Parsimonious Charge Deconvolution for Native Mass Spectrometry

Charge deconvolution infers the mass from mass over charge (<i>m</i>/<i>z</i>) measurements in electrospray ionization mass spectra. When applied over a wide input <i>m</i>/<i>z</i> 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 <i>m</i>/<i>z</i> range of the monomer, gave accurate and interpretable masses for glycoforms that had previously been analyzed manually using <i>m</i>/<i>z</i> 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

Characterizing Protein Glycosylation through On-Chip Glycan Modification and Probing

Glycans are critical to protein biology and are useful as disease biomarkers. Many studies of glycans rely on clinical specimens, but the low amount of sample available for some specimens limits the experimental options. Here we present a method to obtain information about protein glycosylation using a minimal amount of protein. We treat proteins that were captured or directly spotted in small microarrays (2.2 mm × 2.2 mm) with exoglycosidases to successively expose underlying features, and then we probe the native or exposed features using a panel of lectins or glycan-binding reagents. We developed an algorithm to interpret the data and provide predictions about the glycan motifs that are present in the sample. We demonstrated the efficacy of the method to characterize differences between glycoproteins in their sialic acid linkages and N-linked glycan branching, and we validated the assignments by comparing results from mass spectrometry and chromatography. The amount of protein used on-chip was about 11 ng. The method also proved effective for analyzing the glycosylation of a cancer biomarker in human plasma, MUC5AC, using only 20 μL of the plasma. A glycan on MUC5AC that is associated with cancer had mostly 2,3-linked sialic acid, whereas other glycans on MUC5AC had a 2,6 linkage of sialic acid. The on-chip glycan modification and probing (on-chip GMAP) method provides a platform for analyzing protein glycosylation in clinical specimens and could complement the existing toolkit for studying glycosylation in disease

Upregulation of Glycans Containing 3′ Fucose in a Subset of Pancreatic Cancers Uncovered Using Fusion-Tagged Lectins

The fucose post-translational modification is frequently increased in pancreatic cancer, thus forming the basis for promising biomarkers, but a subset of pancreatic cancer patients does not elevate the known fucose-containing biomarkers. We hypothesized that such patients elevate glycan motifs with fucose in linkages and contexts different from the known fucose-containing biomarkers. We used a database of glycan array data to identify the lectins CCL2 to detect glycan motifs with fucose in a 3′ linkage; CGL2 for motifs with fucose in a 2′ linkage; and RSL for fucose in all linkages. We used several practical methods to test the lectins and determine the optimal mode of detection, and we then tested whether the lectins detected glycans in pancreatic cancer patients who did not elevate the sialyl-Lewis A glycan, which is upregulated in ∼75% of pancreatic adenocarcinomas. Patients who did not upregulate sialyl-Lewis A, which contains fucose in a 4′ linkage, tended to upregulate fucose in a 3′ linkage, as detected by CCL2, but they did not upregulate total fucose or fucose in a 2′ linkage. CCL2 binding was high in cancerous epithelia from pancreatic tumors, including areas negative for sialyl-Lewis A and a related motif containing 3′ fucose, sialyl-Lewis X. Thus, glycans containing 3′ fucose may complement sialyl-Lewis A to contribute to improved detection of pancreatic cancer. Furthermore, the use of panels of recombinant lectins may uncover details about glycosylation that could be important for characterizing and detecting cancer

Glycan motifs recognized by three CA 19–9 antibodies.

<p>For each antibody, the binding intensity to the indicated glycans, in relative fluorescence units, is given at three antibody concentrations (in μg/mL). Green indicates strong binding, yellow is moderate binding, and pink is weak binding.</p

Deviations between the assays at specific cutoffs.

<p>Deviations between the assays at specific cutoffs.</p

Marker performance at selected cutoffs.

<p>Marker performance at selected cutoffs.</p

Correlation between Assay 1 and Assay 2.

<p>The log-transformed CA 19–9 values of all subjects from Assay 1 and Assay 2 were plotted with respect to each other. Each circle indicates an individual patient sample. The trendline is the linear-least squares best fit, and the Spearman’s rank correlation coefficient is 0.74.</p

Characteristics of study subjects.

<p>ENH, Evanston Northwestern Healthcare; MSKCC, Memorial Sloan Kettering Cancer Center; Pitt, University of Pittsburgh Medical Center; UMHS, University of Michigan Health Services; UNMC, University of Nebraska Medical Center.</p><p>Characteristics of study subjects.</p