Mass spectrometry (MS) analysis is often challenged by contaminations from detergents, salts, and polymers that compromise data quality and can damage the chromatography and MS instruments. However, researchers often discover contamination issues only after they acquire the data. There is no existing contaminant assay that is sensitive enough to detect trace amounts of contaminants from a few microliters of samples prior to MS analysis. To address this crucial need in the field, we developed a sensitive, rapid, and cost-effective contaminant spot check and removal assay (ContamSPOT) to detect and quantify trace amounts of contaminants, such as detergents, salts, and other chemicals commonly used in the MS sample preparation workflow. Only 1 μL of the sample was used prior to MS injection to quantify contaminants by ContamSPOT colorimetric or fluorometric assay on a thin layer chromatography (TLC) plate. We also optimized contaminant removal methods to salvage samples with minimal loss when ContamSPOT showed a positive result. ContamSPOT was then successfully applied to evaluate commonly used bottom-up proteomic methods regarding the effectiveness of removing detergent, peptide recovery, reproducibility, and proteome coverage. We expect ContamSPOT to be widely adopted by MS laboratories as a last-step quality checkpoint prior to MS injection. We provided a practical decision tree and a step-by-step protocol with a troubleshooting guide to facilitate the use of ContamSPOT by other researchers. ContamSPOT can also provide a unique readout of sample cleanliness for developing new MS-based sample preparation methods in the future.
This study aimed to develop and incorporate a secondary electrospray ionization (SESI) setup in combination with both targeted tandem mass spectrometry (MS/MS) and a hybrid metabolomics technique, globally optimized targeted mass spectrometry (GOT-MS), to sensitively detect volatile metabolites from the headspace of in vitro gut microbial culture in a human colonic model (HCM). Two SESI-tandem mass spectrometry panels with a comparable number of targeted metabolites/features (77 compounds in the targeted SESI-MS/MS panel and 75 features in the SESI-GOT-MS/MS panel) were established. The analytical performance of the SESI-GOT-MS/MS method, as well as its biological capability, were examined and compared with the targeted SESI-MS/MS method. As a result, the SESI-GOT-MS/MS method detected a similar number of metabolic features with good reproducibility (coefficient of variation <10%) compared to the targeted SESI-MS/MS method. Both methods showed a comparable ability to differentiate the gut microbial culture with or without the addition of green tea extract (GTE) to a HCM. The results from analysis of variance (ANOVA) showed that similar numbers of compounds from targeted SESI-MS/MS and metabolic features from SESI-GOT-MS/MS have significant differences when comparing samples collected from different HCM treatment stages. Partial least-squares discriminant analysis (PLS-DA) indicated that both methods could clearly differentiate the stages of GTE treatment. In summary, we demonstrated that SESI-MS/MS in combination with either targeted or GOT approaches can be a useful tool for monitoring gut microbial metabolism and their response to perturbations.
Gun ranges in the United States are underinvestigated regarding indoor air quality (IAQ) and energy efficiency. Navy Environmental Health Center (NEHC), National Guard Bureau (NGB), and Air Force Civil Engineer Support Agency (AFCESA) recommend using 100% outdoor air, but this measure will cause prohibitive energy costs since indoor gun ranges must maintain a low air velocity ranging from 0.25 to 0.36 m/s and a supply temperature close to the range target temperature. Hence, typical commercial firing ranges use a fixed outdoor air ratio (OAR) ranging from 25% to 30% together with high-efficiency particulate air filtration to save energy. This OAR is arbitrary and not supported by any standards or guidelines. To obtain reliable control of contaminant levels and better energy efficiency, we propose a model-based demand control ventilation feedback control strategy in which carbon monoxide is monitored and controlled as a proper IAQ indicator around the clock. Our 52-day field study demonstrates that our demand control ventilation control strategy can ensure proper IAQ, reduce the OAR from 30% to as low as 2.5%, and cut up to 85% cooling load and 70% heating load introduced by ventilation.
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