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Related Experiment Video

Updated: Sep 16, 2025

Robust Comparison of Protein Levels Across Tissues and Throughout Development Using Standardized Quantitative Western Blotting

Robust Comparison of Protein Levels Across Tissues and Throughout Development Using Standardized Quantitative Western Blotting

Published on: April 9, 2019

ProMix: Enhancing Protein Quantification through Experimental Design and Statistical Normalization.

Huaying Fang^1,2, Mei-Chiung Shih^3,4, Lihua Jiang¹

¹Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, United States.

Journal of Proteome Research

|July 7, 2025

Summary

This summary is machine-generated.

This study introduces ProMix, a new framework to enhance protein normalization in mass spectrometry (MS) experiments. By using internal standards and sample replicates, ProMix reduces variability and systematic biases in quantitative proteomic profiling.

Keywords:

experimental design linear mixed model multiplex mass spectrometry protein normalization quantitative proteomics

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Last Updated: Sep 16, 2025

Robust Comparison of Protein Levels Across Tissues and Throughout Development Using Standardized Quantitative Western Blotting

Robust Comparison of Protein Levels Across Tissues and Throughout Development Using Standardized Quantitative Western Blotting

Published on: April 9, 2019

Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification

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Published on: August 17, 2015

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

Area of Science:

Proteomics
Mass Spectrometry
Bioinformatics

Background:

Quantitative proteomic profiling using isobaric labeling and mass spectrometry (MS) is crucial for large-scale biological studies.
Experimental variations can introduce significant sample variability and systematic biases, impacting data accuracy.
Existing methods may not fully address these challenges in complex proteogenomic analyses.

Purpose of the Study:

To develop ProMix, a flexible analytical framework to improve protein normalization in quantitative proteomics.
To address challenges of sample variability and systematic biases in MS-based proteomic profiling.
To leverage enhanced experimental design features for more robust quantitative proteomic data.

Main Methods:

Development of ProMix, an analytical framework incorporating an internal reference sample and specimen replicates.
Application of ProMix to both simulated and real-world proteogenomic datasets.
Evaluation of ProMix's performance in improving protein normalization and reducing bias.

Main Results:

ProMix demonstrates improved performance in protein normalization compared to standard methods.
The framework effectively reduces sample variability and mitigates systematic biases.
Utilizing an internal standard and/or replicates significantly enhances data reliability.

Conclusions:

ProMix offers a flexible and effective solution for improving quantitative proteomic data accuracy.
Enhanced experimental designs, including reference samples and replicates, are vital for robust proteomic profiling.
The ProMix framework provides a valuable tool for researchers in proteomics and proteogenomics.