Islets of Langerhans are the endocrine portion of the pancreas, which release different peptide hormones to control blood glucose. Release of these hormones is perturbed in a number of metabolic diseases, highlighting the necessity for understanding the biology of this tissue. Several notable analytical approaches have been developed to study islets and their secreted hormones, including the Nobel Prize-winning discovery of insulin, the determination of its amino acid sequence, and the development of the radioimmunoassay. Advances in islet research are propelled by progress in analytical methodologies; new tools and technologies are continually being refined by emerging biological insights, creating an ideal environment for discovery. In this article, we provide a critical review of analytical advances in the analysis of islets of Langerhans over the past 5 years. Topics include advances in functional testing, proteomics, metabolomics, mass spectrometry imaging, single-cell -omics, and multimodal detection.

To circumvent multiple challenges associated with Cannabis analysis by conventional methods such as gas chromatography (GC), liquid chromatography (LC), and hyphenated techniques such as GC and LC mass spectrometry (MS), there is increasing interest in the application of ambient ionization mass spectrometry (AIMS) for its chemical characterization, as this approach can in principle address several of the issues associated with interrogation of Cannabis-derived complex matrix samples. Among the advantages that these methods confer are rapid analysis times; limited or no need for sample pretreatment steps; detection of a range of compound classes in a single analysis, including cannabinoids, terpenes, flavonoids, and pesticides; avoidance of nuanced method development tailored to particular analyte classes; and the ability to analyze samples in their native forms. This review highlights the progress thus far in the nascent area of application of AIMS approaches to Cannabis and Cannabis-derived materials, and the further developments required in order for AIMS methods to be more widely adopted for routine analysis.

Immunometabolomics is a multidisciplinary field that explores how metabolic pathways regulate immune cell function, using metabolomics-the large-scale analysis of small molecules (metabolites)-to map these interactions in health and disease. Emerging evidence highlights that metabolic shifts are not merely by-products but key drivers of exercise-induced immune adaptations, with significant implications for performance, recovery, and disease prevention. This narrative review summarizes the latest findings on how exercise shapes immune responses through metabolic pathways. We discuss how key metabolites, such as succinate, itaconate, lactate, short-chain fatty acids, and kynurenine, act as molecular links between energy metabolism and immune regulation during and after exercise. We also investigate the effects of physical exercise on immunometabolic profiles within distinct tissues, elucidating their roles in promoting either proinflammatory or anti-inflammatory adaptations. Methodological advances in metabolomics and multi-omics are also addressed. Our review highlights robust evidence from human trials that physical exercise reprograms immunometabolic pathways in a time-, tissue-, and modality-specific manner, supporting its role in both health maintenance and clinical interventions.

Carbon nanofibers (CNFs) possess inherent 3D-porous architecture that facilitates the development of highly sensitive electrochemical transducers. Their fabrication via laser-induced carbonization provides advantages such as mass production capability, outstanding electroanalytical performance, and preserved full beneficial features even when integrated into miniaturized systems. Moreover, the overall manufacturing cost is relatively low in comparison to conventional techniques, making the laser-generated CNFs favorable as electrochemical transducers in point-of-care testing (POCT). This review highlights the key studies representing the formation of CNF electrodes and their functional hybrids realized from laser-carbonization technology, which reflect a clear new trend in the field. The morphologies of laser-generated CNF electrodes from various substrates, how they can be conveniently integrated into POCT devices, and their superior analytical performance are discussed. The mass-producible 3D-porous CNF electrodes via laser carbonization will pave the way for efficient translation into the next generation of POCT devices, not only for sensing but also for self-powering devices.

Hyaluronan (HA) is an essential polysaccharide found throughout nature where it serves diverse biological roles in a broad range of fundamental biological processes and diseases. A critical aspect of HA is its molecular weight (MW), which varies from a few disaccharides to polymers tens of megadaltons in length. Because the behavior of HA depends distinctly on size, there is great interest in the accurate determination of its MW distribution in different conditions. A solution is presented by the solid-state nanopore platform in which HA molecules are probed individually as they pass electrically through a fabricated pore. The approach has been built into a sensitive and quantitative tool for HA MW assessment through process optimization and coupling with specific HA extraction protocols. These developments have enabled it to be applied to several outstanding biological questions, including the emergent role of heavy chain-modified HA.

Autonomous systems integrating machine learning (ML) and laboratory automation are transforming synthetic chemistry by enabling closed-loop experimentation and discovery. In this review, we examine the state-of-the-art in autonomous systems for organic synthesis, with a focus on the components, configurations, and ML algorithms that enable automated reaction planning, execution, and optimization. We survey representative systems that span applications from reaction discovery to molecular optimization, comparing flow and batch configurations and identifying trends in system design. Emphasis is placed on the critical bottlenecks of purification and analytical measurement, particularly structural elucidation of unexpected products-areas that currently constrain autonomous platforms. We describe recent advances in chromatographic method development, structural elucidation from mass spectrometry and nuclear magnetic resonance, and novel ML-based approaches to quantify complex mixtures without calibration. By focusing on enabling technologies in chemical analysis, we identify opportunities for ML and automation to expand beyond domain-specific platforms and accelerate the pace of synthetic discovery.

Mental health disorders, such as depression, represent a growing global challenge. Depression is difficult to diagnose and treat. These difficulties stem from an insufficient understanding of the underlying mechanisms of the disorder. It is incredibly challenging to improve diagnostic and therapeutic approaches without a clear understanding of depression pathology. Neurotransmitters are low in concentration and fluctuate rapidly, making them difficult to investigate. Progress in methods to study the brain has uncovered clues to the pathology of depression and antidepressant mechanisms. In this review, we first describe the three medical hypotheses of depression: the monoamine, plasticity, and inflammation theories. We highlight key analytical methods that have been employed in depression studies. Lastly, we show how these investigations have advanced our knowledge of depression mechanisms and treatment strategies. Thus, via this review, we present the status quo of how chemical measurements are guiding our understanding of the chemical underpinnings of depression and pointing the community toward new antidepressant treatment targets.

Vibrational circular dichroism (VCD) is an established chiroptical technique that probes molecular handedness via differential IR absorption of left- and right-circularly polarized light. Quantum cascade lasers (QCLs) have revitalized VCD spectroscopy by delivering high-power, narrowband mid-IR sources that, combined with polarization-modulation strategies, have dramatically improved VCD sensitivity and speed-enabling imaging that was not previously attainable. We review the instrumental design of QCL-based VCD imaging and demonstrate its application to spatially resolved chiral biosensing. By mapping VCD signals with micrometer resolution, one can detect and differentiate protein secondary structures, monitor enantiomeric purity in pharmaceutical compounds, and visualize pathological tissue features without labels. We discuss practical challenges, including cell-window birefringence, polarization-sensitive detection, and data processing, and propose optimized configurations for robust imaging. Finally, we outline future directions for QCL-VCD systems and their integration with nonlinear chiroptical techniques, highlighting the potential of QCL-VCD imaging to transform chiral analysis in biological and clinical contexts.

Gas-phase ion-ion reactions lead to well-defined changes in mass and charge that are readily detected via mass spectrometry, they have unusually large cross-sections, which allows for rates on the order of 1–1000 s−1, and, as a result enable a variety of analytically useful measurements. Such applications rely on one or more of a variety of reaction mechanisms, such as proton transfer, electron transfer, metal ion transfer, and selective covalent bond formation. Electrodynamic ion traps make excellent reaction vessels for ion-ion reactions due to their ability to trap one or both polarities of ions, thereby allowing reactions to proceed with high reactant to product conversion. Understanding the underlying phenomena of ion-ion reactions, as well as conditions under which they proceed, is essential to designing future experiments. This tutorial review summarizes the underlying phenomena of gas-phase ion-ion reactions and relates practical considerations needed to optimize these reactions in an electrodynamic ion trap.

The rapid and continued development of mass spectrometry–based technologies has significantly increased the capability to study and characterize lipids while providing new insight into the complex roles of lipids throughout biology. These capabilities have included the ability to quantify, structurally characterize (including resolving isomers that pose significant challenges to lipidomics), and spatially map lipids within numerous complex organisms, revealing new capabilities in emerging areas such as single-cell analysis. With these rapid developments, several challenges have emerged, such as accurate lipid identification, incorrect and overinterpretation of mass spectrometry data and structural assignments, and the need for improved analytical and bioinformatics tools to understand lipidomics data at the pathway and systems levels. This review critically assesses analytical technologies used for lipidomics studies, along with current challenges and technological developments driving the field forward. By highlighting these challenges, and possible avenues to address them, this review emphasizes the excitement for the future of lipidomics and the need for continued development of analytical tools to enhance our understanding lipid biology.