The Strategy for Peptidomic LC-MS/MS Data Analysis: The Case of Urinary Peptidome Study.

The study of urinary peptidome is an important area of research, which concerns the characterization of endogenous peptides, as well as the identification of biomarkers for a wide range of socially significant diseases. First of all, this relates to renal and genitourinary pathologies and/or pathologies associated with proteinuria, such as kidney diseases, bladder, prostate and ovarian cancers, diabetic nephropathy, and pre-eclampsia. Unlike proteins, peptides do not require proteolytic hydrolysis, can be analyzed in their native form and can provide certain information about occurring (patho)physiological processes. Mass spectrometry (MS)-based approaches are the most unbiased and sensitive instruments with high multiplexing capacity and provided most of the current information about endogenous urine peptides. However, despite the large number of urine peptidomic studies, there are certain issues related to the insufficient comparability of their results due to the lack of consistent approaches to their interpretation. Also the development of a custom project-specific protein library for endogenous peptides search and identification is another important point that should be noted in the context of high-throughput peptidomic analysis. Here we propose the custom-specific urinary protein database and the grouping of endogenous urinary peptides with overlapping sequences as useful tools, which can facilitate the acquisition and analysis of LC-MS peptidomic data, as well as the comparison of results of different studies, which should facilitate their more efficient further application.

Quantitative Proteomics of Maternal Blood Plasma in Isolated Intrauterine Growth Restriction.

Intrauterine growth restriction (IUGR) remains a significant concern in modern obstetrics, linked to high neonatal health problems and even death, as well as childhood disability, affecting adult quality of life. The role of maternal and fetus adaptation during adverse pregnancy is still not completely understood. This study aimed to investigate the disturbance in biological processes associated with isolated IUGR via blood plasma proteomics. The levels of 125 maternal plasma proteins were quantified by liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM MS) with corresponding stable isotope-labeled peptide standards (SIS). Thirteen potential markers of IUGR (Gelsolin, Alpha-2-macroglobulin, Apolipoprotein A-IV, Apolipoprotein B-100, Apolipoprotein(a), Adiponectin, Complement C5, Apolipoprotein D, Alpha-1B-glycoprotein, Serum albumin, Fibronectin, Glutathione peroxidase 3, Lipopolysaccharide-binding protein) were found to be inter-connected in a protein-protein network. These proteins are involved in plasma lipoprotein assembly, remodeling, and clearance; lipid metabolism, especially cholesterol and phospholipids; hemostasis, including platelet degranulation; and immune system regulation. Additionally, 18 proteins were specific to a particular type of IUGR (early or late). Distinct patterns in the coagulation and fibrinolysis systems were observed between isolated early- and late-onset IUGR. Our findings highlight the complex interplay of immune and coagulation factors in IUGR and the differences between early- and late-onset IUGR and other placenta-related conditions like PE. Understanding these mechanisms is crucial for developing targeted interventions and improving outcomes for pregnancies affected by IUGR.

Integrating Proteomics and Lipidomics for Evaluating the Risk of Breast Cancer Progression: A Pilot Study.

Metastasis is a serious and often life-threatening condition, representing the leading cause of death among women with breast cancer (BC). Although the current clinical classification of BC is well-established, the addition of minimally invasive laboratory tests based on peripheral blood biomarkers that reflect pathological changes in the body is of utmost importance. In the current study, the serum proteome and lipidome profiles for 50 BC patients with (25) and without (25) metastasis were studied. Targeted proteomic analysis for concertation measurements of 125 proteins in the serum was performed via liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM MS) using the BAK 125 kit (MRM Proteomics Inc., Victoria, BC, Canada). Untargeted label-free lipidomic analysis was performed using liquid chromatography coupled to tandem mass-spectrometry (LC-MS/MS), in both positive and negative ion modes. Finally, 87 serum proteins and 295 lipids were quantified and showed a moderate correlation with tumor grade, histological and biological subtypes, and the number of lymph node metastases. Two highly accurate classifiers that enabled distinguishing between metastatic and non-metastatic BC were developed based on proteomic (accuracy 90%) and lipidomic (accuracy 80%) features. The best classifier (91% sensitivity, 89% specificity, AUC = 0.92) for BC metastasis diagnostics was based on logistic regression and the serum levels of 11 proteins: alpha-2-macroglobulin, coagulation factor XII, adiponectin, leucine-rich alpha-2-glycoprotein, alpha-2-HS-glycoprotein, Ig mu chain C region, apolipoprotein C-IV, carbonic anhydrase 1, apolipoprotein A-II, apolipoprotein C-II and alpha-1-acid glycoprotein 1.

Antitumor Effect of Bleomycin Nanoaerosol in Murine Carcinoma Model.

Bleomycin, which is widely used as an antitumor agent, possesses serious adverse effects such as pulmonary toxicity. Local nanoaerosol deposition for lung cancer treatment is a promising alternative to drug delivery to lung lesions. The aim of this work is to test the hypothesis that bleomycin nanoaerosol can be effectively used to treat multiple lung metastases. To obtain bleomycin nanoaerosol, an aerosol generator based on electrospray of a solution of a nonvolatile substance with gas-phase neutralization of charged aerosol particles was used. Lung metastases in murine Lewis lung carcinoma and B16 melanoma animal models were counted. The effect of inhaled bleomycin nanoparticles on the number and volume of metastases, as well as pulmonary side effects, was investigated. Using a mouse exposure chamber, the dose-dependent effect of inhaled bleomycin on tumor volume was evaluated in comparison with intraperitoneal administration. Bleomycin nanoaerosol reduced the volume of metastases and produced a higher antitumor effect at much lower doses. It has been established that long-term exposure to nanoaerosol with a low dose of bleomycin is capable of suppressing cancer cell growth. The treatment was well tolerated. In the lungs, minor changes were found in the form of focal-diffuse infiltration of the lung parenchyma.

Targeted MRM Quantification of Urinary Proteins in Chronic Kidney Disease Caused by Glomerulopathies.

Glomerulopathies with nephrotic syndrome that are resistant to therapy often progress to end-stage chronic kidney disease (CKD) and require timely and accurate diagnosis. Targeted quantitative urine proteome analysis by mass spectrometry (MS) with multiple-reaction monitoring (MRM) is a promising tool for early CKD diagnostics that could replace the invasive biopsy procedure. However, there are few studies regarding the development of highly multiplexed MRM assays for urine proteome analysis, and the two MRM assays for urine proteomics described so far demonstrate very low consistency. Thus, the further development of targeted urine proteome assays for CKD is actual task. Herein, a BAK270 MRM assay previously validated for blood plasma protein analysis was adapted for urine-targeted proteomics. Because proteinuria associated with renal impairment is usually associated with an increased diversity of plasma proteins being present in urine, the use of this panel was appropriate. Another advantage of the BAK270 MRM assay is that it includes 35 potential CKD markers described previously. Targeted LC-MRM MS analysis was performed for 69 urine samples from 46 CKD patients and 23 healthy controls, revealing 138 proteins that were found in ≥2/3 of the samples from at least one of the groups. The results obtained confirm 31 previously proposed CKD markers. Combination of MRM analysis with machine learning for data processing was performed. As a result, a highly accurate classifier was developed (AUC = 0.99) that enables distinguishing between mild and severe glomerulopathies based on the assessment of only three urine proteins (GPX3, PLMN, and A1AT or SHBG).

Formulae Differences Commence a Database for Interlaboratory Studies of Natural Organic Matter.

Direct comparison of high-resolution mass spectrometry (HRMS) data acquired with different instrumentation or parameters remains problematic as the derived lists of molecular species via HRMS, even for the same sample, appear distinct. This inconsistency is caused by inherent inaccuracies associated with instrumental limitations and sample conditions. Hence, experimental data may not reflect a corresponding sample. We propose a method that classifies HRMS data based on the differences in the number of elements between each pair of molecular formulae within the formulae list to preserve the essence of the given sample. The novel metric, formulae difference chains expected length (FDCEL), allowed for comparing and classifying samples measured by different instruments. We also demonstrate a web application and a prototype for a uniform database for HRMS data serving as a benchmark for future biogeochemical and environmental applications. FDCEL metric was successfully employed for both spectrum quality control and examination of samples of various nature.

Potential Urine Proteomic Biomarkers for Focal Segmental Glomerulosclerosis and Minimal Change Disease.

Primary focal segmental glomerulosclerosis (FSGS), along with minimal change disease (MCD), are diseases with primary podocyte damage that are clinically manifested by the nephrotic syndrome. The pathogenesis of these podocytopathies is still unknown, and therefore, the search for biomarkers of these diseases is ongoing. Our aim was to determine of the proteomic profile of urine from patients with FSGS and MCD. Patients with a confirmed diagnosis of FSGS (n = 30) and MCD (n = 9) were recruited for the study. For a comprehensive assessment of the severity of FSGS a special index was introduced, which was calculated as follows: the first score was assigned depending on the level of eGFR, the second score-depending on the proteinuria level, the third score-resistance to steroid therapy. Patients with the sum of these scores of less than 3 were included in group 1, with 3 or more-in group 2. The urinary proteome was analyzed using liquid chromatography/mass spectrometry. The proteome profiles of patients with severe progressive FSGS from group 2, mild FSGS from group 1 and MCD were compared. Results of the label free analysis were validated using targeted LC-MS based on multiple reaction monitoring (MRM) with stable isotope labelled peptide standards (SIS) available for 47 of the 76 proteins identified as differentiating between at least one pair of groups. Quantitative MRM SIS validation measurements for these 47 proteins revealed 22 proteins with significant differences between at least one of the two group pairs and 14 proteins were validated for both comparisons. In addition, all of the 22 proteins validated by MRM SIS analysis showed the same direction of change as at the discovery stage with label-free LC-MS analysis, i.e., up or down regulation in MCD and FSGS1 against FSGS2. Patients from the FSGS group 2 showed a significantly different profile from both FSGS group 1 and MCD. Among the 47 significantly differentiating proteins, the most significant were apolipoprotein A-IV, hemopexin, vitronectin, gelsolin, components of the complement system (C4b, factors B and I), retinol- and vitamin D-binding proteins. Patients with mild form of FSGS and MCD showed lower levels of Cystatin C, gelsolin and complement factor I.

Proteomic Markers and Early Prediction of Alzheimer's Disease.

Alzheimer's disease (AD) is the most common socially significant neurodegenerative pathology, which currently affects more than 30 million elderly people worldwide. Since the number of patients grows every year and may exceed 115 million by 2050, and due to the lack of effective therapies, early prediction of AD remains a global challenge, solution of which can contribute to the timely appointment of a preventive therapy in order to avoid irreversible changes in the brain. To date, clinical assays for the markers of amyloidosis in cerebrospinal fluid (CSF) have been developed, which, in conjunction with the brain MRI and PET studies, are used either to confirm the diagnosis based on obligate clinical criteria or to predict the risk of AD developing at the stage of mild cognitive impairment (MCI). However, the problem of predicting AD at the asymptomatic stage remains unresolved. In this regard, the search for new protein markers and studies of proteomic changes in CSF and blood plasma are of particular interest and may consequentially identify particular pathways involved in the pathogenesis of AD. Studies of specific proteomic changes in blood plasma deserve special attention and are of increasing interest due to the much less invasive method of sample collection as compared to CSF, which is important when choosing the object for large-scale screening. This review briefly summarizes the current knowledge on proteomic markers of AD and considers the prospects of developing reliable methods for early identification of AD risk factors based on the proteomic profile.

Albumin Stabilized Fe@C Core-Shell Nanoparticles as Candidates for Magnetic Hyperthermia Therapy.

Carbon-encapsulated iron nanoparticles (Fe@C) with a mean diameter of 15 nm have been synthesized using evaporation-condensation flow-levitation method by the direct iron-carbon gas-phase reaction at high temperatures. Further, Fe@C were stabilized with bovine serum albumin (BSA) coating, and their electromagnetic properties were evaluated to test their performance in magnetic hyperthermia therapy (MHT) through a specific absorption rate (SAR). Heat generation was observed at different Fe@C concentrations (1, 2.5, and 5 mg/mL) when applied 331 kHz and 60 kA/m of an alternating magnetic field, resulting in SAR values of 437.64, 129.36, and 50.4 W/g for each concentration, respectively. Having such high SAR values at low concentrations, obtained material is ideal for use in MHT.

Early Prediction of COVID-19 Patient Survival by Targeted Plasma Multi-Omics and Machine Learning.

The recent surge of coronavirus disease 2019 (COVID-19) hospitalizations severely challenges healthcare systems around the globe and has increased the demand for reliable tests predictive of disease severity and mortality. Using multiplexed targeted mass spectrometry assays on a robust triple quadrupole MS setup which is available in many clinical laboratories, we determined the precise concentrations of hundreds of proteins and metabolites in plasma from hospitalized COVID-19 patients. We observed a clear distinction between COVID-19 patients and controls and, strikingly, a significant difference between survivors and nonsurvivors. With increasing length of hospitalization, the survivors' samples showed a trend toward normal concentrations, indicating a potential sensitive readout of treatment success. Building a machine learning multi-omic model that considers the concentrations of 10 proteins and five metabolites, we could predict patient survival with 92% accuracy (area under the receiver operating characteristic curve: 0.97) on the day of hospitalization. Hence, our standardized assays represent a unique opportunity for the early stratification of hospitalized COVID-19 patients.

Prognosis of Alzheimer's Disease Using Quantitative Mass Spectrometry of Human Blood Plasma Proteins and Machine Learning.

Early recognition of the risk of Alzheimer's disease (AD) onset is a global challenge that requires the development of reliable and affordable screening methods for wide-scale application. Proteomic studies of blood plasma are of particular relevance; however, the currently proposed differentiating markers are poorly consistent. The targeted quantitative multiple reaction monitoring (MRM) assay of the reported candidate biomarkers (CBs) can contribute to the creation of a consistent marker panel. An MRM-MS analysis of 149 nondepleted EDTA-plasma samples (MHRC, Russia) of patients with AD (n = 47), mild cognitive impairment (MCI, n = 36), vascular dementia (n = 8), frontotemporal dementia (n = 15), and an elderly control group (n = 43) was performed using the BAK 125 kit (MRM Proteomics Inc., Canada). Statistical analysis revealed a significant decrease in the levels of afamin, apolipoprotein E, biotinidase, and serum paraoxonase/arylesterase 1 associated with AD. Different training algorithms for machine learning were performed to identify the protein panels and build corresponding classifiers for the AD prognosis. Machine learning revealed 31 proteins that are important for AD differentiation and mostly include reported earlier CBs. The best-performing classifiers reached 80% accuracy, 79.4% sensitivity and 83.6% specificity and were able to assess the risk of developing AD over the next 3 years for patients with MCI. Overall, this study demonstrates the high potential of the MRM approach combined with machine learning to confirm the significance of previously identified CBs and to propose consistent protein marker panels.

Impact of ozone treatment on dissolved organic matter in land-based recirculating aquaculture systems studied by Fourier transform ion cyclotron resonance mass spectrometry.

In land-based recirculating aquaculture systems (RAS), the accumulation of dissolved organic matter (DOM) can have detrimental effects on water quality impacting the system performance, microbial community, and consequently fish health and welfare. Ozone is used in the RAS water treatment process to improve water quality and remove DOM. However, little is known about the molecular composition of DOM in RAS and its transformation when exposed to ozone. In this study, we performed a detailed molecular characterization of DOM in RAS and explored its transformation induced by ozonation of RAS waters. Ultra-high resolution (UHR) Fourier transform ion cyclotron resonance mass spectrometry (FTICR MS) was used to characterize the DOM matrix of RAS waters (pump-sump and tanks) and to evaluate its transformation by ozonation. The analysis of DOM extracted from makeup water and feed samples allowed for the determination of DOM sources in RAS prior to ozonation. The CHO and unsaturated group of compounds were the most abundant class found in water samples. On the contrary, the DOM from feed samples was unique and consisted mainly of CHO, CHON and unsaturated group of compounds. After the ozonation of RAS waters, humic-like and unsaturated compounds [positive oxygen subtracted double bond equivalent per carbon (DBE-O)/C)] were decomposed, particularly the CHO-DOM that contained fewer -CH2- features. Fulvic-like compounds and several hundred saturated compounds [negative (DBE-O)/C)] were formed post ozonation, particularly the CHON and CHONS group of compounds that were associated with fish diets, makeup waters and transformation products from the ozonation of the RAS waters. This study showed that the high accuracy of the ultra-high resolution FTICR MS can be applied to characterize and monitor the changes of DOM at a molecular level in RAS waters. To our knowledge, this is the first study where FTICR MS was incorporated for the characterization of DOM and its sources in RAS.

Analysis of 16O/18O and H/D Exchange Reactions between Carbohydrates and Heavy Water Using High-Resolution Mass Spectrometry.

Mono- and polysaccharides are an essential part of every biological system. Identifying underivatized carbohydrates using mass spectrometry is still a challenge because carbohydrates have a low capacity for ionization. Normally, the intensities of protonated carbohydrates are relatively low, and in order to increase the corresponding peak height, researchers add Na+, K+, or NH4+to the solution. However, the fragmentation spectra of the corresponding ions are very poor. Based on this, reliably identifying carbohydrates in complex natural and biological objects can benefit frommeasuring additional molecular descriptors, especially those directly connected to the molecular structure. Previously, we reported that the application of the isotope exchange approach (H/D and 16O/18O) to high-resolution mass spectrometry can increase the reliability of identifying drug-like compounds. Carbohydrates possess many -OH and -COOH groups, making it reasonable to expect that the isotope exchange approach would have considerable potential for detecting carbohydrates. Here, we used a collection of standard carbohydrates to investigate the isotope exchange reaction (H/D and 16O/18O) in carbohydrates and estimate its analytical applications.

PyFragMS-A Web Tool for the Investigation of the Collision-Induced Fragmentation Pathways.

Dissociation induced by the accumulation of internal energy via collisions of ions with neutral molecules is one of the most important fragmentation techniques in mass spectrometry (MS), and the identification of small singly charged molecules is based mainly on the consideration of the fragmentation spectrum. Many research studies have been dedicated to the creation of databases of experimentally measured tandem mass spectrometry (MS/MS) spectra (such as MzCloud, Metlin, etc.) and developing software for predicting MS/MS fragments in silico from the molecular structure (such as MetFrag, CFM-ID, CSI:FingerID, etc.). However, the fragmentation mechanisms and pathways are still not fully understood. One of the limiting obstacles is that protomers (positive ions protonated at different sites) produce different fragmentation spectra, and these spectra overlap in the case of the presence of different protomers. Here, we are proposing to use a combination of two powerful approaches: computing fragmentation trees that carry information of all consecutive fragmentations and consideration of the MS/MS data of isotopically labeled compounds. We have created PyFragMS-a web tool consisting of a database of annotated MS/MS spectra of isotopically labeled molecules (after H/D and/or 16O/18O exchange) and a collection of instruments for computing fragmentation trees for an arbitrary molecule. Using PyFragMS, we investigated how the site of protonation influences the fragmentation pathway for small molecules. Also, PyFragMS offers capabilities for performing database search when MS/MS data of the isotopically labeled compounds are taken into account.

Mass spectrometric studies of the variety of beta-amyloid proteoforms in Alzheimer's disease.

This review covers the results of the application of mass spectrometric (MS) techniques to study the diversity of beta-amyloid (Aß) peptides in human samples. Since Aß is an important hallmark of Alzheimer's disease (AD), which is a socially significant neurodegenerative disorder of the elderly worldwide, analysis of its endogenous variations is of particular importance for elucidating the pathogenesis of AD, predicting increased risks of the disease onset, and developing effective therapy. MS approaches have no alternative for the study of complex samples, including a wide variety of Aß proteoforms, differing in length and modifications. Approaches based on matrix-assisted laser desorption/ionization time-of-flight and liquid chromatography with electrospray ionization tandem MS are most common in Aß studies. However, Aß forms with isomerized and/or racemized Asp and Ser residues require the use of special methods for separation and extra sensitive and selective methods for detection. Overall, this review summarizes current knowledge of Aß species found in human brain, cerebrospinal fluid, and blood plasma; focuses on application of different MS approaches for Aß studies; and considers the potential of MS techniques for further studies of Aß-peptides.

Increasing the reliability of compound identification in biological samples using 16O/18O-exchange mass spectrometry.

The task of multipurpose analysis of biological samples and identification of individual compounds in them is actual for many organizations in various fields; the results of such analyses can affect lives. The most frequently used, most accurate, and highly sensitive method used for this kind of analysis is the combination of gas/liquid chromatography and high-resolution mass spectrometry. However, in some areas, it is necessary to increase the reliability of compound identification. In this paper, we present a method that combines the reaction of oxygen isotope exchange with mass spectrometry; the method allows to increase the reliability of identification of individual compounds. Oxygen isotope exchange reaction is a "selective" one, which means that not all oxygen present in the molecule can exchange, but only in certain functional groups. Thus, by the number of isotope exchanges that have occurred in this molecule, the right structural formula might be more accurately chosen. The method was tested both on pure pharmaceutical substances and on real human urine samples. In both cases, the effectiveness of the method was shown: the number of expected exchanges in known substances coincided with the experimental one, and from several possible structures of unknown substances, the correct one was chosen based on the number of isotope exchanges.

Combined Impact of Magnetic Force and Spaceflight Conditions on Escherichia coli Physiology.

Changes in bacterial physiology caused by the combined action of the magnetic force and microgravity were studied in Escherichia coli grown using a specially developed device aboard the International Space Station. The morphology and metabolism of E. coli grown under spaceflight (SF) or combined spaceflight and magnetic force (SF + MF) conditions were compared with ground cultivated bacteria grown under standard (control) or magnetic force (MF) conditions. SF, SF + MF, and MF conditions provided the up-regulation of Ag43 auto-transporter and cell auto-aggregation. The magnetic force caused visible clustering of non-sedimenting bacteria that formed matrix-containing aggregates under SF + MF and MF conditions. Cell auto-aggregation was accompanied by up-regulation of glyoxylate shunt enzymes and Vitamin B12 transporter BtuB. Under SF and SF + MF but not MF conditions nutrition and oxygen limitations were manifested by the down-regulation of glycolysis and TCA enzymes and the up-regulation of methylglyoxal bypass. Bacteria grown under combined SF + MF conditions demonstrated superior up-regulation of enzymes of the methylglyoxal bypass and down-regulation of glycolysis and TCA enzymes compared to SF conditions, suggesting that the magnetic force strengthened the effects of microgravity on the bacterial metabolism. This strengthening appeared to be due to magnetic force-dependent bacterial clustering within a small volume that reinforced the effects of the microgravity-driven absence of convectional flows.

The Parallel Reaction Monitoring-Parallel Accumulation-Serial Fragmentation (prm-PASEF) Approach for Multiplexed Absolute Quantitation of Proteins in Human Plasma.

Mass spectrometry (MS)-based quantitative proteomic methods have become some of the major tools for protein biomarker discovery and validation. The recently developed parallel reaction monitoring-parallel accumulation-serial fragmentation (prm-PASEF) approach on a Bruker timsTOF Pro mass spectrometer allows the addition of ion mobility as a new dimension to LC-MS-based proteomics and increases proteome coverage at a reduced analysis time. In this study, a prm-PASEF approach was used for the multiplexed absolute quantitation of proteins in human plasma using isotope-labeled peptide standards for 125 plasma proteins, over a broad (104-106) dynamic range. Optimization of LC and MS parameters, such as accumulation time and collision energy, resulted in improved sensitivity for more than half of the targets (73 out of 125 peptides) by increasing the signal-to-noise ratio by a factor of up to 10. Overall, 41 peptides showed up to a 2-fold increase in sensitivity, 25 peptides showed up to a 5-fold increase in sensitivity, and 7 peptides showed up to a 10-fold increase in sensitivity. Implementation of the prm-PASEF method allowed absolute protein quantitation (down to 1.13 fmol) in human plasma samples. A comparison of the concentration values of plasma proteins determined by MRM on a QTRAP instrument and by prm-PASEF on a timsTOF Pro revealed an excellent correlation (R2 = 0.97) with a slope of close to 1 (0.99), demonstrating that prm-PASEF is well suited for "absolute" quantitative proteomics.

Aromaticity Index with Improved Estimation of Carboxyl Group Contribution for Biogeochemical Studies.

Natural organic matter (NOM) components measured with ultrahigh-resolution mass spectrometry (UHRMS) are often assessed by molecular formula-based indices, particularly related to their aromaticity, which are further used as proxies to explain biogeochemical reactivity. An aromaticity index (AI) is calculated mostly with respect to carboxylic groups abundant in NOM. Here, we propose a new constrained AIcon based on the measured distribution of carboxylic groups among individual NOM components obtained by deuteromethylation and UHRMS. Applied to samples from diverse sources (coal, marine, peat, permafrost, blackwater river, and soil), the method revealed that the most probable number of carboxylic groups was two, which enabled to set a reference point n = 2 for carboxyl-accounted AIcon calculation. The examination of the proposed AIcon showed the smallest deviation to the experimentally determined index for all NOM samples under study as well as for individual natural compounds obtained from the Coconut database. In particular, AIcon performed better than AImod for all compound classes in which aromatic moieties are expected: aromatics, condensed aromatics, and unsaturated compounds. Therefore, AIcon referenced with two carboxyl groups is preferred over conventional AI and AImod for biogeochemical studies where the aromaticity of compounds is important to understand the transformations and fate of NOM compounds.

Oxygen Isotope Exchange Reaction for Untargeted LC-MS Analysis.

LC-MS is a key technique for the identification of small molecules in complex samples. Accurate mass, retention time, and fragmentation spectra from LC-MS experiments are compared to reference values for pure chemical standards. However, this information is often unavailable or insufficient, leading to an assignment to a list of candidates instead of a single hit; therefore, additional features are desired to filter candidates. One such promising feature is the number of specific functional groups of a molecule that can be counted via derivatization or isotope-exchange techniques. Hydrogen/deuterium exchange (HDX) is the most widespread implementation of isotope exchange for mass spectrometry, while oxygen 16O/18O exchange is not applied as frequently as HDX. Nevertheless, it is known that some functional groups may be selectively exchanged in 18O enriched media. Here, we propose an implementation of 16O/18O isotope exchange to highlight various functional groups. We evaluated the possibility of using the number of exchanged oxygen atoms as a descriptor to filter database candidates in untargeted LC-MS-based workflows. It was shown that 16O/18O exchange provides 62% (median, n = 45) search space reduction for a panel of drug molecules. Additionally, it was demonstrated that studying the fragmentation spectra after 16O/18O can aid in eliminating false positives and, in some cases, help to annotate fragments formed with water traces in the collisional cell.