Recent trends in two-dimensional liquid chromatography

Multi-dimensional liquid chromatography (MD-LC) continues to gain in popularity for applications where conventional one-dimensional liquid chromatography is insufficient to solve the analytical problem at hand. In this review we have focused on articles published in the years 2019 to early 2023 and look for trends using our previous review published in 2018 as a baseline. We have also explored usage patterns related to involvement of industrial laboratories in the published research. The two major areas of technical development have been continued work on modulation strategies that help mitigate problems associated with mobile phase mismatch when coupling complementary separation mechanisms, and development of computer-aided method development strategies. Progress in these areas is making 2D-LC easier to use, and it appears that this is translating to a shift toward more involvement by industrial laboratories. Indeed, over 34% of the more than 200 publications on 2D-LC in the last four years have had at least one-industry affiliated author. A recent inter-laboratory comparison study focused on the performance of a sophisticated multi-stage, multi-dimensional separation for therapeutic protein characterization is an exemplary indication of the increasing investment of industrial laboratories to MD-LC, and we expect this trend to continue for the foreseeable future

Sample transformation in online separations:how chemical conversion advances analytical technology

While the advent of modern analytical technology has allowed scientists to determine the complexity of mixtures, it also spurred the demand to understand these sophisticated mixtures better. Chemical transformation can be used to provide insights into properties of complex samples such as degradation pathways or molecular heterogeneity that are otherwise unaccessible. In this article, we explore how sample transformation is exploited across different application fields to empower analytical methods. Transformation mechanisms include molecular-weight reduction, controlled degradation, and derivatization. Both offline and online transformation methods have been explored. The covered studies show that sample transformation facilitates faster reactions (e.g. several hours to minutes), reduces sample complexity, unlocks new sample dimensions (e.g. functional groups), provides correlations between multiple sample dimensions, and improves detectability. The article highlights the state-of-the-art and future prospects, focusing in particular on the characterization of protein and nucleic-acid therapeutics, nanoparticles, synthetic polymers, and small molecules

Characterization and comparison of smokeless powders by on-line two-dimensional liquid chromatography

Smokeless powders (SPs) are one of the most commonly used propellants for ammunition but can also be abused as energetic material in improvised explosive devices (IEDs) such as pipe bombs. After a shooting or explosion, unburnt or partially burnt particulates may be observed which can be used for forensic investigation. SPs comprise mainly nitrocellulose (NC) and additives. Therefore, the characterization of both NC and the additives is of significant forensic importance. Typically, the identification, classification, and chemical profiling of smokeless powders are based exclusively on the analysis of the additives. In this study, information regarding the NC base component was combined with the chemical analysis of the additives using two-dimensional liquid chromatography (2D-LC). The system combines size-exclusion chromatography (SEC) and reversed-phase liquid chromatography (RPLC) in an on-line heart-cut 2D-LC configuration. In the first dimension, the NC is characterized by its molecular-weight distribution (MWD) while being separated from the additives. The additives are then transferred to the second-dimension separation using a novel analyte-transfer system. In the second dimension, the additives are separated to obtain a detailed profile of the low-molecular-mass compounds in the SP. With this approach, the MWD of the NC and the composition of the additives in SP have been obtained within an hour. A discrimination power of 90.53% was obtained when studying exclusively the NC MWD, and 99.47% for the additive profile. This novel combination enables detailed forensic comparison of intact SPs. Additionally, no extensive sample preparation is required, making the developed method less labor intensive

On-line microfluidic immobilized-enzyme reactors: A new tool for characterizing synthetic polymers

Biodegradable polymeric materials may eventually replace biostable materials for medical applications, including therapeutic devices, scaffolds for tissue engineering, and drug-delivery vehicles. To further develop such materials, a more fundamental understanding is necessary to correlate parameters including chemical-composition distribution within a macromolecular structure with the final properties of the material, including particle-size. A wide variety of analytical techniques have been applied for the characterization of polymer materials, including hyphenated techniques such as comprehensive two-dimensional liquid chromatography (LC × LC). In this context, we have investigated enzymatic degradation of polyester-based nanoparticles, both in-solution and by the use of an immobilized-enzyme reactor (IMER). We have demonstrated for the first time the implementation of such an IMER in a size-exclusion chromatography system for on-line degradation and subsequent analysis of the polymer degradation products. The effect of residence times ranging from 12 s to 4 min on polymer degradation was assessed. IMER-assisted degradation is much faster compared to in-solution degradation, which requires several hours to days, and opens the possibility to use such reactors in LC × LC modulation interfaces

Predicting the functional states of human iPSC-derived neurons with single-cell RNA-seq and electrophysiology

Human neural progenitors derived from pluripotent stem cells develop into electrophysiologically active neurons at heterogeneous rates, which can confound disease-relevant discoveries in neurology and psychiatry. By combining patch clamping, morphological and transcriptome analysis on single-human neurons in vitro, we defined a continuum of poor to highly functional electrophysiological states of differentiated neurons. The strong correlations between action potentials, synaptic activity, dendritic complexity and gene expression highlight the importance of methods for isolating functionally comparable neurons for in vitro investigations of brain disorders. Although whole-cell electrophysiology is the gold standard for functional evaluation, it often lacks the scalability required for disease modeling studies. Here, we demonstrate a multimodal machine-learning strategy to identify new molecular features that predict the physiological states of single neurons, independently of the time spent in vitro. As further proof of concept, we selected one of the potential neurophysiological biomarkers identified in this study-GDAP1L1-to isolate highly functional live human neurons in vitro