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DPHL: A pan-human protein mass spectrometry library for robust biomarker discovery

Authors :
Tiansheng Zhu
Yi Zhu
Yue Xuan
Huanhuan Gao
Xue Cai
Sander R. Piersma
Thang V. Pham
Tim Schelfhorst
Richard R Goeij De Haas
Irene V. Bijnsdorp
Rui Sun
Liang Yue
Guan Ruan
Qiushi Zhang
Mo Hu
Yue Zhou
Winan J. Van Houdt
T.Y.S Lelarge
J. Cloos
Anna Wojtuszkiewicz
Danijela Koppers-Lalic
Franziska Böttger
Chantal Scheepbouwer
R.H Brakenhoff
G.J.L.H. van Leenders
Jan N.M. Ijzermans
J.W.M. Martens
R.D.M. Steenbergen
N.C. Grieken
Sathiyamoorthy Selvarajan
Sangeeta Mantoo
Sze Sing Lee
Serene Jie Yi Yeow
Syed Muhammad Fahmy Alkaff
Nan Xiang
Yaoting Sun
Xiao Yi
Shaozheng Dai
Wei Liu
Tian Lu
Zhicheng Wu
Xiao Liang
Man Wang
Yingkuan Shao
Xi Zheng
Kailun Xu
Qin Yang
Yifan Meng
Cong Lu
Jiang Zhu
Jin’e Zheng
Bo Wang
Sai Lou
Yibei Dai
Chao Xu
Chenhuan Yu
Huazhong Ying
Tony Kiat-hon Lim
Jianmin Wu
Xiaofei Gao
Zhongzhi Luan
Xiaodong Teng
Peng Wu
Shi’ang Huang
Zhihua Tao
N. Gopalakrishna Iyer
Shuigeng Zhou
Wenguang Shao
Henry Lam
Ding Ma
Jiafu Ji
Oi Lian Kon
Shu Zheng
Ruedi Aebersold
Connie R. Jimenez
Tiannan Guo
Publication Year :
2020
Publisher :
Cold Spring Harbor Laboratory, 2020.

Abstract

To answer the increasing need for detecting and validating protein biomarkers in clinical specimens, proteomic techniques are required that support the fast, reproducible and quantitative analysis of large clinical sample cohorts. Targeted mass spectrometry techniques, specifically SRM, PRM and the massively parallel SWATH/DIA technique have emerged as a powerful method for biomarker research. For optimal performance, they require prior knowledge about the fragment ion spectra of targeted peptides. In this report, we describe a mass spectrometric (MS) pipeline and spectral resource to support data-independent acquisition (DIA) and parallel reaction monitoring (PRM) based biomarker studies. To build the spectral resource we integrated common open-source MS computational tools to assemble an open source computational workflow based on Docker. It was then applied to generate a comprehensive DIA pan-human library (DPHL) from 1,096 data dependent acquisition (DDA) MS raw files, and it comprises 242,476 unique peptide sequences from 14,782 protein groups and 10,943 SwissProt-annotated proteins expressed in 16 types of cancer samples. In particular, tissue specimens from patients with prostate cancer, cervical cancer, colorectal cancer, hepatocellular carcinoma, gastric cancer, lung adenocarcinoma, squamous cell lung carcinoma, diseased thyroid, glioblastoma multiforme, sarcoma and diffuse large B-cell lymphoma (DLBCL), as well as plasma samples from a range of hematologic malignancies were collected from multiple clinics in China, the Netherlands and Singapore and included in the resource. This extensive spectral resource was then applied to a prostate cancer cohort of 17 patients, consisting of 8 patients with prostate cancer (PCa) and 9 with benign prostate hyperplasia (BPH), respectively. Data analysis of DIA data from these samples identified differential expressions of FASN, TPP1 and SPON2 in prostate tumors. Thereafter, PRM validation was applied to a larger PCa cohort of 57 patients and the differential expressions of FASN, TPP1 and SPON2 in prostate tumors were validated. As a second application, the DPHL spectral resource was applied to a patient cohort consisting of samples from 19 DLBCL patients and 18 healthy individuals. Differential expressions of CRP, CD44 and SAA1 between DLBCL cases and healthy controls were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supported that DIA-PRM MS pipeline enables robust protein biomarker discoveries.

Details

Language :
English
Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....0efeaed59bec37bfbae8b1f7d5e11559
Full Text :
https://doi.org/10.1101/2020.02.03.931329