Candidate prioritization for low-abundant differentially expressed proteins in 2D-DIGE datasets
Related Experiment Video
Updated: Apr 18, 2026
A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions
Published on: April 18, 2025
Umesh K Nandal1, Wytze J Vlietstra2, Carsten Byrman3
1Bioinformatics Laboratory, Academic Medical Center, University of Amsterdam, PO Box 22700, DE Amsterdam, 1100, The Netherlands. u.k.nandal@amc.uva.nl.
Background:
Two-dimensional differential gel electrophoresis (2D-DIGE) provides a powerful technique to separate proteins on their isoelectric point and apparent molecular mass and quantify changes in protein expression. Abundantly available proteins in spots can be identified using mass spectrometry-based approaches. However, identification is often not possible for low-abundant proteins.
Results:
We present a novel computational approach to prioritize candidate proteins for unidentified spots. Our approach exploits noisy information on the isoelectric point and apparent molecular mass of a protein spot in combination with functional similarities of candidate proteins to already identified proteins to select and rank candidates. We evaluated our method on a 2D-DIGE dataset comparing protein expression in uninfected and HIV-1 infected T-cells. Using leave-one-out cross-validation, we show that the true-positive rate for the top-5 ranked proteins is 43.8%.
Conclusions:
Our approach shows good performance on a 2D-DIGE dataset comparing protein expression in uninfected and HIV-1 infected T-cells. We expect our method to be highly useful in (re-)mining other 2D-DIGE experiments in which especially the low-abundant protein spots remain to be identified.
