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Optimization for peptide sample preparation for urine peptidomics

Overview of attention for article published in Clinical Proteomics, February 2014
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Title
Optimization for peptide sample preparation for urine peptidomics
Published in
Clinical Proteomics, February 2014
DOI 10.1186/1559-0275-11-7
Pubmed ID
Authors

Tara K Sigdel, Carrie D Nicora, Szu-Chuan Hsieh, Hong Dai, Wei-Jun Qian, David G Camp, Minnie M Sarwal

Abstract

Analysis of native or endogenous peptides in biofluids can provide valuable insights into disease mechanisms. Furthermore, the detected peptides may also have utility as potential biomarkers for non-invasive monitoring of human diseases. The non-invasive nature of urine collection and the abundance of peptides in the urine makes analysis by high-throughput 'peptidomics' methods , an attractive approach for investigating the pathogenesis of renal disease. However, urine peptidomics methodologies can be problematic with regards to difficulties associated with sample preparation. The urine matrix can provide significant background interference in making the analytical measurements that it hampers both the identification of peptides and the depth of the peptidomics read when utilizing LC-MS based peptidome analysis. We report on a novel adaptation of the standard solid phase extraction (SPE) method to a modified SPE (mSPE) approach for improved peptide yield and analysis sensitivity with LC-MS based peptidomics in terms of time, cost, clogging of the LC-MS column, peptide yield, peptide quality, and number of peptides identified by each method. Expense and time requirements were comparable for both SPE and mSPE, but more interfering contaminants from the urine matrix were evident in the SPE preparations (e.g., clogging of the LC-MS columns, yellowish background coloration of prepared samples due to retained urobilin, lower peptide yields) when compared to the mSPE method. When we compared data from technical replicates of 4 runs, the mSPE method provided significantly improved efficiencies for the preparation of samples from urine (e.g., mSPE peptide identification 82% versus 18% with SPE; p = 8.92E-05). Additionally, peptide identifications, when applying the mSPE method, highlighted the biology of differential activation of urine peptidases during acute renal transplant rejection with distinct laddering of specific peptides, which was obscured for most proteins when utilizing the conventional SPE method. In conclusion, the mSPE method was found to be superior to the conventional, standard SPE method for urine peptide sample preparation when applying LC-MS peptidomics analysis due to the optimized sample clean up that provided improved experimental inference from the confidently identified peptides.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 76 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
Hungary 1 1%
Canada 1 1%
Unknown 73 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 25%
Researcher 19 25%
Student > Bachelor 7 9%
Student > Doctoral Student 5 7%
Professor 3 4%
Other 15 20%
Unknown 8 11%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 20 26%
Agricultural and Biological Sciences 19 25%
Chemistry 13 17%
Medicine and Dentistry 8 11%
Pharmacology, Toxicology and Pharmaceutical Science 1 1%
Other 5 7%
Unknown 10 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 07 April 2018.
All research outputs
#13,938,371
of 22,796,179 outputs
Outputs from Clinical Proteomics
#134
of 283 outputs
Outputs of similar age
#115,240
of 221,061 outputs
Outputs of similar age from Clinical Proteomics
#5
of 6 outputs
Altmetric has tracked 22,796,179 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 283 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one is in the 48th percentile – i.e., 48% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 221,061 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one.