The ability to acquire high-quality spatially-resolved mass spectrometry data is sought in many fields of study, but it often comes with high cost of instrumentation and a high level of expertise required. In addition, techniques highly regarded for isotopic analysis applications such as thermal ionization mass spectrometry (TIMS) do not have the ability to acquire spatially-resolved data. Another drawback is that for radioactive materials, which are often of interest for isotopic analysis in geochemistry and nuclear forensics applications, high-end instruments often have restrictions on radioactivity and non-dispersibility requirements. We have applied the use of a traditional microanalysis tool, the focused ion beam/scanning electron microscope (FIB/SEM), for preparation of radioactive materials either for direct analysis by spatially-resolved instruments such as secondary ion mass spectrometry (SIMS) and laser ablation inductively-coupled mass spectrometry (LA-ICP-MS), or similarly to provide some level of spatial resolution to techniques that do not inherently have that ability such as TIMS or quadrupole inductively coupled plasma mass spectrometry (Q-ICP-MS). We applied this preparation technique to various uranium compounds, which was especially useful for reducing sample sizes and ensuring non-dispersibility to allow for entry into non-radiological or ultra-trace facilities. Our results show how this site-specific preparation can provide spatial context for nominally bulk techniques such as TIMS and Q-ICP-MS. In addition, the analysis of samples extracted from a uranium dioxide fuel pellet via all methods, but especially NanoSIMS and LA-ICP-MS, showed enrichment heterogeneities that are important for nuclear forensics and are of interest for fuel performance.
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http://dx.doi.org/10.1016/j.talanta.2020.120720 | DOI Listing |
J Proteome Res
January 2025
Discovery Research, AbbVie, Inc., 1 North Waukegan Rd., North Chicago, Illinois 60064, United States.
Affinity capture (AC) combined with mass spectrometry (MS)-based proteomics is highly utilized throughout the drug discovery pipeline to determine small-molecule target selectivity and engagement. However, the tedious sample preparation steps and time-consuming MS acquisition process have limited its use in a high-throughput format. Here, we report an automated workflow employing biotinylated probes and streptavidin magnetic beads for small-molecule target enrichment in the 96-well plate format, ending with direct sampling from EvoSep Solid Phase Extraction tips for liquid chromatography (LC)-tandem mass spectrometry (MS/MS) analysis.
View Article and Find Full Text PDFIntensive Care Med
January 2025
Center for Disease Mechanisms Research, Faculdade de Medicina da Universidade de Lisboa, Lisbon, Portugal.
Purpose: Major cardiovascular surgery imposes high physiologic stress, often causing severe organ dysfunction and poor outcomes. The underlying mechanisms remain unclear. This study investigated metabolic changes induced by major cardiovascular surgery and the potential role of identified metabolic signatures in postoperative acute kidney injury (AKI).
View Article and Find Full Text PDFAnal Chem
January 2025
Instrumental Analytical Chemistry, University of Duisburg-Essen, Universitätsstraße 5, 45141 Essen, Germany.
Compound-specific stable isotope analysis (CSIA) using liquid chromatography-isotope ratio mass spectrometry (LC-IRMS) is a powerful tool for determining the isotopic composition of carbon in analytes from complex mixtures. However, LC-IRMS methods are constrained to fully aqueous eluents. Previous efforts to overcome this limitation were unsuccessful, as the use of organic eluents in LC-IRMS was deemed impossible.
View Article and Find Full Text PDFJ Exp Bot
January 2025
KU Leuven, BIOSYST-MeBioS, Willem de Croylaan 42, 3001 Leuven, Belgium.
Tomato (Solanum lycopersicum L.) is an important model plant whose fleshy fruit consists of well-differentiated tissues. Recently it was shown that these tissues develop hypoxia during fruit development and ripening.
View Article and Find Full Text PDFAnal Chem
January 2025
State Key Laboratory of Cellular Stress Biology, Institute of Artificial Intelligence, School of Life Sciences, Faculty of Medicine and Life Sciences, National Institute for Data Science in Health and Medicine, XMU-HBN skin biomedical research center, Xiamen University, Xiamen, Fujian 361102, China.
In metabolomic analysis based on liquid chromatography coupled with mass spectrometry, detecting and quantifying intricate objects is a massive job. Current peak picking methods still cause high rates of incorrectly picked peaks to influence the reliability and reproducibility of results. To address these challenges, we developed QuanFormer, a deep learning method based on object detection designed to accurately quantify peak signals.
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