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The skin surface is an important sample source that the metabolomics community has only just begun to explore. Alterations in sebum, the lipid-rich mixture coating the skin surface, correlate with age, sex, ethnicity, diet, exercise, and disease state, making the skin surface an ideal sample source for future noninvasive biomarker exploration, disease diagnosis, and forensic investigation. The potential of sebum sampling has been realized primarily via electrospray ionization mass spectrometry (ESI-MS), an ideal approach to assess the skin surface lipidome. However, a better understanding of sebum collection and subsequent ESI-MS analysis is required before skin surface sampling can be implemented in routine analyses. Challenges include ambiguity in definitive lipid identification, inherent biological variability in sebum production, and methodological, technical variability in analyses. To overcome these obstacles, avoid common pitfalls, and achieve reproducible, robust outcomes, every portion of the workflow—from sample collection to data analysis—should be carefully considered with the specific application in mind. This review details current practices in sebum sampling, sample preparation, ESI-MS data acquisition, and data analysis, and it provides important considerations in acquiring meaningful lipidomic datasets from the skin surface. Forensic researchers investigating sebum as a means for suspect elimination in lieu of adequate fingerprint ridge detail or database matches, as well as clinical researchers interested in noninvasive biomarker exploration, disease diagnosis, and treatment monitoring, can use this review as a guide for developing methods of best-practice. [ABSTRACT FROM AUTHOR]

Glycans introduce complexity to the proteins to which they are attached. These modifications vary during the progression of many diseases; thus, they serve as potential biomarkers for disease diagnosis and prognosis. The immense structural diversity of glycans makes glycosylation analysis and quantitation difficult. Fortunately, recent advances in analytical techniques provide the opportunity to quantify even low‐abundant glycopeptides and glycans derived from complex biological mixtures, allowing for the identification of glycosylation differences between healthy samples and those derived from disease states. Understanding the strengths and weaknesses of different quantitative glycomics analysis methods is important for selecting the best strategy to analyze glycosylation changes in any given set of clinical samples. To provide guidance towards selecting the proper approach, we discuss four widely used quantitative glycomics analysis platforms, including fluorescence‐based analysis of released N‐linked glycans and three different varieties of MS‐based analysis: liquid chromatography (LC)‐mass spectrometry (MS) analysis of glycopeptides, matrix‐assisted laser desorption ionization‐time of flight MS, and LC‐ESI‐MS analysis of released N‐linked glycans. These methods' strengths and weaknesses are compared, particularly associated with the figures of merit that are important for clinical biomarker studies, including: the initial sample requirements, the methods' throughput, sample preparation time, the number of species identified, the methods' utility for isomer separation and structural characterization, method‐related challenges associated with quantitation, repeatability, the expertise required, and the cost for each analysis. This review, therefore, provides unique guidance to researchers who endeavor to undertake a clinical glycomics analysis by offering insights on the available analysis technologies. [ABSTRACT FROM AUTHOR]

Glycosylation affects various biological functions of proteins (e.g., protein binding, inter- or intracell signaling, etc.), and it can serve as an indicator of disease. Therefore, characterization of the glycosylation in proteins is one important step in developing a comprehensive understanding of the biological significance of glycosylation and in facilitating disease diagnosis. Glycopeptide-based MS analysis has proven to be a viable tool for glycopeptide analysis. However, when glycopeptides coexist with peptides, glycopeptide signals are usually suppressed by the strongly ionizing peptides. Toward this end, it would be desirable to seek methods to improve glycopeptide detection. Herein, we performed an in-depth study of maximizing glycosylation coverage on model glycoproteins by optimizing all the aspects of glycopeptide-based analysis, including sample preparation methods, mass spectral techniques, and data analysis strategies. For sample preparation, several approaches, including reversed-phase high-performance liquid chromatography, lectinbased affinity, and hydrophilic affinity using a carbohydrate-based resin, were compared and tested individually as well as in parallel. For mass spectral techniques, profiling glycopeptides in both positive and negative ion mode is essential to obtain complete glycan profiles. For data analysis, incorporating variable modifications in the database search of GlycoPep DB enhances glycopeptide coverage. In addition, the use of PNGase F helps to confirm the presence of weakly ionized glycopeptides when they coelute with strongly ionizing species. In doing so, we created a work flow that is designed specifically to optimize the coverage of glycosylation heterogeneity in terms of the number of glycosylation sites detected and their corresponding glycan profiles. To test the effectiveness of this approach, a glycoprotein with 27 potential glyeosylation sites, and an unknown glycosylation profile, was analyzed; on this protein, more than 300 glycoforms from 23 detected glycosylation sites were identified. This work demonstrates that these strategies significantly improve the glycopeptide detection, thereby, facilitating understanding the functional properties of glycans on the glycoproteins.

Mass spectral analysis is an increasingly common method used to characterize glycoproteins. When more than one glycosylation site is present on a protein, obtaining MS data of glycopeptides is a highly effective way of obtaining glycosylation information because this approach can be used to identify not only what the carbohydrates are but also at which glycosylation site they are attached. Unfortunately, this is not yet a routine analytical approach, in part because data analysis can be quite challenging. We are developing strategies to simplify this analysis. Presented herein is a novel mass spectrometry technique that identifies the peptide moiety of either sulfated, sialylated, or both sialylated and sulfated glycopeptides. This technique correlates product ions in collision-induced dissociation (CID) experiments of suspected glycopeptides to a peptide composition using a newly developed web-based tool, GlycoPep ID. After identifying the peptide portion of glycopeptides with GlycoPep ID, the process of assigning the rest of the glycopeptide composition to the MS data is greatly facilitated because the 'unknown' portion of the mass assignment that remains can be directly attributed to the carbohydrate component. Several examples of the utility and reliability of this method are presented herein.

Mass spectrometry is emerging as a versatile analytical tool for profiling glycan and glycopeptide structures. While the interpretation of MS data remains a challenging and difficult task, substantial efforts have been made to develop informatics tools to alleviate MS data interpretation. Here, we present a web-based tool, GlycoPep DB, designed to facilitate compositional assignment for glycopeptides by comparing experimentally measured masses to all calculated glycopeptide masses from a carbohydrate database with N-linked glycans. GlycoPep DB is an advance over current tools to assign N-linked glycans because it uses a concept of 'smart searching', where only biologically relevant carbohydrate compositions are searched, when matching carbohydrate compositions with the MS data making glycopeptide compositional assignment more efficient. This is in contrast to currently used tools, where many implausible glycan structures are present in the search output, but fewer biologically relevant glycan motifs are predicted. The utility of GlycoPep DB is illustrated in the analysis of glycopeptides derived from a proteolytic digest of follicle stimulating hormone.

Structural analysis of sulfated glycans is essential in understanding their biological significance. Here, we present a new approach to characterize sulfated glycans present on glycoproteins. The analysis is performed on glycopepfides, so information about the sulfated species is obtained in a glycosylation site-specific manner. This method employs an ion-pairing reagent to stabilize the SO3 group of the glycopeptide, allowing useful information to be obtained during MS/MS experiments. The amount of structural information obtained from (+)ESI-MS/MS of the ion-pair complexes for sulfated glycopeptides of equine thyroid stimulating hormone is compared with information obtained by (-)ESI-MS/MS of the underivatized, sulfated glycopeptides. The results indicate that this new method provides detailed insights into the sequence, branching, and type of N-glycans present, compared to analysis via (-)ESI-MS/MS.

Methods to characterize metabolic transformations in a rapid and reliable fashion are required for facilitating the development of all new pharmaceuticals. One metabolic transformation, which is the focus of this study, is lactone hydrolysis. For pharmaceuticals containing lactones, hydrolysis occurs readily due to both enzymatic and nonenzymatic processes. Hydrolysis affects both the bioavailability and the efficacy of lactone-containing drugs and pro-drugs. To facilitate the characterization of lactones and their corresponding hydrolysis products, we have developed a mass spectrometric method that can readily discriminate between a lactone and its corresponding carboxylic acid, even when these changes are accompanied by other modifications that occur during metabolism. This method uses characteristic product ions in MS/MS experiments, and the trends described herein can be applied broadly to several types of lactones. To demonstrate the efficacy of this approach, two different lactones that had undergone multiple modifications were characterized, and in both cases, lactone hydrolysis was readily discernible, based on the MS/MS data.

We have recently developed a new mass spectrometry method, the STEP (statistical test of equivalent pathways) analysis that uses ion abundances in two tandem mass spectrometry experiments to obtain genealogy information about product ions present in mass spectra. The method requires minimal sample, and it can be performed using a conventional quadrupole ion trap mass spectrometer. To obtain genealogy information, STEP ratios are calculated by comparing the relative abundances of product ions in two MS/MS experiments. These ratios are directly related to the origin of the product ions. Product ions that result directly from the precursor ion always have STEP ratios that are [greater than or equal to] 1. Ions that result from secondary fragmentation pathways have STEP ratios that are significantly larger than the primary ions, based on a Q test of statistical significance. Consequently, the type (primary or secondary) of all the product ions in an MS/MS experiment can easily be identified in this analysis. The STEP method is applied herein to peptides and carbohydrates, and the STEP results are consistent with validation data for 95% of the ions in this study. This new method has many applications in carbohydrate and peptide analysis. It can be used to support mechanistic studies of peptide fragmentation, and it is useful for discriminating among various isomeric carbohydrates, without the need for reference standards. Several examples are presented to demonstrate the reliability of this method, and an example showing how the method benefits carbohydrate sequencing is also provided.