Spectronaut and DIA-NN: A Comparison of their Performance in the Analysis of Lung Adenocarcinoma Biopsies
Direct data-independent acquisition (DIA) in liquid chromatography-tandem mass spectrometry (LC-MS/MS) has developed into a powerful methodology for proteomics. Herein, we directly compare the performance of two widely used DIA computational platformsSpectronaut and DIA-NNusing paired tumor and peritumor tissue biopsies from human lung carcinoma (LUAD) patients, samples that one would encounter in a clinical research lab in a hospital setting. The evaluations include the following: (1) protein identification depth, (2) quantitative consistency, and (3) differential expression on the same set of LC-MS/MS runs, using nominally identical computational parameters and settings. Both Spectronaut and DIA-NN identified ∼>7600 proteins in the LUAD samples, with 7180 proteins common to both platforms. Spectronaut reported 1250 upregulated proteins and 266 downregulated proteins (tumor versus peritumor), while DIA-NN reported 1819 and 174 proteins, respectively. A total of 1130 differentially expressed proteins (DEPs) were common to both platforms. Of the top 50 DEPs, 34 were shared between Spectronaut and DIA-NN. Two of these DEPsHSPA5 and CAV1were also among the top 50 proteins showing the highest degrees of protein-protein interaction. The DEPs enabled classification of the LUAD patients into three subtypes; the clinical validity of the current subtypes will need to be substantiated in the future with additional LUAD samples and experimentation.
Authors
- Ivan K. Chu
- Yunzeng Zhang (ORCID: https://orcid.org/0000-0003-3652-9881)
- Zhaoyan Yu
- An Du (ORCID: https://orcid.org/0000-0002-7910-8828)
- Wenyang Zhang (ORCID: https://orcid.org/0000-0003-4007-1949)
- K. W. Michael Siu
- Xiaoxue Ma
- Xiaona Xu
- Yinan Li
Institutions
- Shandong University (CN)
- Fudan University (CN)
- Guangdong Academy of Agricultural Sciences (CN)
- Huashan Hospital (CN)
- University of Windsor (CA)
- University of Hong Kong (HK)
Publication Details
- Journal
- ACS Omega
- Published
- 2026-01-26
- DOI
- https://doi.org/10.1021/acsomega.5c10421
- Primary Topic
- Advanced Proteomics Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00