Automated SP3 Workflow Enables Robust Sample Preparation on Proteonano Ultraplex Proteomics Platform Article Swipe

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Yì Wáng , Libing Wang , Yonghao Zhang , Xiehua Ouyang , Hao Wu ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.1101/2025.05.27.656469

The efficiency and reproducibility of sample preparation are critical prerequisites for obtaining reliable results in bottom-up proteomics. To conclude, we systematically evaluated three commonly used sample preparation methods: automated single-pot solid-phase-enhanced sample preparation (SP3), precipitation by organic solvents, and filter-aided sample preparation method (FASP). When processing varying input amounts of HeLa cell lysate (10, 20, 50, 100, and 150 μg), the SP3 method demonstrated strong linearity (R 2 = 0.95), indicating excellent quantitative consistency across a broad protein input range. A comparison between manual SP3 and manual precipitation protocols revealed comparable protein identification numbers, with a correlation coefficient of approximately 0.97. We further developed an automated version of the SP3 method based on the Proteonano Ultraplex Proteomics platform (autoSP3), which yielded highly consistent results with the manual approach, achieving a protein identification overlap of 98.5% (6,949 out of 7,056). These findings support the suitability of the autoSP3 workflow as a robust and scalable approach for high-throughput and reproducible sample preparation in proteomics sample preparation.

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Workflow Sample (material) Sample preparation Proteomics Computer science Software engineering Data science Chromatography Chemistry Database Gene Biochemistry

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Type: preprint
Language: en
Landing Page: https://doi.org/10.1101/2025.05.27.656469
PDF: https://www.biorxiv.org/content/biorxiv/early/2025/05/30/2025.05.27.656469.full.pdf
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References: 12
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DOI: https://doi.org/10.1101/2025.05.27.656469

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Title: Automated SP3 Workflow Enables Robust Sample Preparation on Proteonano Ultraplex Proteomics Platform

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Type: preprint

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Language: en

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Publication year: 2025

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Publication date: 2025-05-30

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Authors: Yì Wáng, Libing Wang, Yonghao Zhang, Xiehua Ouyang, Hao Wu

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Landing page: https://doi.org/10.1101/2025.05.27.656469

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Concepts: Workflow, Sample (material), Sample preparation, Proteomics, Computer science, Software engineering, Data science, Chromatography, Chemistry, Database, Gene, Biochemistry

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