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Quantitative Analysis of Chromatin Proteomes in Disease
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Benchmarking Quantitative Performance in Label-Free Proteomics.

James A Dowell1, Logan J Wright1, Eric A Armstrong1

  • 1Wisconsin Institute for Discovery, University of Wisconsin-Madison, 330 North Orchard Street, Madison, Wisconsin 53715, United States.

ACS Omega
|February 8, 2021
PubMed
Summary

Optimizing label-free proteomics requires careful consideration of acquisition methods, replicate numbers, and statistical analysis. Combining data-independent acquisition (DIA) with sufficient replicates and appropriate statistical tests maximizes quantitative accuracy for proteins and peptides.

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Area of Science:

  • Proteomics
  • Quantitative Mass Spectrometry
  • Biostatistics

Background:

  • Label-free proteomics relies on instrument acquisition and statistical analysis for quantitative performance.
  • The combined effects of acquisition methodology, replicate number, and false discovery rate (FDR) corrections on quantitative proteomics are not well understood.

Purpose of the Study:

  • To systematically evaluate the combined impact of acquisition methodology, replicate number, statistical approach, and FDR corrections on quantitative proteomics.
  • To identify optimal parameter combinations for accurate protein and peptide quantification.

Main Methods:

  • Utilized a benchmarking standard for systematic evaluation.
  • Assessed data-dependent acquisition (DDA) and data-independent acquisition (DIA) methodologies.
  • Compared different replicate numbers (n=4 and n=8) and statistical approaches, including linear models for microarrays (LIMMA) and reproducibility-optimized test statistic (ROTS).
  • Evaluated the influence of FDR correction methods (e.g., Benjamini-Hochberg, Storey).

Main Results:

  • Complex interactions between parameters significantly impact quantitative fidelity at both protein and peptide levels.
  • At high replicate numbers (n=8), both DDA and DIA provide accurate protein quantification across statistical methods.
  • At low replicate numbers (n=4), DIA combined with LIMMA or ROTS yields high quantitative fidelity for proteins.
  • Peptide quantification is highly dependent on replicate number and acquisition methodology; DIA with sufficient replicates and LIMMA performs best.
  • FDR corrections variably affect true positive rates in DDA workflows at low replicates.

Conclusions:

  • The choice of instrument acquisition methodology must be paired with appropriate replicate numbers and statistical approaches for optimal quantitative proteomics.
  • DIA generally offers superior quantitative fidelity, especially at lower replicate numbers, compared to DDA.
  • Careful selection of parameters is crucial for reliable protein and peptide quantification in label-free mass spectrometry.