BlotGlycoABCTM, an integrated glycoblotting technique for rapid and large scale clinical glycomics

Recent progress in mass spectrometry has led to new challenges in glycomics, including the development of rapid glycan enrichment techniques. A facile technique for exploration of a carbohydrate-related biomarker is important because proteomics research targets glycosylation, a posttranslational modification. Here we report an "all-in-one" protocol for high throughput clinical glycomics. This new technique integrates glycoblotting-based glycan enrichment onto the BlotGlycoABC bead, on-bead stabilization of sialic acids, and fluorescent labeling of oligosaccharides in a single workflow on a multiwell filter plate. The advantage of this protocol and MALDI-TOF MS was demonstrated through differentiation of serum N-glycan profiles of subjects with congenital disorders of glycosylation and hepatocellular carcinoma and healthy donors. The method also permitted total cellular glycomics analysis of human prostate cancer cells and normal human prostate epithelial cells. These results demonstrate the potentials of glycan enrichment/processing for biomarker discovery.     

High dynamic range characterization of the trauma patient plasma proteome

Although human plasma represents an attractive sample for disease biomarker discovery, the extreme complexity and large dynamic range in protein concentrations present significant challenges for characterization, candidate biomarker discovery, and validation. Herein we describe a strategy that combines immunoaffinity subtraction and subsequent chemical fractionation based on cysteinyl peptide and N-glycopeptide captures with two-dimensional LC-MS/MS to increase the dynamic range of analysis for plasma. Application of this "divide-and-conquer" strategy to trauma patient plasma significantly improved the overall dynamic range of detection and resulted in confident identification of 22,267 unique peptides from four different peptide populations (cysteinyl peptides, non-cysteinyl peptides, N-glycopeptides, and non-glycopeptides) that covered 3,654 different proteins with 1,494 proteins identified by multiple peptides. Numerous low abundance proteins were identified, exemplified by 78 "classic" cytokines and cytokine receptors and by 136 human cell differentiation molecules. Additionally a total of 2,910 different N-glycopeptides that correspond to 662 N-glycoproteins and 1,553 N-glycosylation sites were identified. A panel of the proteins identified in this study is known to be involved in inflammation and immune responses. This study established an extensive reference protein database for trauma patients that provides a foundation for future high throughput quantitative plasma proteomic studies designed to elucidate the mechanisms that underlie systemic inflammatory responses.     

A new method for quantitative analysis of cell surface glycoproteome

As the altered glycosylation expressions of cell surface proteins are associated with many diseases, glycoproteomics approach has been widely applied to characterization of surface glycosylation alteration. In general, the abundances of proteolytic glycopeptides derived from corresponding glycoproteins can be measured to determine the abundances of glycoproteins. However, this quantification strategy cannot distinguish whether the changes are results from changes of protein abundance or changes in glycosite occupancy. For the accurate and specific quantification of the cell surface glycosylation profile, we proposed a modified cell surface‐capturing strategy where the glycopeptides were submitted to LC‐MS/MS analysis directly for identification of glycoproteins and the non‐glycopeptides were isotopically labelled for quantification of glycoproteins. This strategy was applied to comparatively analyze cell surface glycoproteins of two human cell lines, i.e. Chang Liver and HepG2 cells. Totally 341 glycoproteins were identified with 82.4% specificity for cell membrane proteins and 33 glycoproteins were quantified with significant expression change between the two cell lines. The differential expressions of two selected proteins (EMMPRIN and BCAM) were validated by Western blotting. This method enables specific and accurate analysis of the cell surface glycoproteins and may have broad application in the field of biomarker and drug target discovery.     

Cell-specific aptamer probes for membrane protein elucidation in cancer cells

Disease biomarkers play critical roles in the management of various pathological conditions of diseases. This involves diagnosing diseases, predicting disease progression and monitoring the efficacy of treatment modalities. While efforts to identify specific disease biomarkers using a variety of technologies has increased the number of biomarkers or augmented information about them, the effective use of disease-specific biomarkers is still scarce. Here, we report that a high expression of protein tyrosine kinase 7 (PTK7), a transmembrane receptor protein tyrosine kinase-like molecule, was discovered in a series of leukemia cell lines using whole cell aptamer selection. With the implementation of a two-step strategy (aptamer selection and biomarker discovery), combined with mass spectrometry, PTK7 was ultimately identified as a potential biomarker for T-cell acute lymphoblastic leukemia (T-ALL). Specifically, the aptamers for T-ALL cells were selected using the cell-SELEX process, without any prior knowledge of the cell biomarker population, conjugated with magnetic beads and then used to capture and purify their binding targets on the leukemia cell surface. This demonstrates that a panel of molecular aptamers can be easily generated for a specific type of diseased cells. It further demonstrates that this two-step strategy, that is, first selecting cancer cell-specific aptamers and then identifying their binding target proteins, has major clinical implications in that the technique promises to substantially improve the overall effectiveness of biomarker discovery. Specifically, our strategy will enable efficient discovery of new malignancy-related biomarkers, facilitate the development of diagnostic tools and therapeutic approaches to cancer, and markedly improve our understanding of cancer biology.     

Glycoscience aids in biomarker discovery

The glycome consists of all glycans (or carbohydrates) within a biological system, and modulates a wide range of important biological activities, from protein folding to cellular communications. The mining of the glycome for disease markers represents a new paradigm for biomarker discovery; however, this effort is severely complicated by the vast complexity and structural diversity of glycans. This review summarizes recent developments in analytical technology and methodology as applied to the fields of glycomics and glycoproteomics. Mass spectrometric strategies for glycan compositional profiling are described, as are potential refinements which allow structure-specific profiling. Analytical methods that can discern protein glycosylation at a specific site of modification are also discussed in detail. Biomarker discovery applications are shown at each level of analysis, highlighting the key role that glycoscience can play in helping scientists understand disease biology.     

基于质谱的蛋白质生物标志物发现中的特征选择与机器学习方法研究进展

随着质谱技术的进步以及生物信息学与统计学算法的发展,以疾病研究为主要目的之一的人类蛋白质组计划正快速推进。蛋白质生物标志物在疾病早期诊断和临床治疗等方面有着非常重要的意义,其发现策略和方法的研究已成为一个重要的热点领域。特征选择与机器学习对于解决蛋白质组数据"高维度"及"稀疏性"问题有较好的效果,因而逐渐被广泛地应用于发现蛋白质生物标志物的研究中。文中主要阐述蛋白质生物标志物的发现策略以及其中特征选择与机器学习方法的原理、应用实例和适用范围,并讨论深度学习方法在本领域的应用前景及局限性,以期为相关研究提供参考。     

Mass spectrometry-based expression profiling of clinical prostate cancer

The maturation of MS technologies has provided a rich opportunity to interrogate protein expression patterns in normal and disease states by applying expression protein profiling methods. Major goals of this research strategy include the identification of protein biomarkers that demarcate normal and disease populations, and the identification of therapeutic biomarkers for the treatment of diseases such as cancer (Celis, J. E., and Gromov, P. (2003) Proteomics in translational cancer research: Toward an integrated approach. Cancer Cell 3, 9-151). Prostate cancer is one disease that would greatly benefit from implementing MS-based expression profiling methods because of the need to stratify the disease based on molecular markers. In this review, we will summarize the current MS-based methods to identify and validate biomarkers in human prostate cancer. Lastly, we propose a reverse proteomic approach implementing a quantitative MS research strategy to identify and quantify biomarkers implicated in prostate cancer development. With this approach, the absolute levels of prostate cancer biomarkers will be identified and quantified in normal and diseased samples by measuring the levels of native peptide biomarkers in relation to a chemically identical but isotopically labeled reference peptide. Ultimately, a centralized prostate cancer peptide biomarker expression database could function as a repository for the identification, quantification, and validation of protein biomarker(s) during prostate cancer progression in men.     

Proteins are potent biomarkers to detect colon cancer progression

Colon cancer is the most common type of cancer and major cause of death worldwide. The detection of colon cancer is difficult in early stages. However, the secretory proteins have been used as ideal biomarker for the detection of colon cancer progress in cancer patients. Serum/tissue protein expression could help general practitioners to identify colon cancer at earlier stages. By this way, we use the biomarkers to evaluate the anticancer drugs and their response to therapy in cancer models. Recently, the biomarker discovery is important in cancer biology and disease management. Also, many measurable specific molecular components have been studied in colon cancer therapeutics. The biomolecules are mainly DNA, RNA, metabolites, enzymes, mRNA, aptamers and proteins. Thus, in this review we demonstrate the important protein biomarker in colon cancer development and molecular identification of protein biomarker discovery.     

Chemical deamidation: a common pitfall in large-scale N-linked glycoproteomic mass spectrometry-based analyses

N-Linked glycoproteins are involved in several diseases and are important as potential diagnostic molecules for biomarker discovery. Therefore, it is important to provide sensitive and reliable analytical methods to identify not only the glycoproteins but also the sites of glycosylation. Recently, numerous strategies to identify N-linked glycosylation sites have been described. These strategies have been applied to cell lines and several tissues with the aim of identifying many hundreds (or thousands) of glycosylation events. With high-throughput strategies however, there is always the potential for false positives. The confusion arises since the protein N-glycosidase F (PNGase F) reaction used to separate N-glycans from formerly glycosylated peptides catalyzes the cleavage and deamidates the asparagine residue. This is typically viewed as beneficial since it acts to highlight the modification site. We have evaluated this common large-scale N-linked glycoproteomic strategy and proved potential pitfalls using Escherichia coli as a model organism, since it lacks the N-glycosylation machinery found in mammalian systems and some pathogenic microbes. After isolation and proteolytic digestion of E. coli membrane proteins, we investigated the presence of deamidated asparagines. The results show the presence of deamidated asparagines especially with close proximity to a glycine residue or other small amino acid, as previously described for spontaneous in vivo deamidation. Moreover, we have identified deamidated peptides with incorporation of (18)O, showing the pitfalls of glycosylation site assignment based on deamidation of asparagine induced by PNGase F in (18)O-water in large-scale analyses. These data experimentally prove the need for more caution in assigning glycosylation sites and "new" N-linked consensus sites based on common N-linked glycoproteomics strategies without proper control experiments. Besides showing the spontaneous deamidation, we provide alternative methods for validation that should be used in such experiments.     

综述: N-连接完整糖肽的质谱分析策略和研究方法

蛋白质糖基化作为最普遍、最重要的蛋白质修饰,一直是组学研究的焦点之一．近十几年来,N-连接糖蛋白质组学研究普遍采用的方法是将糖链与所修饰的多肽分开进行分析．该策略虽降低了分析难度,却也丢失了糖链与蛋白质糖基化位点间重要的对应关系信息．近年来,完整糖肽的质谱分析策略和方法逐步建立起来．总体而言,要实现对完整糖肽的直接质谱分析,首先需要从复杂样品中富集完整糖肽以消除非糖基化多肽对完整糖肽分析的影响,然后在质谱分析中还需要根据糖肽特性调整相应质谱分析参数,最后在后续数据分析中还需要开发相应的分析软件以完成完整糖肽中多肽序列和糖链组成或结构的鉴定．本文即从以上三个主要方面系统阐述目前N-完整糖肽分析中常用的质谱和数据分析策略和方法,并进一步在糖肽谱图识别、母离子单同位素分子质量校正、数据库选择以及假阳性率评估和控制等方面都进行了逐一探讨．完整糖肽的直接质谱分析有助于获取糖链和糖基化位点间的对应关系信息,可为生物标志物发现和疾病致病机理等研究提供更有力的糖蛋白质组学研究工具．     

Low abundance protein enrichment for discovery of candidate plasma protein biomarkers for early detection of breast cancer

Molecular biomarkers of early stage breast cancer may improve the sensitivity and specificity of diagnosis. Plasma biomarkers have additional value in that they can be monitored with minimal invasiveness. Plasma biomarker discovery by genome-wide proteomic methods is impeded by the wide dynamic range of protein abundance and the heterogeneity of protein expression in healthy and disease populations which requires the analysis of a large number of samples. We addressed these issues through the development of a novel protocol that couples a combinatorial peptide ligand library protein enrichment strategy with isobaric label-based 2D LC-MS/MS for the identification of candidate biomarkers in high throughput. Plasma was collected from patients with stage I breast cancer or benign breast lesions. Low abundance proteins were enriched using a bead-based combinatorial library of hexapeptides. This resulted in the identification of 397 proteins, 22% of which are novel plasma proteins. Twenty-three differentially expressed plasma proteins were identified, demonstrating the effectiveness of the described protocol and defining a set of candidate biomarkers to be validated in independent samples. This work can be used as the basis for the design of properly powered investigations of plasma protein expression for biomarker discovery in larger cohorts of patients with complex disease.     

Protein identification and Peptide expression resolver: harmonizing protein identification with protein expression data

Proteomic discovery platforms generate both peptide expression information and protein identification information. Peptide expression data are used to determine which peptides are differentially expressed between study cohorts, and then these peptides are targeted for protein identification. In this paper, we demonstrate that peptide expression information is also a powerful tool for enhancing confidence in protein identification results. Specifically, we evaluate the following hypothesis: tryptic peptides originating from the same protein have similar expression profiles across samples in the discovery study. Evidence supporting this hypothesis is provided. This hypothesis is integrated into a protein identification tool, PIPER (Protein Identification and Peptide Expression Resolver), that reduces erroneous protein identifications below 5%. PIPER's utility is illustrated by application to a 72-sample biomarker discovery study where it is demonstrated that false positive protein identifications can be reduced below 5%. Consequently, it is recommended that PIPER methodology be incorporated into proteomic studies where both protein expression and identification data are collected.     

Development of a Chip/Chip/SRM platform using digital chip isoelectric focusing and LC-Chip mass spectrometry for enrichment and quantitation of low abundance protein biomarkers in human plasma

Protein biomarkers are critical for diagnosis, prognosis, and treatment of disease. The transition from protein biomarker discovery to verification can be a rate limiting step in clinical development of new diagnostics. Liquid chromatography-selected reaction monitoring mass spectrometry (LC-SRM MS) is becoming an important tool for biomarker verification studies in highly complex biological samples. Analyte enrichment or sample fractionation is often necessary to reduce sample complexity and improve sensitivity of SRM for quantitation of clinically relevant biomarker candidates present at the low ng/mL range in blood. In this paper, we describe an alternative method for sample preparation for LC-SRM MS, which does not rely on availability of antibodies. This new platform is based on selective enrichment of proteotypic peptides from complex biological peptide mixtures via isoelectric focusing (IEF) on a digital ProteomeChip (dPC) for SRM quantitation using a triple quadrupole (QQQ) instrument with an LC-Chip (Chip/Chip/SRM). To demonstrate the value of this approach, the optimization of the Chip/Chip/SRM platform was performed using prostate specific antigen (PSA) added to female plasma as a model system. The combination of immunodepletion of albumin and IgG with peptide fractionation on the dPC, followed by SRM analysis, resulted in a limit of quantitation of PSA added to female plasma at the level of ～1-2.5 ng/mL with a CV of ～13%. The optimized platform was applied to measure levels of PSA in plasma of a small cohort of male patients with prostate cancer (PCa) and healthy matched controls with concentrations ranging from 1.5 to 25 ng/mL. A good correlation (r(2) = 0.9459) was observed between standard clinical ELISA tests and the SRM-based assay. Our data demonstrate that the combination of IEF on the dPC and SRM (Chip/Chip/SRM) can be successfully applied for verification of low abundance protein biomarkers in complex samples.     

A new method for detection of tumor driver‐dependent changes of protein sialylation in a colon cancer cell line reveals nectin‐3 as TGFBR2 target

Protein‐linked glycans play key roles in cell differentiation, cell–cell interactions, cell growth, adhesion and immune response. Aberrant glycosylation is a characteristic feature of tumor cells and is involved in tumor growth, escape from apoptosis, metastasis formation, and resistance to therapy. It can serve as cancer biomarker and treatment target. To enable comprehensive screening for the impact of tumor driving mutations in colorectal cancer cells we present a method for specific analysis of tumor driver‐induced glycome changes. The strategy is based on a combination of three technologies, that is recombinase‐mediated cassette exchange (RMCE), Click‐It chemistry and mass spectrometry. The new method is exemplified by the analysis of the impact of inactivating mutations of the TGF‐ß‐receptor type II (TGFBR2) on sialic acid incorporation into protein‐linked glycans of the colon cancer cell line HCT116. Overall, 70 proteins were found to show de novo sialic acid incorporation exclusively upon TGFBR2 expression whereas 7 proteins lost sialylation upon TGFBR2 reconstitution. Validation of detected candidate glycoproteins is demonstrated with the cell surface glycoprotein nectin‐3 known to be involved in metastasis, invasion and prognosis of various cancers. Altogether, our new approach can help to systematically puzzle out the influence of tumor‐specific mutations in a major signaling pathway, as exemplified by the TGFBR2 tumor suppressor, on the tumor glycome. It facilitates the identification of glycan‐based tumor markers that could be used for diagnostic and therapeutic applications. In principle the outlined strategy can be adapted to any cancer cell line, tumor driver mutation and several glycan‐building blocks.     

Highly Efficient Identification of O‐GalNAc Glycosylation by an Acid‐Assisted Glycoform Simplification Approach

Compared with N‐linked glycosylation, the analysis of O‐GalNAc glycosylation is extremely challenging due to the high structure diversity of glycans and lack of glycosidases to release O‐GalNAc glycans. In this work, a glycoform simplification strategy by combining HILIC enrichment with chemical de‐sialylation to characterize O‐GalNAc glycosylation of human serum is presented. This method is first validated by using the bovine fetuin as the test sample. It is found that more than 90% of the sialic acid residues can be removed from bovine fetuin by the acid‐assisted de‐sialylation method, which significantly simplifies the glycan structure and improves identification sensitivity. Indeed, the number of identified peptide backbones increases nearly one fold when this strategy is used. This method is further applied to analyze the human serum sample, where 185 O‐GalNAc modified peptide sequences corresponding to 94 proteins with high confidence (FDR (false detection rate) <1%) are identified. This straight forward strategy can significantly reduce the variations of glycan structures, and is applicable to analysis of other biological samples with high complexity.     

Antibody-based enrichment of peptides on magnetic beads for mass-spectrometry-based quantification of serum biomarkers

A major bottleneck for validation of new clinical diagnostics is the development of highly sensitive and specific assays for quantifying proteins. We previously described a method, stable isotope standards with capture by antipeptide antibodies, wherein a specific tryptic peptide is selected as a stoichiometric representative of the protein from which it is cleaved, is enriched from biological samples using immobilized antibodies, and is quantitated using mass spectrometry against a spiked internal standard to yield a measure of protein concentration. In this study, we optimized a magnetic-bead-based platform amenable to high-throughput peptide capture and demonstrated that antibody capture followed by mass spectrometry can achieve ion signal enhancements on the order of 10(3), with precision (CVs <10%) and accuracy (relative error approximately 20%) sufficient for quantifying biomarkers in the physiologically relevant ng/mL range. These methods are generally applicable to any protein or biological fluid of interest and hold great potential for providing a desperately needed bridging technology between biomarker discovery and clinical application.     

A lectin affinity workflow targeting glycosite-specific, cancer-related carbohydrate structures in trypsin-digested human plasma

Glycans are cell-type-specific, posttranslational protein modifications that are modulated during developmental and disease processes. As such, glycoproteins are attractive biomarker candidates. Here, we describe a mass spectrometry-based workflow that incorporates lectin affinity chromatography to enrich for proteins that carry specific glycan structures. As increases in sialylation and fucosylation are prominent among cancer-associated modifications, we focused on Sambucus nigra agglutinin (SNA) and Aleuria aurantia lectin (AAL), lectins which bind sialic acid- and fucose-containing structures, respectively. Fucosylated and sialylated glycopeptides from human lactoferrin served as positive controls, and high-mannose structures from yeast invertase served as negative controls. The standards were spiked into Multiple Affinity Removal System (MARS) 14-depleted, trypsin-digested human plasma from healthy donors. Samples were loaded onto lectin columns, separated by HPLC into flow-through and bound fractions, and treated with peptide: N-glycosidase F to remove N-linked glycans. The deglycosylated peptide fractions were interrogated by ESI HPLC-MS/MS. We identified a total of 122 human plasma glycoproteins containing 247 unique glycosites. Importantly, several of the observed glycoproteins (e.g., cadherin 5 and neutrophil gelatinase-associated lipocalin) typically circulate in plasma at low nanogram per milliliter levels. Together, these results provide mass spectrometry-based evidence of the utility of incorporating lectin-separation platforms into cancer biomarker discovery pipelines.     

Proteome profiling reveals gender differences in the composition of human serum

Proteome analysis using human serum is a technological advancement that will enable the discovery of novel biomarkers and biomarker patterns of various human diseases. Although proteome analysis using serum has potential in disease prevention, early diagnosis and treatment of diseases, and evaluation of pharmacotherapies, this technology is still in its infancy. Thus, we sought to develop an advanced method of conducting proteome analysis on human serum. In this study, we report the development of the semi‐comprehensive protein analytical technique, which involves the systematic use of iTRAQ labeling, HPLC, nano‐LC and MS. We compared the composition of the serum proteome in males and females using this technique and detected gender‐based differences in serum protein composition. This technology will enable the generation of databases that may ultimately lead to the discovery of specific biomarkers or biomarker patterns of various diseases.     

An alternative method for serum protein depletion/enrichment by precipitation at mildly acidic pH values and low ionic strength

Serum proteome analysis is severely hampered by the extreme dynamic range of protein concentrations, but tools for the specific depletion of highly abundant serum proteins lack for most farm and companion animals. A well‐established alternative strategy to reduce the dynamic range of plasma protein concentrations, treatment with combinatorial peptide ligand libraries (CPLL), is generally applicable but requires large amounts of sample. Therefore, additional depletion/enrichment protocols for plasma and serum samples from animals are desirable. In this respect, we have tested a protein precipitate that formed after withdrawal of salt from human, bovine, or porcine serum at pH 4.2. The bovine sample was composed of over 300 proteins making it a potential source for biomarker discovery. Precipitation was highly reproducible and the concentrations of albumin and other highly abundant serum proteins were strongly reduced. In comparison to the CPLL treatment, precipitation did not introduce any selection bias based on hydrophathy or pI. However, the composition of both preparations was partially complementary. Salt withdrawal at pH 4.2 is suggested as additional depletion/enrichment strategy for serum samples. Also, we point out that the removal of precipitates from serum samples under the described conditions bears the risk of losing a valuable protein fraction.