scholarly journals Data processing pipelines for comprehensive profiling of proteomics samples by label-free LC–MS for biomarker discovery

Talanta ◽  
2011 ◽  
Vol 83 (4) ◽  
pp. 1209-1224 ◽  
Author(s):  
Christin Christin ◽  
Rainer Bischoff ◽  
Péter Horvatovich
Keyword(s):  
Data Processing ◽  
Biomarker Discovery ◽  
Label Free

scholarly journals Proteomics in Diagnosis of Prostate Cancer/ Протеомика Во Дијагноза На Простатниот Карцином

PRILOZI ◽  
2015 ◽  
Vol 36 (1) ◽  
pp. 5-36 ◽  
Author(s):  
Katarina Davalieva ◽  
Momir Polenakovic
Keyword(s):  
Prostate Cancer ◽  
Tumor Tissue ◽  
Biomarker Discovery ◽  
Molecular Level ◽  
Specific Antigen ◽  
Shotgun Proteomics ◽  
Comparative Proteomics ◽  
Label Free ◽  
The Past ◽  
Proteomics Research

Abstract Prostate cancer (PCa) is the second most frequently diagnosed malignancy in men worldwide. The introduction of prostate specific antigen (PSA) has greatly increased the number of men diagnosed with PCa but at the same time, as a result of the low specificity, led to overdiagnosis, resulting to unnecessary biopsies and high medical cost treatments. The primary goal in PCa research today is to find a biomarker or biomarker set for clear and effecttive diagnosis of PCa as well as for distinction between aggressive and indolent cancers. Different proteomic technologies such as 2-D PAGE, 2-D DIGE, MALDI MS profiling, shotgun proteomics with label-based (ICAT, iTRAQ) and label-free (SWATH) quantification, MudPIT, CE-MS have been applied to the study of PCa in the past 15 years. Various biological samples, including tumor tissue, serum, plasma, urine, seminal plasma, prostatic secretions and prostatic-derived exosomes were analyzed with the aim of identifying diagnostic and prognostic biomarkers and developing a deeper understanding of the disease at the molecular level. This review is focused on the overall analysis of expression proteomics studies in the PCa field investigating all types of human samples in the search for diagnostics biomarkers. Emphasis is given on proteomics platforms used in biomarker discovery and characterization, explored sources for PCa biomarkers, proposed candidate biomarkers by comparative proteomics studies and the possible future clinical application of those candidate biomarkers in PCa screening and diagnosis. In addition, we review the specificity of the putative markers and existing challenges in the proteomics research of PCa.

scholarly journals In Situ Mass Spectrometry Diagnostics of Impaired Glucose Tolerance Using Label-Free Metabolomic Signature

Diagnostics ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 1052
Author(s):  
Petr G. Lokhov ◽  
Oxana P. Trifonova ◽  
Dmitry L. Maslov ◽  
Elena E. Balashova
Keyword(s):  
Mass Spectrometry ◽  
Glucose Tolerance ◽  
Data Processing ◽  
Impaired Glucose Tolerance ◽  
Characteristic Curve ◽  
Complex Data ◽  
Label Free ◽  
Mass Peak ◽  
Mass Spectrometers

In metabolomics, mass spectrometry is used to detect a large number of low-molecular substances in a single analysis. Such a capacity could have direct application in disease diagnostics. However, it is challenging because of the analysis complexity, and the search for a way to simplify it while maintaining the diagnostic capability is an urgent task. It has been proposed to use the metabolomic signature without complex data processing (mass peak detection, alignment, normalization, and identification of substances, as well as any complex statistical analysis) to make the analysis more simple and rapid. Methods: A label-free approach was implemented in the metabolomic signature, which makes the measurement of the actual or conditional concentrations unnecessary, uses only mass peak relations, and minimizes mass spectra processing. The approach was tested on the diagnosis of impaired glucose tolerance (IGT). Results: The label-free metabolic signature demonstrated a diagnostic accuracy for IGT equal to 88% (specificity 85%, sensitivity 90%, and area under receiver operating characteristic curve (AUC) of 0.91), which is considered to be a good quality for diagnostics. Conclusions: It is possible to compile label-free signatures for diseases that allow for diagnosing the disease in situ, i.e., right at the mass spectrometer without complex data processing. This achievement makes all mass spectrometers potentially versatile diagnostic devices and accelerates the introduction of metabolomics into medicine.

scholarly journals Genetics Meets Proteomics: Correlating the Portuguese Water Dog Blood Serum Proteome with Genetic Markers

2011 ◽  
Vol 4 ◽  
pp. PRI.S6470
Author(s):  
Sandra Sénéchal ◽  
Martin Kussmann
Keyword(s):  
Blood Serum ◽  
Biomarker Discovery ◽  
Protein Quantification ◽  
Label Free ◽  
Blood Profile ◽  
Serum Proteome ◽  
Genetic Level ◽  
Portuguese Water ◽  
Dog Blood ◽  
Genetically Determined

Blood serum is a body fluid widely used for biomarker discovery and therefore numerous studies aim at defining its proteome. The serum proteome is subject to fluctuations resulting from biological variability (eg, diurnal variations) reflecting both healthy and/or disease-related conditions. Inter-individual differences originate partly at the genetic level and may influence clinical blood profile including the serum proteome. Therefore we investigated whether serum protein abundance is genetically determined: we report the study of a cohort of 146 Portuguese Water Dogs, a dog breed whose genetic background has been well characterized. We generated protein profiles of dog sera on 1D-gels and correlated them with microsatellite markers. We detected correlations between 7 gel bands and 11 genetic regions and developed a label-free protein quantification method to identify and quantify the proteins most accountable for serum proteome variation. An association between the abundance of RBP4 in dog serum and the adiponectin gene was detected.

scholarly journals Affinity Capture Enrichment versus Affinity Depletion: A Comparison of Strategies for Increasing Coverage of Low-Abundant Human Plasma Proteins

2020 ◽  
Vol 21 (16) ◽  
pp. 5903
Author(s):  
Nicolai Bjødstrup Palstrøm ◽  
Lars Melholt Rasmussen ◽  
Hans Christian Beck
Keyword(s):  
Mass Spectrometry ◽  
Small Molecule ◽  
Plasma Proteins ◽  
Biomarker Discovery ◽  
Plasma Samples ◽  
Label Free ◽  
Affinity Capture ◽  
Liquid Chromatography Tandem Mass ◽  
Removal System ◽  
Equalization Method

In the present study, we evaluated four small molecule affinity-based probes based on agarose-immobilized benzamidine (ABA), O-Phospho-L-Tyrosine (pTYR), 8-Amino-hexyl-cAMP (cAMP), or 8-Amino-hexyl-ATP (ATP) for their ability to remove high-abundant proteins such as serum albumin from plasma samples thereby enabling the detection of medium-to-low abundant proteins in plasma samples by mass spectrometry-based proteomics. We compared their performance with the most commonly used immunodepletion method, the Multi Affinity Removal System Human 14 (MARS14) targeting the top 14 most abundant plasma proteins and also the ProteoMiner protein equalization method by label-free quantitative liquid chromatography tandem mass spectrometry (LC-MSMS) analysis. The affinity-based probes demonstrated a high reproducibility for low-abundant plasma proteins, down to picomol per mL levels, compared to the Multi Affinity Removal System (MARS) 14 and the Proteominer methods, and also demonstrated superior removal of the majority of the high-abundant plasma proteins. The ABA-based affinity probe and the Proteominer protein equalization method performed better compared to all other methods in terms of the number of analyzed proteins. All the tested methods were highly reproducible for both high-abundant plasma proteins and low-abundant proteins as measured by correlation analyses of six replicate experiments. In conclusion, our results demonstrated that small-molecule based affinity-based probes are excellent alternatives to the commonly used immune-depletion methods for proteomic biomarker discovery studies in plasma. Data are available via ProteomeXchange with identifier PXD020727.

scholarly journals Semiautomated Alignment of High-Throughput Metabolite Profiles with Chemometric Tools

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Ze-ying Wu ◽  
Zhong-da Zeng ◽  
Zi-dan Xiao ◽  
Daniel Kam-Wah Mok ◽  
Yi-zeng Liang ◽  
...  
Keyword(s):  
Data Processing ◽  
High Throughput ◽  
Biomarker Discovery ◽  
Linear Interpolation ◽  
Time Shift ◽  
Data Partition ◽  
High Throughput Data ◽  
Automated Method ◽  
Metabolite Profiles ◽  
Standard Profile

The rapid increase in the use of metabolite profiling/fingerprinting techniques to resolve complicated issues in metabolomics has stimulated demand for data processing techniques, such as alignment, to extract detailed information. In this study, a new and automated method was developed to correct the retention time shift of high-dimensional and high-throughput data sets. Information from the target chromatographic profiles was used to determine the standard profile as a reference for alignment. A novel, piecewise data partition strategy was applied for the determination of the target components in the standard profile as markers for alignment. An automated target search (ATS) method was proposed to find the exact retention times of the selected targets in other profiles for alignment. The linear interpolation technique (LIT) was employed to align the profiles prior to pattern recognition, comprehensive comparison analysis, and other data processing steps. In total, 94 metabolite profiles of ginseng were studied, including the most volatile secondary metabolites. The method used in this article could be an essential step in the extraction of information from high-throughput data acquired in the study of systems biology, metabolomics, and biomarker discovery.

scholarly journals DirectMS1: MS/MS-free identification of 1000 proteins of cellular proteomes in 5 minutes

2019 ◽  
Author(s):  
Mark V. Ivanov ◽  
Julia A. Bubis ◽  
Vladimir Gorshkov ◽  
Irina A. Tarasova ◽  
Lev I. Levitsky ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Biomarker Discovery ◽  
Tandem Mass ◽  
Label Free ◽  
Hela Cell Line ◽  
Sequence Coverage ◽  
Proteome Coverage ◽  
Successful Attempt ◽  
Higher Sensitivity ◽  
Lengthy Analysis

AbstractProteome characterization relies heavily on tandem mass spectrometry (MS/MS) and is thus associated with instrumentation complexity, lengthy analysis time, and limited duty-cycle. It was always tempting to implement approaches which do not require MS/MS, yet, they were constantly failing in achieving meaningful depth of quantitative proteome coverage within short experimental times, which is particular important for clinical or biomarker discovery applications. Here, we report on the first successful attempt to develop a truly MS/MS-free and label-free method for bottom-up proteomics. We demonstrate identification of 1000 protein groups for a standard HeLa cell line digest using 5-minute LC gradients. The amount of loaded sample was varied in a range from 1 ng to 500 ng, and the method demonstrated 10-fold higher sensitivity compared with the standard MS/MS-based approach. Due to significantly higher sequence coverage obtained by the developed method, it outperforms all popular MS/MS-based label-free quantitation approaches.

Sign in / Sign up

Export Citation Format

Share Document