Rat plasma proteomics: effects of abundant protein depletion on proteomic analysis

The proteomic analysis of plasma and serum samples represents a formidable challenge due to the presence of a few highly abundant proteins such as albumin and immunoglobulins. Detection of low abundance protein biomarkers requires therefore either the specific depletion of high abundance proteins with immunoaffinity columns and/or optimized protein fractionation methods based on charge, size or hydrophobicity. Here we describe the depletion of seven abundant rat plasma proteins with an immunoaffinity column with coupled antibodies directed against albumin, IgG, transferrin, IgM, haptoglobin, fibrinogen and alpha1-anti-trypsin. The IgY-R7-LC2 (Beckman Coulter) column showed high specificity for the targeted proteins and was able to efficiently remove most of the albumin, IgG and transferrin from rat plasma samples as judged by Western blot analysis. Depleted rat plasma protein samples were analyzed by SELDI-TOF MS, 2D SDS-PAGE and 2D-LC and compared to non-depleted plasma samples as well as to the abundant protein fraction that was eluted from the immunoaffinity column. Analysis of the depleted plasma protein fraction revealed improved signal to noise ratios, regardless of which proteomic method was applied. However, only a small number of new proteins were observed in the depleted protein fraction. Immunoaffinity depletion of abundant plasma proteins results in the significant dilution of the original sample which complicates subsequent analysis. Most proteomic approaches require specialized sample preparation procedures during which significant losses of less abundant proteins and potential biomarkers can occur. Even though abundant protein depletion reduces the dynamic range of the plasma proteome by about 2-3 orders of magnitude, the difference between medium-abundant and low abundant plasma proteins is still in the range of 7-8 orders of magnitude and beyond the dynamic range of current proteomic technologies. Thus, exploring the plasma proteome in greater detail remains a daunting task.     

Higher dimensional (Hi-D) separation strategies dramatically improve the potential for cancer biomarker detection in serum and plasma

The plasma proteome has a wide dynamic range of protein concentrations and is dominated by a few highly abundant proteins. Discovery of novel cancer biomarkers using proteomics is particularly challenging because specific biomarkers are expected to be low abundance proteins with normal blood concentrations of low nanograms per milliliter or less. Conventional, one- and two-dimensional proteomic methods including 2D PAGE, 2D DIGE, LC-MS/MS, and LC/LC-MS/MS do not have the capacity to consistently detect many proteins in this range. In contrast, new higher dimensional (Hi-D) separation strategies, utilizing more than two dimensions of fractionation, can profile the low abundance proteome.     

Tissue proteomics for cancer biomarker development: laser microdissection and 2D-DIGE

Novel cancer biomarkers are required to achieve early diagnosis and optimized therapy for individual patients. Cancer is a disease of the genome, and tumor tissues are a rich source of cancer biomarkers as they contain the functional translation of the genome, namely the proteome. Investigation of the tumor tissue proteome allows the identification of proteomic signatures corresponding to clinico-pathological parameters, and individual proteins in such signatures will be good biomarker candidates. Tumor tissues are also a rich source for plasma biomarkers, because proteins released from tumor tissues may be more cancer specific than those from non-tumor cells. Two-dimensional difference gel electrophoresis (2D-DIGE) with novel ultra high sensitive fluorescent dyes (CyDye DIGE Fluor satulation dye) enables the efficient protein expression profiling of laser-microdissected tissue samples. The combined use of laser microdissection allows accurate proteomic profiling of specific cells in tumor tissues. To develop clinical applications using the identified biomarkers, collaboration between research scientists, clinicians and diagnostic companies is essential, particularly in the early phases of the biomarker development projects. The proteomics modalities currently available have the potential to lead to the development of clinical applications, and channeling the wealth of produced information towards concrete and specific clinical purposes is urgent.     

细胞质膜蛋白质组学研究技术进展

质膜蛋白在细胞中执行着非常重要的功能。随着蛋白质组学的发展,细胞质膜蛋白质组学成为蛋白质组学研究的重要组成部分,它为质膜蛋白的生物功能研究及药物靶标的发现提供了新的途径。然而,质膜蛋白丰度低、疏水性强,对现有蛋白质组学研究技术提出了挑战。简要综述了近年来质膜蛋白质组研究的相关技术进展,包括富集、提取分离鉴定方法及定量和生物信息学研究方法等。     

Redefining Tissue Crosstalk via Shotgun Proteomic Analyses of Plasma Extracellular Vesicles

Protein signaling between tissues, or tissue cross‐talk is becoming recognized as a fundamental biological process that is incompletely understood. Shotgun proteomic analyses of tissues and plasma to explore this concept are regularly challenged by high dynamic range of protein abundance, which limits the identification of lower abundance proteins. In this viewpoint article, it is highlighted how a focus on proteins contained within extracellular vesicles (EVs) not only partially addresses this issue, but can also reveal an underappreciated complexity of the circulating proteome in various physiological and pathological contexts. Furthermore, how quantitative proteomics can inform EV mediated crosstalk is highlighted and the importance of high coverage, sensitive proteomic analyses of EVs to identify both the optimal methods to isolate EV subtypes of interest and proteins that characterize them is stressed.     

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.     

High-performance proteomics as a tool in biomarker discovery

Biomarkers allowing early detection of disease or therapy control have a huge influence in curing a disease. A wide variety of methods were applied to find new biomarkers. In contrast to methods focused on DNA or mRNA techniques, approaches considering proteins as potential biomarker candidates have the advantage that proteins are more diverse than DNA or RNA and are more reflective of a biological system. Here, we present an approach for the identification of new biomarkers relying on our experience from the past 10 years of proteomics, outlining a concept of "high-performance proteomics" This approach is based on quantitative proteome analysis using a sufficient number of clinical samples and statistical validation of proteomics data by independent methods, such as Western blot analysis or immunohistochemistry.     

Computational Biomarker Pipeline from Discovery to Clinical Implementation: Plasma Proteomic Biomarkers for Cardiac Transplantation

Recent technical advances in the field of quantitative proteomics have stimulated a large number of biomarker discovery studies of various diseases, providing avenues for new treatments and diagnostics. However, inherent challenges have limited the successful translation of candidate biomarkers into clinical use, thus highlighting the need for a robust analytical methodology to transition from biomarker discovery to clinical implementation. We have developed an end-to-end computational proteomic pipeline for biomarkers studies. At the discovery stage, the pipeline emphasizes different aspects of experimental design, appropriate statistical methodologies, and quality assessment of results. At the validation stage, the pipeline focuses on the migration of the results to a platform appropriate for external validation, and the development of a classifier score based on corroborated protein biomarkers. At the last stage towards clinical implementation, the main aims are to develop and validate an assay suitable for clinical deployment, and to calibrate the biomarker classifier using the developed assay. The proposed pipeline was applied to a biomarker study in cardiac transplantation aimed at developing a minimally invasive clinical test to monitor acute rejection. Starting with an untargeted screening of the human plasma proteome, five candidate biomarker proteins were identified. Rejection-regulated proteins reflect cellular and humoral immune responses, acute phase inflammatory pathways, and lipid metabolism biological processes. A multiplex multiple reaction monitoring mass-spectrometry (MRM-MS) assay was developed for the five candidate biomarkers and validated by enzyme-linked immune-sorbent (ELISA) and immunonephelometric assays (INA). A classifier score based on corroborated proteins demonstrated that the developed MRM-MS assay provides an appropriate methodology for an external validation, which is still in progress. Plasma proteomic biomarkers of acute cardiac rejection may offer a relevant post-transplant monitoring tool to effectively guide clinical care. The proposed computational pipeline is highly applicable to a wide range of biomarker proteomic studies.     

Proteomics and its applications for biomarker discovery in human saliva

Human saliva is a biological fluid with enormous diagnostic potential. Because saliva can be non-invasively collected, it provides an attractive alternative for blood, serum or plasma. It has been postulated that the blood concentrations of many components are reflected in saliva. Saliva harbors a wide array of proteins, which can be informative for the detection of diseases. Profiling the proteins in saliva over the course of disease progression could reveal potential biomarkers indicative of different stages of diseases, which may be useful in medical diagnostics. With advanced instrumentation and developed refined analytical techniques, proteomics is widely envisioned as a useful and powerful approach for salivary proteomic biomarker discovery. As proteomic technologies continue to mature, salivary proteomics have great potential for biomarker research and clinical applications. The progress and current status of salivary proteomics and its application in the biomarker discovery of oral and systematic diseases will be reviewed. The scientific and clinical challenges underlying this approach will also be discussed.     

Proteomics and its applications for biomarker discovery in human saliva

Human saliva is a biological fluid with enormous diagnostic potential. Because saliva can be non-invasively collected, it provides an attractive alternative for blood, serum or plasma. It has been postulated that the blood concentrations of many components are reflected in saliva. Saliva harbors a wide array of proteins, which can be informative for the detection of diseases. Profiling the proteins in saliva over the course of disease progression could reveal potential biomarkers indicative of different stages of diseases, which may be useful in medical diagnostics. With advanced instrumentation and developed refined analytical techniques, proteomics is widely envisioned as a useful and powerful approach for salivary proteomic biomarker discovery. As proteomic technologies continue to mature, salivary proteomics have great potential for biomarker research and clinical applications. The progress and current status of salivary proteomics and its application in the biomarker discovery of oral and systematic diseases will be reviewed. The scientific and clinical challenges underlying this approach will also be discussed.     

Discovery of pathway biomarkers from coupled proteomics and systems biology methods

Background

Breast cancer is worldwide the second most common type of cancer after lung cancer. Plasma proteome profiling may have a higher chance to identify protein changes between plasma samples such as normal and breast cancer tissues. Breast cancer cell lines have long been used by researches as model system for identifying protein biomarkers. A comparison of the set of proteins which change in plasma with previously published findings from proteomic analysis of human breast cancer cell lines may identify with a higher confidence a subset of candidate protein biomarker.

Results

In this study, we analyzed a liquid chromatography (LC) coupled tandem mass spectrometry (MS/MS) proteomics dataset from plasma samples of 40 healthy women and 40 women diagnosed with breast cancer. Using a two-sample t-statistics and permutation procedure, we identified 254 statistically significant, differentially expressed proteins, among which 208 are over-expressed and 46 are under-expressed in breast cancer plasma. We validated this result against previously published proteomic results of human breast cancer cell lines and signaling pathways to derive 25 candidate protein biomarkers in a panel. Using the pathway analysis, we observed that the 25 “activated” plasma proteins were present in several cancer pathways, including ‘Complement and coagulation cascades’, ‘Regulation of actin cytoskeleton’, and ‘Focal adhesion’, and match well with previously reported studies. Additional gene ontology analysis of the 25 proteins also showed that cellular metabolic process and response to external stimulus (especially proteolysis and acute inflammatory response) were enriched functional annotations of the proteins identified in the breast cancer plasma samples. By cross-validation using two additional proteomics studies, we obtained 86% and 83% similarities in pathway-protein matrix between the first study and the two testing studies, which is much better than the similarity we measured with proteins.

Conclusions

We presented a ‘systems biology’ method to identify, characterize, analyze and validate panel biomarkers in breast cancer proteomics data, which includes 1) t statistics and permutation process, 2) network, pathway and function annotation analysis, and 3) cross-validation of multiple studies. Our results showed that the systems biology approach is essential to the understanding molecular mechanisms of panel protein biomarkers.

     

Mass spectrometry-based proteomics for translational research: a technical overview

Mass spectrometry-based investigation of clinical samples enables the high-throughput identification of protein biomarkers. We provide an overview of mass spectrometry-based proteomic techniques that are applicable to the investigation of clinical samples. We address sample collection, protein extraction and fractionation, mass spectrometry modalities, and quantitative proteomics. Finally, we examine the limitations and further potential of such technologies. Liquid chromatography fractionation coupled with tandem mass spectrometry is well suited to handle mixtures of hundreds or thousands of proteins. Mass spectrometry-based proteome elucidation can reveal potential biomarkers and aid in the development of hypotheses for downstream investigation of the molecular mechanisms of disease.     

Proteomics approach and techniques in identification of reliable biomarkers for diseases

Biomarkers, also called biological markers, are indicators to identify a biological case or situation as well as detecting any presence of biological activities and processes. Proteins are considered as a type of biomarkers based on their characteristics. Therefore, proteomics approach is one of the most promising approaches in this field. The purpose of this review is to summarize the use of proteomics approach and techniques to identify proteins as biomarkers for different diseases. This review was obtained by searching in a computerized database. So, different researches and studies that used proteomics approach to identify different biomarkers for different diseases were reviewed. Also, techniques of proteomics that are used to identify proteins as biomarkers were collected. Techniques and methods of proteomics approach are used for the identification of proteins' activities and presence as biomarkers for different types of diseases from different types of samples. There are three essential steps of this approach including: extraction and separation of proteins, identification of proteins, and verification of proteins. Finally, clinical trials for new discovered biomarker or undefined biomarker would be on.     

Proteomic profiling of pancreatic cancer for biomarker discovery

Pancreatic cancer is a uniformly lethal disease that is difficult to diagnose at early stage and even more difficult to cure. In recent years, there has been a substantial interest in applying proteomics technologies to identify protein biomarkers for early detection of cancer. Quantitative proteomic profiling of body fluids, tissues, or other biological samples to identify differentially expressed proteins represents a very promising approach for improving the outcome of this disease. Proteins associated with pancreatic cancer identified through proteomic profiling technologies could be useful as biomarkers for the early diagnosis, therapeutic targets, and disease response markers. In this article, we discuss recent progress and challenges for applying quantitative proteomics technologies for biomarker discovery in pancreatic cancer.     

Albumin depletion of human plasma also removes low abundance proteins including the cytokines

The use of proteomics for efficient, accurate, and complete analysis of clinical samples poses a variety of technical challenges. The presence of higher abundance proteins in the plasma, such as albumin, may mask the detection of lower abundance proteins such as the cytokines. Methods have been proposed to deplete the sample of these higher abundance proteins to facilitate detection of those with lower abundance. In this study, a commercially available albumin depletion kit was used to determine if removal of albumin would measurably reduce detection of lower abundance cytokine proteins in human plasma. The Montage Albumin Deplete Kit (Millipore) was used to deplete albumin from LPS-stimulated whole blood from 15 normal human donors. Albumin depletion was measured using the BCG reagent and SDS-PAGE, and cytokine recovery was determined by a microassay immunoassay that measures both pro- and anti-inflammatory cytokines. Average albumin depletion from the samples was 72%. However, several cytokines were also significantly reduced when the albumin was removed from the plasma. Additionally, there was a variable reduction in cytokine recovery from a known mixture of cytokines in a minimal amount of plasma that were loaded onto the columns. These data demonstrate that there may be a non-specific loss of cytokines following albumin depletion, which may confound subsequent proteomic analysis.     

Proteomics and the dynamic plasma membrane: Quo Vadis?

Quo Vadis: where are you going? Advances in MS-based proteomics have enabled research to move from obtaining the basic protein inventory of cells and organelles to the ability of monitoring their dynamics, including changes in abundance, location and various PTMs. In this respect, the cellular plasma membrane is of particular interest, by not only serving as a barrier between the "cell interior" and the external environment, but moreover by organizing and clustering essential components to enable dynamic responses to internal and external stimuli. Defining and characterizing the dynamic plasma membrane proteome is crucial for understanding fundamental biological processes, disease mechanisms and for finding drug targets. Protein identification, characterization of dynamic PTMs and protein-ligand interactions, and determination of transient changes in protein expression and composition are among the challenges in functional proteomic studies of the plasma membrane. We review the recent progress in MS-based plasma membrane proteomics by presenting key examples from eukaryotic systems, including mammals, yeast and plants. We highlight the importance of enrichment and quantification technologies required for detailed functional and comparative analysis of the dynamic plasma membrane proteome.     

IgY14 and SuperMix immunoaffinity separations coupled with liquid chromatography-mass spectrometry for human plasma proteomics biomarker discovery

Interest in the application of advanced proteomics technologies to human blood plasma- or serum-based clinical samples for the purpose of discovering disease biomarkers continues to grow; however, the enormous dynamic range of protein concentrations in these types of samples (often >10 orders of magnitude) represents a significant analytical challenge, particularly for detecting low-abundance candidate biomarkers. In response, immunoaffinity separation methods for depleting multiple high- and moderate-abundance proteins have become key tools for enriching low-abundance proteins and enhancing detection of these proteins in plasma proteomics. Herein, we describe IgY14 and tandem IgY14-Supermix separation methods for removing 14 high-abundance and up to 60 moderate-abundance proteins, respectively, from human blood plasma and highlight their utility when combined with liquid chromatography-tandem mass spectrometry for interrogating the human plasma proteome.     

Immunodepletion of albumin and immunoglobulin G from bovine plasma

Current MS-based proteomics has facilitated the identification of large numbers of proteins from complex mixtures. The bovine plasma proteome has the potential to provide a wealth of information concerning the biological state of an animal. However, during MS-based experiments, higher abundance proteins such as albumin and immunoglobulin G (IgG) can hinder the identification of potentially important proteins that are present in much lower abundance. While a variety of readily available technologies exist for the depletion of multiple high-abundance proteins from human, mouse and rat samples, there are few available for bovine. In this study, we report the depletion of >97% of albumin and >92% of IgG from bovine plasma.