Nontarget analysis of Bangladeshi surface water samples by LC-HRMS - analyzed by DIA [APCI]-
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Q Exactive HF-X
ORGANISM(S): Surface Water / Bangladesh
SUBMITTER: 
Jonathan W. Martin
PROVIDER: MSV000089704 | MassIVE | Wed Jun 22 03:03:00 BST 2022
REPOSITORIES: MassIVE
Similar Datasets
Project description:Nontarget analysis of Bangladeshi surface water samples by LC-HRMS - analyzed by DIA [APCI]+
Published here: https://doi.org/10.1021/acs.est.2c08200
2022-06-22 | MSV000089703 | MassIVE
Project description:Nontarget analysis of Bangladeshi surface water samples by LC-HRMS - analyzed by DIA [ESI]-
Published here: https://doi.org/10.1021/acs.est.2c08200
2022-06-22 | MSV000089706 | MassIVE
Project description:Nontarget analysis of Bangladeshi surface water samples by LC-HRMS - analyzed by DIA [ESI]+
Published here: https://doi.org/10.1021/acs.est.2c08200
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Project description:A conventional evaluation methodology for drinking water pollution focuses on analysing hundreds of compounds, usually by liquid chromatography-tandem mass spectrometry. High-resolution mass spectrometry allows comprehensive evaluation of all detected signals (compounds) based on their elemental composition, intensity, and numbers. We combined target analysis of 192 emerging micropollutants with nontarget (NT) full-scan/MS/MS methods to describe the impact of treatment steps in detail and assess drinking water treatment efficiency without compound identification. The removal efficiency based on target analytes ranged from - 143 to 97%, depending on the treatment section, technologies, and season. The same effect calculated for all signals detected in raw water by the NT method ranged between 19 and 65%. Ozonation increased the removal of micropollutants from the raw water but simultaneously caused the formation of new compounds. Moreover, ozonation byproducts showed higher persistence than products formed during other types of treatment. We evaluated chlorinated and brominated organics detected by specific isotopic patterns within the developed workflow. These compounds indicated anthropogenic raw water pollution but also potential treatment byproducts. We could match some of these compounds with libraries available in the software. We can conclude that passive sampling combined with nontargeted analysis shows to be a promising approach for water treatment control, especially for long-term monitoring of changes in technology lines because passive sampling dramatically reduces the number of samples and provides time-weighted average information for 2 to 4 weeks.
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Project description: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.
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Project description:Currently, evaluation of quality semen is based on traditional in vitro sperm tests as measures of motility, viability and acrosomic reaction. However, the results are not always well correlated with real male fertility. In the present study, we evaluated Intact Cell MALDI-TOF Mass Spectrometry (ICM-MS) method as diagnostic molecular tool to substitute in vitro tests in order to phenotype semen with more robustness. For that, we investigated a large male population of known fertility that included two highly different genetic lines (meat and egg laying lines). Our optimized ICM-MS-based method through automation and the construction of fertility-predictive mathematical models was able to discriminate individuals on their reproductive capacity in the two lines studied. Furthermore, we showed a better diagnostic accuracy than traditional in vitro sperm quality tests and demonstrated that differential markers presenting a high discriminating power between fertile and subfertile sperm cells in sperm were implicated in fertility processes. To characterize m/z peaks observed in ICM-MS spectra, we performed high-throughput top-down protein identification of sperm cell extracts. Therefore, we identified more than 75% of the m/z peaks, revealing that these comprises mostly degradation products of testis-specific proteins implicated in the most important functional pathways in sperm cells such as energy metabolism and structure/movement.
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