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Related Experiment Video

Updated: Apr 23, 2026

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

Full-DIA enables complete single-cell proteomics from diaPASEF using deep learning.

Jian Song^1,2, Amanda Momenzadeh³, Hebin Liu⁴

¹Cancer Institute, Suzhou Medical College, Soochow University, Suzhou, 215000, China. songjian2022@suda.edu.cn.

|April 22, 2026

Summary

This summary is machine-generated.

Full-DIA software enhances single-cell proteomics by improving proteome coverage and accuracy for diaPASEF data. This deep learning tool generates a complete protein matrix, enabling robust downstream pathway analysis.

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Area of Science:

Proteomics
Biotechnology
Computational Biology

Background:

DiaPASEF enhances ion utilization and sensitivity in mass spectrometry.
Single-cell proteomics presents challenges in data completeness and quantitative accuracy.

Purpose of the Study:

To introduce Full-DIA, a deep learning software for analyzing single-cell diaPASEF data.
To improve proteome coverage, quantitative accuracy, and analysis speed.
To generate a missing-value-free protein matrix for enhanced downstream analysis.

Main Methods:

Development of Full-DIA, a deep learning-driven software.
Application of Full-DIA to single-cell diaPASEF datasets.
Generation of a missing-value-free protein matrix under global FDR control.

Main Results:

Full-DIA significantly enhances proteome coverage and quantitative accuracy compared to DIA-NN.
The software produces a complete protein matrix, eliminating missing values for downstream analyses.
Pathway enrichment analysis on treated and cell-cycle datasets revealed more biologically relevant pathways.

Conclusions:

Deep learning holds significant potential for advancing four-dimensional diaPASEF analysis.
Full-DIA provides an effective solution for addressing missing values in single-cell proteomics.
The generated protein matrix facilitates more reliable and comprehensive biological interpretation.