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The advent of high-resolution and frequency mass spectrometry has ushered in an era of data-independent acquisition (DIA). This approach affords enormous multiplexing capacity and is particularly suitable for clinical biomarker studies. However, DIA-based quantification of clinical plasma samples is a daunting task due to the high complexity of clinical plasma samples, the diversity of peptides within the samples, and the high biologic dynamic range of plasma proteins. Here we applied DIA methodology, including a highly reproducible sample preparation and LC-MS/MS analysis, and assessed its utility for clinical plasma biomarker detection. A pancreatic cancer-relevant plasma spectral library was constructed consisting of over 14 000 confidently identified peptides derived from over 2300 plasma proteins. Using a nonhuman protein as the internal standard, various empirical parameters were explored to maximize the reliability and reproducibility of the DIA quantification. The DIA parameters were optimized based on the quantification cycle times and fragmentation profile complexity. Higher analytical and biological reproducibility was recorded for the tryptic peptides without labile residues and missed cleavages. Quantification reliability was developed for the peptides identified within a consistent retention time and signal intensity. Linear analytical dynamic range and the lower limit of quantification were assessed, suggesting the critical role of sample complexity in optimizing DIA settings. Technical validation of the assay using a cohort of clinical plasma indicated the robustness and unique advantage for targeted analysis of clinical plasma samples in the context of biomarker development.

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., Competing Interests: Disclosure The authors declare no competing financial interest.

Experiments involving the high-throughput quantification of image data require algorithms for automation. A challenge in the development of such algorithms is to properly interpret signals over a broad range of image characteristics, without the need for manual adjustment of parameters. Here we present a new approach for locating signals in image data, called Segment and Fit Thresholding (SFT). The method assesses statistical characteristics of small segments of the image and determines the best-fit trends between the statistics. Based on the relationships, SFT identifies segments belonging to background regions; analyzes the background to determine optimal thresholds; and analyzes all segments to identify signal pixels. We optimized the initial settings for locating background and signal in antibody microarray and immunofluorescence data and found that SFT performed well over multiple, diverse image characteristics without readjustment of settings. When used for the automated analysis of multicolor, tissue-microarray images, SFT correctly found the overlap of markers with known subcellular localization, and it performed better than a fixed threshold and Otsu's method for selected images. SFT promises to advance the goal of full automation in image analysis.

Improved methods for studying glycans could spur significant advances in the understanding and application of glycobiology. The use of affinity reagents such as lectins and glycan-binding antibodies is a valuable complement to methods involving mass spectrometry and chromatography. Many lectins, however, are not useful as analytic tools due to low affinity in vitro. As an approach to increasing lectin avidity to targeted glycans, we tested the use of lectin multimerization. Several biotinylated lectins were linked together through streptavidin interactions. The binding of certain lectins for purified glycoproteins and glycoproteins captured directly out of biological solutions was increased using multimerization, resulting in the detection of lower concentrations of glycoprotein than possible using monomeric detection. The analysis of glycoproteins in plasma samples showed that the level of binding enhancement through multimerization was not equivalent across patient samples. Wheat germ agglutinin (WGA) reactive glycans on fibronectin and thrombospondin-5 were preferentially bound by multimers in pancreatic cancer patient samples relative to control samples, suggesting a cancer-associated change in glycan density that could be detected only through lectin multimerization. This strategy could lead to the more sensitive and informative detection of glycans in biological samples and a broader spectrum of lectins that are useful as analytical reagents.

Biomarkers are most frequently proteins that are measured in the blood. Their development largely relies on antibody creation to test the protein candidate performance in blood samples of diseased versus nondiseased patients. The creation of such antibody assays has been a bottleneck in biomarker progress due to the cost, extensive time, and effort required to complete the task. Targeted proteomics is an emerging technology that is playing an increasingly important role to facilitate disease biomarker development. In this study, we applied a SRM-based targeted proteomics platform to directly detect candidate biomarker proteins in plasma to evaluate their clinical utility for pancreatic cancer detection. The characterization of these protein candidates used a clinically well-characterized cohort that included plasma samples from patients with pancreatic cancer, chronic pancreatitis, and healthy age-matched controls. Three of the five candidate proteins, including gelsolin, lumican, and tissue inhibitor of metalloproteinase 1, demonstrated an AUC value greater than 0.75 in distinguishing pancreatic cancer from the controls. In addition, we provide an analysis of the reproducibility, accuracy, and robustness of the SRM-based proteomics platform. This information addresses important technical issues that could aid in the adoption of the targeted proteomics platform for practical clinical utility.

A mass spectrometric method was developed to elucidate the N-glycan structures of serum glycoproteins and utilize fucosylated glycans as potential markers for pancreatic cancer. This assay was applied to haptoglobin in human serum where N-glycans derived from the serum of 16 pancreatic cancer patients were compared with those from 15 individuals with benign conditions (5 normals, 5 chronic pancreatitis, and 5 type II diabetes). This assay used only 10 μL of serum where haptoglobin was extracted using a monoclonal antibody and quantitative permethylation was performed on desialylated N-glycans followed by MALDI-QIT-TOF MS analysis. Eight desialylated N-glycan structures of haptoglobin were identified where a bifucosylated triantennary structure was reported for the first time in pancreatic cancer samples. Both core and antennary fucosylation were elevated in pancreatic cancer samples compared to samples from benign conditions. Fucosylation degree indices were calculated and show a significant difference between pancreatic cancer patients of all stages and the benign conditions analyzed. This study demonstrates that a serum assay based on haptoglobin fucosylation patterns using mass spectrometric analysis may serve as a novel method for the diagnosis of pancreatic cancer.

Pancreatic cancer (PC) is one of the most lethal malignancies. It has a 5-year survival rate of only 6%, owing in part to the lack of a reliable tumor marker for early diagnosis. Recent research has shown that the mucin protein MUC4 is aberrantly expressed in pancreatic adenocarcinoma cell lines and tissues but is undetectable in normal pancreas and chronic pancreatitis. Thus, the level of MUC4 in patient sera has the potential to function as a diagnostic and prognostic marker for PC. However, the measurement of MUC4 in sera using conventional test platforms (e.g., enzyme linked immunosorbent assay (ELISA) and radioimmunoassay (RIA)) has been unsuccessful. This has prevented the assessment of the utility of this protein as a possible PC marker in sera. In addressing this obstacle, the work herein examines the potential to create a simple diagnostic test for MUC4 through the development of a surface-enhanced Raman scattering (SERS)-based immunoassay, which was then used to demonstrate the first ever detection of MUC4 in cancer patient serum samples. Importantly, these measurements showed that sera from patients with PC produced a significantly higher SERS response for MUC4 compared to sera from healthy individuals and from patients with benign diseases. These results indicate that a SERS-based immunoassay can monitor MUC4 levels in patient sera, representing a much needed first step toward assessing the potential of this protein to serve as a serum marker for the early stage diagnosis of PC. This paper details these and other findings (i.e., the detection of the mucin protein CA19-9), which demonstrate that our SERS assay outperforms conventional assays (i.e., RIA and ELISA) with respect to limits of detection, readout time, and required sample volume.