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Multiparameter Optimization of Two Common Proteomics Quantification Methods for Quantifying Low-Abundance Proteins.

Chengqian Zhang1, Zhaomei Shi1, Ying Han1

  • 1School of Life Science and Technology , ShanghaiTech University , 393 Middle Huaxia Road , Shanghai 201210 , China.

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|November 6, 2018
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Summary
This summary is machine-generated.

This study optimizes quantitative proteomics workflows to enhance the detection of low-abundance proteins. Improved sensitivity and accuracy in label-free quantification and tandem mass tags (TMT) labeling are achieved through multiparameter optimization.

Keywords:
label-free quantificationlow-abundance proteinsmass spectrometryquantitative proteomicstandem mass tags

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Area of Science:

  • Proteomics
  • Biochemistry
  • Analytical Chemistry

Background:

  • Quantitative proteomics is crucial for identifying differentially regulated proteins.
  • Sensitivity and accuracy in quantifying low-abundance proteins remain significant challenges.
  • Systematic evaluation of workflow steps impacting low-abundance protein quantification is lacking.

Purpose of the Study:

  • To improve the sensitivity and accuracy of label-free quantification and tandem mass tags (TMT) labeling for low-abundance proteins.
  • To systematically optimize multiple parameters across the proteomics workflow.
  • To define optimal settings for various steps from sample preparation to data analysis.

Main Methods:

  • Multiparameter optimization using a complex 2-proteome artificial sample mixture.
  • Evaluation of parameters including peptide desalting, injection amount, LC-MS/MS settings (gradient length, AGC targets, ion accumulation time, MS2 resolution), precursor coisolation threshold, data analysis software, and statistical methods.
  • Identification of suitable cutoffs for detecting low-abundance proteins with specific fold changes.

Main Results:

  • Best settings for each optimized parameter were defined.
  • Suitable cutoffs for detecting 1.5-fold changes (label-free) and 2-fold changes (TMT) in low-abundance proteins were identified.
  • The optimization significantly enhances the performance of quantitative proteomics.

Conclusions:

  • Optimized parameters substantially improve the sensitivity and accuracy of quantitative proteomics for low-abundance proteins.
  • This work provides a framework for enhancing quantitative proteomics applications.
  • The findings will promote broader applications of quantitative proteomics in diverse research areas.