exposomics (IDSL.GOA) to conduct GO over-representation analysis for a metabolite list. The IDSL.GOA KB covers 2393 metabolic GO terms and associated 3144 genes, 1,492 EC annotations, and 2621 metabolites. IDSL.GOA analysis of a case study of older versus young female brain cortex metabolome highlighted 82 GO terms being significantly overrepresented (FDR < 0.05). We showed how IDSL.GOA identified key and relevant GO metabolic processes that were not yet covered in other pathway databases. Overall, we suggest that interpretation of metabolite lists should not be limited to only pathway maps and can also leverage GO terms as well. IDSL.GOA provides a useful tool for this purpose, allowing for a more comprehensive and accurate analysis of metabolite pathway data. IDSL.GOA tool can be accessed at https://goa.idsl.me/ .

IDSL.GOA: Gene Ontology Analysis for Interpreting Metabolomic datasets

Biological interpretation of metabolomic datasets often ends at a pathway analysis step to find the over-represented metabolic pathways in the list of statistically significant metabolites. However, definitions of biochemical pathways and metabolite coverage vary among different curated databases, leading to missed interpretations. For the lists of genes, transcripts and proteins, Gene Ontology (GO) terms over-presentation analysis has become a standardized approach for biological interpretation. But, GO analysis has not been achieved for metabolomic datasets. We present a new knowledgebase (KB) and the online tool, Gene Ontology Analysis by the Integrated Data Science Laboratory for Metabolomics and Exposomics (IDSL.GOA) to conduct GO over-representation analysis for a metabolite list. The IDSL.GOA KB covers 2,393 metabolic GO terms and associated 3,144 genes, 1,492 EC annotations, and 2,621 metabolites. IDSL.GOA analysis of a case study of older vs young female brain cortex metabolome highlighted 82 GO terms being significantly overrepresented (FDR <0.05). We showed how IDSL.GOA identified key and relevant GO metabolic processes that were not yet covered in other pathway databases. Overall, we suggest that interpretation of metabolite lists should not be limited to only pathway maps and can also leverage GO terms as well. IDSL.GOA provides a useful tool for this purpose, allowing for a more comprehensive and accurate analysis of metabolite pathway data. IDSL.GOA tool can be accessed at https://goa.idsl.me/.

Proteomics-based mass spectrometry profiling of SARS-CoV-2 infection from human nasopharyngeal samples

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of the on-going global pandemic of coronavirus disease 2019 (COVID-19) that continues to pose a significant threat to public health worldwide. SARS-CoV-2 encodes four structural proteins namely membrane, nucleocapsid, spike, and envelope proteins that play essential roles in viral entry, fusion, and attachment to the host cell. Extensively glycosylated spike protein efficiently binds to the host angiotensin-converting enzyme 2 initiating viral entry and pathogenesis. Reverse transcriptase polymerase chain reaction on nasopharyngeal swab is the preferred method of sample collection and viral detection because it is a rapid, specific, and high-throughput technique. Alternate strategies such as proteomics and glycoproteomics-based mass spectrometry enable a more detailed and holistic view of the viral proteins and host-pathogen interactions and help in detection of potential disease markers. In this review, we highlight the use of mass spectrometry methods to profile the SARS-CoV-2 proteome from clinical nasopharyngeal swab samples. We also highlight the necessity for a comprehensive glycoproteomics mapping of SARS-CoV-2 from biological complex matrices to identify potential COVID-19 markers.

Analyzing Posttranslational Modifications in Single Cells

With the rapid expansion of capabilities in the analysis of proteins in single cells, we can now identify multiple classes of protein posttranslational modifications on some of these proteins. Each new technology that has increased the number of proteins measured per cell has likewise increased our ability to identify and quantify modified peptides. In this chapter, I will discuss our current capabilities, concerns, and challenges specific to this emerging field of study and the inevitable demand for services, providing a general review of concepts that should be considered.

In situ lipidomics of Staphylococcus aureus osteomyelitis using imaging mass spectrometry

Osteomyelitis occurs when Staphylococcus aureus invades the bone microenvironment, resulting in a bone marrow abscess with a spatially defined architecture of cells and biomolecules. Imaging mass spectrometry and microscopy are invaluable tools that can be employed to interrogate the lipidome of S. aureus-infected murine femurs to reveal metabolic and signaling consequences of infection. Here, nearly 250 lipids were spatially mapped to healthy and infection-associated morphological features throughout the femur, establishing composition profiles for tissue types. Ether lipids and arachidonoyl lipids were significantly altered between cells and tissue structures in abscesses, suggesting their roles in abscess formation and inflammatory signaling. Sterols, triglycerides, bis(monoacylglycero)phosphates, and gangliosides possessed ring-like distributions throughout the abscess, indicating dysregulated lipid metabolism in a subpopulation of leukocytes that cannot be discerned with traditional microscopy. These data provide chemical insight into the signaling function and metabolism of cells in the fibrotic border of abscesses, likely characteristic of lipid-laden macrophages.

Lysolipids are prominent in subretinal drusenoid deposits, a high-risk phenotype in age-related macular degeneration

Introduction

Age related macular degeneration (AMD) causes legal blindness worldwide, with few therapeutic targets in early disease and no treatments for 80% of cases. Extracellular deposits, including drusen and subretinal drusenoid deposits (SDD; also called reticular pseudodrusen), disrupt cone and rod photoreceptor functions and strongly confer risk for advanced disease. Due to the differential cholesterol composition of drusen and SDD, lipid transfer and cycling between photoreceptors and support cells are candidate dysregulated pathways leading to deposit formation. The current study explores this hypothesis through a comprehensive lipid compositional analysis of SDD.

Methods

Histology and transmission electron microscopy were used to characterize the morphology of SDD. Highly sensitive tools of imaging mass spectrometry (IMS) and nano liquid chromatography tandem mass spectrometry (nLC-MS/MS) in positive and negative ion modes were used to spatially map and identify SDD lipids, respectively. An interpretable supervised machine learning approach was utilized to compare the lipid composition of SDD to regions of uninvolved retina across 1873 IMS features and to automatically discern candidate markers for SDD. Immunohistochemistry (IHC) was used to localize secretory phospholipase A2 group 5 (PLA2G5).

Results

Among the 1873 detected features in IMS data, three lipid classes, including lysophosphatidylcholine (LysoPC), lysophosphatidylethanolamine (LysoPE) and lysophosphatidic acid (LysoPA) were observed nearly exclusively in SDD while presumed precursors, including phosphatidylcholine (PC), phosphatidylethanolamine (PE) and phosphatidic acid (PA) lipids were detected in SDD and adjacent photoreceptor outer segments. Molecular signals specific to SDD were found in central retina and elsewhere. IHC results indicated abundant PLA2G5 in photoreceptors and retinal pigment epithelium (RPE).

Discussion

The abundance of lysolipids in SDD implicates lipid remodeling or degradation in deposit formation, consistent with ultrastructural evidence of electron dense lipid-containing structures distinct from photoreceptor outer segment disks and immunolocalization of secretory PLA2G5 in photoreceptors and RPE. Further studies are required to understand the role of lipid signals observed in and around SDD.

TOFIMS mass spectrometry-based immunopeptidomics refines tumor antigen identification

T cell recognition of human leukocyte antigen (HLA)-presented tumor-associated peptides is central for cancer immune surveillance. Mass spectrometry (MS)-based immunopeptidomics represents the only unbiased method for the direct identification and characterization of naturally presented tumor-associated peptides, a key prerequisite for the development of T cell-based immunotherapies. This study reports on the implementation of ion mobility separation-based time-of-flight (TOFIMS) MS for next-generation immunopeptidomics, enabling high-speed and sensitive detection of HLA-presented peptides. Applying TOFIMS-based immunopeptidomics, a novel extensive benignTOFIMS dataset was generated from 94 primary benign samples of solid tissue and hematological origin, which enabled the expansion of benign reference immunopeptidome databases with > 150,000 HLA-presented peptides, the refinement of previously described tumor antigens, as well as the identification of frequently presented self antigens and not yet described tumor antigens comprising low abundant mutation-derived neoepitopes that might serve as targets for future cancer immunotherapy development.

SPPUSM: An MS/MS spectra merging strategy for improved low-input and single-cell proteome identification

Single and rare cell analysis provides unique insights into the investigation of biological processes and disease progress by resolving the cellular heterogeneity that is masked by bulk measurements. Although many efforts have been made, the techniques used to measure the proteome in trace amounts of samples or in single cells still lag behind those for DNA and RNA due to the inherent non-amplifiable nature of proteins and the sensitivity limitation of current mass spectrometry. Here, we report an MS/MS spectra merging strategy termed SPPUSM (same precursor-produced unidentified spectra merging) for improved low-input and single-cell proteome data analysis. In this method, all the unidentified MS/MS spectra from multiple test files are first extracted. Then, the corresponding MS/MS spectra produced by the same precursor ion from different files are matched according to their precursor mass and retention time (RT) and are merged into one new spectrum. The newly merged spectra with more fragment ions are next searched against the database to increase the MS/MS spectra identification and proteome coverage. Further improvement can be achieved by increasing the number of test files and spectra to be merged. Up to 18.2% improvement in protein identification was achieved for 1 ng HeLa peptides by SPPUSM. Reliability evaluation by the "entrapment database" strategy using merged spectra from human and E. coli revealed a marginal error rate for the proposed method. For application in single cell proteome (SCP) study, identification enhancement of 28%-61% was achieved for proteins for different SCP data. Furthermore, a lower abundance was found for the SPPUSM-identified peptides, indicating its potential for more sensitive low sample input and SCP studies.

Development of a targeted hydrophilic interaction liquid chromatography-tandem mass spectrometry based lipidomics platform applied to a coronavirus disease severity study

The importance of lipids seen in studies of metabolism, cancer, the recent COVID-19 pandemic and other diseases has brought the field of lipidomics to the forefront of clinical research. Quantitative and comprehensive analysis is required to understand biological interactions among lipid species. However, lipidomic analysis is often challenging due to the various compositional structures, diverse physicochemical properties, and wide dynamic range of concentrations of lipids in biological systems. To study the comprehensive lipidome, a hydrophilic interaction liquid chromatography-tandem mass spectrometry (HILIC-MS/MS)-based screening method with 1200 lipid features across 19 (sub)classes, including both nonpolar and polar lipids, has been developed. HILIC-MS/MS was selected due to its class separation property and fatty acyl chain level information. 3D models of class chromatographic retention behavior were established and evaluations of cross-class and within-class interferences were performed to avoid over-reporting these features. This targeted HILIC-MS/MS method was fully validated, with acceptable analytical parameters in terms of linearity, precision, reproducibility, and recovery. The accurate quantitation of 608 lipid species in the SRM 1950 NIST plasma was achieved using multi-internal standards per class and post-hoc correction, extending current databases by providing lipid concentrations resolved at fatty acyl chain level. The overall correlation coefficients (R2) of measured concentrations with values from literature range from 0.64 to 0.84. The applicability of the developed targeted lipidomics method was demonstrated by discovering 520 differential lipid features related to COVID-19 severity. This high coverage and targeted approach will aid in future investigations of the lipidome in various disease contexts.

Investigating Performance of the SLIM-Based High Resolution Ion Mobility Platform for Separation of Isomeric Phosphatidylcholine Species

Lipids are structurally diverse molecules that play a pivotal role in a plethora of biological processes. However, deciphering the biological roles of the specific lipids is challenging due to the existence of numerous isomers. This high chemical complexity of the lipidome is one of the major challenges in lipidomics research, as the traditional liquid chromatography-mass spectrometry (LC-MS) based approaches are often not powerful enough to resolve these isomeric and isobaric nuances within complex samples. Thus, lipids are uniquely suited to the benefits provided by multidimensional liquid chromatography-ion mobility-mass spectrometry (LC-IM-MS) analysis. However, many forms of lipid isomerism, including double-bond positional isomers and regioisomers, are structurally similar such that their collision cross section (CCS) differences are unresolvable via conventional IM approaches. Here we evaluate the performance of a high resolution ion mobility (HRIM) system based on structures for lossless ion manipulation (SLIM) technology interfaced to a high resolution quadrupole time-of-flight (QTOF) analyzer to address the noted lipidomic isomerism challenge. SLIM implements the traveling wave ion mobility technique along an ∼13 m ion path, providing longer path lengths to enable improved separation of isomeric features. We demonstrate the power of HRIM-MS to dissect isomeric PC standards differing only in double bond (DB) and stereospecific number (SN) positions. The partial separation of protonated DB isomers is significantly enhanced when they are analyzed as metal adducts. For sodium adducts, we achieve close to baseline separation of three different PC 18:1/18:1 isomers with different cis-double bond locations. Similarly, PC 18:1/18:1 (cis-9) can be resolved from the corresponding PC 18:1/18:1 (trans-9) form. The separation capacity is further enhanced when using silver ion doping, enabling the baseline separation of regioisomers that cannot be resolved when measured as sodium adducts. The sensitivity and reproducibility of the approach were assessed, and the performance for more complex mixtures was benchmarked by identifying PC isomers in total brain and liver lipid extracts.

Two-dimensional liquid chromatography and ion mobility-mass spectrometry for the multicomponent characterization of different parts of the medicinal plant Gynostemma longipes

Herba Gynostemma (Jiaogulan) is an herbaceous plant of the genus Gynostemma in the family Cucurbitaceae. Gynostemma longipes has lipid-lowering activity, thus, it is used as a medicinal material. However, its medicinal using parts have been recorded as whole plants or aerial parts in different provincial quality standards; therefore, it is necessary to conduct a comprehensive compositional analysis of the different parts of G. longipes (rhizomes, stems, and leaves) used in traditional medicine. In this study, offline two-dimensional liquid chromatography-ion mobility-quadrupole time-of-flight mass spectrometry (2D-LC/IM-QTOF-MS) was used to analyze the different parts of G. longipes obtained from Shaanxi province, China. By combining the retention times, mass fragments, collision cross-section values, reference standards, and information concerning literature compounds, 396 components were identified from the three parts of the plant, including 94 groups of isomers, and 217 components were identified or tentatively identified as new compounds. In the rhizomes, leaves, and stems, 240, 220, and 168 compounds, respectively, were identified. Differential analysis of the compounds in the rhizomes and aerial parts was also carried out, and 36 differential components were identified, of which 32 had higher contents in the rhizomes. Therefore, these findings indicate that the number of chemical components and the content of major differential components are higher in the rhizomes than the leaves and stems of G. longipes from the Maobaling Planting Base in Pingli county, Shaanxi province. Thus, the rhizomes of G. longipes are also an important part for medicinal use. These results will contribute to the establishment of quality control methods for G. longipes.

Urine Metabolites and Bioactive Compounds from Functional Food: Applications of Liquid Chromatography Mass Spectrometry

Bioactive compounds in functional foods, medicinal plants and others are considered attractive value-added molecules based on their wide range of bioactivity. It is clear that an important role is occupied by polyphenol, phenolic compounds and others. Urine is an effective biofluid to evaluate and monitor alterations in homeostasis and other processes related to metabolism. The current review provides a detailed description of the formation of urine in human body, various aspects relevant to sampling and analysis of urinary metabolites before presenting recent developments leveraging on metabolite profiling of urine. For the profiling of small molecules in urine, advancement of liquid chromatography mass tandem spectrometry (LC/MS/MS), establishment of standardized chemical fragmentation libraries, computational resources, data-analysis approaches with pattern recognition tools have made it an attractive option. The profiling of urinary metabolites gives an overview of the biomarkers associated with the diet and evaluates its biological effects. Metabolic pathways such as glycolysis, tricarboxylic acid cycle, amino acid metabolism, energy metabolism, purine metabolism and others can be evaluated. Finally, a combination of metabolite profiling with chemical standardization and bioassay in functional food and medicinal plants will likely lead to the identification of new biomarkers and novel biochemical insights.

Enhancing the Depth of Analyses with Next-Generation Ion Mobility Experiments

Recent developments in ion mobility (IM) technology have expanded the capability to separate and characterize gas-phase ions of biomolecules, especially when paired with mass spectrometry. This next generation of IM technology has been ushered in by creative innovation focused on both instrument architectures and how electric fields are applied. In this review, we focus on the application of high-resolution and multidimensional IM to biomolecular analyses, encompassing the fields of glycomics, lipidomics, peptidomics, and proteomics. We highlight selected research that demonstrates the application of the new IM toolkit to challenging biomolecular systems. Through our review of recently published literature, we outline the current strengths of respective technologies and perspectives for future applications.

Comparison of Data-Dependent Acquisition, Data-Independent Acquisition, and Parallel Reaction Monitoring in Trapped Ion Mobility Spectrometry-Time-of-Flight Tandem Mass Spectrometry-Based Lipidomics

The parallel accumulation-serial fragmentation (PASEF) approach based on trapped ion mobility spectrometry (TIMS) enables mobility-resolved fragmentation and a higher number of fragments in the same time period compared to conventional MS/MS experiments. Furthermore, the ion mobility dimension offers novel approaches for fragmentation. Using parallel reaction monitoring (prm), the ion mobility dimension allows a more accurate selection of precursor windows, while using data-independent aquisition (dia) spectral quality is improved through ion-mobility filtering. Owing to favorable implementation in proteomics, the transferability of these PASEF modes to lipidomics is of great interest, especially as a result of the high complexity of analytes with similar fragments. However, these novel PASEF modes have not yet been thoroughly evaluated for lipidomics applications. Therefore, data-dependent acquisition (dda)-, dia-, and prm-PASEF were compared using hydrophilic interaction liquid chromatography (HILIC) for phospholipid class separation in human plasma samples. Results show that all three PASEF modes are generally suitable for usage in lipidomics. Although dia-PASEF achieves a high sensitivity in generating MS/MS spectra, the fragment-to-precursor assignment for lipids with both, similar retention time as well as ion mobility, was difficult in HILIC-MS/MS. Therefore, dda-PASEF is the method of choice to investigate unknown samples. However, the best data quality was achieved by prm-PASEF, owing to the focus on fragmentation of specified targets. The high selectivity and sensitivity in generating MS/MS spectra of prm-PASEF could be a potential alternative for targeted lipidomics, e.g., in clinical applications.

High-Throughput Mass Spectrometry Imaging of Biological Systems: Current Approaches and Future Directions

In the past two decades, the power of mass spectrometry imaging (MSI) for the label free spatial mapping of molecules in biological systems has been substantially enhanced by the development of approaches for imaging with high spatial resolution. With the increase in the spatial resolution, the experimental throughput has become a limiting factor for imaging of large samples with high spatial resolution and 3D imaging of tissues. Several experimental and computational approaches have been recently developed to enhance the throughput of MSI. In this critical review, we provide a succinct summary of the current approaches used to improve the throughput of MSI experiments. These approaches are focused on speeding up sampling, reducing the mass spectrometer acquisition time, and reducing the number of sampling locations. We discuss the rate-determining steps for different MSI methods and future directions in the development of high-throughput MSI techniques.

Lipidomic Characterization of Oocytes at Single-Cell Level Using Nanoflow Chromatography-Trapped Ion Mobility Spectrometry-Mass Spectrometry

Mass spectrometry (MS)-based lipidomic has become a powerful tool for studying lipids in biological systems. However, lipidome analysis at the single-cell level remains a challenge. Here, we report a highly sensitive lipidomic workflow based on nanoflow liquid chromatography and trapped ion mobility spectrometry (TIMS)-MS. This approach enables the high-coverage identification of lipidome landscape at the single-oocyte level. By using the proposed method, comprehensive lipid changes in porcine oocytes during their maturation were revealed. The results provide valuable insights into the structural changes of lipid molecules during porcine oocyte maturation, highlighting the significance of sphingolipids and glycerophospholipids. This study offers a new approach to the single-cell lipidomic.

Collision Cross Section Prediction Based on Machine Learning

Ion mobility-mass spectrometry (IM-MS) is a powerful separation technique providing an additional dimension of separation to support the enhanced separation and characterization of complex components from the tissue metabolome and medicinal herbs. The integration of machine learning (ML) with IM-MS can overcome the barrier to the lack of reference standards, promoting the creation of a large number of proprietary collision cross section (CCS) databases, which help to achieve the rapid, comprehensive, and accurate characterization of the contained chemical components. In this review, advances in CCS prediction using ML in the past 2 decades are summarized. The advantages of ion mobility-mass spectrometers and the commercially available ion mobility technologies with different principles (e.g., time dispersive, confinement and selective release, and space dispersive) are introduced and compared. The general procedures involved in CCS prediction based on ML (acquisition and optimization of the independent and dependent variables, model construction and evaluation, etc.) are highlighted. In addition, quantum chemistry, molecular dynamics, and CCS theoretical calculations are also described. Finally, the applications of CCS prediction in metabolomics, natural products, foods, and the other research fields are reflected.

Chewing the fat: How lipidomics is changing our understanding of human health and disease in 2022

Lipids are biological molecules that play vital roles in all living organisms. They perform many cellular functions, such as 1) forming cellular and subcellular membranes, 2) storing and using energy, and 3) serving as chemical messengers during intra- and inter-cellular signal transduction. The large-scale study of the pathways and networks of cellular lipids in biological systems is called "lipidomics" and is one of the fastest-growing omics technologies of the last two decades. With state-of-the-art mass spectrometry instrumentation and sophisticated data handling, clinical studies show how human lipid composition changes in health and disease, thereby making it a valuable medium to collect for clinical applications, such as disease diagnostics, therapeutic decision-making, and drug development. This review gives a comprehensive overview of current workflows used in clinical research, from sample collection and preparation to data and clinical interpretations. This is followed by an appraisal of applications in 2022 and a perspective on the exciting future of clinical lipidomics.

Collision cross-section as a universal molecular descriptor in the analysis of PFAS and use of ion mobility spectrum filtering for improved analytical sensitivities

The massive usage of per- and polyfluoroalkyl substances (PFAS), as well as their high chemical stability, have led to their ubiquitous presence in environmental matrices and direct human exposure through contaminated food, particularly fish. In the analysis of this large group of substances, the use of ion mobility coupled to mass spectrometry is of particular relevance because it uses an additional descriptor, the collision cross-section (CCS), which results in increased selectivity. In the present work, the ^TWCCS_N2 of 24 priority PFAS were experimentally obtained, and the reproducibility of these measurements was evaluated over seven weeks. The average values were employed to critically assess previously reported data and theoretical calculations. This gain in selectivity made it possible to increase the sensitivity of the detection on complex matrices (biota, food and human serum) by using the drift time associated to each analyte as a filter, thus reducing the interferences and background noise and allowing their detection at trace levels.

Evaluating Software Tools for Lipid Identification from Ion Mobility Spectrometry-Mass Spectrometry Lipidomics Data

The unambiguous identification of lipids is a critical component of lipidomics studies and greatly impacts the interpretation and significance of analyses as well as the ultimate biological understandings derived from measurements. The level of structural detail that is available for lipid identifications is largely determined by the analytical platform being used. Mass spectrometry (MS) coupled with liquid chromatography (LC) is the predominant combination of analytical techniques used for lipidomics studies, and these methods can provide fairly detailed lipid identification. More recently, ion mobility spectrometry (IMS) has begun to see greater adoption in lipidomics studies thanks to the additional dimension of separation that it provides and the added structural information that can support lipid identification. At present, relatively few software tools are available for IMS-MS lipidomics data analysis, which reflects the still limited adoption of IMS as well as the limited software support. This fact is even more pronounced for isomer identifications, such as the determination of double bond positions or integration with MS-based imaging. In this review, we survey the landscape of software tools that are available for the analysis of IMS-MS-based lipidomics data and we evaluate lipid identifications produced by these tools using open-access data sourced from the peer-reviewed lipidomics literature.

A mass spectrum-oriented computational method for ion mobility-resolved untargeted metabolomics

Ion mobility (IM) adds a new dimension to liquid chromatography-mass spectrometry-based untargeted metabolomics which significantly enhances coverage, sensitivity, and resolving power for analyzing the metabolome, particularly metabolite isomers. However, the high dimensionality of IM-resolved metabolomics data presents a great challenge to data processing, restricting its widespread applications. Here, we develop a mass spectrum-oriented bottom-up assembly algorithm for IM-resolved metabolomics that utilizes mass spectra to assemble four-dimensional peaks in a reverse order of multidimensional separation. We further develop the end-to-end computational framework Met4DX for peak detection, quantification and identification of metabolites in IM-resolved metabolomics. Benchmarking and validation of Met4DX demonstrates superior performance compared to existing tools with regard to coverage, sensitivity, peak fidelity and quantification precision. Importantly, Met4DX successfully detects and differentiates co-eluted metabolite isomers with small differences in the chromatographic and IM dimensions. Together, Met4DX advances metabolite discovery in biological organisms by deciphering the complex 4D metabolomics data.