Benchmarking informatics workflows for data-independent acquisition single-cell proteomics

Jianwei Wang, Yi Huang, Fanghua Lu, Qinqin Xu, Zhuo Yang, Yirong Jiang, Shaowen Shi, Jianzhang Pan, Yi Yang () and Qun Fang ()
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Jianwei Wang: Zhejiang University, Single-Cell Proteomics Research Center, and Zhejiang Key Laboratory of Intelligent Manufacturing for Functional Chemicals, ZJU-Hangzhou Global Scientific and Technological Innovation Center
Yi Huang: Zhejiang University, Single-Cell Proteomics Research Center, and Zhejiang Key Laboratory of Intelligent Manufacturing for Functional Chemicals, ZJU-Hangzhou Global Scientific and Technological Innovation Center
Fanghua Lu: Zhejiang University, Single-Cell Proteomics Research Center, and Zhejiang Key Laboratory of Intelligent Manufacturing for Functional Chemicals, ZJU-Hangzhou Global Scientific and Technological Innovation Center
Qinqin Xu: Zhejiang University, Department of Chemistry
Zhuo Yang: Zhejiang University, Department of Chemistry
Yirong Jiang: Zhejiang University, Department of Chemistry
Shaowen Shi: Zhejiang University, Single-Cell Proteomics Research Center, and Zhejiang Key Laboratory of Intelligent Manufacturing for Functional Chemicals, ZJU-Hangzhou Global Scientific and Technological Innovation Center
Jianzhang Pan: Zhejiang University, Single-Cell Proteomics Research Center, and Zhejiang Key Laboratory of Intelligent Manufacturing for Functional Chemicals, ZJU-Hangzhou Global Scientific and Technological Innovation Center
Yi Yang: Zhejiang University, Single-Cell Proteomics Research Center, and Zhejiang Key Laboratory of Intelligent Manufacturing for Functional Chemicals, ZJU-Hangzhou Global Scientific and Technological Innovation Center
Qun Fang: Zhejiang University, Single-Cell Proteomics Research Center, and Zhejiang Key Laboratory of Intelligent Manufacturing for Functional Chemicals, ZJU-Hangzhou Global Scientific and Technological Innovation Center

Nature Communications, 2025, vol. 16, issue 1, 1-16

Abstract: Abstract Recent years have seen a rise of single-cell proteomics by data-independent acquisition mass spectrometry (DIA MS). While diverse data analysis strategies have been reported in literature, their impact on the outcome of single-cell proteomic experiments has been rarely investigated. Here, we present a framework for benchmarking data analysis strategies for DIA-based single-cell proteomics. This framework provides a comprehensive comparison of popular DIA data analysis software tools and searching strategies, as well as a systematic evaluation of method combinations in subsequent informatic workflow, including sparsity reduction, missing value imputation, normalization, batch effect correction, and differential expression analysis. Benchmarking on simulated single-cell samples consisting of mixed proteomes and real single-cell samples with a spike-in scheme, recommendations are provided for the data analysis for DIA-based single-cell proteomics.

Date: 2025
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DOI: 10.1038/s41467-025-65174-4

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