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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月21日

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

摘要

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.