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Multidimensional protein profiling technology and its application to human plasma proteome.

In clinical and diagnostic proteomics, it is essential to develop a comprehensive and robust system for proteome analysis. Although multidimensional liquid chromatography/tandem mass spectrometry (LC/MS/MS) systems have been recently developed as powerful tools especially for identification of protein complexes, these systems still some drawbacks in their application to clinical research that requires an analysis of a large number of human samples. Therefore, in this study, we have constructed a technically simple and high throughput protein profiling system comprising a two-dimensional (2D)-LC/MS/MS system which integrates both a strong cation exchange (SCX) chromatography and a microLC/MS/MS system with micro-flowing reversed-phase chromatography. Using the microLC/MS/MS system as the second dimensional chromatography, SCX separation has been optimized as an off-line first dimensional peptide fractionation. To evaluate the performance of the constructed 2D-LC/MS/MS system, the results of detection and identification of proteins were compared using digests mixtures of 6 authentic proteins with those obtained using one-dimensional microLC/MS/MS system. The number of peptide fragments detected and the coverage of protein sequence were found to be more than double through the use of our newly built 2D-LC/MS/MS system. Furthermore, this multidimensional protein profiling system has been applied to plasma proteome in order to examine its feasibility for clinical proteomics. The experimental results revealed the identification of 174 proteins from one serum sample depleted HSA and IgG which corresponds to only 1 microL of plasma, and the total analysis run time was less than half a day, indicating a fairly high possibility of practicing clinical proteomics in a high throughput manner.

Blood Proteins↗
PubMed2005

Rapid detection of fetal aneuploidy using proteomics approaches on amniotic fluid supernatant.

OBJECTIVE: Conventional chromosomal studies or fluorescent in situ hybridization takes days to diagnose fetal aneuploidies during amniocentesis. Here, we evaluated the value of mass spectrometry-based clinical proteomics analysis on amniotic fluid supernatant (AFS) as a rapid detection of fetal aneuploidies. METHODS: Proteomics profiles generated by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) after fractionating samples with functionalized magnetic beads were used for differentiating 60 normal karyotypic from 20 aneuploid AFS. After the discriminating models were generated using genetic algorithm, we evaluated the clinical efficacy of the models in detecting aneuploidies in two batches (each n=30) of AFS prior to the release of chromosomal diagnoses. RESULTS: Within hours, the two-step proteomics analysis of AFS with the C18 model, followed by the weak cation exchange model, was able to detect aneuploid AFS at 3.3% disease prevalence rate with 100% sensitivity, 72 to 96% specificity, 11 to 50% positive predictive value, and 100% negative predictive value. CONCLUSION: Clinical proteomics analysis of AFS using magnetic beads-based sample preparation and MALDI-TOF-MS can be used as a rapid detection for fetal aneuploidies. With perfect sensitivity and negative predictive value of the two-step proteomics method, it may be used for rapid detection of aneuploid AFS immediately after amniocentesis. Further large-scale examinations are apparently needed to verify the clinical value of this rapid detection.

Amniotic Fluid↗
PubMed2026

Pan-Cancer Quantification of Driver Alteration Transmission Across Molecular Layers Reveals Limited Propagation to Protein Abundance.

Precision oncology relies primarily on DNA-level alterations for therapeutic decisions, but the extent to which driver mutations propagate to protein abundance has not been systematically evaluated. Here, I developed a regression-based transmission score (TS_R 2) to quantify driver alteration signal propagation across DNA, mRNA, and protein layers. Applying this framework to matched genomic, transcriptomic, proteomic, and phosphoproteomic data from 754 Clinical Proteomic Tumor Analysis Consortium (CPTAC) tumors across seven cancer types, I analyzed 86 driver gene-cancer type pairs, of which 83 were evaluable for the full two-layer transmission score. I employed covariate-adjusted regression for each molecular transition, assessing significance via permutation testing (n = 1000). Mixed-effects modeling then partitioned gene-intrinsic from cancer-type-dependent effects. Only 5 of 83 evaluable pairs (6%) demonstrated high transmission (TS_R 2 > 0.05), with receptor tyrosine kinases (EGFR, FGFR2) exemplifying this class. The primary bottleneck occurred at the mutation-mRNA transition, not mRNA-protein translation. Gene identity accounted for 49% of transmission efficiency variance, nearly double the contribution of cancer type (29%). Copy number alterations transmitted signals 13.8-fold more efficiently than point mutations, and truncating mutations showed higher transmission than missense variants (Wilcoxon p = 0.005). Microsatellite instability attenuated mRNA-protein transmission in UCEC and COAD. These findings demonstrate that many driver alterations show limited propagation to protein abundance. This challenges DNA-only interpretations in precision oncology and provides a framework for integrated functional driver prioritization.

Humans↗
PubMed2004

The use of proteomics for the assessment of clinical samples in research.

Proteomics, the analysis of expressed proteins, has been an important developing area of research for the past two decades [Anderson, NG, Anderson, NL. Twenty years of two-dimensional electrophoresis: past, present and future. Electrophoresis 1996;17:443-453]. Advances in technology have led to a rapid increase in applications to a wide range of samples; from initial experiments using cell lines, more complex tissues and biological fluids are now being assessed to establish changes in protein expression. A primary aim of clinical proteomics is the identification of biomarkers for diagnosis and therapeutic intervention of disease, by comparing the proteomic profiles of control and disease, and differing physiological states. This expansion into clinical samples has not been without difficulties owing to the complexity and dynamic range in plasma and human tissues including tissue biopsies. The most widely used techniques for analysis of clinical samples are surface-enhanced laser desorption/ionisation mass spectrometry (SELDI-MS) and 2-dimensional gel electrophoresis (2-DE) coupled to matrix-assisted laser desorption ionisation [Person, MD, Monks, TJ, Lau, SS. An integrated approach to identifying chemically induced posttranslational modifications using comparative MALDI-MS and targeted HPLC-ESI-MS/MS. Chem. Res. Toxicol. 2003;16:598-608]-mass spectroscopy (MALDI-MS). This review aims to summarise the findings of studies that have used proteomic research methods to analyse samples from clinical studies and to assess the impact that proteomic techniques have had in assessing clinical samples.

Biomarkers↗
PubMed2005

Clinical-scale high-throughput human plasma proteome analysis: lung adenocarcinoma.

Clinical proteomics requires the stable and reproducible analysis of a large number of human samples. We report a high-throughput comprehensive protein profiling system comprising a fully automated, on-line, two-dimensional microflow liquid chromatography/tandem mass spectrometry (2-D microLC-MS/MS) system for use in clinical proteomics. A linear ion-trap mass spectrometer (ITMS) also known as a 2-D ITMS instrument, which is characterized by high scan speed, was incorporated into the microLC-MS/MS system in order to obtain highly improved sensitivity and resolution in MS/MS acquisition. This system was used to evaluate bovine serum albumin and human 26S proteasome. Application of these high-throughput microLC conditions and the 2-D ITMS resulted in a 10-fold increase in sensitivity in protein identification. Additionally, peptide fragments from the 26S proteasome were identified three-fold more efficiently than by the conventional 3-D ITMS instrument. In this study, the 2-D microLC-MS/MS system that uses linear 2-D ITMS has been applied for the plasma proteome analysis of a few samples from healthy individuals and lung adenocarcinoma patients. Using the 2-D and 1-D microLC-MS/MS analyses, approximately 250 and 100 different proteins were detected, respectively, in each HSA- and IgG-depleted sample, which corresponds to only 0.4 microL of blood plasma. Automatic operation enabled the completion of a single run of the entire 1-D and 2-D microLC-MS/MS analyses within 11 h. Investigation of the data extracted from the protein identification datasets of both healthy and adenocarcinoma groups revealed that several of the group-specific proteins could be candidate protein disease markers expressed in the human blood plasma. Consequently, it was demonstrated that this high-throughput microLC-MS/MS protein profiling system would be practically applicable to the discovery of protein disease markers, which is the primary objective in clinical plasma proteome projects.

Adenocarcinoma↗
PubMed2003

Approaches to proteomic analysis of human tumors.

The completion of the Human Genome Project and the successful use of high-throughput array formats for mRNA measurements has spurred a revival of interest in the field of proteomics. Clinical cancer specimens will be a critical component of proteomic studies of cancer, both for discovering new insights into TSG function, and for validation of discoveries that are made in the laboratory. Global protein analysis methods hold great potential for facilitating the discovery of novel tumor suppressor genes, as well as providing a better understanding of the biochemical role of these proteins.

Electrophoresis, Gel, Two-Dimensional↗
PubMed2026

A pan-cancer analysis of MEX3D in human tumors.

BACKGROUND: MEX3D, a member of the MEX3 RNA-binding protein family, has emerged as a potential regulatory molecule in cancer. However, its role across different tumor types remains largely unexplored. METHODS: We conducted a pan-cancer analysis of MEX3D using transcriptomic and proteomic data from the Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Clinical Proteomic Tumor Analysis Consortium (CPTAC). Expression patterns, clinical correlations, survival outcomes, genetic alterations, RNA modification associations, immune infiltration, and functional enrichment were systematically evaluated. RESULTS: MEX3D was significantly dysregulated in numerous cancers at both mRNA and protein levels. Its expression correlated with tumor stage in ACC, LIHC, OV, SKCM, and THCA. Elevated MEX3D expression was associated with poor overall survival (OS) and disease-specific survival (DSS) in multiple malignancies, including ACC, LGG, LUAD, and MESO. Genetic alteration analysis revealed frequent amplifications and mutations, particularly in SARC and OV. MEX3D was positively correlated with RNA modification-related genes (m1A, m5C, m6A) and immune regulatory genes such as CD276, TGFB1, VEGFA, and ICOSLG. Additionally, MEX3D expression showed significant associations with tumor mutational burden (TMB), microsatellite instability (MSI), and cancer-associated fibroblast infiltration. Functional enrichment analyses indicated that MEX3D-related genes are involved in reproductive cellular processes, RNA binding, the Hippo signaling pathway, and microRNA-related oncogenic pathways. CONCLUSION: This pan-cancer analysis highlights the heterogeneous expression and cancer-specific prognostic significance of MEX3D. MEX3D is associated with immune infiltration, immune regulatory genes, RNA modification-related genes, TMB/MSI, and pathways involved in gene regulation and tumor progression. These findings suggest that MEX3D may participate in cancer-specific post-transcriptional and microenvironmental regulatory networks.

Biomarker↗
PubMed2004

Accurate qualitative and quantitative proteomic analysis of clinical hepatocellular carcinoma using laser capture microdissection coupled with isotope-coded affinity tag and two-dimensional liquid chromatography mass spectrometry.

Laser capture microdissection (LCM) is a powerful tool that enables the isolation of specific cell types from tissue sections, overcoming the problem of tissue heterogeneity and contamination. This study combined the LCM with isotope-coded affinity tag (ICAT) technology and two-dimensional liquid chromatography to investigate the qualitative and quantitative proteomes of hepatocellular carcinoma (HCC). The effects of three different histochemical stains on tissue sections have been compared, and toluidine blue stain was proved as the most suitable stain for LCM followed by proteomic analysis. The solubilized proteins from microdissected HCC and non-HCC hepatocytes were qualitatively and quantitatively analyzed with two-dimensional liquid chromatography tandem mass spectrometry (2D-LC-MS/MS) alone or coupled with cleavable ICAT labeling technology. A total of 644 proteins were qualitative identified, and 261 proteins were unambiguously quantitated. These results show that the clinical proteomic method using LCM coupled with ICAT and 2D-LC-MS/MS can carry out not only large-scale but also accurate qualitative and quantitative analysis.

Affinity Labels↗
PubMed2026

PDZ-binding kinase promotes ovarian cancer cell proliferation and invasion via CCNB1 regulation.

BACKGROUND: Ovarian cancer is one of the most lethal gynecological malignancies, characterized by late diagnosis, frequent recurrence, and high mortality. PDZ-binding kinase (PBK), a serine/threonine kinase of the mitogen-activated protein kinase kinase (MAPKK) family, has been implicated in the tumorigenesis of multiple cancers, yet its role in ovarian cancer remains incompletely characterized. This study aimed to investigate the effect of PBK on the proliferation and invasion of ovarian cancer cells. METHODS: The expression of PBK and cyclin B1 (CCNB1) in normal ovarian tissues and ovarian cancer tissues was analyzed using online databases including Gene Expression Profiling Interactive Analysis 2 (GEPIA2), Clinical Proteomic Tumor Analysis Consortium (CPTAC), and Kaplan-Meier Plotter. Clinical tissue specimens were collected to detect the expression of PBK and CCNB1 by immunohistochemistry. Quantitative real-time polymerase chain reaction (PCR) was performed to detect PBK messenger RNA (mRNA) expression levels in clinical specimens and cell lines. Western blot was used to detect PBK protein expression in ovarian cancer cell lines. ES2 and A2780 cells with higher PBK expression were selected to construct PBK knockdown cell lines using lentiviral interference vectors. Cell Counting Kit-8 (CCK-8) assay, colony formation assay, and 5-ethynyl-2'-deoxyuridine (EdU) assay were performed to explore the effect of PBK knockdown on cell proliferation. Transwell assay was used to investigate the effect on cell invasion. The Cancer Genome Atlas (TCGA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were utilized to analyze PBK-related pathways and predict CCNB1 as the gene most closely related to PBK. RESULTS: PBK was significantly overexpressed in ovarian cancer tissues and cell lines compared with normal controls, and high PBK expression was associated with poor overall survival (OS) and progression-free survival (PFS). Knockdown of PBK expression inhibited the proliferation, colony formation, and invasion of ovarian cancer cells. Bioinformatics analysis revealed that CCNB1 was significantly overexpressed in ovarian cancer and high CCNB1 expression was associated with poor OS. CCNB1 was also significantly highly expressed in ovarian cancer tissues as validated by immunohistochemistry and was associated with lymph node metastasis. PBK and CCNB1 expression showed a significant positive correlation in TCGA ovarian cancer datasets. Knockdown of PBK inhibited CCNB1 expression in ovarian cancer cells. CONCLUSIONS: PBK promotes ovarian cancer cell proliferation and invasion. PBK knockdown leads to CCNB1 downregulation. These findings suggest that CCNB1 contributes to PBK-mediated oncogenic effects and identify the PBK-CCNB1 axis as a potential therapeutic target for ovarian cancer treatment.

PDZ-binding kinase (PBK)↗
PubMed2006

Proteomic-based biomarker discovery with emphasis on cerebrospinal fluid and multiple sclerosis.

Discovery of disease specific biomarkers in human body fluids has become an important challenge in clinical proteomics. Facing the increasing threat of degenerative and disabling diseases like cancer, cardiovascular, neurological and inflammatory diseases in large parts of the world's population, there is an urgent need to improve early diagnostics. In this review we discuss possibilities and limitations connected to using mass spectrometry based proteomics in the search for novel biomarkers, with focus on multiple sclerosis as a typical representative for the large group of non-curable degenerative and disabling disease with the lack of specific tests for early diagnosis. Careful control of the pre-analytical phase including sampling, storage and fractionation of samples, in addition to a thoroughly considered patient selection, is important in order to avoid false biomarkers to appear in the resulting mass spectra. Furthermore, advanced computational tools are needed in order to discover potential biomarkers from the enormous data amounts generated by the mass spectrometers. The development of such computer tools is a research field currently in the start phase and could prove to be a bottle neck in the biomarker discovery the next years. Therefore, a rather detailed review of the most used computational and pre-analytical methods is given in this review. Mass spectrometry based biomarker discovery is undoubtedly still in its early infancy. However, in light of the potential of this technology to provide deep coverage of the body fluid proteomes, it will certainly consolidate its role in developing molecular medicine into clinical practice.

Biomarkers↗
PubMed2006

Research translation: a new frontier for clinical laboratories.

Translational research and translational medicine (referred to hereafter as translational research) are interchangeable terms that underline the pressing need to translate into practical benefits for those affected by disease the extensive investments divested by the private and public sectors in biomedical research. For people more directly involved in clinical practice (physicians, clinical laboratory professionals and patients), translational research responds to the need to accelerate the capture of benefits of research, closing the gap between what we know and what we practice. This basically means the transfer of diagnostic and therapeutic advances proven effective in large, well-conducted trials (and, therefore, evidence-based) to daily medical practice. Translational research should be regarded as a two-way road: bench to bedside, and bedside to bench. In particular, to make possible a more effective translation process, a new road map should be implemented through interaction and cooperation between basic researchers, clinicians, laboratory professionals and manufacturers. Some examples of recent developments in clinical laboratory testing, including markers of cardiovascular diseases, clinical proteomics and recombinant allergens, may explain the importance of careful evaluation of all variables that allow the introduction of such new insights into clinical practice to assure better clinical outcomes. The vital role of laboratory medicine in the delivery of safer and more effective healthcare requires more careful evaluation not only of the analytical characteristics, but also of any other variable that may affect the clinical usefulness and diagnostic performances of laboratory tests, thus allowing more accurate interpretation and utilization of laboratory information.

Albuminuria↗
PubMed2005

The use of proteomics in biomarker discovery in neurodegenerative diseases.

Biomarkers for neurodegenerative diseases should reflect the central pathogenic processes of the diseases. The field of clinical proteomics is especially well suited for discovery of biomarkers in cerebrospinal fluid (CSF), which reflects the proteins in the brain under healthy conditions as well as in several neurodegenerative diseases. Known proteins involved in the pathology of neurodegenerative diseases are, respectively, normal tau protein, beta-amyloid (1-42), synaptic proteins, amyloid precursor protein (APP), apolipoprotein E (apoE), which previously have been studied by protein immunoassays. The objective of this paper was to summarize results from proteomic studies of differential protein patterns in neurodegenerative diseases with focus on Alzheimer's disease (AD). Today, discrimination of AD from controls and from other neurological diseases has been improved by simultaneous analysis of both beta-amyloid (1-42), total-tau, and phosphorylated tau, where a combination of low levels of CSF-beta-amyloid 1-42 and high levels of CSF-tau and CSF-phospho-tau is associated with an AD diagnosis. Detection of new biomarkers will further strengthen diagnosis and provide useful information in drug trials. The combination of immunoassays and proteomic methods show that the CSF proteins express differential protein patterns in AD, FTD, and PD patients, which reflect divergent underlying pathophysiological mechanisms and neuropathological changes in these diseases.

Alzheimer Disease↗
PubMed2026

The molecular similarity landscape of preclinical cancer models to patient tumors.

Selecting appropriate preclinical models is fundamental for translational oncology, yet a large-scale, multi-omic quantitative comparison of their similarity to primary human tumors is lacking. To address this, we integrated transcriptomic, proteomic, and genomic profiles from over 10,000 primary tumors from The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC), alongside 4,000 preclinical models. Using a robust computational framework, we revealed a clear hierarchy of transcriptomic and proteomic similarity to patient tumors: with patient-dervied xenografts (PDXs) having greater transcriptomic and proteomic similarity to patient tumors (>) compared with patient-derived organoids (PDOs), which are equal in hierarchy to that of PDX-dervied organoids (PDXOs) > cell lines. We also quantified high molecular conservation (Pearson correlation coefficient = 0.96) across paired in vitro to in vivo platform (organoids to PDX) transitions. Furthermore, genomic analysis demonstrated that whole-exome sequencing (WES) outperforms RNA-seq in detecting DNA variants, and it identified a clonal complexity hierarchy (cell lines > PDXOs > PDXs > PDOs) reflecting the effect of passaging history on intratumor heterogeneity. Ultimately, this study delivers a comprehensive quantitative benchmark, establishing a population-level hierarchy of molecular similarity between preclinical models and primary tumors and providing a data-driven reference for model selection. These findings offer a data-driven framework for selecting models that balance biological representativeness with experimental practicality.

Humans↗
PubMed2002

SELDI ProteinChip array in oncoproteomic research.

Research into the causes, early detection and treatment of cancers is a primary focus of the health care industry and proteomic-based methodologies are providing an increasingly important role in addressing these issues. The ProteinChip Array technology forms the basis of a clinical proteomics platform designed to expedite the discovery, validation, and characterization of cancer biomarkers at all stages of cancer progression. Being able to detect cancer progression early in turn allows for the possibility of more effective treatment. This short review serves to introduce the technology by highlighting specific examples related to cancer biomarker discoveries.

Biomarkers, Tumor↗
PubMed2003

Detection of cancer-specific markers amid massive mass spectral data.

We propose a comprehensive pattern recognition procedure that will achieve best discrimination between two or more sets of subjects with data in the same coordinate system. Applying the procedure to MS data of proteomic analysis of serum from ovarian cancer patients and serum from cancer-free individuals in the Food and Drug Administration/National Cancer Institute Clinical Proteomics Database, we have achieved perfect discrimination (100% sensitivity, 100% specificity) of patients with ovarian cancer, including early-stage disease, from normal controls for two independent sets of data. Our procedure identifies the best subset of proteomic biomarkers for optimal discrimination between the groups and appears to have higher discriminatory power than other methods reported to date. For large-scale screening for diseases of relatively low prevalence such as ovarian cancer, almost perfect specificity and sensitivity of the detection system is critical to avoid unmanageably high numbers of false-positive cases.

Algorithms↗
PubMed2024

Bridging the Gap From Proteomics Technology to Clinical Application: Highlights From the 68th Benzon Foundation Symposium.

The 68th Benzon Foundation Symposium brought together leading experts to explore the integration of mass spectrometry-based proteomics and artificial intelligence to revolutionize personalized medicine. This report highlights key discussions on recent technological advances in mass spectrometry-based proteomics, including improvements in sensitivity, throughput, and data analysis. Particular emphasis was placed on plasma proteomics and its potential for biomarker discovery across various diseases. The symposium addressed critical challenges in translating proteomic discoveries to clinical practice, including standardization, regulatory considerations, and the need for robust "business cases" to motivate adoption. Promising applications were presented in areas such as cancer diagnostics, neurodegenerative diseases, and cardiovascular health. The integration of proteomics with other omics technologies and imaging methods was explored, showcasing the power of multimodal approaches in understanding complex biological systems. Artificial intelligence emerged as a crucial tool for the acquisition of large-scale proteomic datasets, extracting meaningful insights, and enhancing clinical decision-making. By fostering dialog between academic researchers, industry leaders in proteomics technology, and clinicians, the symposium illuminated potential pathways for proteomics to transform personalized medicine, advancing the cause of more precise diagnostics and targeted therapies.

Proteomics↗
PubMed2024

Development of a Fit-For-Purpose Multi-Marker Panel for Early Diagnosis of Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) suffers from a lack of an effective diagnostic method, which hampers improvement in patient survival. Carbohydrate antigen 19-9 (CA19-9) is the only FDA-approved blood biomarker for PDAC, yet its clinical utility is limited due to suboptimal performance. Liquid chromatography-mass spectrometry (LC-MS) has emerged as a burgeoning technology in clinical proteomics for the discovery, verification, and validation of novel biomarkers. A plethora of protein biomarker candidates for PDAC have been identified using LC-MS, yet few has successfully transitioned into clinical practice. This translational standstill is owed partly to insufficient considerations of practical needs and perspectives of clinical implementation during biomarker development pipelines, such as demonstrating the analytical robustness of proposed biomarkers which is critical for transitioning from research-grade to clinical-grade assays. Moreover, the throughput and cost-effectiveness of proposed assays ought to be considered concomitantly from the early phases of the biomarker pipelines for enhancing widespread adoption in clinical settings. Here, we developed a fit-for-purpose multi-marker panel for PDAC diagnosis by consolidating analytically robust biomarkers as well as employing a relatively simple LC-MS protocol. In the discovery phase, we comprehensively surveyed putative PDAC biomarkers from both in-house data and prior studies. In the verification phase, we developed a multiple-reaction monitoring (MRM)-MS-based proteomic assay using surrogate peptides that passed stringent analytical validation tests. We adopted a high-throughput protocol including a short gradient (<10&#xa0;min) and simple sample preparation (no depletion or enrichment steps). Additionally, we developed our assay using serum samples, which are usually the preferred biospecimen in clinical settings. We developed predictive models based on our final panel of 12 protein biomarkers combined with CA19-9, which showed improved diagnostic performance compared to using CA19-9 alone in discriminating PDAC from non-PDAC controls including healthy individuals and patients with benign pancreatic diseases. A large-scale clinical validation is underway to demonstrate the clinical validity of our novel panel.

Humans↗
PubMed2006

Discovery of regulatory molecular events and biomarkers using 2D capillary chromatography and mass spectrometry.

An important component of proteomic research is the high-throughput discovery of novel proteins and protein-protein interactions that control molecular events that contribute to critical cellular functions and human disease. The interactions of proteins are essential for cellular functions. Identifying perturbation of normal cellular protein interactions is vital for understanding the disease process and intervening to control the disease. A second area of proteomics research is the discovery of proteins that will serve as biomarkers for the early detection, diagnosis and drug treatment response for specific diseases. These studies have been referred to as clinical proteomics. To discover biomarkers, proteomics research employs the quantitative comparison of peptide and protein expression in body fluids and tissues from diseased individuals (case) versus normal individuals (control). Methods that couple 2D capillary liquid chromatography (LC) and tandem mass spectrometry (MS/MS) analysis have greatly facilitated this discovery science. Coupling 2D-LC/MS/MS analysis with automated genome-assisted spectra interpretation allows a direct, high-throughput and high-sensitivity identification of thousands of individual proteins from complex biological samples. The systematic comparison of experimental conditions and controls allows protein function or disease states to be modeled. This review discusses the different purification and quantification strategies that have been developed and used in combination with 2D-LC/MS/MS and computational analysis to examine regulatory protein networks and clinical samples.

Biomarkers↗