Interferences and contaminants encountered in modern mass spectrometry

Where have all the ions gone, long time passing? Tandem quadrupole mass spectrometers with atmospheric pressure ionization sensitivity gains since the mid-1970s. A perspective

The gains in sensitivity since 1975 for quadrupole mass spectrometers equipped with atmospheric pressure ionization (API), and in particular triple quadrupole mass spectrometers (QqQs) since 1981, have been driven by the needs of the environmental, biomedical, agricultural, and other scientific research, industrial, regulatory, legal, and sporting communities to continually achieve lower limits of quantitation and identification. QqQs have realized a one-million-fold improvement in sensitivity attempting to address these needs over the past two score years. It is the purpose of this article to describe how that came about, not through an exhaustive review of the literature, but rather by describing what general approaches were used across the industry to improve sensitivity and provide some examples to illustrate its evolution. The majority of the gains came from the ion source and its interface to the vacuum system. "Sampling efficiency" is a measurement of the losses in this area so will be a focus of this review. The discovery of the phenomenon of collisional focusing was key to improving sampling efficiency because it enabled designs that increased the ion-containing gas loads from the ion source, using staged differential pumping backed by increasingly larger pumps, and prevented the scattering losses of ions in the resulting gas expansion inside vacuum. Likewise, systems with smaller pumps and lower ion-containing gas loads could be designed with size and cost reduction in mind while maintaining reasonable sampling efficiencies. As a consequence, advancements in the designs of both larger and smaller turbomolecular vacuum pumps were accelerated by pump manufacturers to accommodate the explosive growth in the use of API-QqQ and API-ion trap mass spectrometers that occurred in the 1990s and continued into the new millennium. Sampling efficiency was further improved by increasing the ion yield from electrospray by increasing the rate of droplet desolvation. An estimate of the practical limit to further sensitivity improvements beyond what has been achieved to date is provided to shed light on what to expect in the future. Lastly, the implications and unforeseen consequences of the sensitivity gains are considered with a particular focus on how they have enabled a dramatic increase in daily sample throughput on triple quadrupole and other types of mass spectrometers.

Chemical Transformations of Infiltrated Wildfire Smoke on Indoor-Relevant Surfaces

Indoor environments are affected during wildfire events due to the infiltration of smoke. In this study, the fate of wildfire smoke, including gases and particles, on indoor surfaces was investigated through laboratory and field experiments. Fresh smoke was generated from the burning of ponderosa pine woodchips, which produced well-established wildfire and biomass burning tracers, such as levoglucosan, 3-methyl-1,2,3-butanetricarboxylic acid (MBTCA), and 5-hydroxymethylfurfural. The interactions of smoke particles and gases were observed on different indoor-relevant building material surfaces, including glass (windows), rutile (paint and self-cleaning surfaces), and kaolinite (cement proxy and clay). However, the relative abundance of surface-bound species varied depending on the nature of these surfaces, suggesting that preferential adsorption of volatile species and particle deposition onto relevant surfaces play a key role in indoor chemistry and indoor air quality following smoke intrusion. Kaolinite surfaces, in particular, exhibited the formation of surface-initiated products during fresh smoke exposure. Furthermore, the formation of larger particles on a rutile surface was observed following ozone-aged smoke exposure, potentially resulting from the interaction of secondary organic aerosol formed during ozonolysis. Overall, this study demonstrates that different indoor-relevant material surfaces interact uniquely with smoke compounds, leading to distinct chemical transformations.

Fast and comprehensive lipidomic analysis using supercritical fluid chromatography coupled with low and high resolution mass spectrometry

Despite advances in the last few years, the use of supercritical fluid chromatography (SFC) in combination with mass spectrometry (MS) for lipidomic analysis has not reached the popularity of LC-MS. However, SFC presents clear advantages that can be exploited, such as fast, reproducible and class-based separations including nonpolar lipid classes, such as cholesterol esters, triacylglycerols, diacylglycerols, monoacylglycerols and cholesterol. In this study we show how SFC can be used for comprehensive lipidomic analyses after optimization of parameters such as back-pressure regulator (BPR), column temperature or overfeed volume of injection. We also compare the performances of triple quadrupole (QqQ) and quadrupole time-of-flight (QTOF) mass spectrometers coupled to SFC in terms of qualitative and quantitative analyses.

HSA Adductomics in the Shanghai Women's Health Study Links Lung Cancer in Never-Smokers with Air Pollution, Redox Biology, and One-Carbon Metabolism

Nearly one fourth of lung cancers occur among never-smokers and are predominately lung adenocarcinomas (LUADs) that are distinct from smoking-related cancers. Causal links between LUADs in never-smokers have been attributed to reactive oxygen species (ROS) arising from airborne fine particulate matter (PM2.5) and polycyclic aromatic hydrocarbons (PAHs). These effects are pronounced among East Asian women who experience massive exposures to PM2.5 and PAHs and have the highest incidence of LUADs in the world. We employed untargeted adductomics to establish ROS adduct signatures in human serum albumin (HSA) from lung cancer cases and controls from never-smokers in the Shanghai Women's Health Study. Forty-seven HSA adducts were quantified by mass spectrometry, nine of which were selected for association with lung cancer, including Cys34 sulfoxidation products and disulfides of cysteine and homocysteine and two modifications to Lys525. Associated adducts include constituents of redox biology and one-carbon metabolism (OCM), which are pathways associated with lung cancer. Differences in adduct abundance between cases and controls and correlations of adducts with urinary PAHs and dietary factors provide additional evidence linking air pollutants, OCM, and redox biology with lung cancer in never-smokers.

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.

Refinement of Protein Extraction Protocols for Human Peripheral Nerve Tissue

Our aim was to establish an effective method for protein extraction from freshly frozen human peripheral nerves, determine the minimum amount required for consistent protein extraction outcomes, and assess which method produced the highest number of protein identities. Five extraction methods were compared using 8 M urea and Ripa buffer using either the Bullet Blender or Bioruptor. Out of the total 2619 identified proteins, protein extraction using the Ripa buffer combined with either Bioruptor or Bullet Blender resulted in the identification of 1582 (60%) and 1615 (62%) proteins, respectively. In contrast, using 8 M urea and Bioruptor for protein extraction resulted in 1022 proteins (39%), whereas employing Bullet Blender yielded 1446 proteins (55%). Sample amounts, ranging from 0.6 to 10 mg, were prepared with consistent protein extraction outcome obtained for samples ≥1.2 mg. Combining Ripa and 8 M urea with Bullet Blender increased protein identification to 2126 (81%). Proteins were classified by their cell components, molecular functions, and biological processes. Furthermore, a subclassification of proteins involved in the extracellular matrix (ECM) was introduced. We recommend the use of Ripa buffer, in combination with 8 M urea and Bullet Blender for extracting proteins from fresh-frozen human nerves weighing ≥1.2 mg.

MAGPIE: A Machine Learning Approach to Decipher Protein-Protein Interactions in Human Plasma

Immunoprecipitation coupled to tandem mass spectrometry (IP-MS/MS) methods are often used to identify protein-protein interactions (PPIs). While these approaches are prone to false positive identifications through contamination and antibody nonspecific binding, their results can be filtered using negative controls and computational modeling. However, such filtering does not effectively detect false-positive interactions when IP-MS/MS is performed on human plasma samples. Therein, proteins cannot be overexpressed or inhibited, and existing modeling algorithms are not adapted for execution without such controls. Hence, we introduce MAGPIE, a novel machine learning-based approach for identifying PPIs in human plasma using IP-MS/MS, which leverages negative controls that include antibodies targeting proteins not expected to be present in human plasma. A set of negative controls used for false positive interaction modeling is first constructed. MAGPIE then assesses the reliability of PPIs detected in IP-MS/MS experiments using antibodies that target known plasma proteins. When applied to five IP-MS/MS experiments as a proof of concept, our algorithm identified 68 PPIs with an FDR of 20.77%. MAGPIE significantly outperformed a state-of-the-art PPI discovery tool and identified known and predicted PPIs. Our approach provides an unprecedented ability to detect human plasma PPIs, which enables a better understanding of biological processes in plasma.

Mass spectrometric detection of keratins in tear fluid

PURPOSE

Keratin contamination is a common problem in mass spectrometry proteomic analyses, particularly in bottom-up mass spectrometry. The purpose of this study was to determine the protein contaminants introduced during the proteomic analysis of tear fluid.

METHODS

Human tear fluid samples were collected using Schirmer strips. Proteomic analyses were performed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) on blank Schirmer strips and tear fluid samples, with empty vials serving as controls for assessing environmental contaminant proteins.

RESULTS

We detected 26 contaminant proteins (18 keratins and 8 non-keratins). 98.2% of the total protein contamination can be attributed to the 9 keratins, including KRT10 (23.6%), KRT1 (23.5%), KRT2 (15.7%), KRT14 (7.6%), KRT16 (7.0%), KRT5 (6.1%), KRT9 (5.9%), KRT6B (4.6%), and KRT6A (4.3%). A comparison to the proteomic profile of blank Schirmer strips and controls (empty vials) found a strong correlation (R2 = 0.9753), indicating that these proteins were not from the blank Schirmer strips but are environmental contaminants. On the other hand, several keratins including KRT19, KRT13, KRT4, KRT7, KRT15, KRT8 and KRT18 were present in tear fluid, but either not detected or were negligible in blank strips. Another set of keratins, including KRT5, KRT6A, KRT14, KRT16, and KRT17, were identified as components of tear fluid as well as environmental contaminants.

CONCLUSIONS

This study revealed nine major contaminant keratins in the mass spectrometry analysis. Several other keratins were identified as constituents of tear fluid. Background subtraction is necessary for the accurate analysis of tear fluid using mass spectrometry.

Ultrahigh-Resolution Mass Spectrometry Advances for Biogeochemical Analysis: From Seafloor Sediments to Petroleum and Marine Oil Spills

This Perspective explores the transformative impact of ultrahigh-resolution mass spectrometry (UHR-MS), particularly Fourier transform ion cyclotron resonance (FT-ICR-MS), in the characterization of complex environmental and petroleum samples. UHR-MS has significantly advanced our ability to identify molecular formulas in complex mixtures, revolutionizing the study of biogeochemical processes and organic matter evolution on wide time scales. We start by briefly reviewing the main technological advances of UHR-MS in the context of petroleum and environmental applications, highlighting some of the challenges of the technology such as quantitation and structural identification. We then showcase a selection of impactful applications published in the last 20+ years. In the field of environmental lipidomics, high-resolution analysis of lipids in sediments enables multiproxy studies and provides novel insights into past environmental conditions. UHR-MS has also facilitated the characterization of kerogen, a complex, poorly soluble mixture formed from sedimented organic matter over geological time scales, and the identification of polar compounds within its fractions. In petroleum (geo)chemistry, UHR-MS has enabled the identification of biomarkers such as petroporphyrins, asphaltenes, and high-molecular-weight naphthenic acids, shedding light on the molecular complexity of crude oil. The application of UHR-MS in oil spill science has revealed significant molecular transformations during weathering processes, such as photo-oxidation, which are crucial for assessing the environmental impact of past spills and improving the preparedness for future spills. These advancements underscore the role of this maturing analytical technology in deepening our understanding of geochemical processes and biogeochemical cycles, highlighting its potential for future research directions in organic geochemistry.

Early detection of bacterial pneumonia by characteristic induced odor signatures

INTRODUCTION

The ability to detect pathogenic bacteria before the onsets of severe respiratory symptoms and to differentiate bacterial infection allows to improve patient-tailored treatment leading to a significant reduction in illness severity, comorbidity as well as antibiotic resistance. As such, this study refines the application of the non-invasive Secondary Electrospray Ionization-High Resolution Mass Spectrometry (SESI-HRMS) methodology for real-time and early detection of human respiratory bacterial pathogens in the respiratory tract of a mouse infection model.

METHODS

A real-time analysis of changes in volatile metabolites excreted by mice undergoing a lung infection by Staphylococcus aureus or Streptococcus pneumoniae were evaluated using a SESI-HRMS instrument. The infection status was confirmed using classical CFU enumeration and tissue histology. The detected VOCs were analyzed using a pre- and post-processing algorithm along with ANOVA and RASCA statistical evaluation methods.

RESULTS

Characteristic changes in the VOCs emitted from the mice were detected as early as 4-6 h post-inoculation. Additionally, by using each mouse as its own baseline, we mimicked the inherent variation within biological organism and reported significant variations in 25 volatile organic compounds (VOCs) during the course of a lung bacterial infection.

CONCLUSION

the non-invasive SESI-HRMS enables real-time detection of infection specific VOCs. However, further refinement of this technology is necessary to improve clinical patient management, treatment, and facilitate decisions regarding antibiotic use due to early infection detection.

Protein Precipitation by Metal Hydroxides as a Convenient and Alternative Sample Preparation Procedure for Bioanalysis

Protein precipitation is widely used for sample preparation ahead of liquid chromatography. This step is required to analyze small molecules without the interference of proteins contained in the matrix. Organic solvents and acidic chemicals are the two most popular reagents used for this scope. Organic solvents are quite effective precipitating agents, but require a medium-to-large sample dilution. Moreover, a high concentration of organic solvents in sample media can affect reversed phase separations. Therefore, an evaporation step, followed by the resuspension of the analytes in appropriate media, is sometimes required. On the contrary, the addition of acidic compounds is more straightforward, since it keeps the supernatant aqueous and does not require evaporation, but the extreme pH can cause the degradation of analytes and the stationary phase. Herein, an alternative method for protein precipitation using the addition of zinc hydroxide was tested. The main advantages of this method over the other precipitating reagents are the minimal sample dilution required and the maintenance of aqueous media at nearly neutral pH which ensure analyte stability. The protocol ensured an effective protein removal before the analysis of small molecules in biological matrices, resulting in full compatibility with reversed phase chromatography coupled with both UV and mass spectrometric detectors.

Sample Preparation for Metabolomic Analysis in Exercise Physiology

Metabolomics investigates final and intermediate metabolic products in cells. Assessment of the human metabolome relies principally on the analysis of blood, urine, saliva, sweat, and feces. Tissue biopsy is employed less frequently. Understanding the metabolite composition of biosamples from athletes can significantly improve our knowledge of molecular processes associated with the efficiency of training and recovery. Such knowledge may also lead to new management opportunities. Successful execution of metabolomic studies requires simultaneous qualitative and quantitative analyses of numerous small biomolecules in samples under test. Unlike genomics and proteomics, which do not allow for direct assessment of enzymatic activity, metabolomics focuses on biochemical phenotypes, providing unique information about health and physiological features. Crucial factors in ensuring the efficacy of metabolomic analysis are the meticulous selection and pre-treatment of samples.

Challenges of MS-based small extracellular vesicles proteomics

Proteomic profiling of small extracellular vesicles (sEV) is a powerful tool for discovering biomarkers of various diseases. This process most often assisted by mass spectrometry (MS) usually lacks standardization and recognition of challenges which may lead to unreliable results. General recommendations for sEV MS analyses have been briefly given in the MISEV2023 guidelines. The present work goes into detail for every step of sEV protein profiling with an overview of factors influencing such analyses. This includes reporting and defining the sEV source and vesicle isolation, protein solubilization and digestion, 'offline' and 'online' sample complexity reduction, the analysis type itself, and subsequent data analysis. Every stage in this process affects the others, which could result in different outcomes. Although characterization and comparisons of different sEV isolation methods are known and accessible and MS-based profiling details are provided for cell or tissue samples, no consensus work has been ever published to describe the whole process of sEV proteomic analysis. Reliable results can be obtained from sEV profiling provided that the analysis is well planned, prepared for, and backed by pilot studies or appropriate research.

High-resolution acoustic ejection mass spectrometry for high-throughput library screening

An approach is described for high-throughput quality assessment of drug candidate libraries using high-resolution acoustic ejection mass spectrometry (AEMS). Sample introduction from 1536-well plates is demonstrated for this application using 2.5 nL acoustically dispensed sample droplets into an Open Port Interface (OPI) with pneumatically assisted electrospray ionization at a rate of one second per sample. Both positive and negative ionization are shown to be essential to extend the compound coverage of this protease inhibitor-focused library. Specialized software for efficiently interpreting this data in 1536-well format is presented. A new high-throughput method for quantifying the concentration of the components (HTQuant) is proposed that neither requires adding an internal standard to each well nor further encumbers the high-throughput workflow. This approach for quantitation requires highly reproducible peak areas, which is shown to be consistent within 4.4 % CV for a 1536-well plate analysis. An approach for troubleshooting the workflow based on the background ion current signal is also presented. The AEMS data is compared to the industry standard LC/PDA/ELSD/MS approach and shows similar coverage but at 180-fold greater throughput. Despite the same ionization process, both methods confirmed the presence of a small percentage of compounds in wells that the other did not. The data for this relatively small, focused library is compared to a larger, more chemically diverse library to indicate that this approach can be more generally applied beyond this single case study. This capability is particularly timely considering the growing implementation of artificial intelligence strategies that require the input of large amounts of high-quality data to formulate predictions relevant to the drug discovery process. The molecular structures of the 872-compound library analyzed here are included to begin the process of correlating molecular structures with ionization efficiency and other parameters as an initial step in this direction.

Unlocking the Potential of Isopropanol as an Eco-Friendly Eluent for Large-Scale Fractionation of Fulvic Acids via Preparative Reversed-Phase High-Performance Liquid Chromatography and Multidimensional RP-HPLC: Evaluation of Molecular Diversity and Element Composition

Humic substances are organic mixtures of extreme complexity, which significantly complicate their analysis by any method. Fractionation into more homogeneous mixtures seems to be almost the only way to overcome these difficulties. Preparative high-performance liquid chromatography (HPLC) provides almost any amounts of substances required for both further fractionation and studies by other methods. For the first time, isopropyl alcohol (IPA) is proposed for these purposes; its advantages are shown by the example of groundwater fulvic acids (FA). IPA is much safer than conventional solvents and elutes the most nonpolar compounds that are nonelutable by methanol and acetonitrile. The isolated fractions differ significantly in their molecular composition, which is confirmed by ultrahigh-resolution mass spectrometry and molecular spectroscopy. Stepwise IPA-gradient HPLC-UV analysis of each preparative fraction demonstrates the possibilities of multidimensional HPLC for FA. The isolated fractions were studied for contents of a broad range of elements, and the relationships between the molecular and trace-element compositions of the fractions were revealed. Our data reveals the existence of numerous and different organometal compounds in FA composition; thus, the proposed approach can be used for a more in-depth study of the mechanisms of element migration in the environment.

Empirically establishing drug exposure records directly from untargeted metabolomics data

Despite extensive efforts, extracting information on medication exposure from clinical records remains challenging. To complement this approach, we developed the tandem mass spectrometry (MS/MS) based GNPS Drug Library. This resource integrates MS/MS data for drugs and their metabolites/analogs with controlled vocabularies on exposure sources, pharmacologic classes, therapeutic indications, and mechanisms of action. It enables direct analysis of drug exposure and metabolism from untargeted metabolomics data independent of clinical records. Our library facilitates stratification of individuals in clinical studies based on the empirically detected medications, exemplified by drug-dependent microbiota-derived N-acyl lipid changes in a cohort with human immunodeficiency virus. The GNPS Drug Library holds potential for broader applications in drug discovery and precision medicine.

Demonstration of an End-To-End Workflow Using Atmospheric Solids Analysis Probe-Mass Spectrometry (ASAP-MS) With Real-Time Sample Recognition Software for the Identification of Falsified and Substandard Pharmaceutical Tablets

Counterfeit pharmaceuticals are a subclass of falsified and substandard medicines. They are illicit products, purporting to be genuine medicines, that are made and sold by criminal organisations. They represent a significant risk to patient safety, as well as a financial and reputational threat to the companies who make the genuine medicines. It is essential to have analytical methods to determine if suspect samples seized by law enforcement agencies are counterfeit, with mass spectrometry (MS) being a commonly used technique in forensic cases. Speed-to-answer is vital to enable law enforcement agencies to progress investigations, as well as for pharmaceutical companies so that they can notify health authorities of the circulation of counterfeit medicines. In this work, an atmospheric solids analysis probe (ASAP)-MS was assessed as a fast and simple-to-use approach to analyse tablets on a commercially available instrument. Complementing the analytics with real-time sample recognition software demonstrated that the classification of tablets as authentic or counterfeit could be achieved quickly (< 2 min) and without the need for MS interpretation skills. Authentication of five tablets (two authentic pharmaceuticals, one placebo and two counterfeits containing the correct active pharmaceutical ingredient [API] but at lower quantities than in the genuine medicine and with different excipient contents) of unknown origin was achieved with 100% success. This creates the opportunity to deploy the end-to-end workflow as a tool for non-scientists, such as law enforcement officers and border control staff, for use in-territory to obtain fast answers and make data-led decisions to control the illegal trading of medicines.

A strategy for the detection of benzodiazepine drugs using low-resolution paper-spray mass spectrometry for harm reduction drug checking

The ability to detect newly emerging substances is of great importance in reducing harms for people who use drugs. New psychoactive substances including novel benzodiazepines in the illicit drug supply have been linked to high rates of overdose deaths while complicating drug checking as an overdose prevention strategy. Paper-spray mass spectrometry (PS-MS) has emerged as a novel strategy to rapidly detect trace components in street drug samples. While targeted, low-resolution PS-MS methods have proven effective, newly emerging substances are often missed. To address this, a method was applied to low-resolution full-scan PS-MS data to aid in the early detection and identification of novel benzodiazepines in the unregulated drug supply. Using the developed method, true positives rates of 0.89 and 0.75 were achieved for bromazolam and etizolam in street samples obtained in a community drug checking service. The applicability of the method was further demonstrated for a novel benzodiazepine, desalkylgidazepam, that has recently emerged in the illicit drug supply.

Suppressing the background of LC-ESI-MS analysis of permethylated glycans using the active background ion reduction device

Mass spectrometry (MS) has revolutionized analytical chemistry, enabling precise identification and quantification of chemical species, which is pivotal for biomarker discovery and understanding complex biological systems. Despite its versatility, the presence of background ions in MS analysis hinders the sensitive detection of low-abundance analytes. Therefore, studies aimed at lowering background ion levels have become increasingly important. Here, we utilized the commercially available Active Background Ion Reduction Device (ABIRD) to suppress background ions and assess its effect on the liquid chromatography-electrospray ionization (LC-ESI)-MS analyses of N-glycans on the Q Exactive HF mass spectrometer. We also investigated the effect of different solvent vapors in the ESI source on N-glycan analysis by MS. ABIRD generally had no effect on high-mannose and neutral structures but reduced the intensity of some structures that contained sialic acid, fucose, or both when methanol vapor filled the ESI source. Based on our findings on the highest number of identified N-glycans from human serum, methanol vapor in the ion source compartment may enhance N-glycan LC-ESI-MS analyses by improving the desolvation of droplets formed during the ESI process due to its high volatility. This protocol may be further validated and extended to advanced bottom-up proteomic/glycoproteomic studies for the analysis of peptide/glycopeptide ions by MS.

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.

MRMQuant: Automated MRM Data Quantitation for Large-Scale Targeted Metabolomics Analysis

Multiple reaction monitoring (MRM) is a powerful and popular technique used for metabolite quantification in targeted metabolomics. Accurate and consistent quantitation of metabolites from the MRM data is essential for subsequent analyses. Here, we developed an automated tool, MRMQuant, for targeted metabolomic quantitation using high-throughput liquid chromatography-tandem mass spectrometry MRM data to provide users with an easy-to-use tool for accurate MRM data quantitation with minimal human intervention. This tool has many user-friendly functions and features to inspect and correct the quantitation results as required. MRMQuant possesses the following features to ensure accurate quantitation: (1) dynamic signal smoothing, (2) automatic deconvolution of coeluted peaks, (3) absolute quantitation via standard curves and/or internal standards, (4) visualized inspection and correction, (5) corrections applicable to multiple samples, and (6) batch-effect correction.

Fibroblast-derived extracellular vesicles contain SFRP1 and mediate pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a lethal chronic lung disease characterized by aberrant intercellular communication, extracellular matrix deposition, and destruction of functional lung tissue. While extracellular vesicles (EVs) accumulate in the IPF lung, their cargo and biological effects remain unclear. We interrogated the proteome of EV and non-EV fractions during pulmonary fibrosis and characterized their contribution to fibrosis. EVs accumulated 14 days after bleomycin challenge, correlating with decreased lung function and initiated fibrogenesis in healthy precision-cut lung slices. Label-free proteomics of bronchoalveolar lavage fluid EVs (BALF-EVs) collected from mice challenged with bleomycin or control identified 107 proteins enriched in fibrotic vesicles. Multiomic analysis revealed fibroblasts as a major cellular source of BALF-EV cargo, which was enriched in secreted frizzled related protein 1 (SFRP1). Sfrp1 deficiency inhibited the activity of fibroblast-derived EVs to potentiate lung fibrosis in vivo. SFRP1 led to increased transitional cell markers, such as keratin 8, and WNT/β-catenin signaling in primary alveolar type 2 cells. SFRP1 was expressed within the IPF lung and localized at the surface of EVs from patient-derived fibroblasts and BALF. Our work reveals altered EV protein cargo in fibrotic EVs promoting fibrogenesis and identifies fibroblast-derived vesicular SFRP1 as a fibrotic mediator and potential therapeutic target for IPF.

In vitro, in vivo metabolism and quantification of the novel synthetic opioid N-piperidinyl etonitazene (etonitazepipne)

OBJECTIVES

N-piperidinyl etonitazene (etonitazepipne) is a newly synthesized opioid related to the 2-benzylbenzimidazole analog class. Etonitazepipne has been formally notified and placed under intensive monitoring in Europe in January 2022. Nitazenes have high affinity at µ-opioid receptor (MOR). Etonitazepipne, specifically shows a EC50 of 2.49 nM, suggesting about 50 times higher potency combined with higher efficacy compared to morphine. Antinociceptive potency l ('hot plate test' with rats) was 192-fold greater than that of morphine.

METHODS

Here we report on a post-mortem case involving etonitazepipne and its quantification using a standard addition method (SAM) through liquid chromatography tandem mass spectrometry (LC-MS/MS). In addition, characterization and identification of phase I human metabolites using in vitro assay based on pooled human liver microsomes (pHLM) was performed along with the analysis of authentic urine samples by means of high-performance liquid chromatography high-resolution tandem mass spectrometry (LC-HRMS/MS).

RESULTS

The concentration of etonitazepipne in post-mortem blood and urine was 8.3 and 11 ng/mL, respectively. SAM was validated by assessing the following parameters: intraday and interday repeatability, matrix effect and recovery rate in post-mortem blood. A total of 20 and 14 metabolites were identified after pHLM incubation and urine analysis, respectively. Most pronounced in vitro and in vivo transformations were O-deethylation, hydroxylation, ketone reduction, and combinations thereof.

CONCLUSIONS

Considering small traces of the parent drug often found in real cases, the identification of metabolic biomarkers is crucial to identify exposure to this drug. O-deethylated, oxidated metabolites, and combination thereof are proposed as urinary biomarkers along with the parent compound.

Formation of Micelles by Nonionic Detergent Molecules Leads to the Breakthrough Peak in Reversed-Phase Ultraperformance Liquid Chromatography (UPLC)

A peculiar phenomenon known as "breakthrough" occurs under reversed-phase ultraperformance liquid chromatography (UPLC) conditions and has been under scrutiny for decades. This effect takes place when a large volume of analyte solution, prepared in a solvent with an eluotropic strength significantly higher than that of the initial mobile phase solvent, is injected. According to the literature, under specific experimental conditions, a substantial portion of solutes is carried by the mobile phase and detected near the dead time of the chromatographic system. This phenomenon is typically observed when the injected volume of a particular analyte is sufficiently large. However, the underlying physicochemical principles governing this phenomenon have remained elusive. We present evidence demonstrating that breakthroughs can occur even when injecting a sample of a neat solvent devoid of any solute. By mass spectrometric analysis, we identified the breakthrough peak to represent the nonionic detergent Triton. When columns are equilibrated with water, Triton molecules, present as impurities in filtered water, accumulate on the nonpolar stationary phase. Upon the introduction of a solvent with a stronger elution strength, Triton molecules retained on the stationary phase are removed. As detergents, these Triton molecules aggregate into micelles featuring a hydrophobic inner core and a hydrophilic outer shell. These hydrophilic micelles are carried by the polar mobile phase and detected as the breakthrough peak at the dead time of the chromatographic system. When analytes are present, a portion of the injected solutes is captured by the micelles and transported with the breakthrough plug. This assertion was verified and confirmed by liquid chromatography-mass spectrometry (LC-MS) analysis of a methanolic solution of perfluorooctanoic acid (PFOA). The mass spectra corresponding to the breakthrough plug featured a peak for the PFOA anion (m/z 413) in addition to those for Triton.

Native metabolomics for mass spectrometry-based siderophore discovery

Microorganisms, plants, and animals alike have specialized acquisition pathways for obtaining metals, with microorganisms and plants biosynthesizing and secreting small molecule natural products called siderophores and metallophores with high affinities and specificities for iron or other non-iron metals, respectively. This chapter details a novel approach to discovering metal-binding molecules, including siderophores and metallophores, from complex samples ranging from microbial supernatants to biological tissue to environmental samples. This approach, called Native Metabolomics, is a mass spectrometry method in which pH adjustment and metal infusion post-liquid chromatography are interfaced with ion identity molecular networking (IIMN). This rule-based data analysis workflow that enables the identification of metal-binding species based on defined mass (m/z) offsets with the same chromatographic profiles and retention times. Ion identity molecular networking connects compounds that are structurally similar by their fragmentation pattern and species that are ion adducts of the same compound by chromatographic shape correlations. This approach has previously revealed new insights into metal binding metabolites, including that yersiniabactin can act as a biological zincophore (in addition to its known role as a siderophore), that the recently elucidated lepotchelin natural products are cyanobacterial metallophores, and that antioxidants in traditional medicine bind iron. Native metabolomics can be conducted on any liquid chromatography-mass spectrometry system to explore the binding of any metal or multiple metals simultaneously, underscoring the potential for this method to become an essential strategy for elucidating biological metal-binding molecules.

"Tranq-dope": The first fatal intoxication due to xylazine-adulterated heroin in Italy

BACKGROUND AND AIMS

The number of xylazine-involved overdose deaths tremendously increased from 2019 onwards in the US. This is due to the "tranq-dope" trend consisting in mixing opioids with the sedative to reduce drug manufacturing costs and enhance their effects. In this study, we report the first fatality involving xylazine-adulterated heroin in the EU.

MATERIALS AND METHODS

The subject was a 33-year-old Caucasian male with a documented history of drug abuse who was found dead in a public area with puncture marks at the elbow. Peripheral blood and urine were collected at the autopsy and analyzed by liquid chromatography-high-resolution tandem mass spectrometry (LC-HRMS/MS) after protein precipitation.

RESULTS

6-Monoacetylmorphine, total/free morphine, and codeine blood concentrations of 20.3, 236/105, and 38.3 ng/mL, respectively, indicated recent heroin consumption. Methadone blood concentration was below 10 ng/mL. Alprazolam, nordiazepam, and flurazepam blood concentrations were 23.9, 61.4, and 55.0 ng/mL, respectively. Benzoylecgonine blood concentration was below 5 ng/mL. Xylazine blood and urine concentrations were 105 and 72.6 ng/mL, respectively.

CONCLUSION

The combination of central nervous system depressants, i.e., opioids, benzodiazepines, and xylazine, was the principal cause of death by cardiorespiratory failure. The case was promptly reported to the UE Early Warning System on drugs.

Human metabolism of four synthetic benzimidazole opioids: isotonitazene, metonitazene, etodesnitazene, and metodesnitazene

Following isotonitazene scheduling in 2019, the availability of alternative 2-benzylbenzimidazole opioids (nitazenes) on the global drug market increased, resulting in many fatalities worldwide. Nitazenes are potent µ-opioid receptor agonists with strong narcotic/analgesic effects, and their concentrations in biological matrices are low, making the detection of metabolite biomarkers of consumption crucial to document use in clinical and forensic settings. However, there is little to no data on the metabolism of the most recently available nitazenes, especially desnitro-analogues. The aim of the research was to assess isotonitazene, metonitazene, etodesnitazene, and metodesnitazene human metabolism and identify specific metabolite biomarkers of consumption. The four analogues were incubated with 10-donor-pooled human hepatocytes, and the incubates were analyzed by liquid chromatography-high-resolution tandem mass spectrometry and data mining with Compound Discoverer (Thermo Scientific); the analysis was supported by in silico metabolite predictions with GLORYx open-access software. Metabolites were identified in postmortem blood and/or urine samples from two metonitazene-positive and three etodesnitazene-positive cases following the same workflow, with and without glucuronide hydrolysis in urine, to confirm in vitro results. Twelve, nine, twenty-two, and ten metabolites were identified for isotonitazene, metonitazene, etodesnitazene, and metodesnitazene, respectively. The main transformations were N-deethylation at the N,N-diethylethanamine side chain, O-dealkylation, and further O-glucuronidation. In vitro and autopsy results were consistent, demonstrating the efficacy of the 10-donor-pooled human hepatocyte model to predict human metabolism. We suggest the parent and the corresponding O-dealkyl- and N-deethyl-O-dealkyl metabolites as biomarkers of exposure in urine after glucuronide hydrolysis, and the corresponding N-deethyl metabolite as additional biomarker in blood.

3-CMC, 4-CMC, and 4-BMC Human Metabolic Profiling: New Major Pathways to Document Consumption of Methcathinone Analogues?

Synthetic cathinones represent one of the largest and most abused new psychoactive substance classes, and have been involved in numerous intoxications and fatalities worldwide. Methcathinone analogues like 3-methylmethcathinone (3-MMC), 3-chloromethcathinone (3-CMC), and 4-CMC currently constitute most of synthetic cathinone seizures in Europe. Documenting their consumption in clinical/forensic casework is therefore essential to tackle this trend. Targeting metabolite markers is a go-to to document consumption in analytical toxicology, and metabolite profiling is crucial to support investigations. We sought to identify 3-CMC, 4-CMC, and 4-bromomethcathinone (4-BMC) human metabolites. The substances were incubated with human hepatocytes; incubates were screened by liquid chromatography-high-resolution tandem mass spectrometry and data were mined with Compound Discoverer (Themo Scientific). 3-CMC-positive blood, urine, and oral fluid and 4-CMC-positive urine and saliva from clinical/forensic casework were analyzed. Analyses were supported by metabolite predictions with GLORYx freeware. Twelve, ten, and ten metabolites were identified for 3-CMC, 4-CMC, and 4-BMC, respectively, with similar transformations occurring for the three cathinones. Major reactions included ketoreduction and N-demethylation. Surprisingly, predominant metabolites were produced by combination of N-demethylation and ω-carboxylation (main metabolite in 3-CMC-positive urine), and combination of β-ketoreduction, oxidative deamination, and O-glucuronidation (main metabolite in 4-CMC-positive urine). These latter metabolites were detected in negative-ionization mode only and their non-conjugated form was not detected after glucuronide hydrolysis; this metabolic pathway was never reported for any methcathinone analogue susceptible to undergo the same transformations. These results support the need for comprehensive screening strategies in metabolite identification studies, to avoid overlooking significant metabolites and major markers of consumption.

A LC-MS/MS method for the simultaneous quantification of 17 opioids in biosolids

The solid product of wastewater treatment plants is commonly used as a fertiliser to increase sustainability and waste reuse. It has undergone extensive treatment to remove high nutrient loads, pathogens and heavy metals but the extensive matrix of household chemicals, pesticides and pharmaceuticals remains, untargeted by most treatment technologies. These compounds, particularly pharmaceuticals, have been detected in biosolids with there being evidence of uptake by plants. With the current opioid pandemic in North America and overprescription, a simple method is required for the extraction of opioids from a solid medium as to ascertain the concentrations the environment is exposed to. A sonication-liquid-liquid extracted method was developed where biosolids were suspended in water and extracted using ethyl acetate before analysis on LC MS/MS. Sodium and potassium chloride were compared along with acidic and alkaline conditions. The optimised method utilised NaCl at a pH of 12 and was validated for 17 opioids, achieving linearity >0.987, 86-113% matrix effect and 0.1-10 μg/kg limits of detection. Upon analysis of biosolids destined for agriculture, 14 opioids were detected across all samples in a concentration range of 1-289 μg/kg.

Effect of the 35 nm and 70 nm Size Exclusion Chromatography (SEC) Column and Plasma Storage Time on Separated Extracellular Vesicles

The technical difficulty of separating extracellular vesicles (EVs) from plasma proteins in human blood presents a significant hurdle in EV research, particularly during nano ultra-high-performance liquid chromatography-tandem mass spectrometric (UHPLC-MS/MS) analysis, where detecting "vesicular" proteins among abundant plasma proteins is challenging. Standardisation is a pressing issue in EV research, prompting collaborative global efforts to address it. While the MISEV guidelines offer valuable recommendations, unanswered questions remain, particularly regarding sample storage. We compared size exclusion chromatography (SEC) columns with pore sizes of 35 nm and 70 nm to identify fractions with minimal contaminating proteins and the highest concentration of small EVs (sEVs). Following column selection, we explored potential differences in the quality and quantity of sEVs isolated from platelet-free plasma (PFP) after long-term storage at -80 °C (>2.5 years) compared to freshly drawn blood. Our methodologically rigorous study indicates that prolonged storage, under correct storage and processing conditions, does not compromise sEV quality. Both columns effectively isolated vesicles, with the 70 nm column exhibiting a higher abundance of "vesicular" proteins. We propose a relatively rapid and moderately efficient protocol for obtaining a comparatively pure sEV fraction from plasma, facilitating sEV processing in clinical trials.

HowDirty: An R package to evaluate molecular contaminants in LC-MS experiments

Contaminants derived from consumables, reagents, and sample handling often negatively affect LC-MS data acquisition. In proteomics experiments, they can markedly reduce identification performance, reproducibility, and quantitative robustness. Here, we introduce a data analysis workflow combining MS1 feature extraction in Skyline with HowDirty, an R-markdown-based tool, that automatically generates an interactive report on the molecular contaminant level in LC-MS data sets. To facilitate the interpretation of the results, the HTML report is self-contained and self-explanatory, including plots that can be easily interpreted. The R package HowDirty is available from https://github.com/DavidGZ1/HowDirty. To demonstrate a showcase scenario for the application of HowDirty, we assessed the impact of ultrafiltration units from different providers on sample purity after filter-assisted sample preparation (FASP) digestion. This allowed us to select the filter units with the lowest contamination risk. Notably, the filter units with the lowest contaminant levels showed higher reproducibility regarding the number of peptides and proteins identified. Overall, HowDirty enables the efficient evaluation of sample quality covering a wide range of common contaminant groups that typically impair LC-MS analyses, facilitating corrective or preventive actions to minimize instrument downtime.

SpecieScan: semi-automated taxonomic identification of bone collagen peptides from MALDI-ToF-MS

MOTIVATION

Zooarchaeology by Mass Spectrometry (ZooMS) is a palaeoproteomics method for the taxonomic determination of collagen, which traditionally involves challenging manual spectra analysis with limitations in quantitative results. As the ZooMS reference database expands, a faster and reproducible identification tool is necessary. Here we present SpecieScan, an open-access algorithm for automating taxa identification from raw MALDI-ToF mass spectrometry (MS) data.

RESULTS

SpecieScan was developed using R (pre-processing) and Python (automation). The algorithm's output includes identified peptide markers, closest matching taxonomic group (taxon, family, order), correlation scores with the reference databases, and contaminant peaks present in the spectra. Testing on original MS data from bones discovered at Palaeothic archaeological sites, including Denisova Cave in Russia, as well as using publicly-available, externally produced data, we achieved >90% accuracy at the genus-level and ∼92% accuracy at the family-level for mammalian bone collagen previously analysed manually.

AVAILABILITY AND IMPLEMENTATION

The SpecieScan algorithm, along with the raw data used in testing, results, reference database, and common contaminants lists are freely available on Github (https://github.com/mesve/SpecieScan).

Studies of Labware Contamination during Lipid Extraction in Mass Spectrometry-Based Lipidome Analysis

In lipidomic analysis, plasticware is increasingly being used for lipid extraction and other sample processing procedures over glassware. However, a systematic investigation of the consequences of plasticware use on mass spectrometry (MS)-based lipidome analysis is lacking. In this work, we present an analytical approach for detecting and comparing solvent and labware contaminants encountered in lipidomic workflows. It is shown that the contaminant profiles varied widely between microcentrifuge tubes from different manufacturers. The most suitable polypropylene tubes tested introduced 847 labware-originating contaminant m/z's when three different manufacturing batches were tested for Folch lipid extractions. Of particular concern is that 21 primary amide and fatty acid surfactants were introduced that were identical to biological endogenous lipids, 16 of which had not been previously reported as leachables from polypropylene materials. Alternatively, the use of borosilicate glassware and PTFE-lined screw caps introduced 98 different contaminant m/z's across three manufacturing batches tested for Folch extractions. Despite the overwhelming number of labware contaminants introduced, current databases and literature only facilitated the identification of 32 contaminants. To address the dearth of publicly available contaminant information, we provide a comprehensive labware contamination repository containing high-resolution m/z values, adductation information, retention times, and MS/MS spectra. This resource should prove to be valuable for researchers in detecting and distinguishing contaminants from analytes of interest. A companion paper presents a detailed study of how labware contamination can lead to ion-suppression effects on coeluting lipids and interference in the analysis of endogenous lipids, such as those from human sera.

Specification of neural circuit architecture shaped by context-dependent patterned LAR-RPTP microexons

LAR-RPTPs are evolutionarily conserved presynaptic cell-adhesion molecules that orchestrate multifarious synaptic adhesion pathways. Extensive alternative splicing of LAR-RPTP mRNAs may produce innumerable LAR-RPTP isoforms that act as regulatory "codes" for determining the identity and strength of specific synapse signaling. However, no direct evidence for this hypothesis exists. Here, using targeted RNA sequencing, we detected LAR-RPTP mRNAs in diverse cell types across adult male mouse brain areas. We found pronounced cell-type-specific patterns of two microexons, meA and meB, in Ptprd mRNAs. Moreover, diverse neural circuits targeting the same neuronal populations were dictated by the expression of different Ptprd variants with distinct inclusion patterns of microexons. Furthermore, conditional ablation of Ptprd meA+ variants at presynaptic loci of distinct hippocampal circuits impaired distinct modes of synaptic transmission and objection-location memory. Activity-triggered alterations of the presynaptic Ptprd meA code in subicular neurons mediates NMDA receptor-mediated postsynaptic responses in CA1 neurons and objection-location memory. Our data provide the evidence of cell-type- and/or circuit-specific expression patterns in vivo and physiological functions of LAR-RPTP microexons that are dynamically regulated.

Contaminant Spot Check and Removal Assay (ContamSPOT) for Mass Spectrometry Analysis

Mass spectrometry (MS) analysis is often challenged by contaminations from detergents, salts, and polymers that compromise data quality and can damage the chromatography and MS instruments. However, researchers often discover contamination issues only after they acquire the data. There is no existing contaminant assay that is sensitive enough to detect trace amounts of contaminants from a few microliters of samples prior to MS analysis. To address this crucial need in the field, we developed a sensitive, rapid, and cost-effective contaminant spot check and removal assay (ContamSPOT) to detect and quantify trace amounts of contaminants, such as detergents, salts, and other chemicals commonly used in the MS sample preparation workflow. Only 1 μL of the sample was used prior to MS injection to quantify contaminants by ContamSPOT colorimetric or fluorometric assay on a thin layer chromatography (TLC) plate. We also optimized contaminant removal methods to salvage samples with minimal loss when ContamSPOT showed a positive result. ContamSPOT was then successfully applied to evaluate commonly used bottom-up proteomic methods regarding the effectiveness of removing detergent, peptide recovery, reproducibility, and proteome coverage. We expect ContamSPOT to be widely adopted by MS laboratories as a last-step quality checkpoint prior to MS injection. We provided a practical decision tree and a step-by-step protocol with a troubleshooting guide to facilitate the use of ContamSPOT by other researchers. ContamSPOT can also provide a unique readout of sample cleanliness for developing new MS-based sample preparation methods in the future.

Integrating a Multi-label Deep Learning Approach with Protein Information to Compare Bioactive Peptides in Brain and Plasma

Peptide therapeutics is gaining momentum. Advances in the field of peptidomics have enabled researchers to harvest vital information from various organisms and tissue types concerning peptide existence, expression and function. The development of mass spectrometry techniques for high-throughput peptide quantitation has paved the way for the identification and discovery of numerous known and novel peptides. Though much has been achieved, scientists are still facing difficulties when it comes to reducing the search space of the large mass spectrometry-generated peptidomics datasets and focusing on the subset of functionally relevant peptides. Moreover, there is currently no straightforward way to analytically compare the distributions of bioactive peptides in distinct biological samples, which may reveal much useful information when seeking to characterize tissue- or fluid-specific peptidomes. In this chapter, we demonstrate how to identify, rank, and compare predicted bioactive peptides and bioactivity distributions from extensive peptidomics datasets. To aid this task, we utilize MultiPep, a multi-label deep learning approach designed for classifying peptide bioactivities, to identify bioactive peptides. The predicted bioactivities are synergistically combined with protein information from the UniProt database, which assist in navigating through the jungle of putative therapeutic peptides and relevant peptide leads.

Assessing background contamination of sample tubes used in human biomonitoring by non-targeted liquid chromatography-high resolution mass spectrometry

Controlling and minimising background contamination is crucial for maintaining a high quality of samples in human biomonitoring targeting organic chemicals. We assessed the contamination of three previous types and one newly introduced medical-grade type of sample tubes used for storing human body fluids at the German Environmental Specimen Bank. Aqueous extracts from these tubes were analysed by non-targeted liquid chromatography-high resolution mass spectrometry (LC-HRMS) before and after a dedicated cleaning procedure. After peak detection using MZmine, Bayesian hypothesis testing was used to group peaks into those originating either from instrumental and laboratory background contamination, or actual tube contaminants, based on if their peak height was reduced, increased or not affected by the cleaning procedure. For all four tube types 80-90% of the 2475 peaks (1549 in positive and 926 in negative mode) were assigned to laboratory/instrumental background, which we have to consider as potential sample tube contaminants. Among the tube contaminants, results suggest a considerable difference in the contaminant peak inventory and the absolute level of contamination among the different sample tube types. The cleaning procedure did not affect the largest fraction of peaks (50-70%). For the medical grade tubes, the removal of contaminants by the cleaning procedure was strongest compared to the previous tubes, but in all cases a small fraction increased in intensity after cleaning, probably due to a release of oligomers or additives. The identified laboratory background contaminants were mainly semi-volatile polymer additives such as phthalates and phosphate esters. A few compounds could be assigned solely as tube-specific contaminants, such as N,N-dibutylformamide and several constituents of the oligomeric light stabiliser Tinuvin-622. A cleaning procedure before use is an effective way to standardise the used sample tubes and minimises the background contamination, and therefore increases sample quality and therewith analytical results.

Full-Length NAD+-I Riboswitches Bind a Single Cofactor but Cannot Discriminate against Adenosine Triphosphate

Bacterial riboswitches are structured RNAs that bind small metabolites to control downstream gene expression. Two riboswitch classes have been reported to sense nicotinamide adenine dinucleotide (NAD+), which plays a key redox role in cellular metabolism. The NAD+-I (class I) riboswitch stands out because it comprises two homologous, tandemly arranged domains. However, previous studies examined the isolated domains rather than the full-length riboswitch. Crystallography and ligand binding analyses led to the hypothesis that each domain senses NAD+ but with disparate equilibrium binding constants (KD) of 127 μM (domain I) and 3.4 mM (domain II). Here, we analyzed individual domains and the full-length riboswitch by isothermal titration calorimetry to quantify the cofactor affinity and specificity. Domain I senses NAD+ with a KD of 24.6 ± 8.4 μM but with a reduced ligand-to-receptor stoichiometry, consistent with nonproductive domain self-association observed by gel-filtration chromatography; domain II revealed no detectable binding. By contrast, the full-length riboswitch binds a single NAD+ with a KD of 31.5 ± 1.5 μM; dinucleotides NADH and AP2-ribavirin also bind with one-to-one stoichiometry. Unexpectedly, the full-length riboswitch also binds a single ATP equivalent (KD = 11.0 ± 3.5 μM). The affinity trend of the full-length riboswitch is ADP = ATP > NAD+ = AP2-ribavirin > NADH. Although our results support riboswitch sensing of a single NAD+ at concentrations significantly below the intracellular levels of this cofactor, our findings do not support the level of specificity expected for a riboswitch that exclusively senses NAD+. Gene regulatory implications and future challenges are discussed.

CT hyperdense cerebral artery sign reflects distinct proteomic composition in acute ischemic stroke thrombus

BACKGROUND

Hyperdense cerebral artery sign (HCAS) is an imaging biomarker in acute ischemic stroke (AIS) that has been shown to be associated with various clinical outcomes and stroke etiology. While prior studies have correlated HCAS with histopathological composition of cerebral thrombus, it is unknown whether and to what extent HCAS is also associated with distinct clot protein composition.

METHODS

Thromboembolic material from 24 patients with AIS were retrieved via mechanical thrombectomy and evaluated with mass spectrometry in order to characterize their proteomic composition. Presence (+) or absence (-) of HCAS on preintervention non-contrast head CT was then determined and correlated with thrombus protein signature with abundance of individual proteins calculated as a function HCAS status.

RESULTS

24 clots with 1797 distinct proteins in total were identified. 14 patients were HCAS(+) and 10 were HCAS(-). HCAS(+) were most significantly differentially abundant in actin cytoskeletal protein (P=0.002, Z=2.82), bleomycin hydrolase (P=0.007, Z=2.44), arachidonate 12-lipoxygenase (P=0.004, Z=2.60), and lysophospholipase D (P=0.007, Z=2.44), among other proteins; HCAS(-) clots were differentially enriched in soluble N-ethylmaleimide-sensitive factor (NSF) attachment protein (P=0.0009, Z=3.11), tyrosine-protein kinase Fyn (P=0.002, Z=2.84), and several complement proteins (P<0.05, Z>1.71 for all), among numerous other proteins. Additionally, HCAS(-) thrombi were enriched in biological processes involved with plasma lipoprotein and protein-lipid remodeling/assembling, and lipoprotein metabolic processes (P<0.001), as well as cellular components including mitochondria (P<0.001).

CONCLUSIONS

HCAS is reflective of distinct proteomic composition in AIS thrombus. These findings suggest that imaging can be used to identify mechanisms of clot formation or maintenance at the protein level, and might inform future research on thrombus biology and imaging characterization.

Disclosing the molecular profile of the human amniotic mesenchymal stromal cell secretome by filter-aided sample preparation proteomic characterization

BACKGROUND

The secretome of mesenchymal stromal cells isolated from the amniotic membrane (hAMSCs) has been extensively studied for its in vitro immunomodulatory activity as well as for the treatment of several preclinical models of immune-related disorders. The bioactive molecules within the hAMSCs secretome are capable of modulating the immune response and thus contribute to stimulating regenerative processes. At present, only a few studies have attempted to define the composition of the secretome, and several approaches, including multi-omics, are underway in an attempt to precisely define its composition and possibly identify key factors responsible for the therapeutic effect.

METHODS

In this study, we characterized the protein composition of the hAMSCs secretome by a filter-aided sample preparation (FASP) digestion and liquid chromatography-high resolution mass spectrometry (LC-MS) approach. Data were processed for gene ontology classification and functional protein interaction analysis by bioinformatics tools.

RESULTS

Proteomic analysis of the hAMSCs secretome resulted in the identification of 1521 total proteins, including 662 unique elements. A number of 157 elements, corresponding to 23.7%, were found as repeatedly characterizing the hAMSCs secretome, and those that resulted as significantly over-represented were involved in immunomodulation, hemostasis, development and remodeling of the extracellular matrix molecular pathways.

CONCLUSIONS

Overall, our characterization enriches the landscape of hAMSCs with new information that could enable a better understanding of the mechanisms of action underlying the therapeutic efficacy of the hAMSCs secretome while also providing a basis for its therapeutic translation.

Evidence of H/D Exchange within Metal-Adducted Carbohydrates after Ion/Ion-Dissociation Reactions

Tandem mass spectrometry (MS/MS) using fragmentation has become one of the most effective methods for gaining sequence and structural information on biomolecules. Ion/ion reactions are competitive reactions, where either proton transfer (PT) or electron transfer (ET) can occur from interactions between multiply charged cations and singly charged anions. Utilizing ion/ion reactions with fluoranthene has offered a unique method of fragment formation for the structural elucidation of biomolecules. Fluoranthene is considered an ideal anion reagent because it selectively causes electron-transfer dissociation (ETD) and minimizes PT when interacting with peptides. However, limited investigations have sought to understand how fluoranthene─the primary, commercially available anion reagent─interacts with other biomolecules. Here, we apply deuterium labeling to investigate ion/ion reaction mechanisms between fluoranthene and divalent, metal-adducted carbohydrates (Ca2+, Mg2+, Co2+, and Ni2+). Deuterium labeling of carbohydrates allowed us to observe evidence of hydrogen/deuterium exchange (HDX) occurring after ion/ion dissociation reactions. The extent of deuterium loss is dependent on several factors, including the physical properties of the metal ion and the fragment structure. Based on the deuterium labeling data, we have proposed ETD, PTD, and intermolecular PT─also described as HDX─mechanisms. This research provides a fundamental perspective of ion/ion and ion/molecule reaction mechanisms and illustrates properties that impact ion/ion and ion/molecule reactions for carbohydrates. Together, this could improve the capability to distinguish complex and heterogeneous biomolecules, such as carbohydrates.

Photo-caged 2-butene-1,4-dial as an efficient, target-specific photo-crosslinker for covalent trapping of DNA-binding proteins

Covalent trapping of DNA-binding proteins via photo-crosslinking is an advantageous method for studying DNA-protein interactions. However, traditional photo-crosslinkers generate highly reactive intermediates that rapidly and non-selectively react with nearby functional groups, resulting in low target-capture yields and high non-target background capture. Herein, we report that photo-caged 2-butene-1,4-dial (PBDA) is an efficient photo-crosslinker for trapping DNA-binding proteins. Photo-irradiation (360 nm) of PBDA-modified DNA generates 2-butene-1,4-dial (BDA), a small, long-lived intermediate that reacts selectively with Lys residues of DNA-binding proteins, leading in minutes to stable DNA-protein crosslinks in up to 70% yield. In addition, BDA exhibits high specificity for target proteins, leading to low non-target background capture. The high photo-crosslinking yield and target specificity make PBDA a powerful tool for studying DNA-protein interactions.

Prediction of systemic free and total valproic acid by off-line analysis of exhaled breath in epileptic children and adolescents

Therapeutic drug monitoring (TDM) of medications with a narrow therapeutic window is a common clinical practice to minimize toxic effects and maximize clinical outcomes. Routine analyses rely on the quantification of systemic blood concentrations of drugs. Alternative matrices such as exhaled breath are appealing because of their inherent non-invasive nature. This is especially the case for pediatric patients. We have recently showcased the possibility of predicting systemic concentrations of valproic acid (VPA), an anti-seizure medication by real-time breath analysis in two real clinical settings. This approach, however, comes with the limitation of the patients having to physically exhale into the mass spectrometer. This restricts the possibility of sampling from patients not capable or available to exhale into the mass spectrometer located on the hospital premises. In this work, we developed an alternative method to overcome this limitation by collecting the breath samples in customized bags and subsequently analyzing them by secondary electrospray ionization coupled to high-resolution mass spectrometry (SESI-HRMS). A total ofn= 40 patients (mean ± SD, 11.5 ± 3.5 y.o.) diagnosed with epilepsy and taking VPA were included in this study. The patients underwent three measurements: (i) serum concentrations of total and free VPA, (ii) real-time breath analysis and (iii) off-line analysis of exhaled breath collected in bags. The agreement between the real-time and the off-line breath analysis methods was evaluated using Lin's concordance correlation coefficient (CCC). CCC was computed for ten mass spectral predictors of VPA concentrations. Lin's CCC was >0.6 for all VPA-associated features, except for two low-signal intensity isotopic peaks. Finally, free and total serum VPA concentrations were predicted by cross validating the off-line data set. Support vector machine algorithms provided the most accurate predictions with a root mean square error of cross validation of 29.0 ± 7.4 mg l-1and 3.9 ± 1.4 mg l-1for total and free VPA (mean ± SD), respectively. As a secondary analysis, we explored whether exhaled metabolites previously associated with side-effects and response to medication could be rendered by the off-line analysis method. We found that five features associated with side effects showed a CCC > 0.6, whereas none of the drug response-associated peaks reached this cut-off. We conclude that the clinically relevant free fraction of VPA can be predicted by this combination of off-line breath collection with rapid SESI-HRMS analysis. This opens new possibilities for breath based TDM in clinical settings.

Exploring the Impact of Organic Solvent Quality and Unusual Adduct Formation during LC-MS-Based Lipidomic Profiling

Liquid chromatography-mass spectrometry (LC-MS) is the key technique for analyzing complex lipids in biological samples. Various LC-MS modes are used for lipid separation, including different stationary phases, mobile-phase solvents, and modifiers. Quality control in lipidomics analysis is crucial to ensuring the generated data's reliability, reproducibility, and accuracy. While several quality control measures are commonly discussed, the impact of organic solvent quality during LC-MS analysis is often overlooked. Additionally, the annotation of complex lipids remains prone to biases, leading to potential misidentifications and incomplete characterization of lipid species. In this study, we investigate how LC-MS-grade isopropanol from different vendors may influence the quality of the mobile phase used in LC-MS-based untargeted lipidomic profiling of biological samples. Furthermore, we report the occurrence of an unusual, yet highly abundant, ethylamine adduct [M+46.0651]+ that may form for specific lipid subclasses during LC-MS analysis in positive electrospray ionization mode when acetonitrile is part of the mobile phase, potentially leading to lipid misidentification. These findings emphasize the importance of considering solvent quality in LC-MS analysis and highlight challenges in lipid annotation.

A Novel Chromatographic Method to Assess the Binding Ability towards Dicarbonyls

Human exposure to dicarbonyls occurs via ingestion (e.g., food), inhalation (e.g., electronic cigarettes) and dysregulation of endogenous metabolic pathways (e.g., glycolysis). Dicarbonyls are electrophiles able to induce carbonylation of endogenous substrate. They have been associated with the onset and progression of several human diseases. Several studies have advocated the use of dicarbonyl binders as food preservatives or as drugs aimed at mitigating carbonylation. This study presents the setup of an easy and cheap assay for the screening of selective and potent dicarbonyl binders. The method is based on the incubation of the candidate molecules with a molecular probe. The activity is then determined by measuring the residual concentration of the molecular probe over time by liquid chromatography (LC). However, the naturally occurring dicarbonyls (e.g., glyoxal, methylglyoxal) are not appealing as probes since they are hard to separate and detect using the most popular LC variants. Benzylglyoxal (BGO) was therefore synthesized and tested, proving to be a convenient probe that allows a direct quantification of residual dicarbonyls by reversed phase LC without derivatization. The method was qualified by assessing the binding ability of some molecules known as binders of natural occurring dicarbonyls, obtaining results consistent with literature.

Urinary Metabolomic Analysis of Prostate Cancer by UPLC-FTMS and UPLC-Ion Trap MSn

Accumulative evidence suggests metabolic disorders correlate with prostate cancer. Metabolic profiling of urine allows the measurement of numerous metabolites simultaneously. This study set up a metabolomic platform consisting of UPLC-FTMS and UPLC-ion trap MSⁿ for urine metabolome analysis. The platform improved retention time, mass accuracy, and signal stability. Additionally, the product ion spectrum obtained from ion trap MSⁿ facilitated structure elucidation of candidate metabolites, especially when authentic standards were not available. Urine samples from six hernia patients and six BPH patients were used for the initial establishment of the analytic platform. This platform was further employed to analyze the urine samples of 27 PCa and 49 BPH patients. Choosing the upper and lower 16% of metabolites, 258 metabolite candidates were selected. Twenty-four of them with AUC values larger than 0.65 were further selected. Eighteen of the twenty-four features can be matched in METLIN and HMDB. Eleven of the eighteen features can be interpreted by MSⁿ experiments. They were used for the combination achieving the best differential power. Finally, four metabolites were combined to reach the AUC value of 0.842 (CI 95, 0.7559 to 0.9279). This study demonstrates the urinary metabolomic analysis of prostate cancer and sheds light on future research.

Critical review on data processing algorithms in non-target screening: challenges and opportunities to improve result comparability

Non-target screening (NTS) is a powerful environmental and analytical chemistry approach for detecting and identifying unknown compounds in complex samples. High-resolution mass spectrometry has enhanced NTS capabilities but created challenges in data analysis, including data preprocessing, peak detection, and feature extraction. This review provides an in-depth understanding of NTS data processing methods, focusing on centroiding, extracted ion chromatogram (XIC) building, chromatographic peak characterization, alignment, componentization, and prioritization of features. We discuss the strengths and weaknesses of various algorithms, the influence of user input parameters on the results, and the need for automated parameter optimization. We address uncertainty and data quality issues, emphasizing the importance of incorporating confidence intervals and raw data quality assessment in data processing workflows. Furthermore, we highlight the need for cross-study comparability and propose potential solutions, such as utilizing standardized statistics and open-access data exchange platforms. In conclusion, we offer future perspectives and recommendations for developers and users of NTS data processing algorithms and workflows. By addressing these challenges and capitalizing on the opportunities presented, the NTS community can advance the field, improve the reliability of results, and enhance data comparability across different studies.

Augmented region of interest for untargeted metabolomics mass spectrometry (AriumMS) of multi-platform-based CE-MS and LC-MS data

In mass spectrometry (MS)-based metabolomics, there is a great need to combine different analytical separation techniques to cover metabolites of different polarities and apply appropriate multi-platform data processing. Here, we introduce AriumMS (augmented region of interest for untargeted metabolomics mass spectrometry) as a reliable toolbox for multi-platform metabolomics. AriumMS offers augmented data analysis of several separation techniques utilizing a region-of-interest algorithm. To demonstrate the capabilities of AriumMS, five datasets were combined. This includes three newly developed capillary electrophoresis (CE)-Orbitrap MS methods using the recently introduced nanoCEasy CE-MS interface and two hydrophilic interaction liquid chromatography (HILIC)-Orbitrap MS methods. AriumMS provides a novel mid-level data fusion approach for multi-platform data analysis to simplify and speed up multi-platform data processing and evaluation. The key feature of AriumMS lies in the optimized data processing strategy, including parallel processing of datasets and flexible parameterization for processing of individual separation methods with different peak characteristics. As a case study, Saccharomyces cerevisiae (yeast) was treated with a growth inhibitor, and AriumMS successfully differentiated the metabolome based on the augmented multi-platform CE-MS and HILIC-MS investigation. As a result, AriumMS is proposed as a powerful tool to improve the accuracy and selectivity of metabolome analysis through the integration of several HILIC-MS/CE-MS techniques.

Deep Bottom-up Proteomics Enabled by the Integration of Liquid-Phase Ion Trap

In bottom-up proteomics, the complexity of the proteome requires advanced peptide separation and/or fractionation methods to acquire an in-depth understanding of protein profiles. Proposed earlier as a solution-phase ion manipulation device, liquid phase ion traps (LPITs) were used in front of mass spectrometers to accumulate target ions for improved detection sensitivity. In this work, an LPIT-reversed phase liquid chromatography-tandem mass spectrometry (LPIT-RPLC-MS/MS) platform was established for deep bottom-up proteomics. LPIT was used here as a robust and effective method for peptide fractionation, which also shows good reproducibility and sensitivity on both qualitative and quantitative levels. LPIT separates peptides based on their effective charges and hydrodynamic radii, which is orthogonal to that of RPLC. With excellent orthogonality, the integration of LPIT with RPLC-MS/MS could effectively increase the number of peptides and proteins being detected. When HeLa cells were analyzed, peptide and protein coverages were increased by ∼89.2% and 50.3%, respectively. With high efficiency and low cost, this LPIT-based peptide fraction method could potentially be used in routine deep bottom-up proteomics.

Real-Time Volatile Metabolomics Analysis of Dendritic Cells

Dendritic cells (DCs) actively sample and present antigen to cells of the adaptive immune system and are thus vital for successful immune control and memory formation. Immune cell metabolism and function are tightly interlinked, and a better understanding of this interaction offers potential to develop immunomodulatory strategies. However, current approaches for assessing the immune cell metabolome are often limited by end-point measurements, may involve laborious sample preparation, and may lack unbiased, temporal resolution of the metabolome. In this study, we present a novel setup coupled to a secondary electrospray ionization-high resolution mass spectrometric (SESI-HRMS) platform allowing headspace analysis of immature and activated DCs in real-time with minimal sample preparation and intervention, with high technical reproducibility and potential for automation. Distinct metabolic signatures of DCs treated with different supernatants (SNs) of bacterial cultures were detected during real-time analyses over 6 h compared to their respective controls (SN only). Furthermore, the technique allowed for the detection of ¹³C-incorporation into volatile metabolites, opening the possibility for real-time tracing of metabolic pathways in DCs. Moreover, differences in the metabolic profile of naı̈ve and activated DCs were discovered, and pathway-enrichment analysis revealed three significantly altered pathways, including the TCA cycle, α-linolenic acid metabolism, and valine, leucine, and isoleucine degradation.