This review is the tenth update of the original article published in 1999 on the application of matrix-assisted laser desorption/ionization (MALDI) mass spectrometry to the analysis of carbohydrates and glycoconjugates and brings coverage of the literature to the end of 2020. Also included are papers that describe methods appropriate to analysis by MALDI, such as sample preparation techniques, even though the ionization method is not MALDI. The review is basically divided into three sections: (1) general aspects such as theory of the MALDI process, matrices, derivatization, MALDI imaging, fragmentation, quantification and the use of arrays. (2) Applications to various structural types such as oligo- and polysaccharides, glycoproteins, glycolipids, glycosides and biopharmaceuticals, and (3) other areas such as medicine, industrial processes and glycan synthesis where MALDI is extensively used. Much of the material relating to applications is presented in tabular form. The reported work shows increasing use of incorporation of new techniques such as ion mobility and the enormous impact that MALDI imaging is having. MALDI, although invented nearly 40 years ago is still an ideal technique for carbohydrate analysis and advancements in the technique and range of applications show little sign of diminishing. This article is protected by copyright. All rights reserved.

The intact-mass MS measurements are becoming increasingly popular in characterization of a range of biopolymers, especially those of interest to biopharmaceutical industry. However, as the complexity of protein therapeutics and other macromolecular medicines increases, the new challenges arise, one of which is the high levels of structural heterogeneity that are frequently exhibited by such products. The very notion of the molecular mass measurement loses its clear and intuitive meaning when applied to an extremely heterogenous system that cannot be characterized by a unique mass, but instead requires that a mass distribution be considered. Furthermore, convoluted mass distributions frequently give rise to unresolved ionic signal in mass spectra, from which little-to-none meaningful information can be extracted using standard approaches that work well for homogeneous systems. However, a range of technological advances made in the last decade, such as the hyphenation of intact-mass MS measurements with front-end separations, better integration of ion mobility in MS workflows, development of an impressive arsenal of gas-phase ion chemistry tools to supplement MS methods, as well as the revival of the charge detection MS and its triumphant entry into the field of bioanalysis already made impressive contributions towards addressing the structural heterogeneity challenge. An overview of these techniques is accompanied by critical analysis of the strengths and weaknesses of different approaches, and a brief overview of their applications to specific classes of biopharmaceutical products, vaccines, and nonbiological complex drugs.

Jet fuels are complex mixtures composed of many individual compounds that influence crucial chemical and physical properties. Approximately over the last 20 years, mass spectrometry studies provided important and extensive qualitative and quantitative information of the compounds that make up jet fuels. This review presents these main findings, evaluates the analytical methods utilized, and summarizes the hydrocarbons, nitrogen-, oxygen- and sulfur-containing compounds characterized in the jet fuels. Potential areas where mass spectrometry may play important roles in the future will also be discussed.

Chemical analysis by analytical instrumentation has played a major role in disease diagnosis, which is a necessary step for disease treatment. While the treatment process often targets specific organs or compounds, the diagnostic step can occur through various means, including physical or chemical examination. Chemically, the genome may be evaluated to give information about potential genetic outcomes, the transcriptome to provide information about expression actively occurring, the proteome to offer insight on functions causing metabolite expression, or the metabolome to provide a picture of both past and ongoing physiological function in the body. Mass spectrometry (MS) has been elevated among other analytical instrumentation because it can be used to evaluate all four biological machineries of the body. In addition, MS provides enhanced sensitivity, selectivity, versatility, and speed for rapid turnaround time, qualities that are important for instance in clinical procedures involving the diagnosis of a pediatric patient in intensive care or a cancer patient undergoing surgery. In this review, we provide a summary of the use of MS to evaluate biomarkers for newborn screening and cancer diagnosis. As many reviews have recently appeared focusing on MS methods and instrumentation for metabolite analysis, we sought to describe the biological basis for many metabolomic and additional omics biomarkers used in newborn screening and how tandem MS methods have recently been applied, in comparison to traditional methods. Similar comparison is done for cancer screening, with emphasis on emerging MS approaches that allow biological fluids, tissues, and breath to be analyzed for the presence of diagnostic metabolites yielding insight for treatment options based on the understanding of prior and current physiological functions of the body.

Förster Resonance Energy transfer (FRET) is a nonradiative process that may occur from an electronically excited donor to an acceptor when the emission spectrum of the donor overlaps with the absorption spectrum of the acceptor. FRET experiments have been done in the gas phase based on specially designed mass-spectroscopy setups with the goal to obtain structural information on biomolecular ions labeled with a FRET pair (i.e., donor and acceptor dyes) and to shed light on the energy-transfer process itself. Ions are accumulated in a radio-frequency ion trap or a Penning trap where mass selection of those of interest takes place, followed by photoexcitation. Gas-phase FRET is identified from detection of emitted light either from the donor, the acceptor, or both, or from a fragmentation channel that is specific to the acceptor when electronically excited. The challenge associated with the first approach is the collection and detection of photons emitted from a thin ion cloud that is not easily accessible while the second approach relies both on the photophysical and chemical behavior of the acceptor. In this review, we present the different instrumentation used for gas-phase FRET, including a discussion of advantages and disadvantages, and examples on how the technique has provided important structural information that is not easily obtainable otherwise. Furthermore, we describe how the spectroscopic properties of the dyes are affected by nearby electric fields, which is readily discernable from experiments on simple model systems with alkyl or π-conjugated bridges. Such spectral changes can have a significant effect on the FRET efficiency. Ideas for new directions are presented at the end with special focus on cold-ion spectroscopy.

Recent technological advancements in mass spectrometry (MS)-based proteomics technologies have accelerated its application to study greater and greater numbers of human tumor specimens. Over the last several years, the Clinical Proteomic Tumor Analysis Consortium, the International Cancer Proteogenome Consortium, and others have generated MS-based proteomic profiling data combined with corresponding multiomics data on thousands of human tumors to date. Proteomic data sets in the public domain can be re-examined by other researchers with different questions in mind from what the original studies explored. In this review, we examine the increasing role of proteomics in studying cancer, along with the potential for previous studies and their associated data sets to contribute to improving the diagnosis and treatment of cancer in the clinical setting. We also explore publicly available proteomics and multi-omics data from cancer cell line models to show how such data may aid in identifying therapeutic strategies for cancer subsets.