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Related Concept Videos

Proteomics01:33

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term proteomics...

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Related Experiment Video

Updated: May 11, 2026

Comprehensive Workflow of Mass Spectrometry-based Shotgun Proteomics of Tissue Samples
14:51

Comprehensive Workflow of Mass Spectrometry-based Shotgun Proteomics of Tissue Samples

Published on: November 13, 2021

A fast workflow for identification and quantification of proteomes.

Chen Ding1, Jing Jiang, Junying Wei

  • 1State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206, China.

Molecular & Cellular Proteomics : MCP
|May 15, 2013
PubMed
Summary

A new Fast-seq/Fast-quan workflow significantly speeds up proteome analysis using dual HPLC-MS, achieving extensive protein identification and quantification in just half a day. This breakthrough enhances efficiency, making mass spectrometry more accessible for biomedical research.

More Related Videos

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
10:37

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

Published on: November 15, 2017

Related Experiment Videos

Last Updated: May 11, 2026

Comprehensive Workflow of Mass Spectrometry-based Shotgun Proteomics of Tissue Samples
14:51

Comprehensive Workflow of Mass Spectrometry-based Shotgun Proteomics of Tissue Samples

Published on: November 13, 2021

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
10:37

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

Published on: November 15, 2017

Area of Science:

  • Proteomics
  • Analytical Chemistry
  • Biotechnology

Background:

  • Current in-depth proteomics relies on lengthy chromatography gradients, requiring days for extensive proteome coverage.
  • Achieving broad protein identification (e.g., 8000 mammalian gene products) typically demands 3 days of mass spectrometry (MS) runtime.

Purpose of the Study:

  • To develop a rapid proteomics workflow (Fast-seq) for enhanced proteome coverage.
  • To create a quantitative version (Fast-quan) for large-scale protein quantification.
  • To systematically evaluate and optimize parameters affecting quantification accuracy and sensitivity.

Main Methods:

  • Implementation of a dual reverse-phase high-performance liquid chromatography-mass spectrometry (HPLC-MS) workflow with short gradients.
  • Adaptation of the workflow for quantitative analysis (Fast-quan) compatible with large-scale studies.
  • Systematic evaluation of quantification parameters using identical samples and statistical analysis.

Main Results:

  • The Fast-seq workflow achieved comparable proteome coverage to traditional methods in just 0.5 day.
  • Identified and characterized parameters influencing sensitivity and accuracy in label-free quantification.
  • Minimized the rate of falsely quantified differential proteins through optimized parameter settings.
  • Successfully applied the optimized workflow to a real biological process.

Conclusions:

  • The Fast-seq/Fast-quan workflow offers a significant improvement in efficiency and throughput for proteomic analysis.
  • Enables pairwise proteome comparison within 1 day, potentially democratizing MS accessibility.
  • Expected to positively impact biomedical research by accelerating discovery and data generation.