Global and targeted quantitative proteomics for biomarker discovery

Proteomic analysis of the serum in dogs with pulmonary hypertension secondary to myxomatous mitral valve disease: the preliminary study

Background

Pulmonary hypertension (PH) is a common complication in dogs with myxomatous mitral valve disease (MMVD), characterized by elevated blood pressure in pulmonary artery. Echocardiography is a reliable technique for PH diagnosis in veterinary medicine. However, it is limited to use as an early detection method. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has found extensive application in the discovery of serum protein biomarkers for various diseases. The objective of this study was to identify serum proteins in healthy control dogs and MMVD dogs both with and without PH using LC-MS/MS.

Materials and methods

In this research, a total of 81 small-breed dogs participated, and they were categorized into three groups: the control (n = 28), MMVD (n = 24) and MMVD+PH (n = 29) groups. Serum samples were collected and analyzed by LC-MS/MS.

Results

Differentially expressed proteins were identified, and the upregulated and downregulated proteins in MMVD+PH group including Myomesin 1 (MYOM1) and Histone deacetylase 7 (HDAC7), Pleckstrin homology domain containing M3 (PLEKHM3), Diacylglycerol lipase alpha (DAGLA) and Tubulin tyrosine ligase like 6 (TTLL6) were selected as proteins of interest in MMVD dogs with PH.

Conclusion

Different types of proteins have been identified in healthy dogs and MMVD dogs with and without PH. Additional studies are needed to investigate the potential of these proteins as biomarkers for PH in dogs with MMVD.

Low-Abundance Protein Enrichment for Medical Applications: The Involvement of Combinatorial Peptide Library Technique

The discovery of low- and very low-abundance proteins in medical applications is considered a key success factor in various important domains. To reach this category of proteins, it is essential to adopt procedures consisting of the selective enrichment of species that are present at extremely low concentrations. In the past few years pathways towards this objective have been proposed. In this review, a general landscape of the enrichment technology situation is made first with the presentation and the use of combinatorial peptide libraries. Then, a description of this peculiar technology for the identification of early-stage biomarkers for well-known pathologies with concrete examples is given. In another field of medical applications, the determination of host cell protein traces potentially present in recombinant therapeutic proteins, such as antibodies, is discussed along with their potentially deleterious effects on the health of patients on the one hand, and on the stability of these biodrugs on the other hand. Various additional applications of medical interest are disclosed for biological fluids investigations where the target proteins are present at very low concentrations (e.g., protein allergens).

Proteomics analysis of serum from thymoma patients

Thymoma is the most common malignant tumor in thymic epithelial tumors (TETS). This study aimed to identify the changes in serum proteomics in patients with thymoma. Proteins were extracted from twenty patients with thymoma serum and nine healthy controls and prepared for mass spectrometry (MS) analysis. Data independent acquisition (DIA) quantitative proteomics technique was used to examine the serum proteome. Differential proteins of abundance changes in the serum were identified. Bioinformatics was used to examine the differential proteins. Functional tagging and enrichment analysis were conducted using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. The string database was used to assess the interaction of different proteins. In all, 486 proteins were found in all samples. There were differences in 58 serum proteins between patients and healthy blood donors, 35 up-regulated and 23 down-regulated. These proteins are primarily exocrine and serum membrane proteins involved in controlling immunological responses and antigen binding, according to GO functional annotation. KEGG functional annotation showed that these proteins play a significant role in the complement and coagulation cascade and the phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT) signal pathway. Notably, the KEGG pathway (complement and coagulation cascade) is enriched, and three key activators were up-regulated: von willebrand factor (VWF), coagulation factor v (F5) and vitamin k-dependent protein c (PC). Protein-protein interaction (PPI) analysis showed that six proteins ((VWF, F5, thrombin reactive protein 1 (THBS1), mannose-binding lectin-associated serine protease 2 (MASP2), apolipoprotein B (APOB), and apolipoprotein (a) (LPA)) were up-regulated and two proteins (Metalloproteinase inhibitor 1(TIMP1), ferritin light chain (FTL)) were down-regulated. The results of this study showed that several proteins involved in complement and coagulation cascades were up-regulated in the serum of patients.

Mass Spectrometry Techniques: Principles and Practices for Quantitative Proteomics

In the current omics-age of research, major developments have been made in technologies that attempt to survey the entire repertoire of genes, transcripts, proteins, and metabolites present within a cell. While genomics has led to a dramatic increase in our understanding of such things as disease morphology and how organisms respond to medications, it is critical to obtain information at the proteome level since proteins carry out most of the functions within the cell. The primary tool for obtaining proteome-wide information on proteins within the cell is mass spectrometry (MS). While it has historically been associated with the protein identification, developments over the past couple of decades have made MS a robust technology for protein quantitation as well. Identifying quantitative changes in proteomes is complicated by its dynamic nature and the inability of any technique to guarantee complete coverage of every protein within a proteome sample. Fortunately, the combined development of sample preparation and MS methods have made it capable of quantitatively comparing many thousands of proteins obtained from cells and organisms.

Putative serum protein biomarkers for epsilon toxin exposure in mouse model using LC-MS/MS analysis

Epsilon toxin (ETX), produced by Clostridium perfringens Type B or type D strains, is a potential biological and toxin warfare (BTW) agent, largely for its very high toxicity. The toxin is implicated in several animal diseases. Using LC-MS/MS analysis, we report here elucidation of putative serum maker proteins for ETX exposure with an objective of the early diagnosis of intoxication. Of 166 consensus proteins (488 peptides), showing ETX-induced alterations, 119 proteins exhibited increase and 47 proteins showed decreased abundance in serum, as revealed by SWATH (DIA) acquisition on LC-MS/MS and label free quantitative analysis of control and test samples. Complement and coagulation cascade, nitrogen metabolism, negative regulation of peptidase activity, and response to ROS were among the biological processes and pathways perturbed by the ETX exposure. Interaction network indicated enzyme inhibitor activity, detoxification of ROS, and steroid binding functions were the major interaction networks for the proteins with increased abundance, while, hemostasis and structural molecule activity were the prominent networks for the down-regulated proteins. Validation studies were carried out by immunoprecipitation, ELISA, and Western blot analysis of selected proteins to demonstrate diagnostic potential of the putative marker proteins of ETX exposure.

Next-Generation Sequencing and MALDI Mass Spectrometry in the Study of Multiresistant Processed Meat Vancomycin-Resistant Enterococci (VRE)

Vancomycin-resistant enterococci (VRE), due to their intrinsic resistance to various commonly used antibiotics and their malleable genome, make the treatment of infections caused by these bacteria less effective. The aims of this work were to characterize isolates of Enterococcus spp. that originated from processed meat, through phenotypic and genotypic techniques, as well as to detect putative antibiotic resistance biomarkers. The 19 VRE identified had high resistance to teicoplanin (89%), tetracycline (94%), and erythromycin (84%) and a low resistance to kanamycin (11%), gentamicin (11%), and streptomycin (5%). Based on a Next-Generation Sequencing NGS technique, most isolates were vanA-positive. The most prevalent resistance genes detected were erm(B) and aac(6')-Ii, conferring resistance to the classes of macrolides and aminoglycosides, respectively. MALDI-TOF mass spectrometry (MS) analysis detected an exclusive peak of the Enterococcus genus at m/z (mass-to-charge-ratio) 4428 ± 3, and a peak at m/z 6048 ± 1 allowed us to distinguish Enterococcus faecium from the other species. Several statistically significant protein masses associated with resistance were detected, such as peaks at m/z 6358.27 and m/z 13237.3 in ciprofloxacin resistance isolates. These results reinforce the relevance of the combined and complementary NGS and MALDI-TOF MS techniques for bacterial characterization.

Putative Protein Biomarkers of Escherichia coli Antibiotic Multiresistance Identified by MALDI Mass Spectrometry

The commensal bacteria Escherichia coli causes several intestinal and extra-intestinal diseases, since it has virulence factors that interfere in important cellular processes. These bacteria also have a great capacity to spread the resistance genes, sometimes to phylogenetically distant bacteria, which poses an additional threat to public health worldwide. Here, we aimed to use the analytical potential of MALDI-TOF mass spectrometry (MS) to characterize E. coli isolates and identify proteins associated closely with antibiotic resistance. Thirty strains of extended-spectrum beta-lactamase producing E. coli were sampled from various animals. The phenotypes of antibiotic resistance were determined according to Clinical and Laboratory Standards Institute (CLSI) methods, and they showed that all bacterial isolates were multi-resistant to trimethoprim-sulfamethoxazole, tetracycline, and ampicillin. To identify peptides characteristic of resistance to particular antibiotics, each strain was grown in the presence or absence of the different antibiotics, and then proteins were extracted from the cells. The protein fingerprints of the samples were determined by MALDI-TOF MS in linear mode over a mass range of 2 to 20 kDa. The spectra obtained were compared by using the ClinProTools bioinformatics software, using three machine learning classification algorithms. A putative species biomarker was also detected at a peak m/z of 4528.00.

Refinements of LC-MS/MS Spectral Counting Statistics Improve Quantification of Low Abundance Proteins

Mass spectrometry-based spectral count has been a common choice of label-free proteome quantification due to the simplicity for the sample preparation and data generation. The discriminatory nature of spectral count in the MS data-dependent acquisition, however, inherently introduces the spectral count variation for low-abundance proteins in multiplicative LC-MS/MS analysis, which hampers sensitive proteome quantification. As many low-abundance proteins play important roles in cellular processes, deducing low-abundance proteins in a quantitatively reliable manner greatly expands the depth of biological insights. Here, we implemented the Moment Adjusted Imputation error model in the spectral count refinement as a post PLGEM-STN for improving sensitivity for quantitation of low-abundance proteins by reducing spectral count variability. The statistical framework, automated spectral count refinement by integrating the two statistical tools, was tested with LC-MS/MS datasets of MDA-MB468 breast cancer cells grown under normal and glucose deprivation conditions. We identified about 30% more quantifiable proteins that were found to be low-abundance proteins, which were initially filtered out by the PLGEM-STN analysis. This newly developed statistical framework provides a reliable abundance measurement of low-abundance proteins in the spectral count-based label-free proteome quantification and enabled us to detect low-abundance proteins that could be functionally important in cellular processes.

Elucidation of protein biomarkers in plasma and urine for epsilon toxin exposure in mouse model

Epsilon toxin (ETX) is the major virulence determinant of C. perfringens type B or type D strains, causing diseases in animals, besides being a listed biological and toxin warfare (BTW) agent. Keeping in mind the high lethality and the rapid onset of clinical manifestations, early diagnosis of epsilon toxin exposure is of paramount importance for implementation of appropriate medical countermeasures. Using a 2DE-MS approach, the present study is the first comprehensive proteomic elucidation of ETX-induced protein markers in the mouse model, providing putative targets for early diagnosis of ETX exposure. A total of 52 unique proteins showing ETX-induced modulations were identified in plasma and urine samples. Fibrinogen, apolipoprotein, serum amyloid protein, plasminogen, serum albumin, glutathione peroxidase, transferrin, major urinary protein 2, haptoglobin, transthyretin, and vitamin D-binding protein were among the proteins observed in more than one dataset with altered abundance after the ETX-intoxication. The predicted localization, function, and interaction of the ETX-modulated proteins in the plasma and urine indicated involvement of multiple pathways; extracellular proteins, followed by macromolecular complexes associated with blood coagulation and plasminogen activating cascade, being the most prominent among others. The putative markers elucidated here warrants further validation and can be of immense value for the early diagnosis of ETX exposure.

Interstitial Fluid in Gynecologic Tumors and Its Possible Application in the Clinical Practice

Gynecologic cancers are an important cause of worldwide mortality. The interstitium consists of solid and fluid phases, situated between the blood vessels and cells. The interstitial fluid (IF), or fluid phase, is an extracellular fluid bathing and surrounding the tissue cells. The TIF (tumor interstitial fluid) is a dynamic fluid rich in lipids, proteins and enzyme-derived substances. The molecules found in the IF may be associated with pathological changes in tissues leading to cancer growth and metastatization. Proteomic techniques have allowed an extensive study of the composition of the TIF as a source of biomarkers for gynecologic cancers. In our review, we analyze the composition of the TIF, its formation process, the sampling methods, the consequences of its accumulation and the proteomic analyses performed, that make TIF valuable for monitoring different types of cancers.

Contribution of the plasma and lymph Degradome and Peptidome to the MHC Ligandome

Every biological fluid, blood, interstitial fluid and lymph, urine, saliva, lacrimal fluid, nipple aspirate, and spinal fluid, contains a peptidome-degradome derived from the cellular secretome along with byproducts of the metabolic/catabolic activities of each parenchymal organ. Clement et al. (J Proteomics 78:172-187, 2013), Clement et al. (J Biol Chem 291:5576-5595, 2016), Clement et al. (PLoS One 5:e9863, 2010), Clement et al. (Trends Immunol 32:6-11, 2011), Clement et al. (Front Immunol 4:424, 2013), Geho et al. (Curr Opin Chem Biol 10, 50-55, 2006), Interewicz et al. (Lymphology 37:65‑72, 2004), Leak et al. (Proteomics 4:753‑765, 2004), Popova et al. (PLoS One 9:e110873, 2014), Zhou et al. (Electrophoresis 25:1289‑1298, 2004), D'Alessandro et al. (Shock 42:509‑517, 2014), Dzieciatkowska et al. (Shock 42:485‑498, 2014), Dzieciatkowska et al. (Shock 35:331‑338, 2011), Jordan et al. (J Surg Res 143:130‑135, 2007), Peltz et al. (Surgery 146:347‑357, 2009), Zurawel et al. (Clin Proteomics 8:1, 2011), Ling et al. (Clin Proteomics 6:175‑193, 2010), Sturm et al. (Nat Commun 4:1616, 2013). Over the last decade, qualitative and quantitative analysis of the biological fluids peptidome and degradome have provided a dynamic measurement of tissue homeostasis as well as the tissue response to pathological damage. Proteomic profiling has mapped several of the proteases and resulting degradation by-products derived from cell cycle progression, organ/tissue remodeling and cellular growth, physiological apoptosis, hemostasis, and angiogenesis. Currently, a growing interest lies in the degradome observed during pathological conditions such as cancer, autoimmune diseases, and immune responses to pathogens as a way to exploit biological fluids as liquid biopsies for biomarker discovery Dzieciatkowska et al. (Shock 42:485-498, 2014), Dzieciatkowska et al. (Shock 35:331-338, 2011), Ling et al. (Clin Proteomics 6:175-193, 2010), Ugalde et al. (Methods Mol Biol 622:3-29, 2010), Quesada et al. (Nucleic Acids Res 37:D239‑243, 2009), Cal et al. (Front Biosci 12, 4661-4669, 2007), Shen et al. (PLoS One 5:e13133, 2010a), Antwi et al. (Mol Immunol 46:2931-2937, 2009a), Antwi et al. (J Proteome Res 8:4722‑4731, 2009b), Bedin et al. (J Cell Physiol 231, 915‑925, 2016), Bery et al. (Clin Proteomics 11:13, 2014), Bhalla et al. (Sci Rep 7:1511, 2017), Fan et al. (Diagn Pathol 7:45, 2012a), Fang et al. (Shock 34:291‑298, 2010), Fiedler et al. (Clin Cancer Res 15:3812‑3819, 2009), Fredolini et al. (AAPS J 12:504‑518, 2010), Greening et al. (Enzymes 42:27‑64, 2017), He et al. (PLoS One 8:e63724, 2013), Huang et al. (Int J Gynecol Cancer 28:355‑362, 2018), Hashiguchi et al. (Med Hypotheses 73:760‑763, 2009), Liotta and Petricoin (J Clin Invest 116:26‑30, 2006), Petricoin et al. (Nat Rev Cancer 6:961‑967, 2006), Shen et al. (J Proteome Res 9:2339‑2346, 2010a), Shen et al. (J Proteome Res 5:3154‑3160, 2006), Smith (Clin Proteomics 11:23, 2014), Wang et al. (Oncotarget 8:59376‑59386, 2017), Yang et al. (Clin Exp Med 12:79‑87, 2012a), Yang et al. (J Clin Lab Anal 26:148‑154, 2012b), Yang et al. (Anat Rec (Hoboken) 293:2027‑2033, 2010), Zapico-Muniz et al. (Pancreas 39:1293‑1298, 2010), Villanueva et al. (Mol Cell Proteomics 5:1840‑1852, 2006), Robbins et al. (J Clin Oncol 23:4835‑4837, 2005), Klupczynska et al. (Int J Mol Sci 17:410, 2016). In this review, we focus on the current knowledge of the degradome/peptidome observed in two main biological fluids (plasma and lymph) during physiological and pathological conditions and its importance for immune surveillance.

The Lymphatic Fluid

This review will highlight our current understanding of the formation, circulation, and immunological role of lymphatic fluid. The formation of the extracellular fluid depends on the net balance between the hydrostatic and osmotic pressure gradients effective in the capillary beds. Lymph originates from the extracellular fluid and its composition combines the ultrafiltrated plasma proteins with the proteome generated by the metabolic activities of each parenchymal tissue. Several analyses have indicated how the lymph composition reflects the organs' physiological and pathological states. The collected lymphatic fluid moves from the capillaries into progressively larger collectors toward the draining lymph node aided by the lymphangion contractility and unidirectional valves, which prevent backflow. The proteomic composition of the lymphatic fluid is reflected in the MHC II peptidome presented by nodal antigen-presenting cells. Taken together, the past few years have generated new interest in the formation, transport, and immunological role of the lymphatic fluid.

New Technologies for Monitoring Marine Mammal Health

Omics technologies have been developed in recent decades and applied to different subjects, although the greatest advancements have been achieved in human biology and disease. Genome sequencing and the exploration of its coding and noncoding regions are rapidly yielding meaningful answers to diverse questions, relating genome information to protein activity to environmental changes. In the past, marine mammal genetic and transcriptional studies have been restricted due to the lack of reference genomes. But the advance of high-throughput sequencing is revolutionizing the life sciences technologies. As long-lived organisms, at the top of the food chain, marine mammals play an important role in marine ecosystems and while their protected status is in favor of conservation of the species, it also complicates the researcher's approach to traditional measurements of health. Omics data generated by high-throughput technologies will represent an important key for improving the scientific basis for understanding both marine mammal and environment health.

Tear proteome analysis in ocular surface diseases using label-free LC-MS/MS and multiplexed-microarray biomarker validation

We analyzed the tear film proteome of patients with dry eye (DE), meibomian gland dysfunction (MGD), and normal volunteers (CT). Tear samples were collected from 70 individuals. Of these, 37 samples were analyzed using spectral-counting-based LC-MS/MS label-free quantitation, and 33 samples were evaluated in the validation of candidate biomarkers employing customized antibody microarray assays. Comparative analysis of tear protein profiles revealed differences in the expression levels of 26 proteins, including protein S100A6, annexin A1, cystatin-S, thioredoxin, phospholipase A2, antileukoproteinase, and lactoperoxidase. Antibody microarray validation of CST4, S100A6, and MMP9 confirmed the accuracy of previously reported ELISA assays, with an area under ROC curve (AUC) of 87.5%. Clinical endpoint analysis showed a good correlation between biomarker concentrations and clinical parameters. In conclusion, different sets of proteins differentiate between the groups. Apolipoprotein D, S100A6, S100A8, and ceruloplasmin discriminate best between the DE and CT groups. The differences between antileukoproteinase, phospholipase A2, and lactoperoxidase levels allow the distinction between MGD and DE, and the changes in the levels of annexin A1, clusterin, and alpha-1-acid glycoprotein 1, between MGD and CT groups. The functional network analysis revealed the main biological processes that should be examined to identify new candidate biomarkers and therapeutic targets.

Tear proteome analysis in ocular surface diseases using label-free LC-MS/MS and multiplexed-microarray biomarker validation

We analyzed the tear film proteome of patients with dry eye (DE), meibomian gland dysfunction (MGD), and normal volunteers (CT). Tear samples were collected from 70 individuals. Of these, 37 samples were analyzed using spectral-counting-based LC-MS/MS label-free quantitation, and 33 samples were evaluated in the validation of candidate biomarkers employing customized antibody microarray assays. Comparative analysis of tear protein profiles revealed differences in the expression levels of 26 proteins, including protein S100A6, annexin A1, cystatin-S, thioredoxin, phospholipase A2, antileukoproteinase, and lactoperoxidase. Antibody microarray validation of CST4, S100A6, and MMP9 confirmed the accuracy of previously reported ELISA assays, with an area under ROC curve (AUC) of 87.5%. Clinical endpoint analysis showed a good correlation between biomarker concentrations and clinical parameters. In conclusion, different sets of proteins differentiate between the groups. Apolipoprotein D, S100A6, S100A8, and ceruloplasmin discriminate best between the DE and CT groups. The differences between antileukoproteinase, phospholipase A2, and lactoperoxidase levels allow the distinction between MGD and DE, and the changes in the levels of annexin A1, clusterin, and alpha-1-acid glycoprotein 1, between MGD and CT groups. The functional network analysis revealed the main biological processes that should be examined to identify new candidate biomarkers and therapeutic targets.

Protein biomarkers for early detection of diseases: The decisive contribution of combinatorial peptide ligand libraries

The present review deals with biomarker discovery, especially in regard to sample treatment via combinatorial peptide ligand libraries, perhaps the only technique at present allowing deep exploration of biological fluids and tissue extracts in search for low- to very-low-abundance proteins, which could possibly mark the onset of most pathologies. Early-stage biomarkers, in fact, might be the only way to detect the beginning of most diseases thus permitting proper intervention and care. The following cancers are reviewed, with lists of potential biomarkers suggested in various reports: hepatocellular carcinoma, ovarian cancer, breast cancer and pancreatic cancer, together with some other interesting applications. Although panels of proteins have been presented, with robust evidence, as potential early-stage biomarkers in these different pathologies, their approval by FDA as novel biomarkers in routine clinical chemistry settings would require plenty of additional work and efforts from the pharma industry. The science environment in universities could simply not afford such heavy monetary investments.

SIGNIFICANCE

After more than 16years of search for novel biomarkers, to be used in a clinical chemistry set-up, via proteomic analysis (mostly in biological fluids) it was felt a critical review was due. In the present report, though, only papers reporting biomarker discovery via combinatorial peptide ligand libraries are listed and assessed, since this methodology seems to be the most advanced one for digging in depth into low-to very-low-abundance proteins, which might represent important biomarkers for the onset of pathologies. In particular, a large survey has been made for the following diseases, since they appear to have a large incidence on human population and/or represent fatal diseases: ovarian cancer, breast cancer, pancreatic cancer and hepatocellular carcinoma.

Blood-borne biomarkers and bioindicators for linking exposure to health effects in environmental health science

Environmental health science aims to link environmental pollution sources to adverse health outcomes to develop effective exposure intervention strategies that reduce long-term disease risks. Over the past few decades, the public health community recognized that health risk is driven by interaction between the human genome and external environment. Now that the human genetic code has been sequenced, establishing this "G × E" (gene-environment) interaction requires a similar effort to decode the human exposome, which is the accumulation of an individual's environmental exposures and metabolic responses throughout the person's lifetime. The exposome is composed of endogenous and exogenous chemicals, many of which are measurable as biomarkers in blood, breath, and urine. Exposure to pollutants is assessed by analyzing biofluids for the pollutant itself or its metabolic products. New methods are being developed to use a subset of biomarkers, termed bioindicators, to demonstrate biological changes indicative of future adverse health effects. Typically, environmental biomarkers are assessed using noninvasive (excreted) media, such as breath and urine. Blood is often avoided for biomonitoring due to practical reasons such as medical personnel, infectious waste, or clinical setting, despite the fact that blood represents the central compartment that interacts with every living cell and is the most relevant biofluid for certain applications and analyses. The aims of this study were to (1) review the current use of blood samples in environmental health research, (2) briefly contrast blood with other biological media, and (3) propose additional applications for blood analysis in human exposure research.

Complementary PTM Profiling of Drug Response in Human Gastric Carcinoma by Immunoaffinity and IMAC Methods with Total Proteome Analysis

Gaining insight into normal cellular signaling and disease biology is a critical goal of proteomic analyses. The ability to perform these studies successfully to extract the maximum value and discovery of biologically relevant candidate biomarkers is therefore of primary importance. Many successful studies in the past have focused on total proteome analysis (changes at the protein level) combined with phosphorylation analysis by metal affinity enrichment (changes at the PTM level). Here, we use the gastric carcinoma cell line MKN-45 treated with the c-Met inhibitor SU11274 and PKC inhibitor staurosporine to investigate the most efficient and most comprehensive strategies for both total protein and PTM analysis. Under the conditions used, total protein analysis yielded few changes in response to either compound, while analysis of phosphorylation identified thousands of sites that changed differentially between the two treatments. Both metal affinity and antibody-based enrichments were used to assess phosphopeptide changes, and the data generated by the two methods was largely complementary (non-overlapping). Label-free quantitation of peptide peak abundances was used to accurately determine fold-changes between control and treated samples. Protein interaction network analysis allowed the data to be placed in a biologically relevant context, and follow-up validation of selected findings confirmed the accuracy of the proteomic data. Together, this study provides a framework for start-to-finish proteomic analysis of any experimental system under investigation to maximize the value of the proteomic study and yield the best chance for uncovering actionable target candidates.

Tumor Interstitial Fluid Formation, Characterization, and Clinical Implications

The interstitium, situated between the blood and lymph vessels and the cells, consists of a solid or matrix phase and a fluid phase representing the tissue microenvironment. In the present review, we focus on the interstitial fluid phase of solid tumors, the tumor interstitial fluid (TIF), i.e., the fluid bathing the tumor and stroma cells, also including immune cells. This is a component of the internal milieu of a solid tumor that has attracted regained attention. Access to this space may provide important insight into tumor development and therapy response. TIF is formed by transcapillary filtration, and since this fluid is not readily available we discuss available techniques for TIF isolation, results from subsequent characterization and implications of recent findings with respect to fluid filtration and uptake of macromolecular therapeutic agents. There appear to be local gradients in signaling substances from neoplastic tissue to plasma that may provide new understanding of tumor biology. The development of sensitive proteomic technologies has made TIF a valuable source for tumor specific proteins and biomarker candidates. Potential biomarkers will appear locally in high concentrations in tumors and may eventually be found diluted in the plasma. Access to TIF that reliably reflects the local tumor microenvironment enables identification of substances that can be used in early detection and monitoring of disease.

Chemical biology applied to the study of bacterial pathogens

In recent years, chemical biology and chemical genomics have been increasingly applied to the field of microbiology to uncover new potential therapeutics as well as to probe virulence mechanisms in pathogens. The approach offers some clear advantages, as identified compounds (i) can serve as a proof of principle for the applicability of drugs to specific targets; (ii) can serve as conditional effectors to explore the function of their targets in vitro and in vivo; (iii) can be used to modulate gene expression in otherwise genetically intractable organisms; and (iv) can be tailored to a narrow or broad range of bacteria. This review highlights recent examples from the literature to illustrate how the use of small molecules has advanced discovery of novel potential treatments and has been applied to explore biological mechanisms underlying pathogenicity. We also use these examples to discuss practical considerations that are key to establishing a screening or discovery program. Finally, we discuss the advantages and challenges of different approaches and the methods that are emerging to address these challenges.

Comparative proteomic analysis of membranous nephropathy biopsy tissues using quantitative proteomics

Membranous nephropathy (MN) is a common cause of nephrotic syndrome in adults and the second leading cause of end-stage renal disease due to primary glomerulonephritis. The aim of the present study was to identify potential biomarkers of MN and further characterize these proteins by Gene Ontology (GO) analysis. Isobaric tags for relative and absolute quantification were used to compare the protein levels in tissues from MN patients and healthy individuals, and the combined samples were subsequently separated by specialized communications exchange. Mass spectrometry data acquisition was conducted using a 4800 Plus MALDI TOF/TOF tandem mass spectrometry device, and the results were subjected to statistical analysis. A total of 1,903 proteins were identified, with 423 proteins exhibiting a difference of >1.5-fold compared with the control group. Of these, 202 proteins were upregulated, while 221 proteins were downregulated. In conclusion, GO enrichment analysis revealed that the differentially expressed proteins were primarily mapped to the following GO terms: ‘Immune response’, ‘immune effector process’, ‘activation of immune response’ and ‘positive regulation of immune system process’. The affected proteins may be associated with the pathogenesis of MN; thus, may represent candidate MN biomarkers.

On-beads digestion in conjunction with data-dependent mass spectrometry: a shortcut to quantitative and dynamic interaction proteomics

With the advent of the "-omics" era, biological research has shifted from functionally analyzing single proteins to understanding how entire protein networks connect and adapt to environmental cues. Frequently, pathological processes are initiated by a malfunctioning protein network rather than a single protein. It is therefore crucial to investigate the regulation of proteins in the context of a pathway first and signaling network second. In this study, we demonstrate that a quantitative interaction proteomic approach, combining immunoprecipitation, in-solution digestion and label-free quantification mass spectrometry, provides data of high accuracy and depth. This protocol is applicable, both to tagged, exogenous and untagged, endogenous proteins. Furthermore, it is fast, reliable and, due to a label-free quantitation approach, allows the comparison of multiple conditions. We further show that we are able to generate data in a medium throughput fashion and that we can quantify dynamic interaction changes in signaling pathways in response to mitogenic stimuli, making our approach a suitable method to generate data for system biology approaches.

Quantitative proteomics in resected renal cancer tissue for biomarker discovery and profiling

Background:

Proteomics-based approaches for biomarker discovery are promising strategies used in cancer research. We present state-of-art label-free quantitative proteomics method to assess proteome of renal cell carcinoma (RCC) compared with noncancer renal tissues.

Methods:

Fresh frozen tissue samples from eight primary RCC lesions and autologous adjacent normal renal tissues were obtained from surgically resected tumour-bearing kidneys. Proteins were extracted by complete solubilisation of tissues using filter-aided sample preparation (FASP) method. Trypsin digested proteins were analysed using quantitative label-free proteomics approach followed by data interpretation and pathways analysis.

Results:

A total of 1761 proteins were identified and quantified with high confidence (MASCOT ion score threshold of 35 and P-value <0.05). Of these, 596 proteins were identified as differentially expressed between cancer and noncancer tissues. Two upregulated proteins in tumour samples (adipose differentiation-related protein and Coronin 1A) were further validated by immunohistochemistry. Pathway analysis using IPA, KOBAS 2.0, DAVID functional annotation and FLink tools showed enrichment of many cancer-related biological processes and pathways such as oxidative phosphorylation, glycolysis and amino acid synthetic pathways.

Conclusions:

Our study identified a number of differentially expressed proteins and pathways using label-free proteomics approach in RCC compared with normal tissue samples. Two proteins validated in this study are the focus of on-going research in a large cohort of patients.

Differential expression of serum/plasma proteins in various infectious diseases: specific or nonspecific signatures

Apart from direct detection of the infecting organisms or biomarker of the pathogen itself, surrogate host markers are also useful for sensitive and early diagnosis of pathogenic infections. Early detection of pathogenic infections, discrimination among closely related diseases with overlapping clinical manifestations, and monitoring of disease progression can be achieved by analyzing blood biomarkers. Therefore, over the last decade large numbers of proteomics studies have been conducted to identify differentially expressed human serum/plasma proteins in different infectious diseases with the intent of discovering novel potential diagnostic/prognostic biomarkers. However, in-depth review of the literature indicates that many reported biomarkers are altered in the same way in multiple infectious diseases, regardless of the type of infection. This might be a consequence of generic acute phase reactions, while the uniquely modulated candidates in different pathogenic infections could be indicators of some specific responses. In this review article, we will provide a comprehensive analysis of differentially expressed serum/plasma proteins in various infectious diseases and categorize the protein markers associated with generic or specific responses. The challenges associated with the discovery, validation, and translational phases of serum/plasma biomarker establishment are also discussed.

Towards defining biomarkers indicating resistances to targeted therapies

An impressive, but often short objective response was obtained in many tumor patients treated with different targeted therapies, but most of the patients develop resistances against these drugs. So far, a number of distinct mechanisms leading to intrinsic as well as acquired resistances have been identified in tumors of distinct origin. These can arise from genetic alterations, like mutations, truncations, and amplifications or due to deregulated expression of various proteins and signal transduction pathways, but also from cellular heterogeneity within tumors after an initial response. Therefore, biomarkers are urgently needed for cancer prognosis and personalized cancer medicine. The application of "ome"-based technologies including cancer (epi)genomics, next generation sequencing, cDNA microarrays and proteomics might led to the predictive or prognostic stratification of patients to categorize resistance mechanisms and to postulate combinations of treatment strategies. This review discusses the implementation of proteome-based analysis to identify markers of pathway (in)activation in tumors and the resistance mechanisms, which represent major clinical problems as a tool to optimize individually tailored therapies based on targeted drugs. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.

Application of machine learning to proteomics data: classification and biomarker identification in postgenomics biology

Mass spectrometry is an analytical technique for the characterization of biological samples and is increasingly used in omics studies because of its targeted, nontargeted, and high throughput abilities. However, due to the large datasets generated, it requires informatics approaches such as machine learning techniques to analyze and interpret relevant data. Machine learning can be applied to MS-derived proteomics data in two ways. First, directly to mass spectral peaks and second, to proteins identified by sequence database searching, although relative protein quantification is required for the latter. Machine learning has been applied to mass spectrometry data from different biological disciplines, particularly for various cancers. The aims of such investigations have been to identify biomarkers and to aid in diagnosis, prognosis, and treatment of specific diseases. This review describes how machine learning has been applied to proteomics tandem mass spectrometry data. This includes how it can be used to identify proteins suitable for use as biomarkers of disease and for classification of samples into disease or treatment groups, which may be applicable for diagnostics. It also includes the challenges faced by such investigations, such as prediction of proteins present, protein quantification, planning for the use of machine learning, and small sample sizes.

Interstitial fluid-a reflection of the tumor cell microenvironment and secretome

The interstitium or interstitial space describes the space outside the blood and lymphatic vessels. It contains two phases; the interstitial fluid (IF) and the extracellular matrix. In this review we focus on the interstitial fluid phase, which is the physical and biochemical microenvironment of the cells, and more specifically that of tumors. IF is created by transcapillary filtration and cleared by lymphatic vessels, and contains substances that are either produced and secreted locally, thus denoted secretome, or brought to the organ by the circulation. The structure of the interstitium is discussed briefly and moreover techniques for IF isolation focusing on those that are relevant for studies of the secretome. Accumulated data show that tumor IF is hypoxic and acidic compared with subcutaneous IF and plasma, and that there are gradients between IF and plasma giving information on where substances are produced and thereby reflecting the local microenvironment. We review recent data on the origin of tissue specific substances, challenges related to isolating a representative secretome and the use of this as a substrate for biomarker identification. Finally we perform a comparative analysis across human tumor types and techniques and show that there is great variation in the results obtained that may at least partially be due to the isolation method used. We conclude that when care is taken in isolation of substrate, analysis of the secretome may give valuable biological insight and result in identification of biomarker candidates. This article is part of a Special Issue entitled: An Updated Secretome.

Trypsin-mediated ¹⁸O/¹⁶O labeling for biomarker discovery

Differential (18)O/(16)O stable isotopic labeling that relies on post-digestion (18)O exchange is a simple and efficient method for the relative quantitation of proteins in complex mixtures. This method incorporates two (18)O atoms onto the C-termini of proteolytic peptides resulting in a 4 Da mass-tag difference between (18)O- and (16)O-labeled peptides. This allows for wide-range relative quantitation of proteins in complex mixtures using shotgun proteomics. Because of minimal sample consumption and unrestricted peptide tagging, the post-digestion (18)O exchange is suitable for labeling of low-abundance membrane proteins enriched from cancer cell lines or clinical specimens, including tissues and body fluids. This chapter describes a protocol that applies post-digestion (18)O labeling to elucidate putative endogenous tumor hypoxia markers in the plasma membrane fraction enriched from a hypoxia-adapted malignant melanoma cell line. Plasma membrane proteins from hypoxic and normoxic cells were differentially tagged using (18)O/(16)O stable isotopic labeling. The initial tryptic digestion and solubilization of membrane proteins were carried out in a buffer containing 60 % methanol followed by post-digestion (18)O exchange/labeling in buffered 20 % methanol. The differentially labeled peptides were mixed in a 1:1 ratio and fractionated using off-line strong cation exchange (SCX) liquid chromatography followed by on-line reversed-phase nano-flow RPLC-MS identification and quantitation of peptides/proteins in respective SCX fractions. The present protocol illustrates the utility of (18)O/(16)O stable isotope labeling in the context of quantitative shotgun proteomics that provides a basis for the discovery of hypoxia-induced membrane protein markers in malignant melanoma cell lines.

The proteomics big challenge for biomarkers and new drug-targets discovery

In the modern process of drug discovery, clinical, functional and chemical proteomics can converge and integrate synergies. Functional proteomics explores and elucidates the components of pathways and their interactions which, when deregulated, lead to a disease condition. This knowledge allows the design of strategies to target multiple pathways with combinations of pathway-specific drugs, which might increase chances of success and reduce the occurrence of drug resistance. Chemical proteomics, by analyzing the drug interactome, strongly contributes to accelerate the process of new druggable targets discovery. In the research area of clinical proteomics, proteome and peptidome mass spectrometry-profiling of human bodily fluid (plasma, serum, urine and so on), as well as of tissue and of cells, represents a promising tool for novel biomarker and eventually new druggable targets discovery. In the present review we provide a survey of current strategies of functional, chemical and clinical proteomics. Major issues will be presented for proteomic technologies used for the discovery of biomarkers for early disease diagnosis and identification of new drug targets.

The long journey of cancer biomarkers from the bench to the clinic

BACKGROUND

Protein cancer biomarkers serve multiple clinical purposes, both early and late, during disease progression. The search for new and better biomarkers has become an integral component of contemporary cancer research. However, the number of new biomarkers cleared by the US Food and Drug Administration has declined substantially over the last 10 years, raising concerns regarding the efficiency of the biomarker-development pipeline.

CONTENT

We describe different clinical uses of cancer biomarkers and their performance requirements. We also present examples of protein cancer biomarkers currently in clinical use and their limitations. The major barriers that candidate biomarkers need to overcome to reach the clinic are addressed. Finally, the long and arduous journey of a protein cancer biomarker from the bench to the clinic is outlined with an example.

SUMMARY

The journey of a protein biomarker from the bench to the clinic is long and challenging. Every step needs to be meticulously planned and executed to succeed. The history of clinically useful biomarkers suggests that at least a decade is required for the transition of a marker from the bench to the bedside. Therefore, it may be too early to expect that the new technological advances will catalyze the anticipated biomarker revolution any time soon.

Interstitial Fluid and Lymph Formation and Transport: Physiological Regulation and Roles in Inflammation and Cancer

The interstitium describes the fluid, proteins, solutes, and the extracellular matrix (ECM) that comprise the cellular microenvironment in tissues. Its alterations are fundamental to changes in cell function in inflammation, pathogenesis, and cancer. Interstitial fluid (IF) is created by transcapillary filtration and cleared by lymphatic vessels. Herein we discuss the biophysical, biomechanical, and functional implications of IF in normal and pathological tissue states from both fluid balance and cell function perspectives. We also discuss analysis methods to access IF, which enables quantification of the cellular microenvironment; such methods have demonstrated, for example, that there can be dramatic gradients from tissue to plasma during inflammation and that tumor IF is hypoxic and acidic compared with subcutaneous IF and plasma. Accumulated recent data show that IF and its convection through the interstitium and delivery to the lymph nodes have many and diverse biological effects, including in ECM reorganization, cell migration, and capillary morphogenesis as well as in immunity and peripheral tolerance. This review integrates the biophysical, biomechanical, and biological aspects of interstitial and lymph fluid and its transport in tissue physiology, pathophysiology, and immune regulation.

PTMScan direct: identification and quantification of peptides from critical signaling proteins by immunoaffinity enrichment coupled with LC-MS/MS

Proteomic studies of post-translational modifications by metal affinity or antibody-based methods often employ data-dependent analysis, providing rich data sets that consist of randomly sampled identified peptides because of the dynamic response of the mass spectrometer. This can complicate the primary goal of programs for drug development, mutational analysis, and kinase profiling studies, which is to monitor how multiple nodes of known, critical signaling pathways are affected by a variety of treatment conditions. Cell Signaling Technology has developed an immunoaffinity-based LC-MS/MS method called PTMScan Direct for multiplexed analysis of these important signaling proteins. PTMScan Direct enables the identification and quantification of hundreds of peptides derived from specific proteins in signaling pathways or specific protein types. Cell lines, tissues, or xenografts can be used as starting material. PTMScan Direct is compatible with both SILAC and label-free quantification. Current PTMScan Direct reagents target key nodes of many signaling pathways (PTMScan Direct: Multipathway), serine/threonine kinases, tyrosine kinases, and the Akt/PI3K pathway. Validation of each reagent includes score filtering of MS/MS assignments, filtering by identification of peptides derived from expected targets, identification of peptides homologous to expected targets, minimum signal intensity of peptide ions, and dependence upon the presence of the reagent itself compared with a negative control. The Multipathway reagent was used to study sensitivity of human cancer cell lines to receptor tyrosine kinase inhibitors and showed consistent results with previously published studies. The Ser/Thr kinase reagent was used to compare relative levels of kinase-derived phosphopeptides in mouse liver, brain, and embryo, showing tissue-specific activity of many kinases including Akt and PKC family members. PTMScan Direct will be a powerful quantitative method for elucidation of changes in signaling in a wide array of experimental systems, combining the specificity of traditional biochemical methods with the high number of data points and dynamic range of proteomic methods.

After genomics, what proteomics tools could help us understand the antimicrobial resistance of Escherichia coli?

Proteomic approaches have been considerably improved during the past decade and have been used to investigate the differences in protein expression profiles of cells grown under a broad spectrum of growth conditions and with different stress factors including antibiotics. In Europe, the most significant disease threat remains the presence of microorganisms that have become resistant to antimicrobials and so it is important that different scientific tools are combined to achieve the largest amount of knowledge in this area of expertise. The emergence and spread of the antibiotic-resistant Gram-negative pathogens, such as Escherichia coli, can lead to serious problem public health in humans. E. coli, a very well described prokaryote, has served as a model organism for several biological and biotechnological studies increasingly so since the completion of the E. coli genome-sequencing project. The purpose of this review is to present an overview of the different proteomic approaches to antimicrobial-resistant E. coli that will be helpful to obtain a better knowledge of the antibiotic-resistant mechanism(s). This can also aid to understand the molecular determinants involved with pathogenesis, which is essential for the development of effective strategies to combat infection and to reveal new therapeutic targets. This article is part of a Special Issue entitled: Proteomics: The clinical link.

"The quest for biomarkers": are we on the right technical track?

The discovery phase of biomarkers of diagnostic or therapeutic interest started a decade ago with the very rapid development of proteomic investigations. In spite of the development of innovative technologies and multiple approaches, the "harvest" is still modest. Various reasons justified the encountered difficulties and most of them have been circumvented by specific sample treatments or dedicated analytical approaches. Nevertheless, the situation of very modest biomarker discovery level did not change much. This review intends to specifically analyze the main approaches used for biomarker discovery phase and evaluate related advantages and disadvantages. Thus, preliminary sample treatments such as fractionation, depletion and reduction of dynamic concentration range will critically be discussed and then the main differential expression investigation methods analyzed. Combinations of technologies are also discussed along with possible proposals to federate associations of complementary technologies for better chances of success.

Supervised regularized canonical correlation analysis: integrating histologic and proteomic measurements for predicting biochemical recurrence following prostate surgery

BACKGROUND

Multimodal data, especially imaging and non-imaging data, is being routinely acquired in the context of disease diagnostics; however, computational challenges have limited the ability to quantitatively integrate imaging and non-imaging data channels with different dimensionalities and scales. To the best of our knowledge relatively few attempts have been made to quantitatively fuse such data to construct classifiers and none have attempted to quantitatively combine histology (imaging) and proteomic (non-imaging) measurements for making diagnostic and prognostic predictions. The objective of this work is to create a common subspace to simultaneously accommodate both the imaging and non-imaging data (and hence data corresponding to different scales and dimensionalities), called a metaspace. This metaspace can be used to build a meta-classifier that produces better classification results than a classifier that is based on a single modality alone. Canonical Correlation Analysis (CCA) and Regularized CCA (RCCA) are statistical techniques that extract correlations between two modes of data to construct a homogeneous, uniform representation of heterogeneous data channels. In this paper, we present a novel modification to CCA and RCCA, Supervised Regularized Canonical Correlation Analysis (SRCCA), that (1) enables the quantitative integration of data from multiple modalities using a feature selection scheme, (2) is regularized, and (3) is computationally cheap. We leverage this SRCCA framework towards the fusion of proteomic and histologic image signatures for identifying prostate cancer patients at the risk of 5 year biochemical recurrence following radical prostatectomy.

RESULTS

A cohort of 19 grade, stage matched prostate cancer patients, all of whom had radical prostatectomy, including 10 of whom had biochemical recurrence within 5 years of surgery and 9 of whom did not, were considered in this study. The aim was to construct a lower fused dimensional metaspace comprising both the histological and proteomic measurements obtained from the site of the dominant nodule on the surgical specimen. In conjunction with SRCCA, a random forest classifier was able to identify prostate cancer patients, who developed biochemical recurrence within 5 years, with a maximum classification accuracy of 93%.

CONCLUSIONS

The classifier performance in the SRCCA space was found to be statistically significantly higher compared to the fused data representations obtained, not only from CCA and RCCA, but also two other statistical techniques called Principal Component Analysis and Partial Least Squares Regression. These results suggest that SRCCA is a computationally efficient and a highly accurate scheme for representing multimodal (histologic and proteomic) data in a metaspace and that it could be used to construct fused biomarkers for predicting disease recurrence and prognosis.

Applying selected reaction monitoring to targeted proteomics

Selected reaction monitoring (SRM) is a highly selective and sensitive mass spectrometric methodology for precise and accurate quantification of low-abundant proteins in complex mixtures and for characterization of modified peptides, and constitutes the method of choice in targeted proteomics. Owing to its outstanding features, SRM arises as an alternative to antibody-based assays for discovery and validation of clinically relevant biomarkers, a topic that is tackled in this article. Several of the obstacles encountered in SRM experiments, mainly those derived from shared physicochemical properties of peptides (e.g., mass, charge and chromatographic retention time), can compromise selectivity and quantitation. We illustrate how to circumvent these limitations on the basis of using time-scheduled chromatographic approaches and choosing appropriate spectrometric conditions, including the careful selection of the precursor and diagnostic ions.

Urinary glycoprotein biomarker discovery for bladder cancer detection using LC/MS-MS and label-free quantification

BACKGROUND

Cancers of the urinary bladder are the fifth most commonly diagnosed malignancy in the United States. Early clinical diagnosis of bladder cancer remains a major challenge, and the development of noninvasive methods for detection and surveillance is desirable for both patients and health care providers.

APPROACH

To identify urinary proteins with potential clinical utility, we enriched and profiled the glycoprotein component of urine samples by using a dual-lectin affinity chromatography and liquid chromatography/tandem mass spectrometry platform.

RESULTS

From a primary sample set obtained from 54 cancer patients and 46 controls, a total of 265 distinct glycoproteins were identified with high confidence, and changes in glycoprotein abundance between groups were quantified by a label-free spectral counting method. Validation of candidate biomarker alpha-1-antitrypsin (A1AT) for disease association was done on an independent set of 70 samples (35 cancer cases) by using an ELISA. Increased levels of urinary A1AT glycoprotein were indicative of the presence of bladder cancer (P < 0.0001) and augmented voided urine cytology results. A1AT detection classified bladder cancer patients with a sensitivity of 74% and specificity of 80%.

SUMMARY

The described strategy can enable higher resolution profiling of the proteome in biological fluids by reducing complexity. Application of glycoprotein enrichment provided novel candidates for further investigation as biomarkers for the noninvasive detection of bladder cancer.

Proteomic biomarker discovery: it's more than just mass spectrometry

The previous decade witnessed an enormous number of studies with the singular goal of identifying protein biomarkers for diseases such as cancer. A large majority of these studies have focused on comparative studies of serum or plasma obtained from disease-affected and control patients. In these studies, proteins identified in the samples using MS were compared with the hope that differences between samples would reveal useful biomarkers. Unfortunately, finding clinically relevant biomarkers has often been elusive and frustrating. As with most research efforts, both successes and failures, much has been learned about what strategies work and which do not. Part of the problem can be attributed to underestimating the effort required to discover novel biomarkers and depending too heavily on MS analysis of peripheral blood samples. Fortunately, the future for biomarker discovery still appears bright. MS technology continues to increase in sensitivity, throughput, and accuracy while novel types of samples and clever experimental designs coupled with innovative bioinformatics will make this vision of routine biomarker discovery a reality. To achieve ultimate success is going to require concomitant application of a number of different technologies, all providing the information necessary for discovering and validating clinically useful biomarkers.

Proteomic technologies for the identification of disease biomarkers in serum: advances and challenges ahead

Serum is an ideal biological sample that contains an archive of information due to the presence of a variety of proteins released by diseased tissue, and serum proteomics has gained considerable interest for the disease biomarker discovery. Easy accessibility and rapid protein changes in response to disease pathogenesis makes serum an attractive sample for clinical research. Despite these advantages, the analysis of serum proteome is very challenging due to the wide dynamic range of proteins, difficulty in finding low-abundance target analytes due to the presence of high-abundance serum proteins, high levels of salts and other interfering compounds, variations among individuals and paucity of reproducibility. Sample preparation introduces pre-analytical variations and poses major challenges to analyze the serum proteome. The label-free detection techniques such as surface plasmon resonance, microcantilever, few nanotechniques and different resonators are rapidly emerging for the analysis of serum proteome and they have exhibited potential to overcome few limitations of the conventional techniques. In this article, we will discuss the current status of serum proteome analysis for the biomarker discovery and address key technological advancements, with a focus on challenges and amenable solutions.

Quantitative profiling of serum samples using TMT protein labelling, fractionation and LC-MS/MS

Blood-borne biomarkers are urgently required for the early detection, accurate diagnosis and prognosis of disease. Additionally, improved methods of profiling serum and plasma proteins for biomarker discovery efforts are needed. Herein, we report a quantitative method based on amino-group labelling of serum proteins (rather than peptides) with isobaric tandem mass tags (TMT) and incorporating immune-based depletion, gel-based and strong anion exchange separation of proteins prior to differential endoproteinase treatment and liquid chromatography tandem mass spectrometry. We report a generally higher level of quantitative coverage of the serum proteome compared to other peptide-based isobaric tagging approaches and show the potential of the method by applying it to a set of unique samples that pre-date the diagnosis of pancreatic cancer.

Combination of improved (18)O incorporation and multiple reaction monitoring: a universal strategy for absolute quantitative verification of serum candidate biomarkers of liver cancer

Stable isotope dilution-multiple reaction monitoring-mass spectrometry (SID-MRM-MS), which is an alternative to immunoassay methods such as ELISA and Western blotting, has been used to alleviate the bottlenecks of high-throughput verification of biomarker candidates recently. However, the inconvenience and high isotope consumption required to obtain stably labeled peptide impedes the broad application of this method. In our study, the (18)O-labeling method was introduced to generate stable isotope-labeled peptides instead of the Fmoc chemical synthesis and Qconcat recombinant protein synthesis methods. To make (18)O-labeling suitable for absolute quantification, we have added the following procedures: (1) RapiGest SF and microwave heating were added to increase the labeling efficiency; (2) trypsin was deactivated completely by chemical modification using tris(2-carboxyethyl)phosphine (TCEP) and iodoacetamide (IAA) to prevent back-exchange of (18)O to (16)O, and (3) MRM parameters were optimized to maximize specificity and better distinguish between (18)O-labeled and unlabeled peptides. As a result, the (18)O-labeled peptides can be prepared in less than 1 h with satisfactory efficiency (>97%) and remained stable for 1 week, compared to traditional protocols that require 5 h for labeling with poor stability. Excellent separation of (18)O-labeled and unlabeled peptides was achieved by the MRM-MS spectrum. Finally, through the combined improvement in (18)O-labeling with multiple reaction monitoring, an absolute quantification strategy was developed to quantitatively verify hepatocellular carcinoma-related biomarker candidates, namely, vitronectin and clusterin, in undepleted serum samples. Sample preparation and capillary-HPLC analysis were optimized for high-throughput applications. The reliability of this strategy was further evaluated by method validation, with accuracy (%RE) and precision (%RSD) of less than 20% and good linearity (r(2) > 0.99), and clinical validation, which were consistent with previously reported results. In summary, our strategy can promote broader application of SID-MRM-MS for biomarkers from discovery to verification regarding the significant advantages of the convenient and flexible generation of internal standards, the reduction in the sample labeling steps, and the simple transition.

Quality control based on isotopic distributions for high-throughput MALDI-TOF and MALDI-FTICR serum peptide profiling

In this study, we have implemented a new quality control (QC) parameter for peptide profiling based on isotopic distributions. This QC parameter is an objective measure and facilitates automatic sorting of large numbers of peptide spectra. Peptides in human serum samples were enriched using reversed-phase C(18)-functionalized magnetic beads using a high-throughput robotic platform. High-resolution MALDI-TOF and ultrahigh resolution MALDI-FTICR mass spectra were obtained and a workflow was developed for automated analysis and evaluation of these profiles. To this end, the isotopic distributions of multiple peptides were quantified from both MALDI-TOF and MALDI-FTICR spectra. Odd peptide isotope distributions in TOF spectra could be rationalized from ultrahigh resolution FTICR spectra that showed overlap of different peptides. The comparison of isotope patterns with estimated polyaveragine distributions was used to calculate a QC value for each single mass spectrum. Sorting these QC values enabled the best MALDI spectrum to be selected from replicate spots. Moreover, using this approach spectra containing high intensities of polymers or other contaminants and lacking peptides of interest can be efficiently removed from a clinical dataset. In general, this method simplifies the exclusion of low quality spectra from further statistical analysis.

Mass-spectrometry-based clinical proteomics--a review and prospective

This review reports on the current and emerging technologies for the use of mass-spectrometry-based proteomics in clinical applications.

Interstitial fluid: the overlooked component of the tumor microenvironment?

Background

The interstitium, situated between the blood and lymph vessels and the cells, consists of a solid or matrix phase and a fluid phase, together constituting the tissue microenvironment. Here we focus on the interstitial fluid phase of tumors, i.e., the fluid bathing the tumor and stromal cells. Novel knowledge on this compartment may provide important insight into how tumors develop and how they respond to therapy.

Results

We discuss available techniques for interstitial fluid isolation and implications of recent findings with respect to transcapillary fluid balance and uptake of macromolecular therapeutic agents. By the development of new methods it is emerging that local gradients exist in signaling substances from neoplastic tissue to plasma. Such gradients may provide new insight into the biology of tumors and mechanistic aspects linked to therapy. The emergence of sensitive proteomic technologies has made the interstitial fluid compartment in general and that of tumors in particular a highly valuable source for tissue-specific proteins that may serve as biomarker candidates. Potential biomarkers will appear locally at high concentrations in the tissue of interest and will eventually appear in the plasma, where they are diluted.

Conclusions

Access to fluid that reliably reflects the local microenvironment enables us to identify substances that can be used in early detection and monitoring of disease.

Pathway analysis of dilated cardiomyopathy using global proteomic profiling and enrichment maps

Global protein expression profiling can potentially uncover perturbations associated with common forms of heart disease. We have used shotgun MS/MS to monitor the state of biological systems in cardiac tissue correlating with disease onset, cardiac insufficiency and progression to heart failure in a time-course mouse model of dilated cardiomyopathy. However, interpreting the functional significance of the hundreds of differentially expressed proteins has been challenging. Here, we utilize improved enrichment statistical methods and an extensive collection of functionally related gene sets, gaining a more comprehensive understanding of the progressive alterations associated with functional decline in dilated cardiomyopathy. We visualize the enrichment results as an Enrichment Map, where significant gene sets are grouped based on annotation similarity. This approach vastly simplifies the interpretation of the large number of enriched gene sets found. For pathways of specific interest, such as Apoptosis and the MAPK (mitogen-activated protein kinase) cascade, we performed a more detailed analysis of the underlying signaling network, including experimental validation of expression patterns.