Ultrafast analysis of peptides by laser diode thermal desorption-triple quadrupole mass spectrometry.

RATIONALE: The COVID-19 pandemic demonstrated the importance of high-throughput analysis for public health. Given the importance of surface viral proteins for interactions with healthy tissue, they are targets of interest for mass spectrometry-based analysis. For that reason, the possibility of detecting and quantifying peptides using a high-throughput technique, laser diode thermal desorption-triple quadrupole mass spectrometry (LDTD-QqQMS), was explored. METHODS: Two peptides used as models for small peptides (leu-enkephalin and endomorphin-2) and four tryptic peptides (GVYYPDK, NIDGYFK, IADYNYK, and QIAPGQTGK) specific to the SARS-CoV-2 Spike protein were employed. Target peptides were analyzed individually in the positive mode by LDTD-QqQMS. Peptides were quantified by internal calibration using selected reaction monitoring transitions in pure solvents and in samples spiked with 20 µg mL-1 of a bovine serum albumin tryptic digest to represent real analysis conditions. RESULTS: Low-energy fragment ions (b and y ions) as well as high-energy fragment ions (c and x ions) and some of their corresponding water or ammonia losses were detected in the full mass spectra. Only for the smallest peptides, leu-enkephalin and endomorphin-2, were [M + H]+ ions observed. Product ion spectra confirmed that, with the experimental conditions used in the present study, LDTD transfers a considerable amount of energy to the target peptides. Quantitative analysis showed that it was possible to quantify peptides using LDTD-QqQMS with acceptable calibration curve linearity (R2 > 0.99), precision (RSD < 18.2%), and trueness (bias < 8.3%). CONCLUSIONS: This study demonstrated for the first time that linear peptides can be qualitatively and quantitatively analyzed using LDTD-QqQMS. Limits of quantification and dynamic ranges are still inadequate for clinical applications, but other applications where higher levels of proteins must be detected could be possible with LDTD. Given the high-throughput capabilities of LDTD-QqQMS (>15 000 samples in less than 43 h), more studies are needed to improve the sensitivity for peptide analysis of this technique.

Application of Spectral Accuracy to Improve the Identification of Organic Compounds in Environmental Analysis.

Correct identification of a chemical substance in environmental samples based only on accurate mass measurements can be difficult especially for molecules >300 Da. Here is presented the application of spectral accuracy, a tool for the comparison of isotope patterns toward molecular formula generation, as a complementary technique to assist in the identification process of organic micropollutants and their transformation products in surface water. A set of nine common contaminants (five antibiotics, an herbicide, a beta-blocker, an antidepressant, and an antineoplastic) frequently found in surface water were spiked in methanol and surface water extracts at two different concentrations (80 and 300 µg L-1). They were then injected into three different mass analyzers (triple quadrupole, quadrupole-time-of-fight, and quadrupole-orbitrap) to study the impact of matrix composition, analyte concentration, and mass resolution on the correct identification of molecular formulas using spectral accuracy. High spectral accuracy and ranking of the correct molecular formula were in many cases compound-specific due principally to conditions affecting signal intensity such as matrix effects and concentration. However, in general, results showed that higher concentrations and higher resolutions favored ranking the correct formula in the top 10. Using spectral accuracy and mass accuracy it was possible to reduce the number of possible molecular formulas for organic compounds of relative high molecular mass (e.g., between 400 and 900 Da) to less than 10 and in some cases, it was possible to unambiguously assign one specific molecular formula to an experimental isotopic pattern. This study confirmed that spectral accuracy can be used as a complementary diagnostic technique to improve confidence levels for the identification of organic contaminants under environmental conditions.

Identifying congeners and transformation products of organic contaminants within complex chemical mixtures in impacted surface waters with a top-down non-targeted screening workflow.

Over 350,000 compounds are registered for production and use including a high number of congeners found in complex chemical mixtures (CCMs). With such a high number of chemicals being released in the environment and degraded into transformation products (TPs), the challenge of identifying contaminants by non-targeted screening (NTS) is massive. "Bottom-up" studies, where compounds are subjected to conditions simulating environmental degradation to identify new TPs, are time consuming and cannot be relied upon to study the TPs of hundreds of thousands of compounds. Therefore, the development of "top-down" workflows, where the structural elucidation of unknown compounds is carried directly on the sample, is of interest. In this study, a top-down NTS workflow was developed using molecular networking and clustering (MNC). A total of 438 compounds were identified including 176 congeners of consumer product additives and 106 TPs. Reference standards were used to confirm the identification of 53 contaminants among them lesser-known pharmaceuticals (aliskiren, sitagliptin) and consumer product additives (lauramidopropyl betaine, 2,2,4-trimethyl-1,2-dihydroquinoline). The MNC tools allowed to group similar TPs and congeners together. As such, several previously unknown TPs of pesticides (metolachlor) and pharmaceuticals (gliclazide, irbesartan) were identified as tentative candidates or probable structures. Moreover, some congeners that had no entry on global repositories (PubChem, ChemSpider) were identified as probable structures. The workflow worked efficiently with oligomers containing ethylene oxide moieties, and with TPs structurally related to their parent compounds. The top-down approach shown in this study addresses several issues with the identification of congeners of industrial compounds from CCMs. Furthermore, it allows elucidating the structure of TPs directly from samples without relying on bottom-up studies under conditions discussed herein. The top-down workflow and the MNC tools show great potential for data mining and retrospective analysis of previous NTS studies.

Uncovering transformation products of four organic contaminants of concern by photodegradation experiments and analysis of real samples from a local river.

In this study, photodegradation experiments simulating the exposure conditions of sunlight on the commonly detected in surface and wastewater contaminants atorvastatin (ATV), bezafibrate (BEZ), oxybenzone (OXZ), and tris(2-butoxyethyl)phosphate (TBEP) were conducted as the fate of these compounds and their transformation products (TPs) was followed. Then a nontargeted analysis was carried out on an urban river to confirm the environmental occurrence of the TPs after which the ECOSAR software was used to generate predicted effect levels of toxicity of the detected TPs on aquatic organisms. Five TPs of ATV were tentatively identified including two stable ones at the end of the experiment: ATV_TP557a and ATV_TP575, that were the product of hydroxylation. Complete degradation of OXZ was observed in the experiment with no significant TP identified. BEZ remained stable and largely undegraded at the end of the exposure. Five TPs of TBEP were found including four that were stable at the end of the experiment: TBEP_TP413, TBEP_TP415, TBEP_TP429, and TBEP_TP343. In the nontargeted analysis, ATV_TP557b, a positional isomer of ATV_TP557a, ATV_TP575 and the 5 TPs of TBEP were tentatively identified. The predicted concentration for effect levels were lower for ATV_TP557b compared to ATV indicating the TP is potentially more toxic than the parent compound. All the TPs of TBEP showed lower predicted toxicity toward aquatic organisms than their parent compound. These results highlight the importance of conducting complete workflows from laboratory experiments, followed by nontargeted analysis to confirm environmental occurrence to end with predicted toxicity to better communicate concern of the newfound TPs to monitoring programs.

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.