Mass Spectrometry Reviews最新文献

Mass spectrometry (MS) is a widely recognized analytical technique that can be applied to both large and small molecules and is a potent analytical tool that advances both basic science and practical applications in and out of the lab. MS has become a desirable analytical technique in clinical and bioanalytical labs with its high sensitivity, broad applicability, and throughput benefits. Numerous MS approaches, combined with various separation methods including gas and liquid chromatography, have been proposed to examine intricate matrices. Nevertheless, this technique could be limited by the labor-intensive and drawn-out sample preparation needed before MS analysis. By enabling the direct MS analysis of complex materials under ambient settings with little to no sample pretreatment, AIMS has cut down on both analysis time and expense. Nonetheless, these methods' speed and simplicity make them appropriate for use in critical care and point-of-care testing. Because of this, ambient ionization mass spectrometry (AIMS) has inevitably appealed to a wide range of scientists. The field of AIMS has grown tremendously since its foundation in the mid-2000s, marked by the emergence of numerous new ion sources, a plethora of fascinating applications, and a discernible increase in interest from various fields of study. Around the world, ambient ionization techniques are becoming more and more widespread in laboratories as the science advances yearly. This review focuses on the recent bioanalytical applications of AIMS in fields such as clinics, drugs, forensics, food, and environment, with a specific emphasis on the research reported in manuscripts published during the last 5 years. Although certain methodological developments are mentioned in this review, its primary goal is to demonstrate the practical applications of AIMS in bioanalytical fields. A timely overview of AIMS-based procedures and their application in various sample situations is what this endeavor seeks to give bioanalytical researchers. Moreover, several cutting-edge biological applications have been highlighted to explain how the AIMS area can develop in the future. In this chapter, readers will be provided with a quick overview of the main AIMS methodologies and their recent applied use cases.

Glycosylation is an abundant post-translational modification that impacts a wide variety of functions, including protein regulation, cell adhesion, and structural integrity. The application of proteomics methods to glycopeptide assignment faces unique challenges due to high heterogeneity, which results in complex populations with low overall abundance per glycopeptide. In addition, glycans dissociate at a lower collision energy compared to their attached peptide component. The resulting mass spectral data require specialized assignment software, which has caused glycoproteomics to lag traditional proteomics. Existing software primarily focuses on data-dependent acquisition (DDA), but manual validation is frequently required, and experiments are necessarily limited by the stochastic nature of DDA ion-selection. Data-independent acquisition (DIA) allows for a more complete and robust analysis of glycopeptide samples, but analysis software is still sparse. In this review, we discuss the current state of DDA analysis software, the limitations, and how it can inform our forays into DIA glycoproteomics.

Liquid chromatography-mass spectrometry (LC-MS) has become an indispensable tool for elucidating molecular structures and quantifying diverse compounds within complex mixtures. Despite its versatility, it faces various challenges such as ion suppression, low sensitivity, analyte instability, and matrix effects, which are being overcome by different kinds of offline and online derivatization techniques to improve specificity and reduce potential interferences. In this context, considerable advancements have been made in reviewing and critically evaluating a wide range of developed methods and techniques; however, little attention has been given to post-column derivatization (PCD) in LC-MS. Therefore, this comprehensive review highlights state-of-the-art advancements in LC-MS with a specific focus on various types of chemical and physical PCD, and in-source derivatization. It also examines the latest instrumentation developments, highlights methods and influencing factors, and explores applications in food, proteomics, biology, pharmaceuticals, and environmental analysis from the past four decades. Besides, this review critically examines the role of PCD in LC-MS along with outlining its advantages and disadvantages. Furthermore, special emphasis is also made on prospects and insights for developing more versatile LC-PCD-MS techniques and in-source methodologies, to address ongoing challenges and aim to open new research avenues for analysts.

Lipopolysaccharides (LPS) are complex molecules embedded in the outer membrane of Gram-negative bacteria. LPS are highly heterogeneous across species and strains, posing a significant analytical challenge. This review article explores recent advances in the identification and characterization of LPS, with a particular focus on the role of mass spectrometry (MS) techniques. The review highlights how MS, in conjunction with various separation methods and spanning different ionization techniques and dissociation modes, has enabled more precise and sensitive determination of LPS composition, with a focus on lipid A structure. Finally, emerging trends in MS applications for LPS research are discussed and its potential to provide deeper insights into the development of antibiotic resistance among pathogenic bacteria.

Mass spectrometry-based proteomics is essential for advancing preventive and personalised medicine. Technological advancements have greatly increased both the number and sensitivity of spectra generated in a single experiment. Traditionally, spectra are identified using database search engines that depend on large and continuously expanding databases. This expansion enlarges the search space, posing challenges for controlling the false discovery rate in peptide identification. While many bioinformatic workflows employ rescoring algorithms as a post-processing step to manage false discoveries, preprocessing spectra offers a promising alternative. One such method, spectral quality assessment, classifies spectra as “high” quality (likely containing a peptide) or “low” quality (predominantly consisting of noise). This review provides a comprehensive perspective on spectral quality assessment, examining existing tools and their underlying principles. We discuss key considerations such as the definition of spectral quality, normalisation, the use of experimental training data, and future research in the field. By highlighting the potential of spectral quality assessment to improve peptide identification and reduce false discoveries, we aim to elaborate on its potential for the proteomics community.