Work

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

Year: 2026

Type: article

Abstract: diaPASEF improves ion utilization and sensitivity by synchronizing quadrupole isolation with trapped ion mobility separation, making it suitable for single-cell proteomics. We present Full-DIA, a deep learning-driven software that enhances proteome coverage, quantitative accuracy, and analysis speed over DIA-NN for single-cell diaPASEF data. Notably, Full-DIA generates a missing-value-free protein matrix under stringent global FDR control, enabling downstream analyses without data gaps. Applied to LPS-treated and cell-cycle datasets, this matrix yields pathway enrichment results with fewer off-target and more biologically relevant pathways. Full-DIA highlights the potential of deep learning for four-dimensional diaPASEF analysis and offers a solution to missing values.

Source: Genome biology

DOI: 10.1186/s13059-026-04087-x

Authors: Jian Song, Amanda Momenzadeh, Hebin Liu, Chengpin Shen, Jesse G. Meyer, Xiaohui Wu

Institutions: Cedars-Sinai Medical Center, Soochow University

Language: English

Topic: Advanced Proteomics Techniques and Applications

Subfield: Spectroscopy

Field: Chemistry

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $5,490

Funders: National Natural Science Foundation of China, Government of Jiangsu Province, National Institute of General Medical Sciences