Applicability of 2D gel electrophoresis and liquid chromatography in proteomic analysis of urine using mass spectrometry MALDI-TOF

2013 ◽  
Vol 16 (3) ◽  
pp. 587-592 ◽  
Author(s):  
T. Banach ◽  
Ł. Adaszek ◽  
D. Wyłupek ◽  
M. Winiarczyk ◽  
S. Winiarczyk
Keyword(s):  
Liquid Chromatography ◽  
Body Fluid ◽  
Mass Spectra ◽  
Protein Composition ◽  
Human Medicine ◽  
Urine Samples ◽  
Maldi Tof ◽  
2D Gel ◽  
Bruker Daltonics ◽  
Prognostic And Predictive Markers

AbstractProteomics including the studies of the structure, function and dependences between proteins is more and more extensively applied in human medicine and veterinary medicine. The analysis of protein profiles of tissues and body fluid from healthy and ill individuals allows to identify diagnostic, prognostic and predictive markers in various pathological states in people and animals. This paper presents preparation of urine samples for analysis in the mass spectrometer MALDI-TOF (Ultraflextreme, Bruker, Bremen, Germany) by means of two methods: liquid chromatography based on the system Nano-LC (PROTEINER FC II, Bruker Daltonics, Bremen Germany). and two-direction electrophoresis 2DE (GE Healthcare, United Kingdom). Both methods enable separation of the mixture under consideration into individual fractions of high purity indispensable for obtaining readable mass spectra. The purpose of this paper is to determine applicability of these methods in analysis of protein composition of urine samples.

Profiling of Myelin Proteins by 2D-Gel Electrophoresis and Multidimensional Liquid Chromatography Coupled to MALDI TOF−TOF Mass Spectrometry

2005 ◽  
Vol 4 (6) ◽  
pp. 2283-2293 ◽  
Author(s):  
Frank Vanrobaeys ◽  
Rudy Van Coster ◽  
Goedele Dhondt ◽  
Bart Devreese ◽  
Jozef Van Beeumen
Keyword(s):  
Mass Spectrometry ◽  
Liquid Chromatography ◽  
Gel Electrophoresis ◽  
Myelin Proteins ◽  
2D Gel Electrophoresis ◽  
Maldi Tof ◽  
2D Gel

scholarly journals Proteomics of the Autographa californica Nucleopolyhedrovirus Budded Virions

2010 ◽  
Vol 84 (14) ◽  
pp. 7233-7242 ◽  
Author(s):  
RanRan Wang ◽  
Fei Deng ◽  
Dianhai Hou ◽  
Yong Zhao ◽  
Lin Guo ◽  
...  
Keyword(s):  
Liquid Chromatography ◽  
Ion Trap ◽  
Secondary Infection ◽  
Time Of Flight ◽  
Protein Composition ◽  
Autographa Californica ◽  
Enveloped Viruses ◽  
Maldi Tof ◽  
Associated Proteins ◽  
Autographa Californica Nucleopolyhedrovirus

ABSTRACT Baculoviruses produce two progeny phenotypes during their replication cycles. The occlusion-derived virus (ODV) is responsible for initiating primary infection in the larval midgut, and the budded virus (BV) phenotype is responsible for the secondary infection. The proteomics of several baculovirus ODVs have been revealed, but so far, no extensive analysis of BV-associated proteins has been conducted. In this study, the protein composition of the BV of Autographa californica nucleopolyhedrovirus (AcMNPV), the type species of baculoviruses, was analyzed by various mass spectrometry (MS) techniques, including liquid chromatography-triple quadrupole linear ion trap (LC-Qtrap), liquid chromatography-quadrupole time of flight (LC-Q-TOF), and matrix-assisted laser desorption ionization-time of flight (MALDI-TOF). SDS-PAGE and MALDI-TOF analyses showed that the three most abundant proteins of the AcMNPV BV were GP64, VP39, and P6.9. A total of 34 viral proteins associated with the AcMNPV BV were identified by the indicated methods. Thirteen of these proteins, PP31, AC58/59, AC66, IAP-2, AC73, AC74, AC114, AC124, chitinase, polyhedron envelope protein (PEP), AC132, ODV-E18, and ODV-E56, were identified for the first time to be BV-associated proteins. Western blot analyses showed that ODV-E18 and ODV-E25, which were previously thought to be ODV-specific proteins, were also present in the envelop fraction of BV. In addition, 11 cellular proteins were found to be associated with the AcMNPV BV by both LC-Qtrap and LC-Q-TOF analyses. Interestingly, seven of these proteins were also identified in other enveloped viruses, suggesting that many enveloped viruses may commonly utilize certain conserved cellular pathways.

scholarly journals Mass spectrometry analysis of protein blood extracts of animals with experimental brucellos

2019 ◽  
pp. 11-18
Author(s):  
D. V. Ulshina ◽  
D. A. Kovalev ◽  
D. G. Ponomarenko ◽  
D. V. Rusanova ◽  
T. V. Berdnikova ◽  
...  
Keyword(s):  
Mass Spectra ◽  
Direct Detection ◽  
Protein Profiling ◽  
Mass Spectrometry Analysis ◽  
Experimental Conditions ◽  
Spectrometry Analysis ◽  
Maldi Tof ◽  
Maldi Tof Mass Spectra ◽  
Causative Agents ◽  
Bruker Daltonics

Aim. The aim of the present research was to study the possibility of direct detection of the causative agent of brucellosis in a biomaterial under experimental conditions via the MALDI-TOF MS method using Mass-Up program resources and a set of packages for open-source statistical software R. Materials and methods. We used laboratory mice infected with the causative agents of Brucellosis (strains B. melitensis 548, B. abortus 544, B. suis 1330) as models. Protein profiling was performed on a MALDI-TOF Microflex «Bruker Daltonics» mass spectrometer. Results. The bioinformatic-statistical approach used for analyzing MALDI-TOF mass spectra allows to carry out a direct detection of Brucella in the biomaterial; besides, it is possible to determinate their species via the identification of a group of biomarkers. Conclusion. It was experimentally confirmed that the protein profiles of the blood extracts of infected animals contain 11 markers, including 6 genus specific for Brucella spp., which can be associated with Brucella infection.

scholarly journals Rapidly predicting vancomycin resistance of Enterococcus faecium through MALDI-TOF MS spectrum obtained in real-world clinical microbiology laboratory

2020 ◽  
Author(s):  
Hsin-Yao Wang ◽  
Ko-Pei Lu ◽  
Chia-Ru Chung ◽  
Yi-Ju Tseng ◽  
Tzong-Yi Lee ◽  
...  
Keyword(s):  
Enterococcus Faecium ◽  
Antibiotic Susceptibility ◽  
Body Fluid ◽  
Mass Spectra ◽  
External Validation ◽  
Antibiotic Use ◽  
Clinical Microbiology Laboratory ◽  
Maldi Tof Ms ◽  
Maldi Tof ◽  
Tof Ms

AbstractEnterococcus faecium is one of the leading pathogens in the world. In this study, we proposed a strategy to rapidly and accurately distinguish vancomycin-resistant Enterococcus faecium (VREfm) and vancomycin-susceptible E. faecium (VSEfm) to help doctors correctly determine the use of vancomycin by a machine learning (ML)-based algorithm. A predictive model was developed and validated to distinguish VREfm and VSEfm by analyzing MALDI-TOF MS spectra of unique E. faecium isolates from different specimen types. Firstly, 5717 mass spectra, including 2795 VREfm and 2922 VSEfm, were used to develop the algorithm. And 2280 mass spectra of isolates, namely 1222 VREfm and 1058 VSEfm, were used to externally validate the algorithm. The random forest-based algorithm demonstrated good classification performances for overall specimens, whose mean AUROC in 5-fold cross validation, time-wise validation, and external validation was all greater than 0.84. For the detection of VREfm in blood, sterile body fluid, urinary tract, and wound, the AUROC in external validation was also greater than 0.84. The predictions with algorithms were significantly more accurate than empirical antibiotic use. The accuracy of antibiotics administration could be improved by 30%. And the algorithm could provide rapid antibiotic susceptibility results at least 24 hours ahead of routine laboratory tests. The turn-around-time of antibiotic susceptibility could be reduced by 50%. In conclusion, a ML algorithm using MALDI-TOF MS spectra obtained in routine workflow accurately differentiated VREfm from VSEfm, especially in blood and sterile body fluid, which can be applied to facilitate the clinical testing process due to its accuracy, generalizability, and rapidness.

888: Metabolomic Urine Samples Profiling Using High Performance Liquid Chromatography Coupled with Mass Spectrometry to Detect Bladder Cancer

2007 ◽  
Vol 177 (4S) ◽  
pp. 295-295
Author(s):  
Michael Mullerad ◽  
Haleem J. Issaq ◽  
Alexander Kravtsov ◽  
Timothy Waybright ◽  
Brian Luke ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Bladder Cancer ◽  
High Performance Liquid Chromatography ◽  
Liquid Chromatography ◽  
High Performance ◽  
Urine Samples

Interpretable machine learning model to detect chemically adulterated urine samples analyzed by high resolution mass spectrometry

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Gabriel L. Streun ◽  
Andrea E. Steuer ◽  
Lars C. Ebert ◽  
Akos Dobay ◽  
Thomas Kraemer
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Liquid Chromatography ◽  
High Resolution ◽  
Retention Time ◽  
Drug Testing ◽  
High Resolution Mass Spectrometry ◽  
Urine Samples ◽  
Machine Learning Model ◽  
Resolution Mass

Abstract Objectives Urine sample manipulation including substitution, dilution, and chemical adulteration is a continuing challenge for workplace drug testing, abstinence control, and doping control laboratories. The simultaneous detection of sample manipulation and prohibited drugs within one single analytical measurement would be highly advantageous. Machine learning algorithms are able to learn from existing datasets and predict outcomes of new data, which are unknown to the model. Methods Authentic human urine samples were treated with pyridinium chlorochromate, potassium nitrite, hydrogen peroxide, iodine, sodium hypochlorite, and water as control. In total, 702 samples, measured with liquid chromatography coupled to quadrupole time-of-flight mass spectrometry, were used. After retention time alignment within Progenesis QI, an artificial neural network was trained with 500 samples, each featuring 33,448 values. The feature importance was analyzed with the local interpretable model-agnostic explanations approach. Results Following 10-fold cross-validation, the mean sensitivity, specificity, positive predictive value, and negative predictive value was 88.9, 92.0, 91.9, and 89.2%, respectively. A diverse test set (n=202) containing treated and untreated urine samples could be correctly classified with an accuracy of 95.4%. In addition, 14 important features and four potential biomarkers were extracted. Conclusions With interpretable retention time aligned liquid chromatography high-resolution mass spectrometry data, a reliable machine learning model could be established that rapidly uncovers chemical urine manipulation. The incorporation of our model into routine clinical or forensic analysis allows simultaneous LC-MS analysis and sample integrity testing in one run, thus revolutionizing this field of drug testing.

scholarly journals Diagnostic accuracy of MALDI-TOF mass spectrometry for the direct identification of clinical pathogens from urine

Open Medicine ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. 266-273
Author(s):  
Min Tang ◽  
Jia Yang ◽  
Ying Li ◽  
Luhua Zhang ◽  
Ying Peng ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Likelihood Ratio ◽  
Meta Analysis ◽  
Positive Likelihood Ratio ◽  
Negative Likelihood Ratio ◽  
Urine Samples ◽  
Direct Identification ◽  
Maldi Tof Ms ◽  
Maldi Tof ◽  
Tof Ms

AbstractMatrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF MS) has become one of the most popular methods for the rapid and cost-effective detection of clinical pathogenic microorganisms. This study aimed to evaluate and compare the diagnostic performance of MALDI-TOF MS with that of conventional approaches for the direct identification of pathogens from urine samples. A systematic review was conducted based on a literature search of relevant databases. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR) and area under the summary receiver operating characteristic (SROC) curve of the combined studies were estimated. Nine studies with a total of 3920 subjects were considered eligible and included in the meta-analysis. The pooled sensitivity was 0.85 (95% CI 0.79-0.90), and the pooled specificity was 0.93 (95% CI 0.82-0.97). The PLR and NLR were 11.51 (95% CI 4.53-29.26) and 0.16 (95% CI 0.11-0.24), respectively. The area under the SROC curve was 0.93 (95% CI 0.91-0.95). Sensitivity analysis showed that the results of this meta-analysis were stable. MALDI-TOF MS could directly identify microorganisms from urine samples with high sensitivity and specificity.

scholarly journals Network Analysis Based on Unique Spectral Features Enables an Efficient Selection of Genomically Diverse Operational Isolation Units

2021 ◽  
Vol 9 (2) ◽  
pp. 416
Author(s):  
Charles Dumolin ◽  
Charlotte Peeters ◽  
Evelien De Canck ◽  
Nico Boon ◽  
Peter Vandamme
Keyword(s):  
Network Analysis ◽  
Hierarchical Clustering ◽  
Mass Spectra ◽  
Spectral Features ◽  
Maldi Tof ◽  
Maldi Tof Mass Spectra ◽  
Diversity Studies ◽  
Technical Sample ◽  
Efficient Selection ◽  
Selection Of

Culturomics-based bacterial diversity studies benefit from the implementation of MALDI-TOF MS to remove genomically redundant isolates from isolate collections. We previously introduced SPeDE, a novel tool designed to dereplicate spectral datasets at an infraspecific level into operational isolation units (OIUs) based on unique spectral features. However, biological and technical variation may result in methodology-induced differences in MALDI-TOF mass spectra and hence provoke the detection of genomically redundant OIUs. In the present study, we used three datasets to analyze to which extent hierarchical clustering and network analysis allowed to eliminate redundant OIUs obtained through biological and technical sample variation and to describe the diversity within a set of spectra obtained from 134 unknown soil isolates. Overall, network analysis based on unique spectral features in MALDI-TOF mass spectra enabled a superior selection of genomically diverse OIUs compared to hierarchical clustering analysis and provided a better understanding of the inter-OIU relationships.

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