Charge deconvolution infers the mass from mass over charge (m/z) measurements in electrospray ionization mass spectra. When applied over a wide input m/z or broad target mass range, charge-deconvolution algorithms can produce artifacts, such as false masses at one-half or one-third of the correct mass. Indeed, a maximum entropy term in the objective function of MaxEnt, the most commonly used charge deconvolution algorithm, favors a deconvolved spectrum with many peaks over one with fewer peaks. Here we describe a new “parsimonious” charge deconvolution algorithm that produces fewer artifacts. The algorithm is especially well-suited to high-resolution native mass spectrometry of intact glycoproteins and protein complexes. Deconvolution of native mass spectra poses special challenges due to salt and small molecule adducts, multimers, wide mass ranges, and fewer and lower charge states. We demonstrate the performance of the new deconvolution algorithm on a range of samples. On the heavily glycosylated plasma properdin glycoprotein, the new algorithm could deconvolve monomer and dimer simultaneously and, when focused on the m/z range of the monomer, gave accurate and interpretable masses for glycoforms that had previously been analyzed manually using m/z peaks rather than deconvolved masses. On therapeutic antibodies, the new algorithm facilitated the analysis of extensions, truncations, and Fab glycosylation. The algorithm facilitates the use of native mass spectrometry for the qualitative and quantitative analysis of protein and protein assemblies.

Glycoproteomics is a powerful yet analytically challenging research tool. Software packages aiding the interpretation of complex glycopeptide tandem mass spectra have appeared, but their relative performance remains untested. Conducted through the HUPO Human Glycoproteomics Initiative, this community study, comprising both developers and users of glycoproteomics software, evaluates solutions for system-wide glycopeptide analysis. The same mass spectrometrybased glycoproteomics datasets from human serum were shared with participants and the relative team performance for N- and O-glycopeptide data analysis was comprehensively established by orthogonal performance tests. Although the results were variable, several high-performance glycoproteomics informatics strategies were identified. Deep analysis of the data revealed key performance-associated search parameters and led to recommendations for improved ‘high-coverage’ and ‘high-accuracy’ glycoproteomics search solutions. This study concludes that diverse software packages for comprehensive glycopeptide data analysis exist, points to several high-performance search strategies and specifies key variables that will guide future software developments and assist informatics decision-making in glycoproteomics.

Lectin-glycan interactions have critical functions in multiple normal and pathological processes, but the binding partners and functions for many glycans and lectins are not known. An important step in better understanding glycan-lectin biology is enabling systematic quantification and analysis of the interactions. Glycan arrays can provide the experimental information for such analyses, and the thousands of glycan array datasets available through the Consortium for Functional Glycomics provide the opportunity to extend the analyses to a broad scale. We developed software, based on our previously described Motif Segregation algorithm, for the automated analysis of glycan array data, and we analyzed the entire storehouse of 2883 datasets from the Consortium for Functional Glycomics. We mined the resulting database to make comparisons of specificities across multiple lectins and comparisons between glycans in their lectin receptors. Of the lectins in the database, viral lectins were the most different from other organism types, with specificities nearly always restricted to sialic acids, and mammalian lectins had the most diverse range of specificities. Certain mammalian lectins were unique in their specificities for sulfated glycans. Simple modifications to a lactosamine core structure radically altered the types of lectins that were highly specific for the glycan. Unmodified lactosamine was specifically recognized by plant, fungal, viral, and mammalian lectins; sialylation shifted the binding mainly to viral lectins; and sulfation resulted in mainly mammalian lectins with the highest specificities. We anticipate that this analysis program and database will be valuable in fundamental glycobiology studies, detailed analyses of lectin specificities, and practical applications in translational research. Molecular & Cellular Proteomics 12:

Recent research is uncovering unexpected ways that glycans contribute to biology, as well as new strategies for combatting disease using approaches involving glycans. To make full use of glycans for clinical applications, we need more detailed information on the location, nature, and dynamics of glycan expression in vivo. Such studies require the use of specimens acquired directly from patients. Effective studies of clinical specimens require low-volume assays, high precision measurements, and the ability to process many samples. Assays using affinity reagents—lectins and glycan-binding antibodies—can meet these requirements, but further developments are needed to make the methods routine and effective. Recent advances in the use of glycan-binding proteins could meet that need. The advances involve improved determination of specificity using glycan arrays; the availability of databases for mining and analyzing glycan array data; lectin engineering methods; and the ability to quantitatively interpret lectin measurements. Here we describe many of the challenges and opportunities involved in the application of these new approaches to the study of biological samples. The new tools hold promise for developing methods to improve the outcomes of patients afflicted with diseases characterized by aberrant glycan expression.

Background and Aims The CA19-9 antigen is the current best biomarker for pancreatic cancer, but it is not elevated in about 25% of pancreatic cancer patients at a cutoff that gives a 25% false-positive rate. We hypothesized that antigens related to the CA19-9 antigen, which is a glycan called sialyl-Lewis A (sLeA), are elevated in distinct subsets of pancreatic cancers. Methods We profiled the levels of multiple glycans and mucin glycoforms in plasma from 200 subjects with either pancreatic cancer or benign pancreatic disease, and we validated selected findings in additional cohorts of 116 and 100 subjects, the latter run blinded and including cancers that exclusively were early-stage. Results We found significant elevations in two glycans: an isomer of sLeA called sialyl-Lewis X, present both in sulfated and non-sulfated forms; and the sialylated form of a marker for pluripotent stem cells, type 1 N-acetyl-lactosamine. The glycans performed as well as sLeA as individual markers and were elevated in distinct groups of patients, resulting in a 3-marker panel that significantly improved upon any individual biomarker. The panel gave 85% sensitivity and 90% specificity in the combined discovery and validation cohorts, relative to 54% sensitivity and 86% specificity for sLeA; and it gave 80% sensitivity and 84% specificity in the independent test cohort, as opposed to 66% sensitivity and 72% specificity for sLeA. Conclusions Glycans related to sLeA are elevated in distinct subsets of pancreatic cancers and yield improved diagnostic accuracy over CA19-9.