Electrospray ionization efficiency scale of organic compounds

Estimating LoD-s Based on the Ionization Efficiency Values for the Reporting and Harmonization of Amenable Chemical Space in Nontargeted Screening LC/ESI/HRMS

Nontargeted LC/ESI/HRMS aims to detect and identify organic compounds present in the environment without prior knowledge; however, in practice no LC/ESI/HRMS method is capable of detecting all chemicals, and the scope depends on the instrumental conditions. Different experimental conditions, instruments, and methods used for sample preparation and nontargeted LC/ESI/HRMS as well as different workflows for data processing may lead to challenges in communicating the results and sharing data between laboratories as well as reduced reproducibility. One of the reasons is that only a fraction of method performance characteristics can be determined for a nontargeted analysis method due to the lack of prior information and analytical standards of the chemicals present in the sample. The limit of detection (LoD) is one of the most important performance characteristics in target analysis and directly describes the detectability of a chemical. Recently, the identification and quantification in nontargeted LC/ESI/HRMS (e.g., via predicting ionization efficiency, risk scores, and retention times) have significantly improved due to employing machine learning. In this work, we hypothesize that the predicted ionization efficiency could be used to estimate LoD and thereby enable evaluating the suitability of the LC/ESI/HRMS nontargeted method for the detection of suspected chemicals even if analytical standards are lacking. For this, 221 representative compounds were selected from the NORMAN SusDat list (S0), and LoD values were determined by using 4 complementary approaches. The LoD values were correlated to ionization efficiency values predicted with previously trained random forest regression. A robust regression was then used to estimate LoD values of unknown features detected in the nontargeted screening of wastewater samples. These estimated LoD values were used for prioritization of the unknown features. Furthermore, we present LoD values for the NORMAN SusDat list with a reversed-phase C18 LC method.

Characterization of Mass, Diameter, Density, and Surface Properties of Colloidal Nanoparticles Enabled by Charge Detection Mass Spectrometry

A variety of scattering-based, microscopy-based, and mobility-based methods are frequently used to probe the size distributions of colloidal nanoparticles with transmission electron microscopy (TEM) often considered to be the "gold standard". Charge detection mass spectrometry (CDMS) is an alternative method for nanoparticle characterization that can rapidly measure the mass and charge of individual nanoparticle ions with high accuracy. Two low polydispersity, ∼100 nm diameter nanoparticle size standards with different compositions (polymethyl methacrylate/polystyrene copolymer and 100% polystyrene) were characterized using both TEM and CDMS to explore the merits and complementary aspects of both methods. Mass and diameter distributions are rapidly obtained from CDMS measurements of thousands of individual ions of known spherical shape, requiring less time than TEM sample preparation and image analysis. TEM image-to-image variations resulted in a ∼1-2 nm range in the determined mean diameters whereas the CDMS mass precision of ∼1% in these experiments leads to a diameter uncertainty of just 0.3 nm. For the 100% polystyrene nanoparticles with known density, the CDMS and TEM particle diameter distributions were in excellent agreement. For the copolymer nanoparticles with unknown density, the diameter from TEM measurements combined with the mass from CDMS measurements enabled an accurate measurement of nanoparticle density. Differing extents of charging for the two nanoparticle standards measured by CDMS show that charging is sensitive to nanoparticle surface properties. A mixture of the two samples was separated based on their different extents of charging despite having overlapping mass distributions centered at 341.5 and 331.0 MDa.

Automatic Electrochemiluminescence Method for the Detection of Cancerous Exosomes Incorporating Specific Aptamer-Magnetic Beads and Signal Nanoprobes

Exosomes, as an emerging biomarker, have exhibited remarkable promise in early cancer diagnosis. Here, a highly sensitive, selective, and automatic electrochemiluminescence (ECL) method for the detection of cancerous exosomes was developed. Specific aptamer-(EK)4 peptide-tagged magnetic beads (MBs-(EK)4-aptamer) were designed as a magnetic capture probe in which the (EK)4 peptide was used to reduce the steric binding hindrance of cancerous exosomes with a specific aptamer. One new universal ECL signal nanoprobe (CD9 Ab-PEG@SiO2ϵRu(bpy)32+) was designed and synthesized by using microporous SiO2 nanoparticles as the carrier for loading ECL reagent Ru(bpy)32+, polyethylene glycol (PEG) layer, and anticluster of differentiation 9 antibody (CD9 Ab). A "sandwich" biocomplex was formed on the surface of the magnetic capture probe after mixing the capture probe, target exosomes, and ECL signal nanoprobe, and then it was introduced into an automated ECL analyzer for rapid and automatic ECL measurement. It was found that the designed signal nanoprobe shows a 270-fold improvement in the signal-to-noise ratio than that of the ruthenium complex-labeled CD9 antibody signal probe. The relative ECL intensity was proportional to MCF-7 exosomes as a model in the range of 102 to 104 particle/μL, with a detection limit of 11 particle/μL. Furthermore, the ECL method was employed to discriminate cancerous exosomes based on fingerprint responses using the designed multiple magnetic capture probes and the universal ECL signal nanoprobe. This work demonstrates that the utilization of a designed automated ECL tactic using the MBs-(EK)4-aptamer capture probe and the CD9 Ab-PEG@SiO2ϵRu(bpy)32+ signal nanoprobe will provide a unique and robust method for the detection and discrimination of cancerous exosomes.

Photooxidation-Initiated Aqueous-Phase Formation of Organic Peroxides: Delving into Formation Mechanisms

Formation of highly oxygenated molecules (HOMs) such as organic peroxides (ROOR, ROOH, and H2O2) is known to degrade food and organic matter. Gas-phase unimolecular autoxidation and bimolecular RO2 + HO2/RO2 reactions are prominently renowned mechanisms associated with the formation of peroxides. However, the reaction pathways and conditions favoring the generation of peroxides in the aqueous phase need to be evaluated. Here, we identified bulk aqueous-phase ROOHs in varying organic precursors, including a laboratory model compound and monoterpene oxidation products. Our results show that formation of ROOHs is suppressed at enhanced oxidant concentrations but exhibits complex trends at elevated precursor concentrations. Furthermore, we observed an exponential increase in the yield of ROOHs when UV light with longer wavelengths was used in the experiment, comparing UVA, UVB, and UVC. Water-soluble organic compounds represent a significant fraction of ambient cloud-water components (up to 500 μM). Thus, the reaction pathways facilitating the formation of HOMs (i.e., ROOHs) during the aqueous-phase oxidation of water-soluble species add to the climate and health burden of atmospheric particulate matter.

Non-targeted analysis based on quantitative prediction and toxicity assessment for emerging contaminants in tire particle leachates

Non-targeted analysis (NTA) has great potential to screen emerging contaminants in the environment, and some studies have conducted in-depth investigation on environmental samples. Here, we used a NTA workflow to identify emerging contaminants in used tire particle (TP) leachates, followed by quantitative prediction and toxicity assessment based on hazard scores. Tire particles were obtained from four different types of automobiles, representing the most common tires during daily transportation. With the instrumental analysis of TP leachates, a total of 244 positive and 104 negative molecular features were extracted from the mass data. After filtering by a specialized emerging contaminants list and matching by spectral databases, a total of 51 molecular features were tentatively identified as contaminants, including benzothiazole, hexaethylene glycol, 2-hydroxybenzaldehyde, etc. Given that these contaminants have different mass spectral responses in the mass spectrometry, models for predicting the response of contaminants were constructed based on machine learning algorithms, in this case random forest and artificial neural networks. After five-fold cross-validation, the random forest algorithm model had better prediction performance (MAECV = 0.12, Q2 = 0.90), and thus it was chosen to predict the contaminant concentrations. The prediction results showed that the contaminant at the highest concentration was benzothiazole, with 4,875 μg/L in the winter tire sample. In addition, the joint toxicity assessment of four types of tires was conducted in this study. According to different hazard levels, hazard scores increasing by a factor 10 were developed, and hazard scores of all the contaminants identified in each TP leachate were summed to obtain the total hazard score. All four tires were calculated to have relatively high risks, with winter tires having the highest total hazard score of 40,751. This study extended the application of NTA research and led to the direction of subsequent targeting studies on highly concentrated and toxic contaminants.

High-Speed Schlieren Imaging of Vapor Formation in Electrospray Plume

Previous mechanistic descriptions of electrosprays mostly focused on the dynamics of Taylor cones, initial droplets, and progeny droplets. However, vapor formation during droplet desolvation in an electrospray plume has not been discussed to a great extent. Here, we implement a double-pass on-axis schlieren high-speed imaging system to observe generation and propagation of vapors in an offline electrospray source under different conditions. Switching between turbulent and laminar vapor flow was observed for all of the scanned conditions, which may be attributed to randomly occurring disturbances in the sample flow inside the electrospray emitter. Calculation of mean vapor flow velocity and analysis of vapor flow patterns were performed using in-house developed image processing programs. Experiments performed at different electrospray voltages (0-6 kV), solvent flow rates (100-600 μL min-1), and methanol concentrations (50-100%), indicate only a weak dependency between electrospray voltage and mean vapor velocity, implying that the vapor is mostly neutral; thus, the vapor is not accelerated by electric field. On the other hand, electrospraying solutions of analytes (with mass 151 Da or 12 kDa) did not remarkably increase the overall vapor flow velocity. The source of vapor's velocity is attributed to the inertia of the electrospray droplets. Although there are some differences between a modern electrospray ionization (ESI) setup and the setup used in our experiment (e.g., using a higher flow rate and larger emitter), we believe the findings of our study can be projected to a modern ESI setup.

Quantitative Nontarget Analysis of CECs in Environmental Samples Can Be Improved by Considering All Mass Adducts

Quantitative nontarget analysis (qNTA) for liquid chromatography coupled to high-resolution mass spectrometry enables a more comprehensive assessment of environmental samples. Previous studies have shown that correlations between a compound's ionization efficiency and a range of molecular descriptors can predict the compound's concentration within a factor of 5. In this study, the qNTA approach was further improved by considering all mass adducts instead of only the protonated ion. The model was based on a quantitative structure-property relationship (QSPR), including 216 contaminants of emerging concern (CECs), of which 80 exhibited adduct formation that accounted for >10% of the total peak intensity. When all mass adducts were included, the test set coefficient of determination improved to Q2 = 0.855 compared to Q2 = 0.670 when only the protonated ions were considered (test set median RF error factor 1.6). The inclusion of all adducts was also important to transfer the RF QSPR model reliably. It was assumed that RF variations are sequence-dependent; therefore, a second QSPR model for the prediction of the transferability factor was built for each sequence. For validation, samples were analyzed up to two years apart. The median prediction fold change was 1.74 for analytical standards (63 compounds) and 2.4 for enriched wastewater effluent samples (41 compounds), with 80% of the compounds predicted within a fold change of 2.4 and 3.3, respectively. The model was also validated on a second instrument, where 80% of the 26 compounds in wastewater effluent were predicted within a factor of 3.8.

Moderate Signal Enhancement in Electrospray Ionization Mass Spectrometry by Focusing Electrospray Plume with a Dielectric Layer around the Mass Spectrometer's Orifice

Electrospray ionization (ESI) is among the commonly used atmospheric pressure ionization techniques in mass spectrometry (MS). One of the drawbacks of ESI is the formation of divergent plumes composed of polydisperse microdroplets, which lead to low transmission efficiency. Here, we propose a new method to potentially improve the transmission efficiency of ESI, which does not require additional electrical components and complex interface modification. A dielectric plate-made of ceramic-was used in place of a regular metallic sampling cone. Due to the charge accumulation on the dielectric surface, the dielectric layer around the MS orifice distorts the electric field, focusing the charged electrospray cloud towards the MS inlet. The concept was first verified using charge measurement on the dielectric material surface and computational simulation; then, online experiments were carried out to demonstrate the potential of this method in MS applications. In the online experiment, signal enhancements were observed for dielectric plates with different geometries, distances of the electrospray needle axis from the MS inlet, and various compounds. For example, in the case of acetaminophen (15 μM), the signal enhancement was up to 1.82 times (plate B) using the default distance of the electrospray needle axis from the MS inlet (d = 1.5 mm) and 12.18 times (plate C) using a longer distance (d = 7 mm).

Triboelectric Nanogenerator-Coated Blade Spray Mass Spectrometry for Volume-Limited Drug Analysis

The demand for analytical tools for the analysis of low-concentration volume-limited samples has driven researchers to explore new analytical approaches. Mass spectrometry excels at trace analysis due to its high sensitivity and specificity, whereas ambient methods simplify, or completely eliminate sample preparation. Herein, we report a triboelectric nanogenerator-coated blade spray ambient mass spectrometry (TENG-CBS MS) method for the extraction, elution, and ionization of volume-limited, low-concentration small molecule drug samples with minimum sample preparation. Using a TENG device as the CBS power supply, we show it is possible to extract and analyze drug samples in a pulsed fashion at sub-nanogram to picogram levels with good stability and reproducibility. A wide range of analytes polarities were tested. Results indicated this method could also be useful for the analysis of low-level analytes in precious, volume limited samples in a simple single step.

Inclusion of Porous Graphitic Carbon Chromatography Yields Greater Protein Identification and Compartment and Process Coverage and Enables More Reflective Protein-Level Label-Free Quantitation

The ubiquity of mass spectrometry-based bottom-up proteomic analyses as a component of biological investigation mandates the validation of methodologies that increase acquisition efficiency, improve sample coverage, and enhance profiling depth. Chromatographic separation is often ignored as an area of potential improvement, with most analyses relying on traditional reversed-phase liquid chromatography (RPLC); this consistent reliance on a single chromatographic paradigm fundamentally limits our view of the observable proteome. Herein, we build upon early reports and validate porous graphitic carbon chromatography (PGC) as a facile means to substantially enhance proteomic coverage without changes to sample preparation, instrument configuration, or acquisition methods. Analysis of offline fractionated cell line digests using both separations revealed an increase in peptide and protein identifications by 43% and 24%, respectively. Increased identifications provided more comprehensive coverage of cellular components and biological processes independent of protein abundance, highlighting the substantial quantity of proteomic information that may go undetected in standard analyses. We further utilize these data to reveal that label-free quantitative analyses using RPLC separations alone may not be reflective of actual protein constituency. Together, these data highlight the value and comprehension offered through PGC-MS proteomic analyses. RAW proteomic data have been uploaded to the MassIVE repository with the primary accession code MSV000091495.

Mobile phase and column chemistry selection for high sensitivity non-targeted LC/ESI/HRMS screening of water

Systematic selection of mobile phase and column chemistry type can be critical for achieving optimal chromatographic separation, high sensitivity, and low detection limits in liquid chromatography electrospray high resolution mass spectrometry (LC/MS). However, the selection process is challenging for non-targeted screening where the compounds of interest are not preselected nor available for method optimization. To provide general guidance, twenty different mobile phase compositions and four columns were compared for the analysis of 78 compounds with a wide range of physicochemical properties (logP range from -1.46 to 5.48), and analyte sensitivity was compared between methods. The pH, additive type, column, and organic modifier had significant effects on the analyte response factors, and acidic mobile phases (e.g. 0.1% formic acid) yielded highest sensitivity. In some cases, the effect was attributable to the difference in organic modifier content at the time of elution, depending on the mobile phase and column chemistry. Based on these findings, 0.1% formic acid, 0.1% ammonia and 5.0 mM ammonium fluoride were further evaluated for their performance in non-targeted LC/ESI/HRMS analysis of wastewater treatment plan influent and effluent, using a data dependent MS² acquisition and two different data processing workflows (MS-DIAL, patRoon 2.1) to compare number of detected features and sensitivity. Both data-processing workflows indicated that 0.1% formic acid yielded the highest number of features in full scan spectrum (MS¹)^, as well as the highest number of features that triggered fragmentation spectra (MS²) when dynamic exclusion was used.

Bypassing the Identification: MS2Quant for Concentration Estimations of Chemicals Detected with Nontarget LC-HRMS from MS2 Data

Nontarget analysis by liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is now widely used to detect pollutants in the environment. Shifting away from targeted methods has led to detection of previously unseen chemicals, and assessing the risk posed by these newly detected chemicals is an important challenge. Assessing exposure and toxicity of chemicals detected with nontarget HRMS is highly dependent on the knowledge of the structure of the chemical. However, the majority of features detected in nontarget screening remain unidentified and therefore the risk assessment with conventional tools is hampered. Here, we developed MS2Quant, a machine learning model that enables prediction of concentration from fragmentation (MS2) spectra of detected, but unidentified chemicals. MS2Quant is an xgbTree algorithm-based regression model developed using ionization efficiency data for 1191 unique chemicals that spans 8 orders of magnitude. The ionization efficiency values are predicted from structural fingerprints that can be computed from the SMILES notation of the identified chemicals or from MS2 spectra of unidentified chemicals using SIRIUS+CSI:FingerID software. The root mean square errors of the training and test sets were 0.55 (3.5×) and 0.80 (6.3×) log-units, respectively. In comparison, ionization efficiency prediction approaches that depend on assigning an unequivocal structure typically yield errors from 2× to 6×. The MS2Quant quantification model was validated on a set of 39 environmental pollutants and resulted in a mean prediction error of 7.4×, a geometric mean of 4.5×, and a median of 4.0×. For comparison, a model based on PaDEL descriptors that depends on unequivocal structural assignment was developed using the same dataset. The latter approach yielded a comparable mean prediction error of 9.5×, a geometric mean of 5.6×, and a median of 5.2× on the validation set chemicals when the top structural assignment was used as input. This confirms that MS2Quant enables to extract exposure information for unidentified chemicals which, although detected, have thus far been disregarded due to lack of accurate tools for quantification. The MS2Quant model is available as an R-package in GitHub for improving discovery and monitoring of potentially hazardous environmental pollutants with nontarget screening.

Electrospray ionization mass spectrometry adduct formation by mobile phase additives: A case study using nitrile functional groups containing selective androgen receptor modulators

RATIONALE

The formation of mass adducts is common during electrospray ionization mass spectrometry (ESI-MS). However, the mechanism that leads to adduct formation is poorly understood and difficult to control. Multiplication of mass adducts at once will adversely impact the sensitivity of mass analysis and cause misinterpretation of the level of detection. Prior studies on selective androgen receptor modulators (SARMs) revealed an immense mass adduct formation in both positive and negative ESI modes.

METHODS

In this study, additives in the mobile phases are investigated as a potential means of controlling mass adduct formation in various SARMs.

RESULTS

The first evidence of chloride adduct formation when SARMs are detected via ESI-MS has been reported in this research. A series of mobile phase combinations were tested to achieve the optimal condition for HPLC-MS. A comparison was also made between adduct formation on various grades of water used for preparing the mobile phase. A validation study using equine urine and plasma was also conducted to assess the suitability of the developed method.

CONCLUSION

The results of this study will allow for a more accurate identification of SARMs, which will make it easier to investigate their illicit use in horse racing.

Genetic Algorithm Optimized Printed Circuit Board Ion Funnel Tandem Subambient Pressure Ionization with Nanoelectrospray (SPIN) for High Sensitivity Mass Spectrometry

The SPIN tandem ion funnel (IF) structure allows for highly sensitive mass spectrometry due to reduced ion losses in the interface region and during transmission; however, IF has an inherent mass discrimination problem, which can greatly restrain the ion transmission efficiency (TE) and therefore requires certain optimization methods. Conventional optimization methods ignore the combined effects of multiple IF characteristic parameters (electrical and dimensional parameters) and are unable to achieve efficient ion transmission over a wide mass range, thus requiring significant tuning time. In this paper, a genetic algorithm (GA)-optimized printed circuit board ion funnel (PCBIF) was designed, fabricated, preliminarily evaluated, and integrated into the SPIN interface to address the ion loss that can occur when mass spectrometers transfer ions at subambient pressure. Simulation studies have showed clearly that the effective automated GA can increase the PCBIF optimization, design, and the ion TE (finding the optimal characteristic parameters within 4 h and achieving 96% ion TE for ions with m/z between 50 and 700). Preliminary tests on built SPIN-PCBIF-MS can lead to an LOD of 0.01 nM and also indirectly suggest the effectiveness of the GA-optimized PCBIF. The proposed GA method helps to guide the design of IF and can also be used for other multivariate mass analyzers or ion transmission devices.

Ion transmission in an electrospray ionization-mass spectrometry interface using an S-lens

We present the design and performance of an in-house built electrospray ionization-mass spectrometry (ESI-MS) interface equipped with an S-lens ion guide. The ion source was designed specifically for our ion beam experiments to investigate the chemical reactivity and deposition of the clusters and nanoparticles. It includes standard ESI-MS interface components, such as nanoelectrospray, ion transfer capillary, and the S-lens. A custom design enables systematic optimization of all relevant factors influencing ion formation and transfer through the interface. By varying the ESI voltage and flow rate, we determined the optimal operating conditions for selected silica emitters. A comparison of the pulled silica emitters with different tip inner diameters reveals that the total ion current is highest for the largest tip, whereas a tip with the smallest diameter exhibited the highest transmission efficiency through the ESI-MS interface. Ion transmission through the transfer capillary is strongly limited by its length, but the loss of ions can be reduced by increasing the capillary voltage and temperature. The S-lens was characterized over a wide range of RF frequencies and amplitudes. Maximum ion current was detected at RF amplitudes greater than 50 V peak-to-peak (p/p) and frequencies above 750 kHz, with a stable ion transmission region of about 20%. A factor of 2.6 increase in total ion current is observed for 650 kHz as RF amplitudes reach 400 V p/p. Higher RF amplitudes also focus the ions into a narrow beam, which mitigates their losses when passing through the ion guide.

Electrospray Ionization Efficiency Predictions and Analytical Standard Free Quantification for SFC/ESI/HRMS

Supercritical fluid chromatography (SFC) is a promising, sustainable, and complementary alternative to liquid chromatography (LC) and has often been coupled with high resolution mass spectrometry (HRMS) for nontarget screening (NTS). Recent developments in predicting the ionization efficiency for LC/ESI/HRMS have enabled quantification of chemicals detected in NTS even if the analytical standards of the detected and tentatively identified chemicals are unavailable. This poses the question of whether analytical standard free quantification can also be applied in SFC/ES/HRMS. We evaluate both the possibility to transfer an ionization efficiency predictions model, previously trained on LC/ESI/HRMS data, to SFC/ESI/HRMS as well as training a new predictive model on SFC/ESI/HRMS data for 127 chemicals. The response factors of these chemicals ranged over 4 orders of magnitude in spite of a postcolumn makeup flow, expectedly enhancing the ionization of the analytes. The ionization efficiency values were predicted based on a random forest regression model from PaDEL descriptors and predicted values showed statistically significant correlation with the measured response factors (p < 0.05) with Spearman's rho of 0.584 and 0.669 for SFC and LC data, respectively. Moreover, the most significant descriptors showed similarities independent of the chromatography used for collecting the training data. We also investigated the possibility to quantify the detected chemicals based on predicted ionization efficiency values. The model trained on SFC data showed very high prediction accuracy with median prediction error of 2.20×, while the model pretrained on LC/ESI/HRMS data yielded median prediction error of 5.11×. This is expected, as the training and test data for SFC/ESI/HRMS have been collected on the same instrument with the same chromatography. Still, the correlation observed between response factors measured with SFC/ESI/HRMS and predicted with a model trained on LC data hints that more abundant LC/ESI/HRMS data prove useful in understanding and predicting the ionization behavior in SFC/ESI/HRMS.

Predicting liquid chromatography-electrospray ionization/mass spectrometry signal from the structure of model compounds and experimental factors; case study of aripiprazole and its impurities

A priori estimation of analyte response is crucial for the efficient development of liquid chromatography-electrospray ionization/mass spectrometry (LC-ESI/MS) methods, but remains a demanding task given the lack of knowledge about the factors affecting the experimental outcome. In this research, we address the challenge of discovering the interactive relationship between signal response and structural properties, method parameters and solvent-related descriptors throughout an approach featuring quantitative structure-property relationship (QSPR) and design of experiments (DoE). To systematically investigate the experimental domain within which QSPR prediction should be undertaken, we varied LC and instrumental factors according to the Box-Behnken DoE scheme. Seven compounds, including aripiprazole and its impurities, were subjected to 57 different experimental conditions, resulting in 399 LC-ESI/MS data endpoints. To obtain a more standard distribution of the measured response, the peak areas were log-transformed before modeling. QSPR predictions were made using features selected by Genetic Algorithm (GA) and providing Gradient Boosted Trees (GBT) with training data. Proposed model showed satisfactory performance on test data with a RMSEP of 1.57 % and a of 96.48 %. This is the first QSPR study in LC-ESI/MS that provided a holistic overview of the analyte's response behavior across the experimental and chemical space. Since intramolecular electronic effects and molecular size were given great importance, the GA-GBT model improved the understanding of signal response generation of model compounds. It also highlighted the need to fine-tune the parameters affecting desolvation and droplet charging efficiency.

Gigaohm and Teraohm Resistors in Femtoamp and Picoamp Electrospray Ionization

The femtoamp electrospray ionization (femtoESI) mode has been shown to exhibit unique characteristics that may facilitate ionization efficiency studies and experiments requiring low ion beam flux. Investigation of femtoESI was hindered by a tiny, applied voltage window of 10-100 V, beyond which ionization currents quickly jumped to nanoamps. This window was difficult to locate because the exact onset voltage fluctuates due to variations in ion source alignments. Large resistors (0.1-100 TΩ) in series effectively expanded the femtoESI applied voltage range, up to 1400 V. By swapping resistors, rapid alternation allows for the comparison of both ESI modes under the same alignment. In peptide mixtures, analytes with lower surface activity are suppressed in the nanoESI mode whereas the femtoESI mode shows signal enhancement of less surface-active species. For protein solutions, there is little change in the charge states generated but the femtoESI mode does show a decrease in the average charge state of protein peaks. Peptides and proteins analyzed in the femtoESI mode also tend to generate higher intensity sodiated peaks over protonated peaks at specific charge states compared with nanoESI mode operation.

Mass Spectrometric Methods for Non-Targeted Screening of Metabolites: A Future Perspective for the Identification of Unknown Compounds in Plant Extracts

Phyto products are widely used in natural products, such as medicines, cosmetics or as so-called “superfoods”. However, the exact metabolite composition of these products is still unknown, due to the time-consuming process of metabolite identification. Non-target screening by LC-HRMS/MS could be a technique to overcome these problems with its capacity to identify compounds based on their retention time, accurate mass and fragmentation pattern. In particular, the use of computational tools, such as deconvolution algorithms, retention time prediction, in silico fragmentation and sophisticated search algorithms, for comparison of spectra similarity with mass spectral databases facilitate researchers to conduct a more exhaustive profiling of metabolic contents. This review aims to provide an overview of various techniques and tools for non-target screening of phyto samples using LC-HRMS/MS.

Tuning Electrospray Ionization with Low-Frequency Sound

Electrospray ionization (ESI) mass spectrometry (MS) is one of the key techniques used in biomolecular analysis nowadays. It relies on formation of polydisperse microdroplets, which undergo desolvation and liberate ions to the gas phase. Here we demonstrate low-frequency-sound-modulated ESI for analysis of biomolecules. By using a low-frequency (50-350 Hz) sound, it is possible to deflect electrospray microdroplets toward the mass spectrometer's orifice. Microdroplets of different sizes are deflected to a different extent leading to a partial size segregation. This effect leads to either an increase or decrease of MS signal intensity as well as signal-to-noise ratio. It also affects the selectivity of the ESI-MS analysis. The observations are rationalized by taking into account different pathways of ion formation and the likelihood of deflecting microdroplets of certain size. The online ESI-MS observations are supported with offline shadowgraphs obtained at varied sound frequencies, signal amplitudes, and phase shifts.

Determination of estrogens in human serum using a novel chemical derivatization-assisted liquid chromatography-electrospray ionization-tandem mass spectrometry method

RATIONALE

Assessing estrogen concentrations in biological systems can provide valuable information on physiological processes, which is crucial for the early diagnosis of many diseases. Because estrogens are present in the human body in low concentrations and in a wide dynamic range, analytical methods with high sensitivity and specificity are required for their determination in complex biological matrices.

METHODS

To discover an appropriate derivatization reagent for estrogen mass spectrometry (MS) analysis, we compared five sulfonyl chloride derivatization reagents, namely 3-methyl-8-quinolinesulfonyl chloride (MQSCl) and 8-quinolinesulfonyl chloride (QSCl), 1-methyl-1H-pyrazole-4-sulfonyl chloride, 1,2-methyl-imidazole-5-sulfonyl chloride, and dansyl chloride. By selecting the derivatization reagent with the best performance, we developed and validated a novel chemical derivatization-assisted-liquid chromatography-electrospray ionization-tandem mass spectrometry (CD-LC-ESI-MS/MS) method to simultaneously determine the concentrations of estrone, estradiol, and estriol (E1, E2, and E3) in human serum.

RESULTS

It was found that among the five investigated reagents, MQSCl-derivatized estrogens presented the highest sensitivity using LC-ESI-MS/MS. Based on this discovery, MQSCl was chosen to derivatize the analyzed estrogens to assist LC-ESI-MS/MS analysis. The limit of quantification of E1, E2, and E3 was measured as 2.7, 4.6, and 5.1 pg/mL, respectively. Inter- and intra-day precision, expressed as the coefficient of variation, was shown to be lower than 13.2% for all concentrations. The mean recovery was 72.4% overall, with good reproducibility at low, medium, and high concentrations in the calibration range.

CONCLUSIONS

The developed method was successfully applied to the quantitative determination of estrogens in clinical human serum from pediatric and adult women, demonstrating the suitability of estrogen analysis in the biological matrix at low concentration (pg/mL).

Water promoted 9-fluorenylmethyloxycarbonyl detachment from amino acids in charged microdroplets

Aqueous microdroplets exhibit unique properties and can trigger reactions that do not occur in bulk solution. Herein, we have demonstrated that water, in microdroplets, can reduce the energy barrier for the lone H transfer of 9-fluorenylmethyloxycarbonyl and promote its detachment from the amino group. This strategy works on various amino acids and opens opportunities of aqueous microdroplets in triggering organic reactions.

Trace metal stoichiometry of dissolved organic matter in the Amazon plume

Dissolved organic matter (DOM) is a distinct component of Earth's hydrosphere and provides a link between the biogeochemical cycles of carbon, nutrients, and trace metals (TMs). Binding of TMs to DOM is thought to result in a TM pool with DOM-like biogeochemistry. Here, we determined elemental stoichiometries of aluminum, iron, copper, nickel, zinc, cobalt, and manganese associated with a fraction of the DOM pool isolated by solid-phase extraction at ambient pH (DOM_SPE-amb) from the Amazon plume. We found that the rank order of TM stoichiometry within the DOM_SPE-amb fraction was underpinned by the chemical periodicity of the TM. Furthermore, the removal of the TM_SPE-amb pool at low salinity was related to the chemical hardness of the TM ion. Thus, the biogeochemistry of TMs bound to the DOM_SPE-amb component in the Amazon plume was determined by the chemical nature of the TM and not by that of the DOM_SPE-amb.

Equilibration for Electrospray Ionization Mass Spectrometry in Quantitative Analysis

Electrospray ionization mass spectrometry (ESI-MS) is widely used in drug development, therapeutic drug monitoring, and other fields. However, unstable mass spectral signals, especially during the initial stages of instrument operation, plague analysts. Generally, in quantitative experiments, the stability of response can be achieved by running the analytical system for some time. However, the equilibration time required for the responses of different compounds to stabilize has been elusive. To investigate the response stability of the ESI-MS system, 72 compounds with different physicochemical properties were employed on three systems, and flow injection analysis was performed in positive ion mode. With the use of 5.00% (response stable factor, RSF) as the stability limit, about 80% of the compounds were stable within 60 min. Under a 2.00% criterion, the stabilization time was significantly longer. The stabilization time varies with different instruments and physicochemical properties of the compounds. When positive ion detection is performed in an acidic mobile phase, the octanol-water partition coefficient (Log P), molecular weight, and molar volume can all affect the time required to stabilize the response. In general, it is necessary to balance the ESI-MS system for an appropriate time before sample detection, especially for the analysis of compounds with strong hydrophilicity, small molecular weight, or small molar volume under the conditions above.

Quantitative identification of halo-methyl-benzoquinones as disinfection byproducts in drinking water using a pseudo-targeted LC-MS/MS method

Halobenzoquinones (HBQs) as disinfection byproducts (DBPs) in drinking water is prioritized for research due to their prevalent occurrence and high toxicity. However, only fifteen HBQs can be identified among a high diversity using targeted LC-MS/MS analysis in previous studies due to the lack of chemical standards. In this study, we developed a pseudo-targeted LC-MS/MS method for detecting and quantifying diverse HBQs. Distinct fragment characteristics of HBQs was observed according to the halogen substituent effects, and extended to the development of a multiple-reaction-monitoring (MRM) method for the quantification of the 46 HBQs that were observed in simulated drinking water using non-targeted analysis. The fragmentation mechanism was supported by the changes of Gibbs free energy (ΔG), and a linear relationship between the ΔG and the ionization efficiency of analytes was developed accordingly for quantification of these 46 HBQs, 30 of which were lack of chemical standards. It is noted that 29 of the 30 newly-identified HBQs were halo-methyl-benzoquinones (HMBQs), which were predicted to be carcinogens related with drinking-water bladder cancer risk and be more toxic than non-methyl HBQs. Using the new method, twelve HMBQs were detected in actual drinking water samples with concentrations up to 100.4 ng/L, 3 times higher than that reported previously. The cytotoxicity in CHO cells of HMBQs was over 1-fold higher than that of non-methyl-HBQs. Therefore, HMBQs are an essential, highly toxic group of HBQs in drinking water, which deserve particular monitoring and control.

Selected Ion Monitoring Using Low-Cost Mass Spectrum Detectors Provides a Rapid, General, and Accurate Method for Enantiomeric Excess Determination in High-Throughput Experimentation

High-Throughput Experimentation (HTE) workflows are efficient means of surveying a broad array of chiral catalysts in the development of catalytic asymmetric reactions. However, use of traditional HPLC-UV/vis methodology to determine enantiomeric excess (ee) from the resulting reactions is often hampered by co-elution of other reaction components, resulting in erroneous ee determination when crude samples are used, and ultimately requiring product isolation prior to ee analysis. In this study, using four published reactions selected as model systems, we demonstrate that the use of LC-MS, SFC-MS, and selected ion monitoring (SIM) mass chromatography provides a highly accurate means to determine ee of products in crude reaction samples using commonplace, low-cost MS detectors. By using ion selection, co-eluting signals can be deconvoluted to provide accurate integrations of the target analytes. We also show that this method is effective for samples lacking UV/vis chromophores, making it ideal for HTE workflows in asymmetric catalysis.

Standard-Free Quantification of Dicarboxylic Acids: Case Studies with Salt-Rich Effluents and Serum

The present study evaluates the ionization efficiency (IE) of linear and branched C₂-C₁₄ dicarboxylic acids (DCAs) by electrospray ionization (ESI) under different conditions. The influence of the concentration of organic modifier (MeOH); mobile phase additive; and its concentration, pH, and DCA structure on IE values is studied using flow injection analysis. The IE values of DCAs increase with the increase of MeOH concentration but also decrease with an increase of pH. The former is due to the increase in solvent evaporation rates; the latter is caused by an ion-pairing between the diacid and the cation (ammonium), which is confirmed by the study with different amines. The investigation of DCA ionization in the presence of different acidic mobile phase additives showed that a significant improvement in the (-)ESI responses of analytes was achieved in the presence of weak hydrophobic carboxylic acids, such as butyric or propanoic acid. Conversely, the use of strong carboxylic acids, such as trichloroacetic acid, was found to cause signal suppression. The results of the IE studies were used to develop the liquid chromatography-high-resolution mass spectrometry (LC-HRMS) method that provided instrumental limits of detection in the range from 6 to 180 pg. Furthermore, upon applying the nonparametric Gaussian process, a model for the prediction of IE values was developed, which contains the number of carbons in the molecule and MeOH concentration as model parameters. As a case study, dicarboxylic acids are quantified in salt-rich effluent and blood serum samples using the developed LC-HRMS method.

Sodium adduct formation with graph-based machine learning can aid structural elucidation in non-targeted LC/ESI/HRMS

Non-targeted screening with LC/ESI/HRMS aims to identify the structure of the detected compounds using their retention time, exact mass, and fragmentation pattern. Challenges remain in differentiating between isomeric compounds. One untapped possibility to facilitate identification of isomers relies on different ionic species formed in electrospray. In positive ESI mode, both protonated molecules and adducts can be formed; however, not all isomeric structures form the same ionic species. The complicated mechanism of adduct formation has hindered the use of this molecular characteristic in the structural elucidation in non-targeted screening. Here, we have studied the adduct formation for 94 small molecules with ion mobility spectra and compared collision cross-sections of the respective ions. Based on the results we developed a fast support vector machine classifier with polynomial kernels for accurately predicting the sodium adduct formation in ESI/HRMS. The model is trained on five independent data sets from different laboratories and uses the graph-based connectivity of functional groups and PubChem fingerprints to predict the sodium adduct formation in ESI/HRMS. The validation of the model showed an accuracy of 74.7% (balanced accuracy 70.0%) on a dataset from an independent laboratory, which was not used in the training of the model. Lastly, we applied the classification algorithm to the SusDat database by NORMAN network to evaluate the proportion of isomeric compounds that could be distinguished based on predicted sodium adduct formation. It was observed that sodium adduct formation probability can provide additional selectivity for about one quarter of the exact masses and, therefore, shows practical utility for structural assignment in non-targeted screening.

A novel workflow for semi-quantification of emerging contaminants in environmental samples analyzed by LC-HRMS

There is an increasing need for developing a strategy to quantify the newly identified substances in environmental samples, where there are not always reference standards available. The semi-quantitative analysis can assist risk assessment of chemicals and their environmental fate. In this study, a rigorously tested and system-independent semi-quantification workflow is proposed based on ionization efficiency measurement of emerging contaminants analyzed in liquid chromatography-high-resolution mass spectrometry. The quantitative structure-property relationship (QSPR)-based model was built to predict the ionization efficiency of unknown compounds which can be later used for their semi-quantification. The proposed semi-quantification method was applied and tested in real environmental seawater samples. All semi-quantification-related calculations can be performed online and free of access at http://trams.chem.uoa.gr/semiquantification/ .

Single Molecule-Level Detection via Liposome-Based Signal Amplification Mass Spectrometry Counting Assay

Because of the low atomization and/or ionization efficiencies of many biological macromolecules, the application of mass spectrometry to the direct quantitative detection of low-abundance proteins and nucleic acids remains a significant challenge. Herein, we report mass spectrum tags (MS-tags) based upon gold nanoparticle (AuNP)-templated phosphatidylcholine phospholipid (DSPC) liposomes, which exhibit high and reliable signals via electrospray ionization (ESI). Using these MS-tags, we constructed a liposome signal amplification-based mass spectrometric (LSAMS) "digital" counting assay to enable ultrasensitive detection of target nucleic acids. The LSAMS system consists of liposomes modified with a gold nanoparticle core and surface-anchored photocleavable DNA. In the presence of target nucleic acids, the modified liposome and a magnetic bead simultaneously hybridize with the target nucleic acid. After magnetic separation and photolysis, the MS-tag is released and can be analyzed by ESI-MS. At very low target concentrations, one liposome particle corresponds to one target molecule; thus, the concentration of the target can be estimated by counting the number of liposomes. With this assay, hepatitis C (HCV) virus RNA was successfully analyzed in clinical samples.

Nitrogen-Containing Compounds Enhance Light Absorption of Aromatic-Derived Brown Carbon

The formation of secondary brown carbon (BrC) is chemically complex, leading to an unclear relationship between its molecular composition and optical properties. Here, we present an in-depth investigation of molecular-specific optical properties and aging of secondary BrC produced from the photooxidation of ethylbenzene at varied NO_x levels for the first time. Due to the pronounced formation of unsaturated products, the mass absorption coefficient (MAC) of ethylbenzene secondary organic aerosols (ESOA) at 365 nm was higher than that of biogenic SOA by a factor of 10. A high NO_x level ([ethylbenzene]₀/[NO_x]₀ < 10 ppbC ppb^-1) was found to significantly increase the average MAC_300-700nm of ESOA by 0.29 m² g^-1. The data from two complementary high-resolution mass spectrometers and quantum chemical calculations suggested that nitrogen-containing compounds were largely responsible for the enhanced light absorption of high-NO_x ESOA, and multifunctional nitroaromatic compounds (such as C₈H₉NO₃ and C₈H₉NO₄) were identified as important BrC chromophores. High-NO_x ESOA underwent photobleaching upon direct exposure to ultraviolet light. Photolysis did not lead to the significant decomposition of C₈H₉NO₃ and C₈H₉NO₄, indicating that nitroaromatic compounds may serve as relatively stable nitrogen reservoirs and would effectively absorb solar radiation during the daytime.

Machine Learning for Absolute Quantification of Unidentified Compounds in Non-Targeted LC/HRMS

LC/ESI/HRMS is increasingly employed for monitoring chemical pollutants in water samples, with non-targeted analysis becoming more common. Unfortunately, due to the lack of analytical standards, non-targeted analysis is mostly qualitative. To remedy this, models have been developed to evaluate the response of compounds from their structure, which can then be used for quantification in non-targeted analysis. Still, these models rely on tentatively known structures while for most detected compounds, a list of structural candidates, or sometimes only exact mass and retention time are identified. In this study, a quantification approach was developed, where LC/ESI/HRMS descriptors are used for quantification of compounds even if the structure is unknown. The approach was developed based on 92 compounds analyzed in parallel in both positive and negative ESI mode with mobile phases at pH 2.7, 8.0, and 10.0. The developed approach was compared with two baseline approaches- one assuming equal response factors for all compounds and one using the response factor of the closest eluting standard. The former gave a mean prediction error of a factor of 29, while the latter gave a mean prediction error of a factor of 1300. In the machine learning-based quantification approach developed here, the corresponding prediction error was a factor of 10. Furthermore, the approach was validated by analyzing two blind samples containing 48 compounds spiked into tap water and ultrapure water. The obtained mean prediction error was lower than a factor of 6.0 for both samples. The errors were found to be comparable to approaches using structural information.

Enhancement of sensitivity and quantification quality in the LC-MS/MS measurement of large biomolecules with sum of MRM (SMRM)

Liquid chromatography (LC) coupled with tandem mass spectrometry (MS/MS) provides a simple and efficient means for the measurement of analytes in biological matrices with high selectivity and specificity. LC-MS/MS plays an important role in the pharmaceutical industry and biomedical research, but it requires analytes to be in an ionized form in order to be detected. This can pose a challenge for large molecules such as proteins and peptides, because they can exist in multiple charged forms, and this will reduce the total analyte signal by distributing it into multiple ion peaks with a different number of charges in a mass spectrum. In conventional LC-MS/MS analysis of such macromolecules, one charged form is selected as the precursor ion which is then fragmented by collision-induced dissociation (CID) in MS/MS to generate product ions, a process referred to as multiple-reaction monitoring (MRM). The MRM method minimizes interference from endogenous molecules within biological matrices that share the same molecular weight of the precursor ion, but at the expense of signal intensity as compared to precursor ion intensity. We describe here an approach to boost detection sensitivity and expand dynamic range in the quantitation of large molecules while maintaining analytical specificity using summation of MRM (SMRM) transitions and LC separation technique. Protein image from PDB-101 (PDB101.rscb.org).

Quantitative non-targeted analysis: Bridging the gap between contaminant discovery and risk characterization

Chemical risk assessments follow a long-standing paradigm that integrates hazard, dose-response, and exposure information to facilitate quantitative risk characterization. Targeted analytical measurement data directly support risk assessment activities, as well as downstream risk management and compliance monitoring efforts. Yet, targeted methods have struggled to keep pace with the demands for data regarding the vast, and growing, number of known chemicals. Many contemporary monitoring studies therefore utilize non-targeted analysis (NTA) methods to screen for known chemicals with limited risk information. Qualitative NTA data has enabled identification of previously unknown compounds and characterization of data-poor compounds in support of hazard identification and exposure assessment efforts. In spite of this, NTA data have seen limited use in risk-based decision making due to uncertainties surrounding their quantitative interpretation. Significant efforts have been made in recent years to bridge this quantitative gap. Based on these advancements, quantitative NTA data, when coupled with other high-throughput data streams and predictive models, are poised to directly support 21st-century risk-based decisions. This article highlights components of the chemical risk assessment process that are influenced by NTA data, surveys the existing literature for approaches to derive quantitative estimates of chemicals from NTA measurements, and presents a conceptual framework for incorporating NTA data into contemporary risk assessment frameworks.

ESI-MS reveals preferential complex formation of carbohydrates with Z-sinapinic acid compared with the E-isomer

ZSA + carbohydrate complex preferential formation and higher stability (ESI) support the previously proposed model for ZSA differential efficiency as the MALDI-MS matrix.

Quantitative electrospray ionization efficiency scale: 10 years after

RATIONALE

The first comprehensive quantitative scale of the efficiency of electrospray ionization (ESI) in the positive mode by monoprotonation, containing 62 compounds, was published in 2010. Several trends were found between the compound structure and ionization efficiency (IE) but, possibly because of the limited diversity of the compounds, some questions remained. This work undertakes to align the new data with the originally published IE scale and carry out statistical analysis of the resulting more extensive and diverse data set to derive more grounded relationships and offer a possibility of predicting logIE values.

METHODS

Recently, several new IE studies with numerous compounds have been conducted. In several of them, more detailed investigations of the influence of compound structure, solvent properties, or instrument settings have been conducted. IE data from these studies and results from this work were combined, and the multilinear regression method was applied to relate IE to various compound parameters.

RESULTS

The most comprehensive IE scale available, containing 334 compounds of highly diverse chemical nature and spanning 6 orders of magnitude of IE, has been compiled. Several useful trends were revealed.

CONCLUSIONS

The ESI ionization efficiency of a compound by protonation is mainly affected by three factors: basicity (expressed by pK_aH in water), molecular size (expressed by molar volume or surface area), and hydrophobicity of the ion (expressed by charge delocalization in the ion or its partition coefficient between a water-acetonitrile mixture and hexane). The presented models can be used for tentative prediction of logIE of new compounds (under the used conditions) from parameters that can be computed using commercially available software. The root mean square error of prediction is in the range of 0.7-0.8 log units.

Towards a comprehensive characterisation of the human internal chemical exposome: Challenges and perspectives

The holistic characterisation of the human internal chemical exposome using high-resolution mass spectrometry (HRMS) would be a step forward to investigate the environmental ætiology of chronic diseases with an unprecedented precision. HRMS-based methods are currently operational to reproducibly profile thousands of endogenous metabolites as well as externally-derived chemicals and their biotransformation products in a large number of biological samples from human cohorts. These approaches provide a solid ground for the discovery of unrecognised biomarkers of exposure and metabolic effects associated with many chronic diseases. Nevertheless, some limitations remain and have to be overcome so that chemical exposomics can provide unbiased detection of chemical exposures affecting disease susceptibility in epidemiological studies. Some of these limitations include (i) the lack of versatility of analytical techniques to capture the wide diversity of chemicals; (ii) the lack of analytical sensitivity that prevents the detection of exogenous (and endogenous) chemicals occurring at (ultra) trace levels from restricted sample amounts, and (iii) the lack of automation of the annotation/identification process. In this article, we discuss a number of technological and methodological limitations hindering applications of HRMS-based methods and propose initial steps to push towards a more comprehensive characterisation of the internal chemical exposome. We also discuss other challenges including the need for harmonisation and the difficulty inherent in assessing the dynamic nature of the internal chemical exposome, as well as the need for establishing a strong international collaboration, high level networking, and sustainable research infrastructure. A great amount of research, technological development and innovative bio-informatics tools are still needed to profile and characterise the "invisible" (not profiled), "hidden" (not detected) and "dark" (not annotated) components of the internal chemical exposome and concerted efforts across numerous research fields are paramount.

Soil Organic Matter Characterization by Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FTICR MS): A Critical Review of Sample Preparation, Analysis, and Data Interpretation

The biogeochemical cycling of soil organic matter (SOM) plays a central role in regulating soil health, water quality, carbon storage, and greenhouse gas emissions. Thus, many studies have been conducted to reveal how anthropogenic and climate variables affect carbon sequestration and nutrient cycling. Among the analytical techniques used to better understand the speciation and transformation of SOM, Fourier transform ion cyclotron resonance mass spectrometry (FTICR MS) is the only technique that has sufficient mass resolving power to separate and accurately assign elemental compositions to individual SOM molecules. The global increase in the application of FTICR MS to address SOM complexity has highlighted the many challenges and opportunities associated with SOM sample preparation, FTICR MS analysis, and mass spectral interpretation. Here, we provide a critical review of recent strategies for SOM characterization by FTICR MS with emphasis on SOM sample collection, preparation, analysis, and data interpretation. Data processing and visualization methods are presented with suggested workflows that detail the considerations needed for the application of molecular information derived from FTICR MS. Finally, we highlight current research gaps, biases, and future directions needed to improve our understanding of organic matter chemistry and cycling within terrestrial ecosystems.

Guide to Semi-Quantitative Non-Targeted Screening Using LC/ESI/HRMS

Non-targeted screening (NTS) with reversed phase liquid chromatography electrospray ionization high resolution mass spectrometry (LC/ESI/HRMS) is increasingly employed as an alternative to targeted analysis; however, it is not possible to quantify all compounds found in a sample with analytical standards. As an alternative, semi-quantification strategies are, or at least should be, used to estimate the concentrations of the unknown compounds before final decision making. All steps in the analytical chain, from sample preparation to ionization conditions and data processing can influence the signals obtained, and thus the estimated concentrations. Therefore, each step needs to be considered carefully. Generally, less is more when it comes to choosing sample preparation as well as chromatographic and ionization conditions in NTS. By combining the positive and negative ionization mode, the performance of NTS can be improved, since different compounds ionize better in one or the other mode. Furthermore, NTS gives opportunities for retrospective analysis. In this tutorial, strategies for semi-quantification are described, sources potentially decreasing the signals are identified and possibilities to improve NTS are discussed. Additionally, examples of retrospective analysis are presented. Finally, we present a checklist for carrying out semi-quantitative NTS.

Synergistic sequence contributions bias glycation outcomes

The methylglyoxal-derived hydroimidazolone isomer, MGH-1, is an abundant advanced glycation end-product (AGE) associated with disease and age-related disorders. As AGE formation occurs spontaneously and without an enzyme, it remains unknown why certain sites on distinct proteins become modified with specific AGEs. Here, we use a combinatorial peptide library to determine the chemical features that favor MGH-1. When properly positioned, tyrosine is found to play an active mechanistic role that facilitates MGH-1 formation. This work offers mechanistic insight connecting multiple AGEs, including MGH-1 and carboxyethylarginine (CEA), and reconciles the role of negative charge in influencing glycation outcomes. Further, this study provides clear evidence that glycation outcomes can be influenced through long- or medium-range cooperative interactions. This work demonstrates that these chemical features also predictably template selective glycation on full-length protein targets expressed in mammalian cells. This information is vital for developing methods that control glycation in living cells and will enable the study of glycation as a functional post-translational modification.

Advanced glycation end-products (AGEs), such as methylglyoxal-derived hydroimidazolone isomer (MGH-1), are associated with disease and age-related disorders, and occur spontaneously, so it is unclear why specific protein sites become modified with specific AGEs. Here, the authors use a combinatorial peptide library to determine the chemical features that favour MGH-1 formation for short peptides and demonstrate a key role of tyrosine in this process.

Development and Application of a Novel Semi-quantification Approach in LC-QToF-MS Analysis of Natural Products

Use of high-resolution mass spectrometry (HRMS) including a MS calibration method has enabled simultaneous identification and quantification of knowns/unknowns. This has expanded our knowledge about the existing sample relevant chemical space in a way beyond reconciliation with a quantification task. This is largely due to fact that reference standards are not always available to achieve quantitative analysis. In this scenario, a semi-quantitative approach can fill the gap and provide a rough estimation of concentration. This research aimed to develop and compare several semi-quantification approaches based on chemical similarity or properties. The ionization efficiency scale was created for several groups of natural products. Advanced modeling approach based on a support vector machine was conducted to learn from the experimental ionization efficiency and apply it to unknowns or suspected compounds to predict their ionization efficiency in electrospray ionization mode. The developed semi-quantification workflows could be useful in most HRMS based "omics" areas, especially in natural products discovery.

An Automated Methodology for Non-targeted Compositional Analysis of Small Molecules in High Complexity Environmental Matrices Using Coupled Ultra Performance Liquid Chromatography Orbitrap Mass Spectrometry

The life-critical matrices of air and water are among the most complex chemical mixtures that are ever encountered. Ultrahigh-resolution mass spectrometers, such as the Orbitrap, provide unprecedented analytical capabilities to probe the molecular composition of such matrices, but the extraction of non-targeted chemical information is impractical to perform via manual data processing. Automated non-targeted tools rapidly extract the chemical information of all detected compounds within a sample dataset. However, these methods have not been exploited in the environmental sciences. Here, we provide an automated and (for the first time) rigorously tested methodology for the non-targeted compositional analysis of environmental matrices using coupled liquid chromatography-mass spectrometric data. First, the robustness and reproducibility was tested using authentic standards, evaluating performance as a function of concentration, ionization potential, and sample complexity. The method was then used for the compositional analysis of particulate matter and surface waters collected from worldwide locations. The method detected >9600 compounds in the individual environmental samples, arising from critical pollutant sources, including carcinogenic industrial chemicals, pesticides, and pharmaceuticals among others. This methodology offers considerable advances in the environmental sciences, providing a more complete assessment of sample compositions while significantly increasing throughput.

Quantitative analysis of the relationship of derivatization reagents and detection sensitivity of electrospray ionization-triple quadrupole tandem mass spectrometry: Hydrazines as prototypes

Chemical derivatization-assisted electrospray ionization-triple quadrupole mass spectrometry (ESI-QqQ-MS) has become an efficient tool for the quantification of low-molecular-weight molecules. Many studies found that the derivatives of the same analytes derivatized by different derivatization reagents with the same reaction group had different detection sensitivity, even under the same conditions of electrospray ionization-mass spectrometry (ESI-MS). This phenomenon was suggested to be caused by the different modifying groups in the derivatization reagents. However, there is still a lack of systematic study on how modifying groups in the derivatization reagents affect the detection sensitivity of their corresponding derivatives of analytes, especially theoretical investigations. In this study, we employed a quantitative structure-activity relationship (QSAR) modeling approach to explore the relationship between modifying group structures and the detection sensitivity of derivatization reagents and their derivatives during ESI-MS detection. A total of 110 derivatization reagents of the hydrazine family and their hexanal derivatives (substituted hydrazones) were selected as the prototypes to construct QSAR models. The established models suggested that several molecular descriptors, related to hydrophobicity, electronegativity, and molecular shape, were related to the detection sensitivity of hexanal derivatives induced by different modifying groups in the derivatization reagents. Besides, we found that the detection sensitivity of compounds detected in selected ion mode (SIM) showed a positive correlation with that obtained in multiple reaction monitoring mode (MRM), and the ionization efficiency was the key factor on the detection sensitivity in both modes.

Cholesterol was identified as a biomarker in human melanocytic nevi using DESI and DESI/PI mass spectrometry imaging

The rapid differentiation between diseased tissue and healthy normal tissue is of great importance for the intraoperative diagnosis. Herein, desorption electrospray ionization (DESI) and DESI/post-photoionization (DESI/PI) mass spectrometry imaging were combined to in situ visualize the distribution of biochemicals within the tissue regions of human melanocytic nevi under the ambient condition with a spatial resolution of around 200 μm. Plenty of polar and nonpolar lipids were found to be specifically distributed in melanocytic nevi with statistical significance and could be used to differentiate the healthy normal tissue and melanocytic nevi. Cholesterol was further confirmed to be a potential biomarker for melanocytic nevi diagnosis by multivariate statistical analysis and immunohistochemistry of 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) and translocator protein (TSPO) enzymes. This work provides a visual way for the diagnosis of human melanocytic nevi by lipid profiling, which benefits the understanding of the pathological mechanism of melanocytic nevi and provides a new insight to control melanin growth from the synthesis, transport, and metabolism of cholesterol.

Non-targeted screening of trace organic contaminants in surface waters by a multi-tool approach based on combinatorial analysis of tandem mass spectra and open access databases

Non-targeted screening (NTS) in mass spectrometry (MS) helps alleviate the shortcoming of targeted analysis such as missing the presence of concerning compounds that are not monitored and its lack of retrospective analysis to subsequently look for new contaminants. Most NTS workflows include high resolution tandem mass spectrometry (HRMS²) and structure annotation with libraries which are still limited. However, in silico combinatorial fragmentation tools that simulate MS² spectra are available to help close the gap of missing compounds in empirical libraries. Three NTS tools were combined and used to detect and identify unknown contaminants at ultra-trace levels in surface waters in real samples in this qualitative study. Two of them were based on combinatorial fragmentation databases, MetFrag and the Similar Partition Searching algorithm (SPS), and the third, the Global Natural Products Social Networking (GNPS), was an ensemble of empirical databases. The three NTS tools were applied to the analysis of real samples from a local river. A total of 253 contaminants were identified by combining all three tools: 209 were assigned a probable structure and 44 were confirmed using reference standards. The two major classes of contaminants observed were pharmaceuticals and consumer product additives. Among the confirmed compounds, octylphenol ethoxylates, denatonium, irbesartan and telmisartan are reported for the first time in surface waters in Canada. The workflow presented in this work uses three highly complementary NTS tools and it is a powerful approach to help identify and strategically select contaminants and their transformation products for subsequent targeted analysis and uncover new trends in surface water contamination.

Enhancing the power of liquid chromatography-Mass spectrometry for chemical fingerprinting of phytotoxins in the environment

Phytotoxins are plant secondary metabolites. They have recently been considered as chemicals of emerging concern (CECs) and there is a growing interest in their environmental fate and potential threat to public health. Dedicated target and non-target screening (NTS) analysis of phytotoxins in environmental samples are sparse, meanwhile phytotoxins are rarely detected in NTS-based analysis due to lack of an efficient methodology. Development of new analytical measurement methods is therefore highly needed. In this study, we for the first time investigated key parameters of reversed phase liquid chromatography-high resolution mass spectrometry (RPLC-HRMS) for five major classes of phytotoxins (alkaloids, steroids, terpenoids, flavonoids and aromatic polyketides) in environmental matrices; the investigation included analytical conditions which have not yet been explored by others, e.g. ionization at alkaline pH above 9. As the outcome we established a new analytical method for target/suspect screening and NTS of phytotoxins in the environment, which significantly improved the detection sensitivity with up to 40 times compared to previous methods, and enabled the discovery of over 30 phytotoxins in a NTS-based environmental study. We also observed that the negative ionization of phenols could be facilitated by the number of hydroxyl groups on the ring rather than their position of substitution. This study is of interest for a better fundamental understanding of the behavior of phytotoxins in LC-MS. Dedicated target/suspect screening and NTS methods will facilitate a better risk characterization of phytotoxins in the environment and stimulate implementation of new public regulation on phytotoxins.