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Related Experiment Video

Updated: Jun 20, 2026

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

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Published on: December 16, 2014

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

Jianwei Wang¹, Yi Huang^1,2, Fanghua Lu^1,2

¹Single-Cell Proteomics Research Center, and Zhejiang Key Laboratory of Intelligent Manufacturing for Functional Chemicals, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, China.

Nature Communications

|November 21, 2025

Summary

This summary is machine-generated.

This study introduces a framework to benchmark data analysis strategies for single-cell proteomics using data-independent acquisition mass spectrometry (DIA MS). It compares software tools and informatic workflows to guide DIA MS data analysis.

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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

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Area of Science:

Proteomics
Mass Spectrometry
Bioinformatics

Background:

Single-cell proteomics using data-independent acquisition mass spectrometry (DIA MS) is rapidly advancing.
The impact of various data analysis strategies on single-cell proteomic outcomes remains under-investigated.

Purpose of the Study:

To establish a framework for benchmarking data analysis strategies in DIA-based single-cell proteomics.
To systematically evaluate different software tools, searching strategies, and informatic workflow combinations.

Main Methods:

Developed a benchmarking framework for DIA-based single-cell proteomics.
Compared popular DIA data analysis software and searching strategies.
Evaluated informatic workflow combinations: sparsity reduction, missing value imputation, normalization, batch effect correction, and differential expression analysis.

Main Results:

Comprehensive comparison of DIA data analysis software and searching strategies.
Systematic evaluation of informatic workflow method combinations.
Benchmarking performed on simulated and real single-cell samples with spike-in.

Conclusions:

Provides recommendations for data analysis in DIA-based single-cell proteomics.
Highlights the importance of selecting appropriate data analysis strategies for reliable results.
Facilitates improved data interpretation in single-cell proteomic studies.