蛋白质芯片SELDI-TOFMS技术的研究进展及其在临床中的应用

蛋白质芯片为新一代的蛋白质组研究技术,由美国Ciphergen生物系统公司引进,表面增强激光解吸电离-飞行时间质谱(SELDI-TOFMS)提供一个高通量和高灵敏度的检测平台。投放至今虽短短10来年,但卓越的成果已广为医学科学界重视,尤其在恶性肿瘤的早期诊断、监控和预后研究上。蛋白质是细胞内执行生物功能的最终分子,蛋白质组学研究让人类更深入了解疾病和生命的本源,不断发现的特异性肿瘤标志物更为攻克癌症带来新希望。这里除对表面增强激光解吸电离_飞行时间质谱作较详尽的介绍外,更重点阐述其近年来蛋白质芯片近期的研究进展和在临床中的应用,并就其优劣和发展前景作出评估。     

Global and targeted quantitative proteomics for biomarker discovery

The extraordinary developments made in proteomic technologies in the past decade have enabled investigators to consider designing studies to search for diagnostic and therapeutic biomarkers by scanning complex proteome samples using unbiased methods. The major technology driving these studies is mass spectrometry (MS). The basic premises of most biomarker discovery studies is to use the high data-gathering capabilities of MS to compare biological samples obtained from healthy and disease-afflicted patients and identify proteins that are differentially abundant between the two specimen. To meet the need to compare the abundance of proteins in different samples, a number of quantitative approaches have been developed. In this article, many of these will be described with an emphasis on their advantageous and disadvantageous for the discovery of clinically useful biomarkers.     

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.     

Accurate inclusion mass screening: a bridge from unbiased discovery to targeted assay development for biomarker verification

Verification of candidate biomarker proteins in blood is typically done using multiple reaction monitoring (MRM) of peptides by LC-MS/MS on triple quadrupole MS systems. MRM assay development for each protein requires significant time and cost, much of which is likely to be of little value if the candidate biomarker is below the detection limit in blood or a false positive in the original discovery data. Here we present a new technology, accurate inclusion mass screening (AIMS), designed to provide a bridge from unbiased discovery to MS-based targeted assay development. Masses on the software inclusion list are monitored in each scan on the Orbitrap MS system, and MS/MS spectra for sequence confirmation are acquired only when a peptide from the list is detected with both the correct accurate mass and charge state. The AIMS experiment confirms that a given peptide (and thus the protein from which it is derived) is present in the plasma. Throughput of the method is sufficient to qualify up to a hundred proteins/week. The sensitivity of AIMS is similar to MRM on a triple quadrupole MS system using optimized sample preparation methods (low tens of ng/ml in plasma), and MS/MS data from the AIMS experiments on the Orbitrap can be directly used to configure MRM assays. The method was shown to be at least 4-fold more efficient at detecting peptides of interest than undirected LC-MS/MS experiments using the same instrumentation, and relative quantitation information can be obtained by AIMS in case versus control experiments. Detection by AIMS ensures that a quantitative MRM-based assay can be configured for that protein. The method has the potential to qualify large number of biomarker candidates based on their detection in plasma prior to committing to the time- and resource-intensive steps of establishing a quantitative assay.     

Sampling and analytical strategies for biomarker discovery using mass spectrometry

There is an often unspoken truth behind the course of scientific investigation that involves not what is necessarily academically worthy of study, but rather what is scientifically worthy in the eyes of funding agencies. The perception of worthy research is, as cost is driven in the simplest sense in economics, often driven by demand. Presently, the demand for novel diagnostic and therapeutic protein biomarkers that possess high sensitivity and specificity is placing major impact on the field of proteomics. The focal discovery technology that is being relied on is mass spectrometry (MS), whereas the challenge of biomarker discovery often lies not in the application of MS but in the underlying proteome sampling and bioinformatic processing strategies. Although biomarker discovery research has been historically technology-driven, it is clear from the meager success in generating validated biomarkers that increasing attention must be placed at the pre-analytic stage, such as sample retrieval and preparation. As diseases vary, so do the combinations of sampling and sample analyses necessary to discover novel biomarkers. In this review, we highlight different strategies used toward biomarker discovery and discuss them in terms of their reliance on technology and methodology.     

Validation of an HPLC/MS/MS method with isotopic dilution for quantitative determination of trans,trans-muconic acid in urine samples of workers exposed to low benzene concentrations

Urinary trans,trans-muconic acid (t,t-MA), a biomarker of benzene exposure, is usually determined by HPLC methods with detection by either UV or, more recently, electrospray tandem mass spectrometry. However, not all these methods have been fully validated for quantitative analysis. This paper presents an HPLC/MS/MS method for reliable quantitative determination of t,t-MA that uses a commercial deuterium-labeled isotope as internal standard; the matrix effect has been evaluated and LOD is 0.22 microg/L. We used this method to test 200 urine samples, 175 of them collected at end-of-shift from workers in an oil refinery.     

Targeted and Untargeted Proteomics Approaches in Biomarker Development

An enormous amount of research effort has been devoted to biomarker discovery and validation. With the completion of the human genome, proteomics is now playing an increasing role in this search for new and better biomarkers. Here, what leads to successful biomarker development is reviewed and how these features may be applied in the context of proteomic biomarker research is considered. The “fit‐for‐purpose” approach to biomarker development suggests that untargeted proteomic approaches may be better suited for early stages of biomarker discovery, while targeted approaches are preferred for validation and implementation. A systematic screening of published biomarker articles using MS‐based proteomics reveals that while both targeted and untargeted technologies are used in proteomic biomarker development, most researchers do not combine these approaches. i) The reasons for this discrepancy, (ii) how proteomic technologies can overcome technical challenges that seem to limit their translation into the clinic, and (iii) how MS can improve, complement, or replace existing clinically important assays in the future are discussed.     

A quantitative proteomic approach to prion disease biomarker research: delving into the glycoproteome

Mass spectrometry (MS) -- based proteomic approaches have evolved as powerful tools for the discovery of biomarkers. However, the identification of potential protein biomarkers from biofluid samples is challenging because of the limited dynamic range of detection. Currently there is a lack of sensitive and reliable premortem diagnostic test for prion diseases. Here, we describe the use of a combined MS-based approach for biomarker discovery in prion diseases from mouse plasma samples. To overcome the limited dynamic range of detection and sample complexity of plasma samples, we used lectin affinity chromatography and multidimensional separations to enrich and isolate glycoproteins at low abundance. Relative quantitation of a panel of proteins was obtained by a combination of isotopic labeling and validated by spectral counting. Overall 708 proteins were identified, 53 of which showed more than 2-fold increase in concentration whereas 58 exhibited more than 2-fold decrease. A few of the potential candidate markers were previously associated with prion or other neurodegenerative diseases.     

The nucleotide-independent Fe(III)-binding site is located on beta subunit of the mitochondrial F(1)-ATPase

The need for methods to identify disease biomarkers is underscored by the survival-rate of patients diagnosed at early stages of cancer progression. Surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) is a novel approach to biomarker discovery that combines two powerful techniques: chromatography and mass spectrometry. One of the key features of SELDI-TOF MS is its ability to provide a rapid protein expression profile from a variety of biological and clinical samples. It has been used for biomarker identification as well as the study of protein-protein, and protein-DNA interaction. The versatility of SELDI-TOF MS has allowed its use in projects ranging from the identification of potential diagnostic markers for prostate, bladder, breast, and ovarian cancers and Alzheimer's disease, to the study of biomolecular interactions and the characterization of posttranslational modifications. In this minireview we discuss the application of SELDI-TOF MS to protein biomarker discovery and profiling.     

Biomarker discovery for kidney diseases by mass spectrometry

By the development of soft ionization such as matrix-assisted laser desorption/ionization (MALDI) and electrospray ionization (ESI), mass spectrometry (MS) has become an indispensable technique to analyze proteins. The combination of protein separation and identification such as two-dimensional gel electrophoresis and MS, surface-enhanced laser desorption/ionization-MS, liquid chromatography/MS, and capillary electrophoresis/MS has been successfully applied for proteome analysis of urine and plasma to discover biomarkers of kidney diseases. Some urinary proteins and their proteolytic fragments have been identified as biomarker candidates for kidney diseases. This article reviews recent advances in the application of proteomics using MS to discover biomarkers for kidney diseases.     

Rapidly maturing field of proteomics: A gateway to studying diseases

The proteomics work reported by Smith et al. represents a giant step forward in characterizing the cerebrospinal fluid (CSF) proteome in mouse models of human diseases. Whereas prior studies were limited to analysis of CSF pools, Smith et al. (Proteomics 2014, 14, 1102–1106) base their conclusions on data derived from individual mice, thereby capturing a fuller range of the biological diversity present. These results underscore how far proteomics has come in the past few years, developing into a modern tool with the capacity to remove bottlenecks in the study of neuropsychiatric diseases. Past efforts with mass spectrometry (MS) have been hampered by limitations in access to CSF samples, and small volumes when available. These barriers have been overcome with newer MS platforms and advances in sample preparation. We are far closer than before to producing the production of clinically useful proteomic data for biomarker discovery and for deriving insights into pathogenesis that can lead to more effective treatments for many diseases.     

Evaluation of a pretreatment method for two-dimensional gel electrophoresis of synovial fluid using cartilage oligomeric matrix protein as a marker

Osteoarthritis (OA) is the most common rheumatic pathology. One of the major objectives of OA research is the development of early diagnostic strategies such as those using proteomic technology. Synovial fluid (SF) in OA patients is a potential source of biomarkers for OA. The efficient and reliable preparation of SF proteomes is a critical step towards biomarker discovery. In this study, we have optimized a pretreatment method for two-dimensional gel electrophoresis (2DE) separation of the SF proteome, by enriching low-abundance proteins and simultaneously removing hyaluronic acid, albumin, and IgG. SF samples pretreated using this optimized method were then evaluated by 1DE and 2DE separation followed by immunodetection of cartilage oligomeric matrix protein (COMP), a known OA biomarker, and by the identification of 3 proteins (apolipoprotein, haptoglobin precursor, and fibrinogen D fragment) that are related to joint diseases.     

Development of quantitative plasma N-glycoproteomics using label-free 2-D LC-MALDI MS and its applicability for biomarker discovery in hepatocellular carcinoma

There has been rapid progress in the development of clinical proteomic methodologies with improvements in mass spectrometric technologies and bioinformatics, leading to many new methodologies for biomarker discovery from human plasma. However, it is not easy to find new biomarkers because of the wide dynamic range of plasma proteins and the need for their quantification. Here, we report a new methodology for relative quantitative proteomic analysis combining large-scale glycoproteomics with label-free 2-D LC-MALDI MS. In this method, enrichment of glycopeptides using hydrazide resin enables focusing on plasma proteins with lower abundance corresponding to the tissue leakage region. On quantitative analysis, signal intensities by 2-D LC-MALDI MS were normalized using a peptide internal control, and the values linked to LC data were treated with DeView? software. Our proteomic method revealed that the quantitative dynamic ranged from 102 to 10? pg/mL of plasma proteins with good reproducibility, and the limit of detection was of the order of a few ng/mL of proteins in biological samples. To evaluate the applicability of our method for biomarker discovery, we performed a feasibility study using plasma samples from patients with hepatocellular carcinoma, and identified biomarker candidates, including ceruloplasmin, alpha-1 antichymotrypsin, and multimerin-1.     

A computational platform for MALDI-TOF mass spectrometry data: Application to serum and plasma samples

BackgroundMass spectrometry (MS) is becoming the gold standard for biomarker discovery. Several MS-based bioinformatics methods have been proposed for this application, but the divergence of the findings by different research groups on the same MS data suggests that the definition of a reliable method has not been achieved yet. In this work, we propose an integrated software platform, MASCAP, intended for comparative biomarker detection from MALDI-TOF MS data.ResultsMASCAP integrates denoising and feature extraction algorithms, which have already shown to provide consistent peaks across mass spectra; furthermore, it relies on statistical analysis and graphical tools to compare the results between groups. The effectiveness in mass spectrum processing is demonstrated using MALDI-TOF data, as well as SELDI-TOF data. The usefulness in detecting potential protein biomarkers is shown comparing MALDI-TOF mass spectra collected from serum and plasma samples belonging to the same clinical population.ConclusionsThe analysis approach implemented in MASCAP may simplify biomarker detection, by assisting the recognition of proteomic expression signatures of the disease. A MATLAB implementation of the software and the data used for its validation are available at http://www.unich.it/proteomica/bioinf.     

Targeted proteomics: a bridge between discovery and validation

New technologies in mass spectrometry are beginning to mature and show unique advantages for the identification and quantitation of proteins. In recent years, one of the significant goals of clinical proteomics has been to identify biomarkers that can be used for clinical diagnosis. As technology has progressed, the list of potential biomarkers has grown. However, the verification and validation of these potential biomarkers is increasingly challenging and require high-throughput quantitative assays, targeting specific candidates. Targeted proteomics bridges the gap between biomarker discovery and the development of clinically applicable biomarker assays.     

LC‐MS/MS‐based serum proteomics for identification of candidate biomarkers for hepatocellular carcinoma

Associating changes in protein levels with the onset of cancer has been widely investigated to identify clinically relevant diagnostic biomarkers. In the present study, we analyzed sera from 205 patients recruited in the United States and Egypt for biomarker discovery using label‐free proteomic analysis by LC‐MS/MS. We performed untargeted proteomic analysis of sera to identify candidate proteins with statistically significant differences between hepatocellular carcinoma (HCC) and patients with liver cirrhosis. We further evaluated the significance of 101 proteins in sera from the same 205 patients through targeted quantitation by MRM on a triple quadrupole mass spectrometer. This led to the identification of 21 candidate protein biomarkers that were significantly altered in both the United States and Egyptian cohorts. Among the 21 candidates, ten were previously reported as HCC‐associated proteins (eight exhibiting consistent trends with our observation), whereas 11 are new candidates discovered by this study. Pathway analysis based on the significant proteins reveals upregulation of the complement and coagulation cascades pathway and downregulation of the antigen processing and presentation pathway in HCC cases versus patients with liver cirrhosis. The results of this study demonstrate the power of combining untargeted and targeted quantitation methods for a comprehensive serum proteomic analysis, to evaluate changes in protein levels and discover novel diagnostic biomarkers. All MS data have been deposited in the ProteomeXchange with identifier PXD001171 ( http://proteomecentral.proteomexchange.org/dataset/PXD001171 ).     

Biomarker discovery from body fluids using mass spectrometry

Systems analysis of body fluids by mass spectrometry (MS) is an upcoming field of biomarker research. This approach is extremely attractive because it does not require biomarker candidates and the sample preparation is simple. However, during the development of the technique strong critical comments were made on the poor reproducibility, the special characteristics of blood as a source of peptides and on the frequent non-adequate statistical analysis of the data. Here we discuss the efforts made in the last few years to develop suitable protocols, which could lead to biomarker discovery from body fluids by mass spectrometry. Our review focuses on the systems analysis of non-digested blood serum or plasma samples by MALDI-TOF and SELDI-TOF.     

Initial development and validation of a novel extraction method for quantitative mining of the formalin-fixed, paraffin-embedded tissue proteome for biomarker investigations

Annotated formalin-fixed, paraffin-embedded (FFPE) tissue archives constitute a valuable resource for retrospective biomarker discovery. However, proteomic exploration of archival tissue is impeded by extensive formalin-induced covalent cross-linking. Robust methodology enabling proteomic profiling of archival resources is urgently needed. Recent work is beginning to support the feasibility of biomarker discovery in archival tissues, but further developments in extraction methods which are compatible with quantitative approaches are urgently needed. We report a cost-effective extraction methodology permitting quantitative proteomic analyses of small amounts of FFPE tissue for biomarker investigation. This surfactant/heat-based approach results in effective and reproducible protein extraction in FFPE tissue blocks. In combination with a liquid chromatography-mass spectrometry-based label-free quantitative proteomics methodology, the protocol enables the robust representative and quantitative analyses of the archival proteome. Preliminary validation studies in renal cancer tissues have identified typically 250-300 proteins per 500 ng of tissue with 1D LC-MS/MS with comparable extraction in FFPE and fresh frozen tissue blocks and preservation of tumor/normal differential expression patterns (205 proteins, r = 0.682; p < 10(-15)). The initial methodology presented here provides a quantitative approach for assessing the potential suitability of the vast FFPE tissue archives as an alternate resource for biomarker discovery and will allow exploration of methods to increase depth of coverage and investigate the impact of preanalytical factors.     

An exogenous marker: a novel approach for the characterization of oxidative stress

Oxidative stress (OS) and its consequences which promote alterations in biomolecules, to tissue damage and to the development of pathological conditions, continue to attract many investigators. The identification of reliable biomarker is essential for the characterization of OS and possibly for early discovery of OS-associated diseases. The aim of the present study was to offer a new concept in the development of novel probes for OS, based on the design, synthesis, and utilization of exogenous markers, as alternative to the search for endogenous markers. This article describes: (a) the synthesis of such a marker, linoleoyl tyrosine 2-deoxyguanosyl ester (LTG), constructed from three endogenous subunits: linoleic acid, tyrosine, and 2'-deoxyguanosine, representing the three major groups from which the body is composed, unsaturated fatty acids (USFA), proteins, and DNA, respectively, all bound covalently and (b) the development of analytical tools (LC/MS/MS) to enable the identification of the different LTG oxidized products formed under OS by exposure of LTG to different reactive oxygen species (ROS) such as, copper ions and hypochlorous acid.     

Clinical application of quantitative glycomics

ABSTRACT

Introduction: Aberrant glycosylation has been associated with many diseases. Decades of research activities have reported many reliable glycan biomarkers of different diseases which enable effective disease diagnostics and prognostics. However, none of the glycan markers have been approved for clinical diagnosis. Thus, a review of these studies is needed to guide the successful clinical translation.

Area covered: In this review, we describe and discuss advances in analytical methods enabling clinical glycan biomarker discovery, focusing only on studies of released glycans. This review also summarizes the different glycobiomarkers identified for cancers, Alzheimer’s disease, diabetes, hepatitis B and C, and other diseases.

Expert commentary: Along with the development of techniques in quantitative glycomics, more glycans or glycan patterns have been reported as better potential biomarkers of different diseases and proved to have greater diagnostic/diagnostic sensitivity and specificity than existing markers. However, to successfully apply glycan markers in clinical diagnosis, more studies and verifications on large biological cohorts need to be performed. In addition, faster and more efficient glycomic strategies need to be developed to shorten the turnaround time. Thus, glycan biomarkers have an immense chance to be used in clinical prognosis and diagnosis of many diseases in the near future.