From “Clinical Proteomics” to “Clinical Chemistry Proteomics”: considerations using quantitative mass-spectrometry as a model approach

2012 ◽  
Vol 50 (2) ◽  
Author(s):  
Sylvain Lehmann ◽  
Pauline Poinot ◽  
Laurent Tiers ◽  
Christophe Junot ◽  
François Becher ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Biomarker Discovery ◽  
Clinical Chemistry ◽  
Biological Fluids ◽  
Clinical Proteomics ◽  
Detection Methods ◽  
Patient Treatment ◽  
Quantitative Detection ◽  
New Biomarkers

AbstractClinical Proteomics biomarker discovery programs lead to the selection of putative new biomarkers of human pathologies. Following an initial discovery phase, validation of these candidates in larger populations is a major task that recently started relying upon the use of mass spectrometry approaches, especially in cases where classical immune-detection methods were lacking. Thanks to highly sensitive spectrometers, adapted measurement methods like selective reaction monitoring (SRM) and various pre-fractionation methods, the quantitative detection of protein/peptide biomarkers in low concentrations is now feasible from complex biological fluids. This possibility leads to the use of similar methodologies in clinical biology laboratories, within a new proteomic field that we shall name “Clinical Chemistry Proteomics” (CCP). Such evolution of Clinical Proteomics adds important constraints with regards to the in vitro diagnostic (IVD) application. As measured values of analytes will be used to diagnose, follow-up and adapt patient treatment on a routine basis; medical utility, robustness, reference materials and clinical feasibility are among the new issues of CCP to consider.

P1.49 Mass spectrometry based clinical proteomics for biomarker discovery in Duchenne muscular dystrophy

2011 ◽  
Vol 21 (9-10) ◽  
pp. 656
Author(s):  
F.C. Martin ◽  
S. Oonk ◽  
P.A.C. ’t Hoen ◽  
V.D. Nadarajah ◽  
A. Chaouch ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Duchenne Muscular Dystrophy ◽  
Muscular Dystrophy ◽  
Biomarker Discovery ◽  
Clinical Proteomics

Use of SELDI-TOF mass spectrometry for identification of new biomarkers: potential and limitations

2007 ◽  
Vol 45 (11) ◽  
Author(s):  
Michael Kiehntopf ◽  
Robert Siegmund ◽  
Thomas Deufel
Keyword(s):  
Mass Spectrometry ◽  
Biomarker Discovery ◽  
Clinical Results ◽  
Protein Profiling ◽  
High Throughput Analysis ◽  
Pilot Studies ◽  
Flight Mass Spectrometry ◽  
Analytical Phase ◽  
New Biomarkers ◽  
Tof Ms

AbstractSurface-enhanced laser desorption time of flight mass spectrometry (SELDI-TOF-MS) is an important proteomic technology that is immediately available for the high throughput analysis of complex protein samples. Over the last few years, several studies have demonstrated that comparative protein profiling using SELDI-TOF-MS breaks new ground in diagnostic protein analysis particularly with regard to the identification of novel biomarkers. Importantly, researchers have acquired a better understanding also of the limitations of this technology and various pitfalls in biomarker discovery. Bearing these in mind, great emphasis must be placed on the development of rigorous standards and quality control procedures for the pre-analytical as well as the analytical phase and subsequent bioinformatics applied to analysis of the data. To avoid the risk of false-significant results studies must be designed carefully and control groups accurately selected. In addition, appropriate tools, already established for analysis of highly complex microarray data, need to be applied to protein profiling data. To validate the significance of any candidate biomarker derived from pilot studies in appropriately designed prospective multi-center studies is mandatory; reproducibility of the clinical results must be shown over time and in different diagnostic settings. SELDI-TOF-MS-based studies that are in compliance with these requirements are now required; only a few have been published so far. In the meantime, further evaluation and optimization of both technique and marker validation strategies are called for before MS-based proteomic algorithms can be translated into routine laboratory testing.Clin Chem Lab Med 2007;45:1435–49.

scholarly journals The Use of Proteomics in Biomarker Discovery in Neurodegenerative Diseases

2005 ◽  
Vol 21 (2) ◽  
pp. 81-92 ◽  
Author(s):  
Pia Davidsson ◽  
Magnus Sjögren
Keyword(s):  
Neurodegenerative Diseases ◽  
Biomarker Discovery ◽  
Neurological Diseases ◽  
Synaptic Proteins ◽  
Clinical Proteomics ◽  
Protein Patterns ◽  
Differential Protein ◽  
Csf Proteins ◽  
New Biomarkers ◽  
The Brain

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,β-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 bothβ-amyloid (1-42), total-tau, and phosphorylated tau, where a combination of low levels of CSF-β-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.

scholarly journals New insights on microRNAs in diabetic nephropathy: potential biomarkers for diagnosis and therapeutic targets

2017 ◽  
Vol 20 (1) ◽  
pp. 42-50 ◽  
Author(s):  
Elena Sergeevna Kamyshova ◽  
Irina Nikolaevna Bobkova ◽  
Irina Mikhailovna Kutyrina
Keyword(s):  
Diabetic Nephropathy ◽  
Early Detection ◽  
Early Stage ◽  
Biological Fluids ◽  
Renal Tissue ◽  
In Vivo Models ◽  
New Biomarkers

Diabetic nephropathy (DN) is a severe complication of diabetes mellitus associated with the progressive deterioration of renal function. Although microalbuminuria is considered as a gold standard for DN diagnosis, it has limited predictive powers and specificity as a diagnostic tool for the early stage of DN. Therefore, new biomarkers are required for the early detection of DN. Studies using in vitro and in vivo models of DN have revealed an important role of microRNAs (miRNAs), short non-coding RNAs that modulate physiological and pathological processes by inhibiting target gene expression, in DN development. Recent studies have shown that the dysregulation of miRNAs, which is associated with the key features of DN, such as the mesangial expansion and accumulation of extracellular matrix proteins, is related to fibrosis and glomerular dysfunction. Thus, the up- and downregulation of miRNA expression in the renal tissue or biological fluids, including urine, may represent new biomarkers for the diagnosis and monitoring of DN progression. In this review, we highlight the significance of miRNAs as biomarkers for the early detection of DN and emphasise their potential role as a therapeutic target.

scholarly journals A rigorous evaluation of optimal peptide targets for MS-based clinical diagnostics of Coronavirus Disease 2019 (COVID-19).

2021 ◽  
Author(s):  
Andrew T Rajczewski ◽  
Subina T Mehta ◽  
Dinh Duy An Ngyuen ◽  
Björn Andreas Grüning ◽  
James E Johnson ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Direct Detection ◽  
Clinical Diagnostics ◽  
Clinical Proteomics ◽  
Clinical Samples ◽  
P Value ◽  
Upper Respiratory Tract ◽  
Proteomics Data ◽  
High Throughput Analysis

The Coronavirus Disease 2019 (COVID19) global pandemic has had a profound, lasting impact on the world's population. A key aspect to providing care for those with COVID19 and checking its further spread is early and accurate diagnosis of infection, which has been generally done via methods for amplifying and detecting viral RNA molecules. Detection and quantitation of peptides using targeted mass spectrometry-based strategies has been proposed as an alternative diagnostic tool due to direct detection of molecular indicators from non-invasively collected samples as well as the potential for high-throughput analysis in a clinical setting; many studies have revealed the presence of viral peptides within easily accessed patient samples. However, evidence suggests that some viral peptides could serve as better indicators of COVID19 infection status than others, due to potential misidentification of peptides derived from human host proteins, poor spectral quality, high limits of detection etc. In this study we have compiled a list of 636 peptides identified from Sudden Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) samples, including from in vitro and clinical sources. These datasets were rigorously analyzed using automated, Galaxy-based workflows containing tools such as PepQuery, BLAST-P, and the Multi-omic Visualization Platform as well as the open-source tools MetaTryp and Proteomics Data Viewer (PDV). Using PepQuery for confirming peptide spectrum matches, we were able to narrow down the 639 peptide possibilities to 87 peptides which were most robustly detected and specific to the SARS-CoV-2 virus. The specificity of these sequences to coronavirus taxa was confirmed using Unipept and BLAST-P. Through stringent p-value cutoff combined with manual verification of peptide spectrum match quality, 4 peptides derived from the nucleocapsid phosphoprotein and membrane protein were found to be most robustly detected across all cell culture and clinical samples, including those collected non-invasively. We propose that these peptides would be of the most value for clinical proteomics applications seeking to detect COVID-19 from a variety of sample types. We also contend that samples taken from the upper respiratory tract and oral cavity have the highest potential for diagnosis of SARS-CoV-2 infection from easily collected patient samples using mass spectrometry-based proteomics assays.

scholarly journals Bioanalytical and Mass Spectrometric Methods for Aldehyde Profiling in Biological Fluids

Toxics ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 32 ◽  
Author(s):  
Romel P. Dator ◽  
Morwena J. Solivio ◽  
Peter W. Villalta ◽  
Silvia Balbo
Keyword(s):  
Mass Spectrometry ◽  
High Performance ◽  
Single Cell Analysis ◽  
Current Knowledge ◽  
Biological Fluids ◽  
Cellular Level ◽  
Mass Spectrometric ◽  
Parkinson’S Diseases ◽  
New Biomarkers ◽  
Epidemiology Studies

Human exposure to aldehydes is implicated in multiple diseases including diabetes, cardiovascular diseases, neurodegenerative disorders (i.e., Alzheimer’s and Parkinson’s Diseases), and cancer. Because these compounds are strong electrophiles, they can react with nucleophilic sites in DNA and proteins to form reversible and irreversible modifications. These modifications, if not eliminated or repaired, can lead to alteration in cellular homeostasis, cell death and ultimately contribute to disease pathogenesis. This review provides an overview of the current knowledge of the methods and applications of aldehyde exposure measurements, with a particular focus on bioanalytical and mass spectrometric techniques, including recent advances in mass spectrometry (MS)-based profiling methods for identifying potential biomarkers of aldehyde exposure. We discuss the various derivatization reagents used to capture small polar aldehydes and methods to quantify these compounds in biological matrices. In addition, we present emerging mass spectrometry-based methods, which use high-resolution accurate mass (HR/AM) analysis for characterizing carbonyl compounds and their potential applications in molecular epidemiology studies. With the availability of diverse bioanalytical methods presented here including simple and rapid techniques allowing remote monitoring of aldehydes, real-time imaging of aldehydic load in cells, advances in MS instrumentation, high performance chromatographic separation, and improved bioinformatics tools, the data acquired enable increased sensitivity for identifying specific aldehydes and new biomarkers of aldehyde exposure. Finally, the combination of these techniques with exciting new methods for single cell analysis provides the potential for detection and profiling of aldehydes at a cellular level, opening up the opportunity to minutely dissect their roles and biological consequences in cellular metabolism and diseases pathogenesis.

scholarly journals Studying in vitro metabolism of the first and second generation of antisense oligonucleotides with the use of ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry

2020 ◽  
Vol 412 (27) ◽  
pp. 7453-7467
Author(s):  
Anna Kilanowska ◽  
Łukasz Nuckowski ◽  
Sylwia Studzińska
Keyword(s):  
Mass Spectrometry ◽  
Liquid Chromatography ◽  
Liver Microsomes ◽  
Time Of Flight ◽  
Ion Pair ◽  
In Vitro Metabolism ◽  
Detection Methods ◽  
Ion Pair Chromatography ◽  
Flight Mass Spectrometry

Abstract The aim of the present investigation was the analysis and identification of antisense oligonucleotide metabolism products after incubation with human liver microsomes regarding four different oligonucleotide modifications. Separation and detection methods based on the use of liquid chromatography coupled with quadrupole time-of-flight mass spectrometry were developed for this purpose. Firstly, the optimization of mass spectrometer parameters was done to select those which ensure the highest possible sensitivity of oligonucleotide analysis. This step was conducted for two chromatographic modes—ion pair chromatography and hydrophilic interaction liquid chromatography—due to their common application in oligonucleotide analysis. Based on sensitivity results, ion pair chromatography coupled with mass spectrometry was selected for the separation of model oligonucleotide mixtures in order to verify its selectivity for N-deleted metabolite separation. Next, the developed method was applied in the examination of oligonucleotides in vitro metabolism. First, wide optimization of incubation parameters was conducted including the concentration of the reaction buffer components. Obtained results indicated that both 3′-exonucleases and 5′-exonucleases contributed to the biotransformation of oligonucleotides. Moreover, it may be concluded that the number of metabolites depends on oligonucleotide modification and consequently its resistance to enzymatic attack. Thus, the number of the oligonucleotide metabolites decreased with the decrease of the resultant polarity of oligonucleotide caused by chemical modification.

Emerging protein biomarkers in epithelial ovarian cancer prognosis: An aid for Multivariate indexing

2021 ◽  
Vol 22 ◽  
Author(s):  
Priya Paliwal ◽  
Hemangi Ranade ◽  
Dignya Desai ◽  
Manali Datta
Keyword(s):  
Ovarian Cancer ◽  
Life Expectancy ◽  
Epithelial Ovarian Cancer ◽  
Biomarker Discovery ◽  
Polycystic Ovary ◽  
Biological Fluids ◽  
Cancer Prognosis ◽  
Accurate Identification ◽  
Improve Life Expectancy

: Epithelial ovarian cancer (EOC) is a chronic and degenerative disease propelled by mutation in BRCA1/2 genes, familial history, smoking and polycystic ovary syndrome. Although lifetime risk of ovarian cancer is low, yet it is the fifth leading cause of cancer related deaths. Surprisingly, EOC represents 90% of all ovarian cancers, out of which 70% women are diagnosed with the malignancy at its advanced III-IV stages. Early detection may increase the life expectancy up to 5 years. Thus, it has become need of the hour to attain improvement of clinical outcomes of EOC and improve life expectancy of patients. Plethora of proteins in different biological fluids may serve as prospective identifiers for the disease. Over the years, accurate identification of proteins secreted by EOC cells has been perfected by in vitro and in silico state of art technologies. Multivariate test, consisting of histo-pathological data in combination with protein biomarker panel has paved way for enhanced and accurate assessment for EOC, still there is a chance of further improvement. This review encompasses the inputs made in ovarian cancer biomarker discovery and demonstrates their potential usefulness for design of early diagnostics of EOC.

scholarly journals Urinary Proteome Analysis using Capillary Electrophoresis Coupled to Mass Spectrometry: A Powerful Tool in Clinical Diagnosis, Prognosis and Therapy Evaluation

2009 ◽  
Vol 28 (4) ◽  
pp. 223-234 ◽  
Author(s):  
Harald Mischak ◽  
Eric Schiffer ◽  
Petra Zürbig ◽  
Mohammed Dakna ◽  
Jochen Metzger
Keyword(s):  
Mass Spectrometry ◽  
Capillary Electrophoresis ◽  
Clinical Diagnosis ◽  
Biological Fluids ◽  
Proteome Analysis ◽  
Clinical Proteomics ◽  
Proteomic Profiling ◽  
Two Dimensional Gel Electrophoresis ◽  
Therapy Evaluation ◽  
Urinary Proteome

Urinary Proteome Analysis using Capillary Electrophoresis Coupled to Mass Spectrometry: A Powerful Tool in Clinical Diagnosis, Prognosis and Therapy EvaluationProteome analysis has emerged as a powerful tool to decipher (patho) physiological processes, resulting in the establishment of the field of clinical proteomics. One of the main goals is to discover biomarkers for diseases from tissues and body fluids. Due to the enormous complexity of the proteome, a separation step is required for mass spectrometry (MS)-based proteome analysis. In this review, the advantages and limitations of protein separation by two-dimensional gel electrophoresis, liquid chromatography, surface-enhanced laser desorption/ionization and capillary electrophoresis (CE) for proteomic analysis are described, focusing on CE-MS. CE-MS enables separation and detection of the small molecular weight proteome in biological fluids with high reproducibility and accuracy in one single processing step and in a short time. As sensitive and specific single biomarkers generally may not exist, a strategy to overcome this diagnostic void is shifting from single analyte detection to simultaneous analysis of multiple analytes that together form a disease-specific pattern. Such approaches, however, are accompanied with additional challenges, which we will outline in this review. Besides the choice of adequate technological platforms, a high level of standardization of proteomic measurements and data processing is also necessary to establish proteomic profiling. In this regard, demands concerning study design, choice of specimens, sample preparation, proteomic data mining, and clinical evaluation should be considered before performing a proteomic study.

Accounting for isotopic clustering in Fourier transform mass spectrometry data analysis for clinical diagnostic studies

2016 ◽  
Vol 15 (5) ◽  
Author(s):  
Alexia Kakourou ◽  
Werner Vach ◽  
Simone Nicolardi ◽  
Yuri van der Burgt ◽  
Bart Mertens
Keyword(s):  
Mass Spectrometry ◽  
High Throughput ◽  
Biomarker Discovery ◽  
Protein Profiling ◽  
Mass Spectrometry Data ◽  
Clinical Proteomics ◽  
Intensity Level ◽  
Data Set ◽  
Fticr Mass Spectrometry ◽  
Mass Spectrometry Technology

AbstractMass spectrometry based clinical proteomics has emerged as a powerful tool for high-throughput protein profiling and biomarker discovery. Recent improvements in mass spectrometry technology have boosted the potential of proteomic studies in biomedical research. However, the complexity of the proteomic expression introduces new statistical challenges in summarizing and analyzing the acquired data. Statistical methods for optimally processing proteomic data are currently a growing field of research. In this paper we present simple, yet appropriate methods to preprocess, summarize and analyze high-throughput MALDI-FTICR mass spectrometry data, collected in a case-control fashion, while dealing with the statistical challenges that accompany such data. The known statistical properties of the isotopic distribution of the peptide molecules are used to preprocess the spectra and translate the proteomic expression into a condensed data set. Information on either the intensity level or the shape of the identified isotopic clusters is used to derive summary measures on which diagnostic rules for disease status allocation will be based. Results indicate that both the shape of the identified isotopic clusters and the overall intensity level carry information on the class outcome and can be used to predict the presence or absence of the disease.

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