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Updated: Jul 17, 2025

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

Multi-omics data integration using ratio-based quantitative profiling with Quartet reference materials.

Yuanting Zheng¹, Yaqing Liu², Jingcheng Yang^2,3

¹State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China. zhengyuanting@fudan.edu.cn.

Nature Biotechnology

|September 7, 2023

Summary

This summary is machine-generated.

This study introduces multi-omics reference materials for reproducible data integration. A ratio-based profiling method using these references overcomes challenges in combining diverse omics datasets.

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Area of Science:

Genomics
Proteomics
Metabolomics
Systems Biology
Biotechnology

Background:

Integrating multi-omics data (genome, epigenome, transcriptome, proteome, metabolome) is challenging due to the lack of standardized reference materials and ground truth.
Current methods often suffer from irreproducibility across different batches, laboratories, and platforms, hindering comprehensive biological insights.

Purpose of the Study:

To develop and characterize publicly available multi-omics reference materials for DNA, RNA, protein, and metabolites.
To establish a robust and reproducible method for multi-omics data integration using these reference materials.
To identify the root causes of irreproducibility in multi-omics measurements and propose a solution.

Main Methods:

Creation of matched DNA, RNA, protein, and metabolite reference materials from a family quartet, including monozygotic twin daughters.
Development and application of a ratio-based profiling approach, scaling study samples against a common reference.
Validation of the ratio-based method for data comparability and integration across omics types and experimental conditions.

Main Results:

The developed reference materials provide inherent ground truth based on familial relationships and biological information flow (DNA to RNA to protein).
Ratio-based profiling demonstrated reproducible and comparable multi-omics data suitable for integration across diverse settings.
Reference-free 'absolute' quantification was identified as a primary cause of irreproducibility in multi-omics data integration.

Conclusions:

Publicly available multi-omics reference materials are crucial for advancing data integration and reproducibility.
Ratio-based multi-omics profiling with common reference materials offers a standardized approach to overcome current integration challenges.
This methodology facilitates more reliable cross-platform, cross-laboratory, and cross-omics data analysis for deeper biological understanding.