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Urine Proteomics in Kidney Disease Biomarker Discovery

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Cover of 'Urine Proteomics in Kidney Disease Biomarker Discovery'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Urine Is a Better Biomarker Source Than Blood Especially for Kidney Diseases
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    Chapter 2 Urine Reflection of Changes in Blood
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    Chapter 3 Urimem Facilitates Kidney Disease Biomarker Research
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    Chapter 4 Human urine proteome: a powerful source for clinical research.
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    Chapter 5 Exosomes in Urine Biomarker Discovery
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    Chapter 6 Urinary Proteins with Post-translational Modifications.
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    Chapter 7 Applications of Peptide retention time in proteomic data analysis.
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    Chapter 8 Urine Sample Preparation in 96-well Filter Plates to Characterize Inflammatory and Infectious Diseases of the Urinary Tract.
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    Chapter 9 Variations of human urinary proteome.
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    Chapter 10 Evolution of the urinary proteome during human renal development and maturation.
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    Chapter 11 Hormone-dependent changes in female urinary proteome.
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    Chapter 12 Effects of exercise on the urinary proteome.
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    Chapter 13 Effects of diuretics on urinary proteins.
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    Chapter 14 Applications of urinary proteomics in renal disease research using animal models.
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    Chapter 15 The application of urinary proteomics for the detection of biomarkers of kidney diseases.
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    Chapter 16 Dynamic changes of urinary proteins in focal segmental glomerulosclerosis model.
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    Chapter 17 Using isolated rat kidney to discover kidney origin biomarkers in urine.
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    Chapter 18 Comparing plasma and urinary proteomes to understand kidney function.
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    Chapter 19 Urinary protein biomarker database: a useful tool for biomarker discovery.
Attention for Chapter 8: Urine Sample Preparation in 96-well Filter Plates to Characterize Inflammatory and Infectious Diseases of the Urinary Tract.
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Chapter title
Urine Sample Preparation in 96-well Filter Plates to Characterize Inflammatory and Infectious Diseases of the Urinary Tract.
Chapter number 8
Book title
Urine Proteomics in Kidney Disease Biomarker Discovery
Published in
Advances in experimental medicine and biology, October 2014
DOI 10.1007/978-94-017-9523-4_8
Pubmed ID
Book ISBNs
978-9-40-179522-7, 978-9-40-179523-4
Authors

Yu Y, Pieper R, Yanbao Yu, Rembert Pieper

Abstract

Urine has been an important body fluid source for diagnostic and prognostic biomarkers of diseases for a long time. Technological advances during the last two decades have enabled a fundamental shift from the discovery of candidate protein biomarkers using single-assay platforms to highly parallel liquid chromatography tandem mass spectrometry (LC-MS/MS)-based proteomic analysis platforms. MS/MS-based approaches such as multiple reaction monitoring (MRM) are also being used increasingly for targeted protein biomarker validation. In large part due to the fact that the majority of protein in voided urine is soluble, such studies have focused on the analysis of urine supernatants, whereas the pellets were discarded after centrifugal sedimentation. Urine sediments are of particular value in the analysis of urinary tract infections (UTI). The LC-MS/MS methods now have sufficient resolving power and sensitivity to survey metaproteomes-the entirety of proteins derived from multiple organisms that interact with each other in mutualistic or antagonistic fashion. Challenges of proteomic analysis of urine include the high dynamic range of protein abundance, high levels of protein post-translational modifications, and high quantities of natural protease inhibitors. Recently, a robust and scalable workflow that can parallelize the processing of multiple urinary supernatant and sediment samples was developed and validated in our lab. This method utilizes 96-well format filter-aided sample preparation (96FASP) strategy and was shown to successfully identify large numbers of proteins from urine samples. Processing 10-50 µg total protein in single experiment, LC-MS/MS with a Q-Exactive mass spectrometer resulted in more than 1,100 distinct human protein identifications from urine supernatants, and around 400 microbial and 1,400 human protein identifications from urine sediments. The surveys are a rich data resource not only for biomarker discovery but also to interrogate mechanisms of pathogenesis in the urinary system.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Professor > Associate Professor 3 27%
Researcher 3 27%
Student > Ph. D. Student 2 18%
Student > Master 1 9%
Student > Bachelor 1 9%
Other 1 9%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 45%
Agricultural and Biological Sciences 2 18%
Medicine and Dentistry 2 18%
Social Sciences 1 9%
Computer Science 1 9%
Other 0 0%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 March 2015.
All research outputs
#3,459,197
of 4,888,368 outputs
Outputs from Advances in experimental medicine and biology
#858
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Outputs of similar age
#95,113
of 139,605 outputs
Outputs of similar age from Advances in experimental medicine and biology
#22
of 72 outputs
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So far Altmetric has tracked 1,471 research outputs from this source. They receive a mean Attention Score of 2.3. This one is in the 27th percentile – i.e., 27% of its peers scored the same or lower than it.
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