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DPHL: A DIA Pan-human Protein Mass Spectrometry Library for Robust Biomarker Discovery.


ABSTRACT: To address the increasing need for detecting and validating protein biomarkers in clinical specimens, mass spectrometry (MS)-based targeted proteomic techniques, including the selected reaction monitoring (SRM), parallel reaction monitoring (PRM), and massively parallel data-independent acquisition (DIA), have been developed. For optimal performance, they require the fragment ion spectra of targeted peptides as prior knowledge. In this report, we describe a MS pipeline and spectral resource to support targeted proteomics studies for human tissue samples. To build the spectral resource, we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on Docker. We then applied the workflow to generate DPHL, a comprehensive DIA pan-human library, from 1096 data-dependent acquisition (DDA) MS raw files for 16 types of cancer samples. This extensive spectral resource was then applied to a proteomic study of 17 prostate cancer (PCa) patients. Thereafter, PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was validated. As a second application, the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma (DLBCL) patients and 18 healthy control subjects. Differentially expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker discovery. DPHL is freely accessible at https://www.iprox.org/page/project.html?id=IPX0001400000.

SUBMITTER: Zhu T 

PROVIDER: S-EPMC7646093 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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DPHL: A DIA Pan-human Protein Mass Spectrometry Library for Robust Biomarker Discovery.

Zhu Tiansheng T   Zhu Yi Y   Xuan Yue Y   Gao Huanhuan H   Cai Xue X   Piersma Sander R SR   Pham Thang V TV   Schelfhorst Tim T   Haas Richard R G D RRGD   Bijnsdorp Irene V IV   Sun Rui R   Yue Liang L   Ruan Guan G   Zhang Qiushi Q   Hu Mo M   Zhou Yue Y   Van Houdt Winan J WJ   Le Large Tessa Y S TYS   Cloos Jacqueline J   Wojtuszkiewicz Anna A   Koppers-Lalic Danijela D   Böttger Franziska F   Scheepbouwer Chantal C   Brakenhoff Ruud H RH   van Leenders Geert J L H GJLH   Ijzermans Jan N M JNM   Martens John W M JWM   Steenbergen Renske D M RDM   Grieken Nicole C NC   Selvarajan Sathiyamoorthy S   Mantoo Sangeeta S   Lee Sze S SS   Yeow Serene J Y SJY   Alkaff Syed M F SMF   Xiang Nan N   Sun Yaoting Y   Yi Xiao X   Dai Shaozheng S   Liu Wei W   Lu Tian T   Wu Zhicheng Z   Liang Xiao X   Wang Man M   Shao Yingkuan Y   Zheng Xi X   Xu Kailun K   Yang Qin Q   Meng Yifan Y   Lu Cong C   Zhu Jiang J   Zheng Jin'e J   Wang Bo B   Lou Sai S   Dai Yibei Y   Xu Chao C   Yu Chenhuan C   Ying Huazhong H   Lim Tony K TK   Wu Jianmin J   Gao Xiaofei X   Luan Zhongzhi Z   Teng Xiaodong X   Wu Peng P   Huang Shi'ang S   Tao Zhihua Z   Iyer Narayanan G NG   Zhou Shuigeng S   Shao Wenguang W   Lam Henry H   Ma Ding D   Ji Jiafu J   Kon Oi L OL   Zheng Shu S   Aebersold Ruedi R   Jimenez Connie R CR   Guo Tiannan T  

Genomics, proteomics & bioinformatics 20200401 2


To address the increasing need for detecting and validating protein biomarkers in clinical specimens, mass spectrometry (MS)-based targeted proteomic techniques, including the selected reaction monitoring (SRM), parallel reaction monitoring (PRM), and massively parallel data-independent acquisition (DIA), have been developed. For optimal performance, they require the fragment ion spectra of targeted peptides as prior knowledge. In this report, we describe a MS pipeline and spectral resource to s  ...[more]

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