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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

DeepQuanTR: MALDI-MS-based label-free quantification of proteins in complex biological samples.

Tim Fugmann¹, Dario Neri, Christoph Roesli

¹Institute of Pharmaceutical Sciences, ETH Zurich, Zurich, Switzerland.

|May 11, 2010

Summary

DeepQuanTR software enhances proteomic analysis by identifying and quantifying differentially expressed proteins. This tool aids in comparing protein abundance across diverse biological samples for research.

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Peptide and Protein Quantification Using Automated Immuno-MALDI (iMALDI)

Peptide and Protein Quantification Using Automated Immuno-MALDI (iMALDI)

Published on: August 18, 2017

Area of Science:

Proteomics
Biochemistry
Computational Biology

Background:

Accurate quantification of protein abundance changes is crucial for understanding biological processes and disease.
Existing methods for differential protein expression analysis can be complex and require specialized expertise.

Purpose of the Study:

To develop and validate DeepQuanTR, a novel software for the identification and relative quantification of differentially expressed proteins.
To demonstrate the software's utility in analyzing complex biological samples using Liquid Chromatography-Matrix Assisted Laser Desorption/Ionization-Mass Spectrometry (LC-MALDI-MS).

Main Methods:

Enzymatic digestion of proteins followed by High-Performance Liquid Chromatography (HPLC) peptide separation.
Normalization of MALDI-MS signal intensities using internal standards.
DeepQuanTR software for peptide feature extraction, retention time adjustment, and relative quantification, generating a Protein Quant Value.

Main Results:

DeepQuanTR successfully analyzed 24 human serum samples spiked with varying protein amounts and eight complex vascular protein samples from human kidneys.
The software enabled the identification and validation of differentially regulated proteins in cancerous kidney lesions compared to normal tissue.
Demonstrated the software's capability to facilitate relative protein quantification across diverse sample types.

Conclusions:

DeepQuanTR is a validated software tool that simplifies the identification and relative quantification of differentially expressed proteins.
The software integrates seamlessly with established proteomic workflows, offering a powerful solution for comparative proteomics.
DeepQuanTR facilitates robust inter-sample comparisons of protein abundance, advancing basic and applied research.