Deglycosylation and label-free quantitative LC-MALDI MS applied to efficient serum biomarker discovery of lung cancer

<p>Abstract</p> <p>Background</p> <p>Serum is an ideal source of biomarker discovery and proteomic profiling studies are continuously pursued on serum samples. However, serum is featured by high level of protein glycosylations that often cause ionization suppression and confound accurate quantification analysis by mass spectrometry. Here we investigated the effect of N-glycan and sialic acid removal from serum proteins on the performance of label-free quantification results.</p> <p>Results</p> <p>Serum tryptic digests with or without deglycosylation treatment were analyzed by LC-MALDI MS and quantitatively compared on the Expressionist Refiner MS module. As a result, 345 out of 2,984 peaks (11.6%) showed the specific detection or the significantly improved intensities in deglycosylated serum samples (<it>P </it>< 0.01). We then applied this deglycosylation-based sample preparation to the identification of lung cancer biomarkers. In comparison between 10 healthy controls and 20 lung cancer patients, 40 peptides were identified to be differentially presented (<it>P </it>< 0.01). Their quantitative accuracies were further verified by multiple reaction monitoring. The result showed that deglycosylation was needed for the identification of some unique candidates, including previously unreported O-linked glycopeptide of complement component C9.</p> <p>Conclusions</p> <p>We demonstrated here that sample deglycosylation improves the quantitative performance of shotgun proteomics, which can be effectively applied to any samples with high glycoprotein contents.</p

A Comprehensive Peptidome Profiling Technology for the Identification of Early Detection Biomarkers for Lung Adenocarcinoma

The mass spectrometry-based peptidomics approaches have proven its usefulness in several areas such as the discovery of physiologically active peptides or biomarker candidates derived from various biological fluids including blood and cerebrospinal fluid. However, to identify biomarkers that are reproducible and clinically applicable, development of a novel technology, which enables rapid, sensitive, and quantitative analysis using hundreds of clinical specimens, has been eagerly awaited. Here we report an integrative peptidomic approach for identification of lung cancer-specific serum peptide biomarkers. It is based on the one-step effective enrichment of peptidome fractions (molecular weight of 1,000–5,000) with size exclusion chromatography in combination with the precise label-free quantification analysis of nano-LC/MS/MS data set using Expressionist proteome server platform. We applied this method to 92 serum samples well-managed with our SOP (standard operating procedure) (30 healthy controls and 62 lung adenocarcinoma patients), and quantitatively assessed the detected 3,537 peptide signals. Among them, 118 peptides showed significantly altered serum levels between the control and lung cancer groups (p<0.01 and fold change >5.0). Subsequently we identified peptide sequences by MS/MS analysis and further assessed the reproducibility of Expressionist-based quantification results and their diagnostic powers by MRM-based relative-quantification analysis for 96 independently prepared serum samples and found that APOA4 273–283, FIBA 5–16, and LBN 306–313 should be clinically useful biomarkers for both early detection and tumor staging of lung cancer. Our peptidome profiling technology can provide simple, high-throughput, and reliable quantification of a large number of clinical samples, which is applicable for diverse peptidome-targeting biomarker discoveries using any types of biological specimens

Quantitative Structural Characterization of Local N‑Glycan Microheterogeneity in Therapeutic Antibodies by Energy-Resolved Oxonium Ion Monitoring

Site-specific characterization of glycoform heterogeneity currently requires glycan structure assignment and glycopeptide quantification in two independent experiments. We present here a new method combining multiple reaction monitoring mass spectrometry with energy-resolved structural analysis, which we termed “energy-resolved oxonium ion monitoring”. We demonstrated that monitoring the yields of oligosaccharide-derived fragment ions (oxonium ions) over a wide range of collision induced dissociation (CID) energy applied to a glycopeptide precursor exhibits a glycan structure-unique fragmentation pattern. In the analysis of purified immunoglobulin glycopeptides, the energy-resolved oxonium ion profile was shown to clearly distinguish between isomeric glycopeptides. Moreover, limit of detection (LOD) of glycopeptide detection was 30 attomole injection, and quantitative dynamic range spanned 4 orders magnitude. Therefore, both quantification of glycopeptides and assignment of their glycan structures were achieved by a simple analysis procedure. We assessed the utility of this method for characterizing site-specific N-glycan microheterogeneity on therapeutic antibodies, including validation of lot-to-lot glycoform variability. A significant change in the degree of terminal galactosylation was observed in different production lots of trastuzumab and bevacizumab. Cetuximab Fab glycosylation, previously known to cause anaphylaxis, was also analyzed, and several causative antigens including Lewis X motifs were quantitatively detected. The data suggests that energy-resolved oxonium ion monitoring could fulfill the regulatory requirement on the routine quality control analysis of forthcoming biosimilar therapeutics

Plasma Low-Molecular-Weight Proteome Profiling Identified Neuropeptide‑Y as a Prostate Cancer Biomarker Polypeptide

In prostate cancer diagnosis, PSA test has greatly contributed to the early detection of prostate cancer; however, expanding overdiagnosis and unnecessary biopsies have emerged as serious issues. To explore plasma biomarkers complementing the specificity of PSA test, we developed a unique proteomic technology QUEST-MS (Quick Enrichment of Small Targets for Mass Spectrometry). The QUEST-MS method based on 96-well formatted sequential reversed-phase chromatography allowing efficient enrichment of <20 kDa proteins quickly and reproducibly. Plasma from 24 healthy controls, 19 benign prostate hypertrophy patients, and 73 prostate cancer patients were purified with QUEST-MS and analyzed by LC/MS/MS. Among 153 057 nonredundant peptides, 189 peptides showed prostate cancer specific detection pattern, which included a neurotransmitter polypeptide neuropeptide-Y (NPY). We further validated the screening results by targeted multiple reaction monitoring technology using independent sample set (<i>n</i> = 110). The ROC curve analysis revealed that logistic regression-based combination of NPY, and PSA showed 81.5% sensitivity and 82.2% specificity for prostate cancer diagnosis. Thus QUEST-MS technology allowed comprehensive and high-throughput profiling of plasma polypeptides and had potential to effectively uncover very low abundant tumor-derived small molecules, such as neurotransmitters, peptide hormones, or cytokines