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

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.