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Title: Long-Gradient Separations Coupled with Selected Reaction Monitoring for Highly Sensitive, Large Scale Targeted Protein Quantification in a Single Analysis

Abstract

Long-gradient separations coupled to tandem MS were recently demonstrated to provide a deep proteome coverage for global proteomics; however, such long-gradient separations have not been explored for targeted proteomics. Herein, we investigate the potential performance of the long-gradient separations coupled with selected reaction monitoring (LG-SRM) for targeted protein quantification. Direct comparison of LG-SRM (5 h gradient) and conventional LC-SRM (45 min gradient) showed that the long-gradient separations significantly reduced background interference levels and provided an 8- to 100-fold improvement in LOQ for target proteins in human female serum. Based on at least one surrogate peptide per protein, an LOQ of 10 ng/mL was achieved for the two spiked proteins in non-depleted human serum. The LG-SRM detection of seven out of eight endogenous plasma proteins expressed at ng/mL or sub-ng/mL levels in clinical patient sera was also demonstrated. A correlation coefficient of >0.99 was observed for the results of LG-SRM and ELISA measurements for prostate-specific antigen (PSA) in selected patient sera. Further enhancement of LG-SRM sensitivity was achieved by applying front-end IgY14 immunoaffinity depletion. Besides improved sensitivity, LG-SRM offers at least 3 times higher multiplexing capacity than conventional LC-SRM due to ~3-fold increase in average peak widths for a 300-min gradientmore » compared to a 45-min gradient. Therefore, LG-SRM holds great potential for bridging the gap between global and targeted proteomics due to its advantages in both sensitivity and multiplexing capacity.« less

Authors:
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1130711
Report Number(s):
PNNL-SA-96700
400412000
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
Analytical Chemistry, 85(19):9196-9203
Additional Journal Information:
Journal Name: Analytical Chemistry, 85(19):9196-9203
Country of Publication:
United States
Language:
English

Citation Formats

Shi, Tujin, Fillmore, Thomas L., Gao, Yuqian, Zhao, Rui, He, Jintang, Schepmoes, Athena A., Nicora, Carrie D., Wu, Chaochao, Chambers, Justin L., Moore, Ronald J., Kagan, Jacob, Srivastava, Sudhir, Liu, Alvin Y., Rodland, Karin D., Liu, Tao, Camp, David G., Smith, Richard D., and Qian, Weijun. Long-Gradient Separations Coupled with Selected Reaction Monitoring for Highly Sensitive, Large Scale Targeted Protein Quantification in a Single Analysis. United States: N. p., 2013. Web. doi:10.1021/ac402105s.
Shi, Tujin, Fillmore, Thomas L., Gao, Yuqian, Zhao, Rui, He, Jintang, Schepmoes, Athena A., Nicora, Carrie D., Wu, Chaochao, Chambers, Justin L., Moore, Ronald J., Kagan, Jacob, Srivastava, Sudhir, Liu, Alvin Y., Rodland, Karin D., Liu, Tao, Camp, David G., Smith, Richard D., & Qian, Weijun. Long-Gradient Separations Coupled with Selected Reaction Monitoring for Highly Sensitive, Large Scale Targeted Protein Quantification in a Single Analysis. United States. https://doi.org/10.1021/ac402105s
Shi, Tujin, Fillmore, Thomas L., Gao, Yuqian, Zhao, Rui, He, Jintang, Schepmoes, Athena A., Nicora, Carrie D., Wu, Chaochao, Chambers, Justin L., Moore, Ronald J., Kagan, Jacob, Srivastava, Sudhir, Liu, Alvin Y., Rodland, Karin D., Liu, Tao, Camp, David G., Smith, Richard D., and Qian, Weijun. 2013. "Long-Gradient Separations Coupled with Selected Reaction Monitoring for Highly Sensitive, Large Scale Targeted Protein Quantification in a Single Analysis". United States. https://doi.org/10.1021/ac402105s.
@article{osti_1130711,
title = {Long-Gradient Separations Coupled with Selected Reaction Monitoring for Highly Sensitive, Large Scale Targeted Protein Quantification in a Single Analysis},
author = {Shi, Tujin and Fillmore, Thomas L. and Gao, Yuqian and Zhao, Rui and He, Jintang and Schepmoes, Athena A. and Nicora, Carrie D. and Wu, Chaochao and Chambers, Justin L. and Moore, Ronald J. and Kagan, Jacob and Srivastava, Sudhir and Liu, Alvin Y. and Rodland, Karin D. and Liu, Tao and Camp, David G. and Smith, Richard D. and Qian, Weijun},
abstractNote = {Long-gradient separations coupled to tandem MS were recently demonstrated to provide a deep proteome coverage for global proteomics; however, such long-gradient separations have not been explored for targeted proteomics. Herein, we investigate the potential performance of the long-gradient separations coupled with selected reaction monitoring (LG-SRM) for targeted protein quantification. Direct comparison of LG-SRM (5 h gradient) and conventional LC-SRM (45 min gradient) showed that the long-gradient separations significantly reduced background interference levels and provided an 8- to 100-fold improvement in LOQ for target proteins in human female serum. Based on at least one surrogate peptide per protein, an LOQ of 10 ng/mL was achieved for the two spiked proteins in non-depleted human serum. The LG-SRM detection of seven out of eight endogenous plasma proteins expressed at ng/mL or sub-ng/mL levels in clinical patient sera was also demonstrated. A correlation coefficient of >0.99 was observed for the results of LG-SRM and ELISA measurements for prostate-specific antigen (PSA) in selected patient sera. Further enhancement of LG-SRM sensitivity was achieved by applying front-end IgY14 immunoaffinity depletion. Besides improved sensitivity, LG-SRM offers at least 3 times higher multiplexing capacity than conventional LC-SRM due to ~3-fold increase in average peak widths for a 300-min gradient compared to a 45-min gradient. Therefore, LG-SRM holds great potential for bridging the gap between global and targeted proteomics due to its advantages in both sensitivity and multiplexing capacity.},
doi = {10.1021/ac402105s},
url = {https://www.osti.gov/biblio/1130711}, journal = {Analytical Chemistry, 85(19):9196-9203},
number = ,
volume = ,
place = {United States},
year = {Tue Oct 01 00:00:00 EDT 2013},
month = {Tue Oct 01 00:00:00 EDT 2013}
}