8 research outputs found
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
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