Best of the Week: ASMS Interviews, Blood Analysis, Oligonucleotides

This week, LCGC International published a variety of articles on the hottest topics in chromatography and beyond. Below, we’ve highlighted some of the most popular articles, according to our readers. Happy reading!

ASMS 2024: An Interview with Christopher Rüger of the University of Rostark

Aaron Acevedo

During ASMS 2024, many scientists will present research on the latest advancements in mass spectrometry. One scientist who will be presenting is Christopher Rüger of the University of Rostock. Rüger is a professor at the Institute of Chemistry in the University of Rostock in Rostock, Germany. Since July 2019, he has been a research group leader at the University of Rostock. His main research topics include high-resolution mass spectrometry, atmospheric-pressure ionisation (particularly photo/laser ionization), thermal analysis and other coupling techniques for gas-phase inlets, and development of software-applications for automated data analysis/processing. Here, we discuss his upcoming presentation, “Advances in Laser- and Lamp-based Photoionization High Resolution Mass Spectrometry: Novel Insights in Complex Mixtures in Energy and Environmental Research,” as well as his expectations for the ASMS conference.

Analyzing Dried Blood Spots for Steroids with LC-MS/MS

John Chasse

In a recent study published in Advanced Sample Preparation, scientists showed the potential liquid chromatography-tandem mass spectrometry (LC-MS/MS) has as a driver for sample preparation from dried blood spot (DBS) samples to quantify the steroids testosterone (T), 17α-hydroxyprogesterone (17-OHP), progesterone (P), and cortisol (C), simultaneously. The study also shows how electric field can be applied for metabolomic sample preparation and discusses devices that can eliminate the electric double layer effect in the process.

Advances and Challenges Relating to Hydrophilic Interaction Chromatography in Analyzing Therapeutic Oligonucleotides

John Chasse

Hydrophilic interaction chromatography (HILIC) is a liquid chromatography (LC) technique that uses a polar stationary phase in conjunction with a mobile phase containing an appreciable quantity of water combined with a higher proportion of a less polar solvent. As for therapeutic oligonucleotides (ONs), this has begun growing as a strong alternative for treating various diseases. Drug manufacturers view this approach as a rapidly expanding category of products that can evolve into personalized treatment. However, chemically synthesizing ON therapeutics has limited use and development. In this study, scientists from the Institute of Pharmaceutical Sciences of Western Switzerland at the University of Geneva (1), present recent advances, as well as current challenges, relating to HILIC for the analysis of therapeutic ONs.

Covalent Organic Frameworks Analyzed Using New Solid-Phase Extraction

Aaron Acevedo

Covalent organic frameworks (COFs) are a type of crystalline porous polymeric material that are constructed by organic monomers via different covalent linkages. Recently, COFs have been drawing increased attention in catalysis, sensing, gas storage, photoelectric behavior, energy storage, and especially separation applications. This stems from their various unique properties, such as readily pre–designable structure, predictable pore sizes, tailored functionalities, excellent chemical stability and large specific surface area. In this study, Chinese and American scientists tested a new solid-phase extraction system for analyzing COFs.

PLS-DA With Univariate Filtration Refines Discovery of Untargeted Metabolomics Data Using LC–MS

Patrick Lavery

Partial least squares–discriminant analysis (PLS-DA) is a supervised multivariate statistical method employed to identify and quantify differences between sample groups based on mathematical comparisons of their mass spectra. A recent study shows a newly created PLS-DA method for analyzing untargeted metabolomics data both preceded and followed by univariate filtration (UVA) and after multivariate analysis (MVA) for differential features selection.