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Kohler D, Staniak M, Yu F, Nesvizhskii AI, Vitek O. An MSstats workflow for detecting differentially abundant proteins in large-scale data-independent acquisition mass spectrometry experiments with FragPipe processing. Nat Protoc 2024; 19:2915-2938. [PMID: 38769142 DOI: 10.1038/s41596-024-01000-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 03/11/2024] [Indexed: 05/22/2024]
Abstract
Technological advances in mass spectrometry and proteomics have made it possible to perform larger-scale and more-complex experiments. The volume and complexity of the resulting data create major challenges for downstream analysis. In particular, next-generation data-independent acquisition (DIA) experiments enable wider proteome coverage than more traditional targeted approaches but require computational workflows that can manage much larger datasets and identify peptide sequences from complex and overlapping spectral features. Data-processing tools such as FragPipe, DIA-NN and Spectronaut have undergone substantial improvements to process spectral features in a reasonable time. Statistical analysis tools are needed to draw meaningful comparisons between experimental samples, but these tools were also originally designed with smaller datasets in mind. This protocol describes an updated version of MSstats that has been adapted to be compatible with large-scale DIA experiments. A very large DIA experiment, processed with FragPipe, is used as an example to demonstrate different MSstats workflows. The choice of workflow depends on the user's computational resources. For datasets that are too large to fit into a standard computer's memory, we demonstrate the use of MSstatsBig, a companion R package to MSstats. The protocol also highlights key decisions that have a major effect on both the results and the processing time of the analysis. The MSstats processing can be expected to take 1-3 h depending on the usage of MSstatsBig. The protocol can be run in the point-and-click graphical user interface MSstatsShiny or implemented with minimal coding expertise in R.
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Affiliation(s)
- Devon Kohler
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
- Barnett Institute for Chemical and Biological Analysis, Northeastern University, Boston, MA, USA
| | | | - Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Alexey I Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Olga Vitek
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA.
- Barnett Institute for Chemical and Biological Analysis, Northeastern University, Boston, MA, USA.
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2
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Gu K, Kumabe H, Yamamoto T, Tashiro N, Masuda T, Ito S, Ohtsuki S. Improving Proteomic Identification Using Narrow Isolation Windows with Zeno SWATH Data-Independent Acquisition. J Proteome Res 2024; 23:3484-3495. [PMID: 38978496 DOI: 10.1021/acs.jproteome.4c00149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Data-independent acquisition (DIA) techniques such as sequential window acquisition of all theoretical mass spectra (SWATH) acquisition have emerged as the preferred strategies for proteomic analyses. Our study optimized the SWATH-DIA method using a narrow isolation window placement approach, improving its proteomic performance. We optimized the acquisition parameter combinations of narrow isolation windows with different widths (1.9 and 2.9 Da) on a ZenoTOF 7600 (Sciex); the acquired data were analyzed using DIA-NN (version 1.8.1). Narrow SWATH (nSWATH) identified 5916 and 7719 protein groups on the digested peptides, corresponding to 400 ng of protein from mouse liver and HEK293T cells, respectively, improving identification by 7.52 and 4.99%, respectively, compared to conventional SWATH. The median coefficient of variation of the quantified values was less than 6%. We further analyzed 200 ng of benchmark samples comprising peptides from known ratios ofEscherichia coli, yeast, and human peptides using nSWATH. Consequently, it achieved accuracy and precision comparable to those of conventional SWATH, identifying an average of 95,456 precursors and 9342 protein groups across three benchmark samples, representing 12.6 and 9.63% improved identification compared to conventional SWATH. The nSWATH method improved identification at various loading amounts of benchmark samples, identifying 40.7% more protein groups at 25 ng. These results demonstrate the improved performance of nSWATH, contributing to the acquisition of deeper proteomic data from complex biological samples.
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Affiliation(s)
- Kongxin Gu
- Department of Pharmaceutical Microbiology, Graduate School of Pharmaceutical Sciences, Kumamoto University, 5-1 Oe-honmachi, Chuo-ku, Kumamoto 862-0973, Japan
| | - Haruka Kumabe
- Department of Pharmaceutical Microbiology, Graduate School of Pharmaceutical Sciences, Kumamoto University, 5-1 Oe-honmachi, Chuo-ku, Kumamoto 862-0973, Japan
| | - Takumi Yamamoto
- Department of Pharmaceutical Microbiology, Graduate School of Pharmaceutical Sciences, Kumamoto University, 5-1 Oe-honmachi, Chuo-ku, Kumamoto 862-0973, Japan
| | - Naoto Tashiro
- Department of Pharmaceutical Microbiology, School of Pharmacy, Kumamoto University, 5-1 Oe-honmachi, Chuo-ku, Kumamoto 862-0973, Japan
| | - Takeshi Masuda
- Department of Pharmaceutical Microbiology, Graduate School of Pharmaceutical Sciences, Kumamoto University, 5-1 Oe-honmachi, Chuo-ku, Kumamoto 862-0973, Japan
- Department of Pharmaceutical Microbiology, School of Pharmacy, Kumamoto University, 5-1 Oe-honmachi, Chuo-ku, Kumamoto 862-0973, Japan
- Institute for Advanced Biosciences, Keio University, 403-1 Nipponkoku, Daihoji, Tsuruoka, Yamagata 997-0017, Japan
- Department of Pharmaceutical Microbiology, Faculty of Life Sciences, Kumamoto University, 5-1 Oe-honmachi, Chuo-ku, Kumamoto 862-0973, Japan
| | - Shingo Ito
- Department of Pharmaceutical Microbiology, Graduate School of Pharmaceutical Sciences, Kumamoto University, 5-1 Oe-honmachi, Chuo-ku, Kumamoto 862-0973, Japan
- Department of Pharmaceutical Microbiology, School of Pharmacy, Kumamoto University, 5-1 Oe-honmachi, Chuo-ku, Kumamoto 862-0973, Japan
- Department of Pharmaceutical Microbiology, Faculty of Life Sciences, Kumamoto University, 5-1 Oe-honmachi, Chuo-ku, Kumamoto 862-0973, Japan
| | - Sumio Ohtsuki
- Department of Pharmaceutical Microbiology, Graduate School of Pharmaceutical Sciences, Kumamoto University, 5-1 Oe-honmachi, Chuo-ku, Kumamoto 862-0973, Japan
- Department of Pharmaceutical Microbiology, School of Pharmacy, Kumamoto University, 5-1 Oe-honmachi, Chuo-ku, Kumamoto 862-0973, Japan
- Department of Pharmaceutical Microbiology, Faculty of Life Sciences, Kumamoto University, 5-1 Oe-honmachi, Chuo-ku, Kumamoto 862-0973, Japan
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3
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Qian L, Zhu J, Xue Z, Zhou Y, Xiang N, Xu H, Sun R, Gong W, Cai X, Sun L, Ge W, Liu Y, Su Y, Lin W, Zhan Y, Wang J, Song S, Yi X, Ni M, Zhu Y, Hua Y, Zheng Z, Guo T. Proteomic landscape of epithelial ovarian cancer. Nat Commun 2024; 15:6462. [PMID: 39085232 PMCID: PMC11291745 DOI: 10.1038/s41467-024-50786-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 07/19/2024] [Indexed: 08/02/2024] Open
Abstract
Epithelial ovarian cancer (EOC) is a deadly disease with limited diagnostic biomarkers and therapeutic targets. Here we conduct a comprehensive proteomic profiling of ovarian tissue and plasma samples from 813 patients with different histotypes and therapeutic regimens, covering the expression of 10,715 proteins. We identify eight proteins associated with tumor malignancy in the tissue specimens, which are further validated as potential circulating biomarkers in plasma. Targeted proteomics assays are developed for 12 tissue proteins and 7 blood proteins, and machine learning models are constructed to predict one-year recurrence, which are validated in an independent cohort. These findings contribute to the understanding of EOC pathogenesis and provide potential biomarkers for early detection and monitoring of the disease. Additionally, by integrating mutation analysis with proteomic data, we identify multiple proteins related to DNA damage in recurrent resistant tumors, shedding light on the molecular mechanisms underlying treatment resistance. This study provides a multi-histotype proteomic landscape of EOC, advancing our knowledge for improved diagnosis and treatment strategies.
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Affiliation(s)
- Liujia Qian
- School of Medicine, Westlake University, Hangzhou, Zhejiang Province, China
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China
- Research Center for Industries of the Future, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, Zhejiang, China
| | - Jianqing Zhu
- Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Zhangzhi Xue
- School of Medicine, Westlake University, Hangzhou, Zhejiang Province, China
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China
- Research Center for Industries of the Future, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, Zhejiang, China
| | - Yan Zhou
- School of Medicine, Westlake University, Hangzhou, Zhejiang Province, China
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China
- Research Center for Industries of the Future, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, Zhejiang, China
| | - Nan Xiang
- School of Medicine, Westlake University, Hangzhou, Zhejiang Province, China
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China
- Research Center for Industries of the Future, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, Zhejiang, China
| | - Hong Xu
- MOE Key Laboratory of Biosystems Homeostasis and Protection, Institute of Biophysics, College of Life Science, Zhejiang University, Hangzhou, China
| | - Rui Sun
- School of Medicine, Westlake University, Hangzhou, Zhejiang Province, China
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China
- Research Center for Industries of the Future, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, Zhejiang, China
| | - Wangang Gong
- Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Xue Cai
- School of Medicine, Westlake University, Hangzhou, Zhejiang Province, China
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China
- Research Center for Industries of the Future, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, Zhejiang, China
| | - Lu Sun
- Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Weigang Ge
- Westlake Omics (Hangzhou) Biotechnology Co., Ltd., Hangzhou, Zhejiang Province, China
| | - Yufeng Liu
- MOE Key Laboratory of Biosystems Homeostasis and Protection, Institute of Biophysics, College of Life Science, Zhejiang University, Hangzhou, China
| | - Ying Su
- Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Wangmin Lin
- Westlake Omics (Hangzhou) Biotechnology Co., Ltd., Hangzhou, Zhejiang Province, China
| | - Yuecheng Zhan
- Westlake Omics (Hangzhou) Biotechnology Co., Ltd., Hangzhou, Zhejiang Province, China
| | - Junjian Wang
- Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Shuang Song
- MOE Key Laboratory of Biosystems Homeostasis and Protection, Institute of Biophysics, College of Life Science, Zhejiang University, Hangzhou, China
| | - Xiao Yi
- School of Medicine, Westlake University, Hangzhou, Zhejiang Province, China
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China
- Research Center for Industries of the Future, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, Zhejiang, China
| | - Maowei Ni
- Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Yi Zhu
- School of Medicine, Westlake University, Hangzhou, Zhejiang Province, China.
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China.
- Research Center for Industries of the Future, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China.
- Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, Zhejiang, China.
| | - Yuejin Hua
- MOE Key Laboratory of Biosystems Homeostasis and Protection, Institute of Biophysics, College of Life Science, Zhejiang University, Hangzhou, China.
| | - Zhiguo Zheng
- Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China.
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
| | - Tiannan Guo
- School of Medicine, Westlake University, Hangzhou, Zhejiang Province, China.
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China.
- Research Center for Industries of the Future, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China.
- Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, Zhejiang, China.
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4
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Shi J, Liu Y, Xu YJ. MS based foodomics: An edge tool integrated metabolomics and proteomics for food science. Food Chem 2024; 446:138852. [PMID: 38428078 DOI: 10.1016/j.foodchem.2024.138852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 02/05/2024] [Accepted: 02/24/2024] [Indexed: 03/03/2024]
Abstract
Foodomics has become a popular methodology in food science studies. Mass spectrometry (MS) based metabolomics and proteomics analysis played indispensable roles in foodomics research. So far, several methodologies have been developed to detect the metabolites and proteins in diets and consumers, including sample preparation, MS data acquisition, annotation and interpretation. Moreover, multiomics analysis integrated metabolomics and proteomics have received considerable attentions in the field of food safety and nutrition, because of more comprehensive and deeply. In this context, we intended to review the emerging strategies and their applications in MS-based foodomics, as well as future challenges and trends. The principle and application of multiomics were also discussed, such as the optimization of data acquisition, development of analysis algorithm and exploration of systems biology.
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Affiliation(s)
- Jiachen Shi
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Research Center for Functional Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, 1800 Lihu Road, Wuxi 214122, Jiangsu, People's Republic of China.
| | - Yuanfa Liu
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Research Center for Functional Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, 1800 Lihu Road, Wuxi 214122, Jiangsu, People's Republic of China.
| | - Yong-Jiang Xu
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Research Center for Functional Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, 1800 Lihu Road, Wuxi 214122, Jiangsu, People's Republic of China.
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5
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Schachner LF, Mullen C, Phung W, Hinkle JD, Beardsley MI, Bentley T, Day P, Tsai C, Sukumaran S, Baginski T, DiCara D, Agard NJ, Masureel M, Gober J, ElSohly AM, Melani R, Syka JEP, Huguet R, Marty MT, Sandoval W. Exposing the molecular heterogeneity of glycosylated biotherapeutics. Nat Commun 2024; 15:3259. [PMID: 38627419 PMCID: PMC11021452 DOI: 10.1038/s41467-024-47693-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 04/10/2024] [Indexed: 04/19/2024] Open
Abstract
The heterogeneity inherent in today's biotherapeutics, especially as a result of heavy glycosylation, can affect a molecule's safety and efficacy. Characterizing this heterogeneity is crucial for drug development and quality assessment, but existing methods are limited in their ability to analyze intact glycoproteins or other heterogeneous biotherapeutics. Here, we present an approach to the molecular assessment of biotherapeutics that uses proton-transfer charge-reduction with gas-phase fractionation to analyze intact heterogeneous and/or glycosylated proteins by mass spectrometry. The method provides a detailed landscape of the intact molecular weights present in biotherapeutic protein preparations in a single experiment. For glycoproteins in particular, the method may offer insights into glycan composition when coupled with a suitable bioinformatic strategy. We tested the approach on various biotherapeutic molecules, including Fc-fusion, VHH-fusion, and peptide-bound MHC class II complexes to demonstrate efficacy in measuring the proteoform-level diversity of biotherapeutics. Notably, we inferred the glycoform distribution for hundreds of molecular weights for the eight-times glycosylated fusion drug IL22-Fc, enabling correlations between glycoform sub-populations and the drug's pharmacological properties. Our method is broadly applicable and provides a powerful tool to assess the molecular heterogeneity of emerging biotherapeutics.
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Affiliation(s)
- Luis F Schachner
- Department of Microchemistry, Proteomics and Lipidomics, Genentech, Inc., South San Francisco, CA, USA
| | - Christopher Mullen
- Life Sciences Mass Spectrometry, Thermo Fisher Scientific, Inc., San Jose, CA, USA
| | - Wilson Phung
- Department of Microchemistry, Proteomics and Lipidomics, Genentech, Inc., South San Francisco, CA, USA
| | - Joshua D Hinkle
- Life Sciences Mass Spectrometry, Thermo Fisher Scientific, Inc., San Jose, CA, USA
| | | | - Tracy Bentley
- Pharmaceutical Technical Development, Genentech, Inc., South San Francisco, CA, USA
| | - Peter Day
- Pharmaceutical Technical Development, Genentech, Inc., South San Francisco, CA, USA
| | - Christina Tsai
- Pharmaceutical Technical Development, Genentech, Inc., South San Francisco, CA, USA
- Protein Analytical Development, Ascendis Pharma, Palo Alto, CA, USA
| | - Siddharth Sukumaran
- Pharmaceutical Technical Development, Genentech, Inc., South San Francisco, CA, USA
- Translational Pharmacometrics, Janssen, Horsham, PA, USA
| | - Tomasz Baginski
- Pharmaceutical Technical Development, Genentech, Inc., South San Francisco, CA, USA
| | - Danielle DiCara
- Department of Antibody Engineering, Genentech, Inc., South San Francisco, CA, USA
| | - Nicholas J Agard
- Department of Antibody Engineering, Genentech, Inc., South San Francisco, CA, USA
| | - Matthieu Masureel
- Department of Structural Biology, Genentech, Inc., South San Francisco, CA, USA
| | - Joshua Gober
- Department of Protein Chemistry, Genentech, Inc., South San Francisco, CA, USA
| | - Adel M ElSohly
- Department of Protein Chemistry, Genentech, Inc., South San Francisco, CA, USA
| | - Rafael Melani
- Life Sciences Mass Spectrometry, Thermo Fisher Scientific, Inc., San Jose, CA, USA
| | - John E P Syka
- Life Sciences Mass Spectrometry, Thermo Fisher Scientific, Inc., San Jose, CA, USA
| | - Romain Huguet
- Life Sciences Mass Spectrometry, Thermo Fisher Scientific, Inc., San Jose, CA, USA
| | - Michael T Marty
- Department of Chemistry and Biochemistry, University of Arizona, Tucson, AZ, USA
| | - Wendy Sandoval
- Department of Microchemistry, Proteomics and Lipidomics, Genentech, Inc., South San Francisco, CA, USA.
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6
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Yue L, Gong T, Jiang W, Qian L, Gong W, Sun Y, Cai X, Xu H, Liu F, Wang H, Li S, Zhu Y, Zheng Z, Wu Q, Guo T. Proteomic profiling of ovarian clear cell carcinomas identifies prognostic biomarkers for chemotherapy. Proteomics 2024; 24:e2300242. [PMID: 38171885 DOI: 10.1002/pmic.202300242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 11/17/2023] [Accepted: 11/20/2023] [Indexed: 01/05/2024]
Abstract
Clear cell ovarian carcinoma (CCOC) is a relatively rare subtype of ovarian cancer (OC) with high degree of resistance to standard chemotherapy. Little is known about the underlying molecular mechanisms, and it remains a challenge to predict its prognosis after chemotherapy. Here, we first analyzed the proteome of 35 formalin-fixed paraffin-embedded (FFPE) CCOC tissue specimens from a cohort of 32 patients with CCOC (H1 cohort) and characterized 8697 proteins using data-independent acquisition mass spectrometry (DIA-MS). We then performed proteomic analysis of 28 fresh frozen (FF) CCOC tissue specimens from an independent cohort of 24 patients with CCOC (H2 cohort), leading to the identification of 9409 proteins with DIA-MS. After bioinformatics analysis, we narrowed our focus to 15 proteins significantly correlated with the recurrence free survival (RFS) in both cohorts. These proteins are mainly involved in DNA damage response, extracellular matrix (ECM), and mitochondrial metabolism. Parallel reaction monitoring (PRM)-MS was adopted to validate the prognostic potential of the 15 proteins in the H1 cohort and an independent confirmation cohort (H3 cohort). Interferon-inducible transmembrane protein 1 (IFITM1) was observed as a robust prognostic marker for CCOC in both PRM data and immunohistochemistry (IHC) data. Taken together, this study presents a CCOC proteomic data resource and a single promising protein, IFITM1, which could potentially predict the recurrence and survival of CCOC.
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Affiliation(s)
- Liang Yue
- School of Life Sciences, Fudan University, Shanghai, China
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China
- Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China
| | - Tingting Gong
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University
| | - Wenhao Jiang
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China
- Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China
| | - Liujia Qian
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China
- Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China
| | - Wangang Gong
- Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Yaoting Sun
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China
- Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China
| | - Xue Cai
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China
- Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China
| | - Heli Xu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Fanghua Liu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - He Wang
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China
- Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China
| | - Sainan Li
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China
- Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- National Health Commission Key Laboratory of Reproductive Health, Institute of Reproductive and Child Health, Peking University, Beijing, China
| | - Yi Zhu
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China
- Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China
| | - Zhiguo Zheng
- Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Qijun Wu
- Department of Clinical Epidemiology, Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Tiannan Guo
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China
- Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China
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7
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Fröhlich K, Furrer R, Schori C, Handschin C, Schmidt A. Robust, Precise, and Deep Proteome Profiling Using a Small Mass Range and Narrow Window Data-Independent-Acquisition Scheme. J Proteome Res 2024; 23:1028-1038. [PMID: 38275131 PMCID: PMC10913089 DOI: 10.1021/acs.jproteome.3c00736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 12/20/2023] [Accepted: 12/26/2023] [Indexed: 01/27/2024]
Abstract
In recent years, a plethora of different data-independent acquisition methods have been developed for proteomics to cover a wide range of requirements. Current deep proteome profiling methods rely on fractionations, elaborate chromatography, and mass spectrometry setups or display suboptimal quantitative precision. We set out to develop an easy-to-use one shot DIA method that achieves high quantitative precision and high proteome coverage. We achieve this by focusing on a small mass range of 430-670 m/z using small isolation windows without overlap. With this new method, we were able to quantify >9200 protein groups in HEK lysates with an average coefficient of variance of 3.2%. To demonstrate the power of our newly developed narrow mass range method, we applied it to investigate the effect of PGC-1α knockout on the skeletal muscle proteome in mice. Compared to a standard data-dependent acquisition method, we could double proteome coverage and, most importantly, achieve a significantly higher quantitative precision, as compared to a previously proposed DIA method. We believe that our method will be especially helpful in quantifying low abundant proteins in samples with a high dynamic range. All raw and result files are available at massive.ucsd.edu (MSV000092186).
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Affiliation(s)
- Klemens Fröhlich
- Proteomics
Core Facility, Biozentrum Basel, University
of Basel, 4056 Basel, Switzerland
| | - Regula Furrer
- Biozentrum
Basel, University of Basel, 4056 Basel, Switzerland
| | - Christian Schori
- Proteomics
Core Facility, Biozentrum Basel, University
of Basel, 4056 Basel, Switzerland
| | | | - Alexander Schmidt
- Proteomics
Core Facility, Biozentrum Basel, University
of Basel, 4056 Basel, Switzerland
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8
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Lou R, Shui W. Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023. Mol Cell Proteomics 2024; 23:100712. [PMID: 38182042 PMCID: PMC10847697 DOI: 10.1016/j.mcpro.2024.100712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/27/2023] [Accepted: 01/02/2024] [Indexed: 01/07/2024] Open
Abstract
Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics. This review provides a comprehensive overview of recent advances in both the experimental and computational methods for DIA proteomics, from data acquisition schemes to analysis strategies and software tools. DIA acquisition schemes are categorized based on the design of precursor isolation windows, highlighting wide-window, overlapping-window, narrow-window, scanning quadrupole-based, and parallel accumulation-serial fragmentation-enhanced DIA methods. For DIA data analysis, major strategies are classified into spectrum reconstruction, sequence-based search, library-based search, de novo sequencing, and sequencing-independent approaches. A wide array of software tools implementing these strategies are reviewed, with details on their overall workflows and scoring approaches at different steps. The generation and optimization of spectral libraries, which are critical resources for DIA analysis, are also discussed. Publicly available benchmark datasets covering global proteomics and phosphoproteomics are summarized to facilitate performance evaluation of various software tools and analysis workflows. Continued advances and synergistic developments of versatile components in DIA workflows are expected to further enhance the power of DIA-based proteomics.
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Affiliation(s)
- Ronghui Lou
- iHuman Institute, ShanghaiTech University, Shanghai, China; School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
| | - Wenqing Shui
- iHuman Institute, ShanghaiTech University, Shanghai, China; School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
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9
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Ma M, Lu M, Sun R, Zhu Z, Fuller DQ, Guo J, He G, Yang X, Tan L, Lu Y, Dong J, Liu R, Yang J, Li B, Guo T, Li X, Zhao D, Zhang Y, Wang CC, Dong G. Forager-farmer transition at the crossroads of East and Southeast Asia 4900 years ago. Sci Bull (Beijing) 2024; 69:103-113. [PMID: 37914610 DOI: 10.1016/j.scib.2023.10.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 08/16/2023] [Accepted: 08/16/2023] [Indexed: 11/03/2023]
Abstract
The southward expansion of East Asian farmers profoundly influenced the social evolution of Southeast Asia by introducing cereal agriculture. However, the timing and routes of cereal expansion in key regions are unclear due to limited empirical evidence. Here we report macrofossil, microfossil, multiple isotopic (C/N/Sr/O) and paleoproteomic data directly from radiocarbon-dated human samples, which were unearthed from a site in Xingyi in central Yunnan and which date between 7000 and 3300 a BP. Dietary isotopes reveal the earliest arrival of millet ca. 4900 a BP, and greater reliance on plant and animal agriculture was indicated between 3800 and 3300 a BP. The dietary differences between hunter-gatherer and agricultural groups are also evident in the metabolic and immune system proteins analysed from their skeletal remains. The results of paleoproteomic analysis indicate that humans had divergent biological adaptations, with and without farming. The combined application of isotopes, archaeobotanical data and proteomics provides a new approach to documenting dietary and health changes across major subsistence transitions.
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Affiliation(s)
- Minmin Ma
- Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China; Zhaotong University, Zhaotong 657000, China
| | - Minxia Lu
- Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
| | - Rui Sun
- Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Zhonghua Zhu
- Yunnan Provincial Institute of Cultural Relics and Archaeology, Kunming 650118, China
| | - Dorian Q Fuller
- Institute of Archaeology, University College London, London WC1H 0PY, UK; School of Cultural Heritage, Northwest University, Xi'an 710069, China
| | - Jianxin Guo
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen 361102, China; Department of Anthropology and Ethnology, Institute of Anthropology, School of Sociology and Anthropology, Xiamen University, Xiamen 361005, China; State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen 361102, China
| | - Guanglin He
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen 361102, China; Department of Anthropology and Ethnology, Institute of Anthropology, School of Sociology and Anthropology, Xiamen University, Xiamen 361005, China; State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen 361102, China
| | - Xiaomin Yang
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen 361102, China; Department of Anthropology and Ethnology, Institute of Anthropology, School of Sociology and Anthropology, Xiamen University, Xiamen 361005, China; State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen 361102, China
| | - Lingling Tan
- Westlake Omics (Hangzhou) Biotechnology Co., Ltd., Hangzhou 310024, China
| | - Yongxiu Lu
- Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
| | - Jiajia Dong
- Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
| | - Ruiliang Liu
- The Department of Asia, British Museum, London WC1E 7JW, UK; School of Cultural Heritage, Northwest University, Xi'an 710069, China
| | - Jishuai Yang
- Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
| | - Bo Li
- Tonghai Cultural Relics Management, Yuxi 652700, China
| | - Tiannan Guo
- Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Xiaorui Li
- Yunnan Provincial Institute of Cultural Relics and Archaeology, Kunming 650118, China
| | - Dongyue Zhao
- School of Cultural Heritage, Northwest University, Xi'an 710069, China
| | - Ying Zhang
- Group of Alpine Paleoecology and Human Adaptation, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China; State Key Laboratory of Tibetan Plateau Earth System, Resources and Environment, Beijing 100101, China
| | - Chuan-Chao Wang
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen 361102, China; Department of Anthropology and Ethnology, Institute of Anthropology, School of Sociology and Anthropology, Xiamen University, Xiamen 361005, China; State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen 361102, China; Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai 200433, China.
| | - Guanghui Dong
- Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China; Zhaotong University, Zhaotong 657000, China.
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10
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Chen J, Sun Y, Li J, Lyu M, Yuan L, Sun J, Chen S, Hu C, Wei Q, Xu Z, Guo T, Cheng X. In-depth metaproteomics analysis of tongue coating for gastric cancer: a multicenter diagnostic research study. MICROBIOME 2024; 12:6. [PMID: 38191439 PMCID: PMC10773145 DOI: 10.1186/s40168-023-01730-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Accepted: 11/21/2023] [Indexed: 01/10/2024]
Abstract
BACKGROUND Our previous study revealed marked differences in tongue images between individuals with gastric cancer and those without gastric cancer. However, the biological mechanism of tongue images as a disease indicator remains unclear. Tongue coating, a major factor in tongue appearance, is the visible layer on the tongue dorsum that provides a vital environment for oral microorganisms. While oral microorganisms are associated with gastric and intestinal diseases, the comprehensive function profiles of oral microbiota remain incompletely understood. Metaproteomics has unique strength in revealing functional profiles of microbiota that aid in comprehending the mechanism behind specific tongue coating formation and its role as an indicator of gastric cancer. METHODS We employed pressure cycling technology and data-independent acquisition (PCT-DIA) mass spectrometry to extract and identify tongue-coating proteins from 180 gastric cancer patients and 185 non-gastric cancer patients across 5 independent research centers in China. Additionally, we investigated the temporal stability of tongue-coating proteins based on a time-series cohort. Finally, we constructed a machine learning model using the stochastic gradient boosting algorithm to identify individuals at high risk of gastric cancer based on tongue-coating microbial proteins. RESULTS We measured 1432 human-derived proteins and 13,780 microbial proteins from 345 tongue-coating samples. The abundance of tongue-coating proteins exhibited high temporal stability within an individual. Notably, we observed the downregulation of human keratins KRT2 and KRT9 on the tongue surface, as well as the downregulation of ABC transporter COG1136 in microbiota, in gastric cancer patients. This suggests a decline in the defense capacity of the lingual mucosa. Finally, we established a machine learning model that employs 50 microbial proteins of tongue coating to identify individuals at a high risk of gastric cancer, achieving an area under the curve (AUC) of 0.91 in the independent validation cohort. CONCLUSIONS We characterized the alterations in tongue-coating proteins among gastric cancer patients and constructed a gastric cancer screening model based on microbial-derived tongue-coating proteins. Tongue-coating proteins are shown as a promising indicator for identifying high-risk groups for gastric cancer. Video Abstract.
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Affiliation(s)
- Jiahui Chen
- Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China
| | - Yingying Sun
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- School of Medicine, School of Life Sciences, Westlake University, Hangzhou, China
- Research Center for Industries of the Future, Westlake University, Hangzhou, China
| | - Jie Li
- Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing, China
- MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China
| | - Mengge Lyu
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- School of Medicine, School of Life Sciences, Westlake University, Hangzhou, China
- Research Center for Industries of the Future, Westlake University, Hangzhou, China
| | - Li Yuan
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China
| | - Jiancheng Sun
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Shangqi Chen
- Department of General Surgery, HwaMei Hospital, University of Chinese Academy of Sciences, Ningbo, China
| | - Can Hu
- Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China
| | - Qing Wei
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China
| | - Zhiyuan Xu
- Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
- Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China.
| | - Tiannan Guo
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.
- School of Medicine, School of Life Sciences, Westlake University, Hangzhou, China.
- Research Center for Industries of the Future, Westlake University, Hangzhou, China.
| | - Xiangdong Cheng
- Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
- Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China.
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11
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Chatterjee S, Zaia J. Proteomics-based mass spectrometry profiling of SARS-CoV-2 infection from human nasopharyngeal samples. MASS SPECTROMETRY REVIEWS 2024; 43:193-229. [PMID: 36177493 PMCID: PMC9538640 DOI: 10.1002/mas.21813] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 09/07/2022] [Accepted: 09/09/2022] [Indexed: 05/12/2023]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of the on-going global pandemic of coronavirus disease 2019 (COVID-19) that continues to pose a significant threat to public health worldwide. SARS-CoV-2 encodes four structural proteins namely membrane, nucleocapsid, spike, and envelope proteins that play essential roles in viral entry, fusion, and attachment to the host cell. Extensively glycosylated spike protein efficiently binds to the host angiotensin-converting enzyme 2 initiating viral entry and pathogenesis. Reverse transcriptase polymerase chain reaction on nasopharyngeal swab is the preferred method of sample collection and viral detection because it is a rapid, specific, and high-throughput technique. Alternate strategies such as proteomics and glycoproteomics-based mass spectrometry enable a more detailed and holistic view of the viral proteins and host-pathogen interactions and help in detection of potential disease markers. In this review, we highlight the use of mass spectrometry methods to profile the SARS-CoV-2 proteome from clinical nasopharyngeal swab samples. We also highlight the necessity for a comprehensive glycoproteomics mapping of SARS-CoV-2 from biological complex matrices to identify potential COVID-19 markers.
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Affiliation(s)
- Sayantani Chatterjee
- Department of Biochemistry, Center for Biomedical Mass SpectrometryBoston University School of MedicineBostonMassachusettsUSA
| | - Joseph Zaia
- Department of Biochemistry, Center for Biomedical Mass SpectrometryBoston University School of MedicineBostonMassachusettsUSA
- Bioinformatics ProgramBoston University School of MedicineBostonMassachusettsUSA
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12
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Sun R, Tan L, Ding X, A J, Xue Z, Cai X, Li S, Guo T. A pathway activity-based proteomic classifier stratifies prostate tumors into two subtypes. Clin Proteomics 2023; 20:50. [PMID: 37950160 PMCID: PMC10638831 DOI: 10.1186/s12014-023-09441-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 10/25/2023] [Indexed: 11/12/2023] Open
Abstract
Prostate cancer (PCa) is the second most common cancer in males worldwide. The risk stratification of PCa is mainly based on morphological examination. Here we analyzed the proteome of 667 tumor samples from 487 Chinese PCa patients and characterized 9576 protein groups by PulseDIA mass spectrometry. Then we developed a pathway activity-based classifier concerning 13 proteins from seven pathways, and dichotomized the PCa patients into two subtypes, namely PPS1 and PPS2. PPS1 is featured with enhanced innate immunity, while PPS2 with suppressed innate immunity. This classifier exhibited a correlation with PCa progression in our cohort and was further validated by two published transcriptome datasets. Notably, PPS2 was significantly correlated with poor biochemical recurrence (BCR)/metastasis-free survival (log-rank P-value < 0.05). The PPS2 was also featured with cell proliferation activation. Together, our study presents a novel pathway activity-based stratification scheme for PCa.
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Affiliation(s)
- Rui Sun
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China.
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, 310024, China.
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, 310024, China.
| | - Lingling Tan
- Westlake Omics (Hangzhou) Biotechnology Co., Ltd., Hangzhou, 310024, China
| | - Xuan Ding
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, 310024, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, 310024, China
| | - Jun A
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, 310024, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, 310024, China
| | - Zhangzhi Xue
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, 310024, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, 310024, China
| | - Xue Cai
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, 310024, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, 310024, China
| | - Sainan Li
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, 310024, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, 310024, China
| | - Tiannan Guo
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China.
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, 310024, China.
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, 310024, China.
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13
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Kitata RB, Yang JC, Chen YJ. Advances in data-independent acquisition mass spectrometry towards comprehensive digital proteome landscape. MASS SPECTROMETRY REVIEWS 2023; 42:2324-2348. [PMID: 35645145 DOI: 10.1002/mas.21781] [Citation(s) in RCA: 37] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 12/17/2021] [Accepted: 01/21/2022] [Indexed: 06/15/2023]
Abstract
The data-independent acquisition mass spectrometry (DIA-MS) has rapidly evolved as a powerful alternative for highly reproducible proteome profiling with a unique strength of generating permanent digital maps for retrospective analysis of biological systems. Recent advancements in data analysis software tools for the complex DIA-MS/MS spectra coupled to fast MS scanning speed and high mass accuracy have greatly expanded the sensitivity and coverage of DIA-based proteomics profiling. Here, we review the evolution of the DIA-MS techniques, from earlier proof-of-principle of parallel fragmentation of all-ions or ions in selected m/z range, the sequential window acquisition of all theoretical mass spectra (SWATH-MS) to latest innovations, recent development in computation algorithms for data informatics, and auxiliary tools and advanced instrumentation to enhance the performance of DIA-MS. We further summarize recent applications of DIA-MS and experimentally-derived as well as in silico spectra library resources for large-scale profiling to facilitate biomarker discovery and drug development in human diseases with emphasis on the proteomic profiling coverage. Toward next-generation DIA-MS for clinical proteomics, we outline the challenges in processing multi-dimensional DIA data set and large-scale clinical proteomics, and continuing need in higher profiling coverage and sensitivity.
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Affiliation(s)
| | - Jhih-Ci Yang
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan
- Sustainable Chemical Science and Technology, Taiwan International Graduate Program, Academia Sinica and National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Applied Chemistry, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Yu-Ju Chen
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan
- Sustainable Chemical Science and Technology, Taiwan International Graduate Program, Academia Sinica and National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Chemistry, National Taiwan University, Taipei, Taiwan
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14
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Hay BN, Akinlaja MO, Baker TC, Houfani AA, Stacey RG, Foster LJ. Integration of data-independent acquisition (DIA) with co-fractionation mass spectrometry (CF-MS) to enhance interactome mapping capabilities. Proteomics 2023; 23:e2200278. [PMID: 37144656 DOI: 10.1002/pmic.202200278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 04/03/2023] [Accepted: 04/14/2023] [Indexed: 05/06/2023]
Abstract
Proteomics technologies are continually advancing, providing opportunities to develop stronger and more robust protein interaction networks (PINs). In part, this is due to the ever-growing number of high-throughput proteomics methods that are available. This review discusses how data-independent acquisition (DIA) and co-fractionation mass spectrometry (CF-MS) can be integrated to enhance interactome mapping abilities. Furthermore, integrating these two techniques can improve data quality and network generation through extended protein coverage, less missing data, and reduced noise. CF-DIA-MS shows promise in expanding our knowledge of interactomes, notably for non-model organisms (NMOs). CF-MS is a valuable technique on its own, but upon the integration of DIA, the potential to develop robust PINs increases, offering a unique approach for researchers to gain an in-depth understanding into the dynamics of numerous biological processes.
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Affiliation(s)
- Brenna N Hay
- Michael Smith Laboratories and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Mopelola O Akinlaja
- Michael Smith Laboratories and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Teesha C Baker
- Michael Smith Laboratories and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Aicha Asma Houfani
- Michael Smith Laboratories and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - R Greg Stacey
- Michael Smith Laboratories and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Leonard J Foster
- Michael Smith Laboratories and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC, Canada
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15
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Pan C, He Y, Wang H, Yu Y, Li L, Huang L, Lyu M, Ge W, Yang B, Sun Y, Guo T, Liu Z. Identifying Patients With Rapid Progression From Hormone-Sensitive to Castration-Resistant Prostate Cancer: A Retrospective Study. Mol Cell Proteomics 2023; 22:100613. [PMID: 37394064 PMCID: PMC10491655 DOI: 10.1016/j.mcpro.2023.100613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 06/19/2023] [Accepted: 06/28/2023] [Indexed: 07/04/2023] Open
Abstract
Prostate cancer (PCa) is the second most prevalent malignancy and the fifth cause of cancer-related deaths in men. A crucial challenge is identifying the population at risk of rapid progression from hormone-sensitive prostate cancer (HSPC) to lethal castration-resistant prostate cancer (CRPC). We collected 78 HSPC biopsies and measured their proteomes using pressure cycling technology and a pulsed data-independent acquisition pipeline. We quantified 7355 proteins using these HSPC biopsies. A total of 251 proteins showed differential expression between patients with a long- or short-term progression to CRPC. Using a random forest model, we identified seven proteins that significantly discriminated long- from short-term progression patients, which were used to classify PCa patients with an area under the curve of 0.873. Next, one clinical feature (Gleason sum) and two proteins (BGN and MAPK11) were found to be significantly associated with rapid disease progression. A nomogram model using these three features was generated for stratifying patients into groups with significant progression differences (p-value = 1.3×10-4). To conclude, we identified proteins associated with a fast progression to CRPC and an unfavorable prognosis. Based on these proteins, our machine learning and nomogram models stratified HSPC into high- and low-risk groups and predicted their prognoses. These models may aid clinicians in predicting the progression of patients, guiding individualized clinical management and decisions.
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Affiliation(s)
- Chenxi Pan
- Department of Urology, The Second Hospital of Dalian Medical University, Dalian, China
| | - Yi He
- Department of Urology, The Second Hospital of Dalian Medical University, Dalian, China
| | - He Wang
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China; Research Center for Industries of the Future, Westlake University, Hangzhou, China
| | - Yang Yu
- Department of Urology, The Second Hospital of Dalian Medical University, Dalian, China
| | - Lu Li
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China; Research Center for Industries of the Future, Westlake University, Hangzhou, China; College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Lingling Huang
- Westlake Omics (Hangzhou) Biotechnology Co., Ltd, Hangzhou, China
| | - Mengge Lyu
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China; Research Center for Industries of the Future, Westlake University, Hangzhou, China
| | - Weigang Ge
- Westlake Omics (Hangzhou) Biotechnology Co., Ltd, Hangzhou, China
| | - Bo Yang
- Department of Urology, The Second Hospital of Dalian Medical University, Dalian, China.
| | - Yaoting Sun
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China; Research Center for Industries of the Future, Westlake University, Hangzhou, China.
| | - Tiannan Guo
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China; Research Center for Industries of the Future, Westlake University, Hangzhou, China
| | - Zhiyu Liu
- Department of Urology, The Second Hospital of Dalian Medical University, Dalian, China.
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16
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Beaumal C, Beck A, Hernandez-Alba O, Carapito C. Advanced mass spectrometry workflows for accurate quantification of trace-level host cell proteins in drug products: Benefits of FAIMS separation and gas-phase fractionation DIA. Proteomics 2023; 23:e2300172. [PMID: 37148167 DOI: 10.1002/pmic.202300172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 04/19/2023] [Accepted: 04/19/2023] [Indexed: 05/08/2023]
Abstract
Therapeutic monoclonal antibodies (mAb) production relies on multiple purification steps before release as a drug product (DP). A few host cell proteins (HCPs) may co-purify with the mAb. Their monitoring is crucial due to the considerable risk they represent for mAb stability, integrity, and efficacy and their potential immunogenicity. Enzyme-linked immunosorbent assays (ELISA) commonly used for global HCP monitoring present limitations in terms of identification and quantification of individual HCPs. Therefore, liquid chromatography tandem mass spectrometry (LC-MS/MS) has emerged as a promising alternative. Challenging DP samples show an extreme dynamic range requiring high performing methods to detect and reliably quantify trace-level HCPs. Here, we investigated the benefits of adding high-field asymmetric ion mobility spectrometry (FAIMS) separation and gas phase fractionation (GPF) prior to data independent acquisition (DIA). FAIMS LC-MS/MS analysis allowed the identification of 221 HCPs among which 158 were reliably quantified for a global amount of 880 ng/mg of NIST mAb Reference Material. Our methods have also been successfully applied to two FDA/EMA approved DPs and allowed digging deeper into the HCP landscape with the identification and quantification of a few tens of HCPs with sensitivity down to the sub-ng/mg of mAb level.
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Affiliation(s)
- Corentin Beaumal
- Laboratoire de Spectrométrie de Masse BioOrganique, IPHC UMR 7178, CNRS, Université de Strasbourg, Strasbourg, France
- Infrastructure Nationale de Protéomique ProFI - FR2048, Strasbourg, France
| | - Alain Beck
- IRPF, Centre d'Immunologie Pierre-Fabre (CIPF), Saint-Julien-en-Genevois, France
| | - Oscar Hernandez-Alba
- Laboratoire de Spectrométrie de Masse BioOrganique, IPHC UMR 7178, CNRS, Université de Strasbourg, Strasbourg, France
- Infrastructure Nationale de Protéomique ProFI - FR2048, Strasbourg, France
| | - Christine Carapito
- Laboratoire de Spectrométrie de Masse BioOrganique, IPHC UMR 7178, CNRS, Université de Strasbourg, Strasbourg, France
- Infrastructure Nationale de Protéomique ProFI - FR2048, Strasbourg, France
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17
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Yi X, Zhu J, Liu W, Peng L, Lu C, Sun P, Huang L, Nie X, Huang S, Guo T, Zhu Y. Proteome Landscapes of Human Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma. Mol Cell Proteomics 2023; 22:100604. [PMID: 37353004 PMCID: PMC10413158 DOI: 10.1016/j.mcpro.2023.100604] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 04/12/2023] [Accepted: 06/20/2023] [Indexed: 06/25/2023] Open
Abstract
Liver cancer is among the top leading causes of cancer mortality worldwide. Particularly, hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (CCA) have been extensively investigated from the aspect of tumor biology. However, a comprehensive and systematic understanding of the molecular characteristics of HCC and CCA remains absent. Here, we characterized the proteome landscapes of HCC and CCA using the data-independent acquisition (DIA) mass spectrometry (MS) method. By comparing the quantitative proteomes of HCC and CCA, we found several differences between the two cancer types. In particular, we found an abnormal lipid metabolism in HCC and activated extracellular matrix-related pathways in CCA. We next developed a three-protein classifier to distinguish CCA from HCC, achieving an area under the curve (AUC) of 0.92, and an accuracy of 90% in an independent validation cohort of 51 patients. The distinct molecular characteristics of HCC and CCA presented in this study provide new insights into the tumor biology of these two major important primary liver cancers. Our findings may help develop more efficient diagnostic approaches and new targeted drug treatments.
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Affiliation(s)
- Xiao Yi
- Center for ProtTalks, Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China
| | - Jiang Zhu
- Center for Stem Cell Research and Application, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China; Key laboratory of Biological Targeted Therapy, The Ministry of Education, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Wei Liu
- Westlake Omics (Hangzhou) Biotechnology Co, Ltd, Hangzhou, Zhejiang, China
| | - Li Peng
- Department of Pathology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Cong Lu
- Center for Stem Cell Research and Application, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China; Key laboratory of Biological Targeted Therapy, The Ministry of Education, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Ping Sun
- Department of Hepatobiliary Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Lingling Huang
- Westlake Omics (Hangzhou) Biotechnology Co, Ltd, Hangzhou, Zhejiang, China
| | - Xiu Nie
- Department of Pathology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Shi'ang Huang
- Center for Stem Cell Research and Application, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China; Key laboratory of Biological Targeted Therapy, The Ministry of Education, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Tiannan Guo
- Center for ProtTalks, Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China.
| | - Yi Zhu
- Center for ProtTalks, Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China.
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18
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Xu K, Yin X, Zhou B, Zheng X, Wang H, Chen J, Cai X, Gao H, Xu X, Wang L, Shen L, Guo T, Zheng S, Li B, Shao Y, Wang J. FOSL2 promotes intertumoral infiltration of T cells and increases pathological complete response rates in locally advanced rectal cancer patients. Cancer Lett 2023; 562:216145. [PMID: 36997107 DOI: 10.1016/j.canlet.2023.216145] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 03/21/2023] [Accepted: 03/21/2023] [Indexed: 03/30/2023]
Abstract
The outcome of neoadjuvant chemoradiotherapy (nCRT) remains highly unpredictable for individuals with locally advanced rectal cancer (LARC). We set out to characterize effective biomarkers that promote a pathological complete response (pCR). We quantified the abundances of 6483 high-confidence proteins in pre-nCRT biopsies of 58 LARC patients from two hospitals with pressure cycling technology (PCT)-assisted pulse data-independent acquisition (PulseDIA) mass spectrometry. Compared with non-pCR patients, pCR patients achieved long-term disease-free survival (DFS) and had higher tumor immune infiltration, especially CD8+ T cell infiltration, before nCRT. FOSL2 was selected as the candidate biomarker for predicting pCR and was found to be significantly upregulated in pCR patients, which was verified in another 54 pre-nCRT biopsies of LARC patients by immunohistochemistry. FOSL2 expression was able to predict pCR by multiple reaction monitoring (MRM) with high efficiency (Area under curve (AUC) = 0.939, specificity = 1.000, sensitivity = 0.850), and high FOSL2 expression was associated with long-term DFS (p = 0.044). When treated with simulated nCRT, FOSL2 sufficiency resulted in more significant inhibition of cell proliferation, and more significant promotion of cell cycle arrest and cell apoptosis. Moreover, CXCL10 secretion with abnormal cytosolic dsDNA accumulation was found in FOSL2-wildtype (FOSL2-WT) tumor cells over nCRT, which might elevate CD8+ T-cell infiltration and CD8+ T-cell-mediated cytotoxicity to promote nCRT-induced antitumor immunity. Our study revealed proteomic profiles in LARC patients before nCRT and highlighted immune activation in the tumors of patients who achieved pCR. We identified FOSL2 as a promising biomarker to predict pCR and promote long-term DFS by contributing to CD8+ T-cell infiltration.
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Affiliation(s)
- Kailun Xu
- Department of Breast Surgery and Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), Cancer Institute, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang, China; Zhejiang Provincial Clinical Research Center for Cancer, China; Cancer Center of Zhejiang University, China.
| | - Xiaoyang Yin
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, Shandong, China.
| | - Biting Zhou
- Zhejiang Provincial Clinical Research Center for Cancer, China; Cancer Center of Zhejiang University, China; Department of Radiation Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang, China.
| | - Xi Zheng
- Zhejiang Provincial Clinical Research Center for Cancer, China; Cancer Center of Zhejiang University, China; Department of Radiation Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang, China.
| | - Hao Wang
- Zhejiang Provincial Clinical Research Center for Cancer, China; Cancer Center of Zhejiang University, China; Department of Colorectal Surgery and Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), Cancer Institute, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang, China.
| | - Jing Chen
- Zhejiang Provincial Clinical Research Center for Cancer, China; Cancer Center of Zhejiang University, China; Department of Colorectal Surgery and Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), Cancer Institute, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang, China.
| | - Xue Cai
- Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China.
| | - Huanhuan Gao
- Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China.
| | - Xiaoming Xu
- Department of Pathology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang, China.
| | - Liuhong Wang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang, China.
| | - Li Shen
- Department of Radiation Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang, China.
| | - Tiannan Guo
- Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China.
| | - Shu Zheng
- Zhejiang Provincial Clinical Research Center for Cancer, China; Cancer Center of Zhejiang University, China; Department of Colorectal Surgery and Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), Cancer Institute, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang, China.
| | - Baosheng Li
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, Shandong, China.
| | - Yingkuan Shao
- Department of Breast Surgery and Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), Cancer Institute, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang, China; Zhejiang Provincial Clinical Research Center for Cancer, China; Cancer Center of Zhejiang University, China.
| | - Jian Wang
- Zhejiang Provincial Clinical Research Center for Cancer, China; Cancer Center of Zhejiang University, China; Department of Colorectal Surgery and Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), Cancer Institute, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang, China.
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19
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Gao X, Sun R, Jiao N, Liang X, Li G, Gao H, Wu X, Yang M, Chen C, Sun X, Chen L, Wu W, Cong Y, Zhu R, Guo T, Liu Z. Integrative multi-omics deciphers the spatial characteristics of host-gut microbiota interactions in Crohn's disease. Cell Rep Med 2023:101050. [PMID: 37172588 DOI: 10.1016/j.xcrm.2023.101050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 02/07/2023] [Accepted: 04/20/2023] [Indexed: 05/15/2023]
Abstract
Dysregulated host-microbial interactions play critical roles in initiation and perpetuation of gut inflammation in Crohn's disease (CD). However, the spatial distribution and interaction network across the intestine and its accessory tissues are still elusive. Here, we profile the host proteins and tissue microbes in 540 samples from the intestinal mucosa, submucosa-muscularis-serosa, mesenteric adipose tissues, mesentery, and mesenteric lymph nodes of 30 CD patients and spatially decipher the host-microbial interactions. We observe aberrant antimicrobial immunity and metabolic processes across multi-tissues during CD and determine bacterial transmission along with altered microbial communities and ecological patterns. Moreover, we identify several candidate interaction pairs between host proteins and microbes associated with perpetuation of gut inflammation and bacterial transmigration across multi-tissues in CD. Signature alterations in host proteins (e.g., SAA2 and GOLM1) and microbes (e.g., Alistipes and Streptococcus) are further imprinted in serum and fecal samples as potential diagnostic biomarkers, thus providing a rationale for precision diagnosis.
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Affiliation(s)
- Xiang Gao
- Center for Inflammatory Bowel Disease Research and Department of Gastroenterology, The Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China
| | - Ruicong Sun
- Center for Inflammatory Bowel Disease Research and Department of Gastroenterology, The Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China
| | - Na Jiao
- National Clinical Research Center for Child Health, The Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Xiao Liang
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China; Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Gengfeng Li
- Center for Inflammatory Bowel Disease Research and Department of Gastroenterology, The Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China
| | - Han Gao
- Center for Inflammatory Bowel Disease Research and Department of Gastroenterology, The Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China
| | - Xiaohan Wu
- Center for Inflammatory Bowel Disease Research and Department of Gastroenterology, The Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China
| | - Muqing Yang
- Center for Difficult and Complicated Abdominal Surgery, The Shanghai Tenth People's Hospital, Tongji University, Shanghai 200072, China
| | - Chunqiu Chen
- Center for Difficult and Complicated Abdominal Surgery, The Shanghai Tenth People's Hospital, Tongji University, Shanghai 200072, China
| | - Xiaomin Sun
- Center for Inflammatory Bowel Disease Research and Department of Gastroenterology, The Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China
| | - Liang Chen
- Center for Inflammatory Bowel Disease Research and Department of Gastroenterology, The Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China
| | - Wei Wu
- Center for Inflammatory Bowel Disease Research and Department of Gastroenterology, The Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China
| | - Yingzi Cong
- Department of Microbiology and Immunology, University of Texas Medical Branch, Galveston, TX 77555, USA
| | - Ruixin Zhu
- Center for Inflammatory Bowel Disease Research and Department of Gastroenterology, The Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China; Department of Bioinformatics, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China.
| | - Tiannan Guo
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China; Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China.
| | - Zhanju Liu
- Center for Inflammatory Bowel Disease Research and Department of Gastroenterology, The Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China.
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20
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Ni M, Zhou J, Gong W, Jiang R, Li X, Dai W, Yin Z, Chen Z, Zheng Z, Zhu J. Proteomic analysis reveals CAAP1 negatively correlates with platinum resistance in ovarian cancer. J Proteomics 2023; 277:104864. [PMID: 36870674 DOI: 10.1016/j.jprot.2023.104864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 02/21/2023] [Accepted: 02/27/2023] [Indexed: 03/06/2023]
Abstract
The present study sought to investigate the correlation between CAAP1 and platinum resistance in ovarian cancer and to preliminarily explore the potential biological function of CAAP1. Proteomic analysis was used to analyze differentially expressed proteins in platinum-sensitive and -resistant tissue samples of ovarian cancer. The Kaplan-Meier plotter was used for prognostic analysis. Immunohistochemistry assay and chi-square test were employed to explore the relationship between CAAP1 and platinum resistance in tissue samples. Lentivirus transfection, immunoprecipitation-mass spectrometry, and bioinformatics analysis were used to determine the potential biological function of CAAP1. Based on results, the expression level of CAAP1 was significantly higher in platinum-sensitive tissues compared to that in resistant tissues. Chi-square test demonstrated that there is a negative correlation between high expression of CAAP1 and platinum resistance. Overexpression of CAAP1 increased cis‑platinum sensitivity of the A2780/DDP cell line likely via the mRNA splicing pathway by interacting with the splicing factor AKAP17A. In summary, there is a negative correlation between high expression of CAAP1 and platinum resistance. CAAP1 might be a potential biomarker for platinum resistance in ovarian cancer. SIGNIFICANCE: Platinum resistance is a key factor affecting the survival of ovarian cancer patients. Understanding the mechanisms of platinum resistance is highly important for ovarian cancer management. Here, we performed the DIA- and DDA-based proteomics to analyze differentially expressed proteins in tissue and cell samples of ovarian cancer. We found that the protein identified as CAAP1, which was first reported to be involved in the regulation of apoptosis, may be negatively correlates with platinum resistance in ovarian cancer. In addition, we also found that CAAP1 enhanced the sensitivity of platinum-resistant cells to cis‑platinum via the mRNA splicing pathway by interacting with the splicing factor AKAP17A. Our data would be useful to reveal novel molecular mechanisms of platinum resistance in ovarian cancer.
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Affiliation(s)
- Maowei Ni
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
| | - Jie Zhou
- Center for Medicinal Resources Research, Zhejiang Academy of Traditional Chinese Medicine, Hangzhou, Zhejiang, China
| | - Wangang Gong
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
| | - Ruibin Jiang
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
| | - Xia Li
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
| | - Wumin Dai
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
| | - Zhuomin Yin
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
| | - Zhongbo Chen
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
| | - Zhiguo Zheng
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China.
| | - Jianqing Zhu
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China.
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21
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Woo J, Zhang Q. A Streamlined High-Throughput Plasma Proteomics Platform for Clinical Proteomics with Improved Proteome Coverage, Reproducibility, and Robustness. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023; 34:754-762. [PMID: 36975161 PMCID: PMC10080683 DOI: 10.1021/jasms.3c00022] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 03/16/2023] [Accepted: 03/20/2023] [Indexed: 06/18/2023]
Abstract
Mass spectrometry-based clinical proteomics requires high throughput, reproducibility, robustness, and comprehensive coverage to serve the needs of clinical diagnosis, prognosis, and personalized medicine. Oftentimes these requirements are contradictory to each other. We report the development of a streamlined High-Throughput Plasma Proteomics (sHTPP) platform for untargeted profiling of the blood plasma proteome, which includes 96-well plates and simplified procedures for sample preparation, disposable trap column for peptide loading, robust liquid chromatographic system for separation, data-independent acquisition in tandem mass spectrometry, and DIA-NN, FragPipe, and in-house peptide spectral library-based data analysis. Using the optimized platform at a throughput of 60 samples per day, over 600 protein groups including 57 FDA-approved biomarkers can be consistently identified from whole human plasma, and more than 85% of the detected proteins have 100% completeness in quantitative values across 300 samples. The balance achieved between proteome coverage, throughput, and reproducibility of this sHTPP platform makes it promising in clinical settings, where a large number of samples are to be measured quickly and reliably to support various needs of clinical medicine.
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Affiliation(s)
- Jongmin Woo
- Center
for Translational Biomedical Research, University
of North Carolina at Greensboro, North Carolina Research Campus, Kannapolis, North Carolina 28081, United States
| | - Qibin Zhang
- Center
for Translational Biomedical Research, University
of North Carolina at Greensboro, North Carolina Research Campus, Kannapolis, North Carolina 28081, United States
- Department
of Chemistry & Biochemistry, University
of North Carolina at Greensboro, Greensboro, North Carolina 27402, United States
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22
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Qian L, Zhu J, Xue Z, Gong T, Xiang N, Yue L, Cai X, Gong W, Wang J, Sun R, Jiang W, Ge W, Wang H, Zheng Z, Wu Q, Zhu Y, Guo T. Resistance prediction in high-grade serous ovarian carcinoma with neoadjuvant chemotherapy using data-independent acquisition proteomics and an ovary-specific spectral library. Mol Oncol 2023. [PMID: 36855266 PMCID: PMC10399723 DOI: 10.1002/1878-0261.13410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 12/25/2022] [Accepted: 02/27/2023] [Indexed: 03/02/2023] Open
Abstract
High-grade serous ovarian carcinoma (HGSOC) is the most common subtype of ovarian cancer with 5-year survival rates below 40%. Neoadjuvant chemotherapy (NACT) followed by interval debulking surgery (IDS) is recommended for patients with advanced-stage HGSOC unsuitable for primary debulking surgery (PDS). However, about 40% of patients receiving this treatment exhibited chemoresistance of uncertain molecular mechanisms and predictability. Here, we built a high-quality ovary-specific spectral library containing 130 735 peptides and 10 696 proteins on Orbitrap instruments. Compared to a published DIA pan-human spectral library (DPHL), this spectral library provides 10% more ovary-specific and 3% more ovary-enriched proteins. This library was then applied to analyze data-independent acquisition (DIA) data of tissue samples from an HGSOC cohort treated with NACT, leading to 10 070 quantified proteins, which is 9.73% more than that with DPHL. We further established a six-protein classifier by parallel reaction monitoring (PRM) to effectively predict the resistance to additional chemotherapy after IDS (Log-rank test, P = 0.002). The classifier was validated with 57 patients from an independent clinical center (P = 0.014). Thus, we have developed an ovary-specific spectral library for targeted proteome analysis, and propose a six-protein classifier that could potentially predict chemoresistance in HGSOC patients after NACT-IDS treatment.
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Affiliation(s)
- Liujia Qian
- School of Medicine, Zhejiang University, Hangzhou, China.,Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Jianqing Zhu
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Zhangzhi Xue
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Tingting Gong
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Nan Xiang
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Liang Yue
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Xue Cai
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Wangang Gong
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Junjian Wang
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Rui Sun
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Wenhao Jiang
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Weigang Ge
- Westlake Omics (Hangzhou) Biotechnology Co., Ltd., China
| | - He Wang
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Zhiguo Zheng
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Qijun Wu
- Department of Clinical Epidemiology, Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yi Zhu
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Tiannan Guo
- School of Medicine, Zhejiang University, Hangzhou, China.,Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
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23
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Li L, Sun C, Sun Y, Dong Z, Wu R, Sun X, Zhang H, Jiang W, Zhou Y, Cen X, Cai S, Xia H, Zhu Y, Guo T, Piatkevich KD. Spatially resolved proteomics via tissue expansion. Nat Commun 2022; 13:7242. [PMID: 36450705 PMCID: PMC9712279 DOI: 10.1038/s41467-022-34824-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 11/04/2022] [Indexed: 12/12/2022] Open
Abstract
Spatially resolved proteomics is an emerging approach for mapping proteome heterogeneity of biological samples, however, it remains technically challenging due to the complexity of the tissue microsampling techniques and mass spectrometry analysis of nanoscale specimen volumes. Here, we describe a spatially resolved proteomics method based on the combination of tissue expansion with mass spectrometry-based proteomics, which we call Expansion Proteomics (ProteomEx). ProteomEx enables quantitative profiling of the spatial variability of the proteome in mammalian tissues at ~160 µm lateral resolution, equivalent to the tissue volume of 0.61 nL, using manual microsampling without the need for custom or special equipment. We validated and demonstrated the utility of ProteomEx for streamlined large-scale proteomics profiling of biological tissues including brain, liver, and breast cancer. We further applied ProteomEx for identifying proteins associated with Alzheimer's disease in a mouse model by comparative proteomic analysis of brain subregions.
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Affiliation(s)
- Lu Li
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Key Laboratory of Structural Biology of Zhejiang Province, Westlake University, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.13402.340000 0004 1759 700XCollege of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310024 Zhejiang China
| | - Cuiji Sun
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China
| | - Yaoting Sun
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Key Laboratory of Structural Biology of Zhejiang Province, Westlake University, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China
| | - Zhen Dong
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Key Laboratory of Structural Biology of Zhejiang Province, Westlake University, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China
| | - Runxin Wu
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Key Laboratory of Structural Biology of Zhejiang Province, Westlake University, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.21107.350000 0001 2171 9311Whiting School of Engineering, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218 USA
| | - Xiaoting Sun
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China
| | - Hanbin Zhang
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China
| | - Wenhao Jiang
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Key Laboratory of Structural Biology of Zhejiang Province, Westlake University, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China
| | - Yan Zhou
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Key Laboratory of Structural Biology of Zhejiang Province, Westlake University, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China
| | - Xufeng Cen
- grid.13402.340000 0004 1759 700XDepartment of Biochemistry & Molecular Medical Center, Zhejiang University School of Medicine, Hangzhou, 310058 China
| | - Shang Cai
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China
| | - Hongguang Xia
- grid.13402.340000 0004 1759 700XDepartment of Biochemistry & Molecular Medical Center, Zhejiang University School of Medicine, Hangzhou, 310058 China ,grid.452661.20000 0004 1803 6319Research Center for Clinical Pharmacy & Key Laboratory for Drug Evaluation and Clinical Research of Zhejiang Province, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003 China ,grid.13402.340000 0004 1759 700XZhejiang Laboratory for Systems & Precision Medicine, Zhejiang University Medical Center, 1369 West Wenyi Road, Hangzhou, 311121 China
| | - Yi Zhu
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Key Laboratory of Structural Biology of Zhejiang Province, Westlake University, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China
| | - Tiannan Guo
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Key Laboratory of Structural Biology of Zhejiang Province, Westlake University, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China
| | - Kiryl D. Piatkevich
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China
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Tissue-Characteristic Expression of Mouse Proteome. Mol Cell Proteomics 2022; 21:100408. [PMID: 36058520 PMCID: PMC9562433 DOI: 10.1016/j.mcpro.2022.100408] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 07/23/2022] [Accepted: 08/24/2022] [Indexed: 01/18/2023] Open
Abstract
The mouse is a valuable model organism for biomedical research. Here, we established a comprehensive spectral library and the data-independent acquisition-based quantitative proteome maps for 41 mouse organs, including some rarely reported organs such as the cornea, retina, and nine paired organs. The mouse spectral library contained 178,304 peptides from 12,320 proteins, including 1678 proteins not reported in previous mouse spectral libraries. Our data suggested that organs from the nervous system and immune system expressed the most distinct proteome compared with other organs. We also found characteristic protein expression of immune-privileged organs, which may help understanding possible immune rejection after organ transplantation. Each tissue type expressed characteristic high-abundance proteins related to its physiological functions. We also uncovered some tissue-specific proteins which have not been reported previously. The testis expressed highest number of tissue-specific proteins. By comparison of nine paired organs including kidneys, testes, and adrenal glands, we found left organs exhibited higher levels of antioxidant enzymes. We also observed expression asymmetry for proteins related to the apoptotic process, tumor suppression, and organ functions between the left and right sides. This study provides a comprehensive spectral library and a quantitative proteome resource for mouse studies.
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25
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High-throughput proteomic sample preparation using pressure cycling technology. Nat Protoc 2022; 17:2307-2325. [PMID: 35931778 PMCID: PMC9362583 DOI: 10.1038/s41596-022-00727-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Accepted: 05/24/2022] [Indexed: 11/09/2022]
Abstract
High-throughput lysis and proteolytic digestion of biopsy-level tissue specimens is a major bottleneck for clinical proteomics. Here we describe a detailed protocol of pressure cycling technology (PCT)-assisted sample preparation for proteomic analysis of biopsy tissues. A piece of fresh frozen or formalin-fixed paraffin-embedded tissue weighing ~0.1–2 mg is placed in a 150 μL pressure-resistant tube called a PCT-MicroTube with proper lysis buffer. After closing with a PCT-MicroPestle, a batch of 16 PCT-MicroTubes are placed in a Barocycler, which imposes oscillating pressure to the samples from one atmosphere to up to ~3,000 times atmospheric pressure. The pressure cycling schemes are optimized for tissue lysis and protein digestion, and can be programmed in the Barocycler to allow reproducible, robust and efficient protein extraction and proteolysis digestion for mass spectrometry-based proteomics. This method allows effective preparation of not only fresh frozen and formalin-fixed paraffin-embedded tissue, but also cells, feces and tear strips. It takes ~3 h to process 16 samples in one batch. The resulting peptides can be analyzed by various mass spectrometry-based proteomics methods. We demonstrate the applications of this protocol with mouse kidney tissue and eight types of human tumors. High-throughput lysis and proteolytic digestion of biopsy-level tissue specimens is a major bottleneck for clinical proteomics. This protocol describes pressure cycling technology (PCT)-assisted sample preparation of biopsy tissues.
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26
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Abstract
Proteins are the molecular effectors of the information encoded in the genome. Proteomics aims at understanding the molecular functions of proteins in their biological context. In contrast to transcriptomics and genomics, the study of proteomes provides deeper insight into the dynamic regulatory layers encoded at the protein level, such as posttranslational modifications, subcellular localization, cell signaling, and protein-protein interactions. Currently, mass spectrometry (MS)-based proteomics is the technology of choice for studying proteomes at a system-wide scale, contributing to clinical biomarker discovery and fundamental molecular biology. MS technologies are continuously being developed to fulfill the requirements of speed, resolution, and quantitative accuracy, enabling the acquisition of comprehensive proteomes. In this review, we present how MS technology and acquisition methods have evolved to meet the requirements of cutting-edge proteomics research, which is describing the human proteome and its dynamic posttranslational modifications with unprecedented depth. Finally, we provide a perspective on studying proteomes at single-cell resolution. Expected final online publication date for the Annual Review of Genomics and Human Genetics, Volume 23 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Ana Martinez-Val
- Novo Nordisk Foundation Center for Protein Research, Proteomics Program, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark;
| | - Ulises H Guzmán
- Novo Nordisk Foundation Center for Protein Research, Proteomics Program, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark;
| | - Jesper V Olsen
- Novo Nordisk Foundation Center for Protein Research, Proteomics Program, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark;
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27
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Sun Y, Li L, Zhou Y, Ge W, Wang H, Wu R, Liu W, Chen H, Xiao Q, Cai X, Dong Z, Zhang F, Xiao J, Wang G, He Y, Gao J, Kon OL, Iyer NG, Guan H, Teng X, Zhu Y, Zhao Y, Guo T. Stratification of follicular thyroid tumors using data-independent acquisition proteomics and a comprehensive thyroid tissue spectral library. Mol Oncol 2022; 16:1611-1624. [PMID: 35194950 PMCID: PMC9019893 DOI: 10.1002/1878-0261.13198] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 12/17/2021] [Accepted: 02/21/2022] [Indexed: 11/23/2022] Open
Abstract
Thyroid nodules occur in about 60% of the population. A major challenge in thyroid nodule diagnosis is to distinguish between follicular adenoma (FA) and carcinoma (FTC). Here, we present a comprehensive thyroid spectral library covering five types of thyroid tissues. This library includes 121 960 peptides and 9941 protein groups. This spectral library can be used to quantify up to 7863 proteins from thyroid tissues, and can also be used to develop parallel reaction monitoring (PRM) assays for targeted protein quantification. Next, to stratify follicular thyroid tumours, we compared the proteomes of 24 FA and 22 FTC samples, and identified 204 differentially expressed proteins (DEPs). Our data suggest altered ferroptosis pathways in malignant follicular carcinoma. In all, 31 selected proteins effectively distinguished follicular tumours. Of those DEPs, nine proteins were further verified by PRM in an independent cohort of 18 FA and 19 FTC. Together, we present a comprehensive spectral library for DIA and targeted proteomics analysis of thyroid tissue specimens, and identified nine proteins that could potentially distinguish FA and FTC.
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Affiliation(s)
- Yaoting Sun
- Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China.,Westlake Laboratory of Life Sciences and Biomedicine, No.18 Shilongshan Road, Hangzhou, 310024, China.,School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China.,Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, No.18 Shilongshan Road, Hangzhou, 310024, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, No.18 Shilongshan Road, Hangzhou, 310024, China
| | - Lu Li
- Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China.,Westlake Laboratory of Life Sciences and Biomedicine, No.18 Shilongshan Road, Hangzhou, 310024, China.,School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China.,Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, No.18 Shilongshan Road, Hangzhou, 310024, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, No.18 Shilongshan Road, Hangzhou, 310024, China
| | - Yan Zhou
- Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China.,Westlake Laboratory of Life Sciences and Biomedicine, No.18 Shilongshan Road, Hangzhou, 310024, China.,School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China.,Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, No.18 Shilongshan Road, Hangzhou, 310024, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, No.18 Shilongshan Road, Hangzhou, 310024, China
| | - Weigang Ge
- Westlake Omics (Hangzhou) Biotechnology Co., Ltd., No.1 Yunmeng Road, Hangzhou, 310024, China
| | - He Wang
- Westlake Laboratory of Life Sciences and Biomedicine, No.18 Shilongshan Road, Hangzhou, 310024, China.,School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China.,Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, No.18 Shilongshan Road, Hangzhou, 310024, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, No.18 Shilongshan Road, Hangzhou, 310024, China
| | - Runxin Wu
- Westlake Laboratory of Life Sciences and Biomedicine, No.18 Shilongshan Road, Hangzhou, 310024, China.,School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China.,Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, No.18 Shilongshan Road, Hangzhou, 310024, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, No.18 Shilongshan Road, Hangzhou, 310024, China.,Whiting School of Engineering, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218-2625, USA
| | - Wei Liu
- Westlake Omics (Hangzhou) Biotechnology Co., Ltd., No.1 Yunmeng Road, Hangzhou, 310024, China
| | - Hao Chen
- Westlake Omics (Hangzhou) Biotechnology Co., Ltd., No.1 Yunmeng Road, Hangzhou, 310024, China
| | - Qi Xiao
- Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China.,Westlake Laboratory of Life Sciences and Biomedicine, No.18 Shilongshan Road, Hangzhou, 310024, China.,School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China.,Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, No.18 Shilongshan Road, Hangzhou, 310024, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, No.18 Shilongshan Road, Hangzhou, 310024, China
| | - Xue Cai
- Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China.,Westlake Laboratory of Life Sciences and Biomedicine, No.18 Shilongshan Road, Hangzhou, 310024, China.,School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China.,Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, No.18 Shilongshan Road, Hangzhou, 310024, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, No.18 Shilongshan Road, Hangzhou, 310024, China
| | - Zhen Dong
- Westlake Laboratory of Life Sciences and Biomedicine, No.18 Shilongshan Road, Hangzhou, 310024, China.,School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China.,Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, No.18 Shilongshan Road, Hangzhou, 310024, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, No.18 Shilongshan Road, Hangzhou, 310024, China
| | - Fangfei Zhang
- Westlake Laboratory of Life Sciences and Biomedicine, No.18 Shilongshan Road, Hangzhou, 310024, China.,School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China.,Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, No.18 Shilongshan Road, Hangzhou, 310024, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, No.18 Shilongshan Road, Hangzhou, 310024, China
| | - Junhong Xiao
- Department of General Surgery, The Second Hospital of Dalian Medical University, Dalian, 116023, China
| | - Guangzhi Wang
- Department of General Surgery, The Second Hospital of Dalian Medical University, Dalian, 116023, China
| | - Yi He
- Department of Urology, The Second Hospital of Dalian Medical University, Dalian, 116023, China
| | - Jinlong Gao
- Westlake Laboratory of Life Sciences and Biomedicine, No.18 Shilongshan Road, Hangzhou, 310024, China.,School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China.,Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, No.18 Shilongshan Road, Hangzhou, 310024, China.,Whiting School of Engineering, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218-2625, USA
| | - Oi Lian Kon
- Division of Medical Sciences, National Cancer Centre Singapore, Singapore, 169610, Republic of Singapore
| | - N Gopalakrishna Iyer
- Division of Medical Sciences, National Cancer Centre Singapore, Singapore, 169610, Republic of Singapore.,Department of Head and Neck Surgery, National Cancer Centre Singapore, Republic of Singapore
| | - Haixia Guan
- Department of Endocrinology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan erlu, Guangzhou, 510080, China
| | - Xiaodong Teng
- Department of Pathology, the First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, 310063, China
| | - Yi Zhu
- Westlake Laboratory of Life Sciences and Biomedicine, No.18 Shilongshan Road, Hangzhou, 310024, China.,School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China.,Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, No.18 Shilongshan Road, Hangzhou, 310024, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, No.18 Shilongshan Road, Hangzhou, 310024, China
| | - Yongfu Zhao
- Department of General Surgery, The Second Hospital of Dalian Medical University, Dalian, 116023, China
| | - Tiannan Guo
- Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China.,Westlake Laboratory of Life Sciences and Biomedicine, No.18 Shilongshan Road, Hangzhou, 310024, China.,School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China.,Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, No.18 Shilongshan Road, Hangzhou, 310024, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, No.18 Shilongshan Road, Hangzhou, 310024, China
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Huang Q, Fei X, Zhong Z, Zhou J, Gong J, Chen Y, Li Y, Wu X. Stratification of diabetic kidney diseases via data-independent acquisition proteomics-based analysis of human kidney tissue specimens. Front Endocrinol (Lausanne) 2022; 13:995362. [PMID: 36465646 PMCID: PMC9714485 DOI: 10.3389/fendo.2022.995362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 10/31/2022] [Indexed: 11/18/2022] Open
Abstract
AIM The aims of this study were to analyze the proteomic differences in renal tissues from patients with diabetes mellitus (DM) and diabetic kidney disease (DKD) and to select sensitive biomarkers for early identification of DKD progression. METHODS Pressure cycling technology-pulse data-independent acquisition mass spectrometry was employed to investigate protein alterations in 36 formalin-fixed paraffin-embedded specimens. Then, bioinformatics analysis was performed to identify important signaling pathways and key molecules. Finally, the target proteins were validated in 60 blood and 30 urine samples. RESULTS A total of 52 up- and 311 down-regulated differential proteins were identified as differing among the advanced DKD samples, early DKD samples, and DM controls (adjusted p<0.05). These differentially expressed proteins were mainly involved in ion transport, apoptosis regulation, and the inflammatory response. UniProt database analysis showed that these proteins were mostly enriched in signaling pathways related to metabolism, apoptosis, and inflammation. NBR1 was significantly up-regulated in both early and advanced DKD, with fold changes (FCs) of 175 and 184, respectively (both p<0.01). In addition, VPS37A and ATG4B were significantly down-regulated with DKD progression, with FCs of 0.140 and 0.088, respectively, in advanced DKD and 0.533 and 0.192, respectively, in early DKD compared with the DM control group (both p<0.01). Bioinformatics analysis showed that NBR1, VPS37A, and ATG4B are closely related to autophagy. We also found that serum levels of the three proteins and urine levels of NBR1 decreased with disease progression. Moreover, there was a significant difference in serum VPS37A and ATG4B levels between patients with early and advanced DKD (both p<0.05). The immunohistochemistry assaay exhibited that the three proteins were expressed in renal tubular cells, and NBR1 was also expressed in the cystic wall of renal glomeruli. CONCLUSION The increase in NBR1 expression and the decrease in ATG4B and VPS37 expression in renal tissue are closely related to inhibition of the autophagy pathway, which may contribute to DKD development or progression. These three proteins may serve as sensitive serum biomarkers for early identification of DKD progression.
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Affiliation(s)
- Qinghua Huang
- Department of Endocrinology, Geriatric Medicine Center, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
- Key Laboratory of Endocrine Gland Diseases of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Xianming Fei
- Laboratory Medicine Center, Department of Clinical Laboratory, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Zhaoxian Zhong
- Department of Commerce, Westlake Omics (Hangzhou) Biotechnology Co., Ltd., Hangzhou, Zhejiang, China
| | - Jieru Zhou
- Graduate School, Jinzhou Medical University, Jinzhou, Liaoning, China
| | - Jianguang Gong
- Laboratory of Kidney Disease, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Yuan Chen
- Department of Pathology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Yiwen Li
- Laboratory of Kidney Disease, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Xiaohong Wu
- Department of Endocrinology, Geriatric Medicine Center, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
- Key Laboratory of Endocrine Gland Diseases of Zhejiang Province, Hangzhou, Zhejiang, China
- *Correspondence: Xiaohong Wu,
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Liu W, Sun Y, Ge W, Zhang F, Gan L, Zhu Y, Guo T, Liu K. DIA-based Proteomics Identifies IDH2 as a Targetable Regulator of Acquired Drug Resistance in Chronic Myeloid Leukemia. Mol Cell Proteomics 2021; 21:100187. [PMID: 34922009 PMCID: PMC8800142 DOI: 10.1016/j.mcpro.2021.100187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 11/08/2021] [Accepted: 12/13/2021] [Indexed: 11/26/2022] Open
Abstract
Drug resistance is a critical obstacle to effective treatment in patients with chronic myeloid leukemia (CML). To understand the underlying resistance mechanisms in response to imatinib (IMA) and adriamycin (ADR), the parental K562 cells were treated with low doses of IMA or ADR for two months to generate derivative cells with mild, intermediate and severe resistance to the drugs as defined by their increasing resistance index (RI). PulseDIA-based quantitative proteomics was then employed to reveal the proteome changes in these resistant cells. In total, 7082 proteotypic proteins from 98,232 peptides were identified and quantified from the dataset using four DIA software tools including OpenSWATH, Spectronaut, DIA-NN, and EncyclopeDIA. Sirtuin Signaling Pathway was found to be significantly enriched in both ADR- and IMA-resistant K562 cells. In particular, IDH2 was identified as a potential drug target correlated with the drug resistance phenotype, and its inhibition by the antagonist AGI-6780 reversed the acquired resistance in K562 cells to either ADR or IMA. Together, our study has implicated IDH2 as a potential target that can be therapeutically leveraged to alleviate the drug resistance in K562 cells when treated with IMA and ADR.
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Affiliation(s)
- Wei Liu
- Department of Clinical Pharmacology, College of Pharmacy, Dalian Medical University, Dalian, Liaoning, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China; Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China
| | - Yaoting Sun
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China; Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China
| | - Weigang Ge
- Westlake Omics (Hangzhou) Biotechnology Co., Ltd., Hangzhou 310024, China
| | - Fangfei Zhang
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China; Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China
| | - Lin Gan
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China; Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China
| | - Yi Zhu
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China; Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China
| | - Tiannan Guo
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China; Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China.
| | - Kexin Liu
- Department of Clinical Pharmacology, College of Pharmacy, Dalian Medical University, Dalian, Liaoning, China.
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Zhang H, Bensaddek D. Narrow Precursor Mass Range for DIA-MS Enhances Protein Identification and Quantification in Arabidopsis. Life (Basel) 2021; 11:982. [PMID: 34575131 PMCID: PMC8469718 DOI: 10.3390/life11090982] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 09/03/2021] [Accepted: 09/15/2021] [Indexed: 12/02/2022] Open
Abstract
Data independent acquisition-mass spectrometry (DIA-MS) is becoming widely utilised for robust and accurate quantification of samples in quantitative proteomics. Here, we describe the systematic evaluation of the effects of DIA precursor mass range on total protein identification and quantification. We show that a narrow mass range of precursors (~250 m/z) for DIA-MS enables a higher number of protein identifications. Subsequent application of DIA with narrow precursor range (from 400 to 650 m/z) on an Arabidopsis sample with spike-in known proteins identified 34.7% more proteins than in conventional DIA (cDIA) with a wide precursor range of 400-1200 m/z. When combining several DIA-MS analyses with narrow precursor ranges (i.e., 400-650, 650-900 and 900-1200 m/z), we were able to quantify 10,099 protein groups with a median coefficient of variation of <6%. These findings represent a 54.7% increase in the number of proteins quantified than with cDIA analysis. This is particularly important for low abundance proteins, as exemplified by the six-protein mix spike-in. In cDIA only five out of the six-protein mix were quantified while our approach allowed accurate quantitation of all six proteins.
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Affiliation(s)
| | - Dalila Bensaddek
- Core Labs, King Abdullah University of Science and Technology, Thuwal 23500-6900, Saudi Arabia;
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Salovska B, Li W, Di Y, Liu Y. BoxCarmax: A High-Selectivity Data-Independent Acquisition Mass Spectrometry Method for the Analysis of Protein Turnover and Complex Samples. Anal Chem 2021; 93:3103-3111. [PMID: 33533601 PMCID: PMC8959401 DOI: 10.1021/acs.analchem.0c04293] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The data-independent acquisition (DIA) performed in the latest high-resolution, high-speed mass spectrometers offers a powerful analytical tool for biological investigations. The DIA mass spectrometry (DIA-MS) combined with the isotopic labeling approach holds a particular promise for increasing the multiplexity of DIA-MS analysis, which could assist the relative protein quantification and the proteome-wide turnover profiling. However, the wide MS1 isolation windows employed in conventional DIA methods lead to a limited efficiency in identifying and quantifying isotope-labeled peptide pairs through peptide fragment ions. Here, we optimized a high-selectivity DIA-MS named BoxCarmax that supports the analysis of complex samples, such as those generated from Stable isotope labeling by amino acids in cell culture (SILAC) and pulse SILAC (pSILAC) experiments. BoxCarmax enables multiplexed acquisition at both MS1 and MS2 levels, through the integration of BoxCar and MSX features, as well as a gas-phase separation strategy. We found BoxCarmax significantly improved the quantitative accuracy in SILAC and pSILAC samples by mitigating the ratio suppression of isotope-peptide pairs. We further applied BoxCarmax to measure protein degradation regulation during serum starvation stress in cultured cells, revealing valuable biological insights. Our study offered an alternative and accurate approach for the MS analysis of protein turnover and complex samples.
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Affiliation(s)
- Barbora Salovska
- Yale Cancer Biology Institute, Yale University, West Haven, CT, USA
| | - Wenxue Li
- Yale Cancer Biology Institute, Yale University, West Haven, CT, USA
| | - Yi Di
- Yale Cancer Biology Institute, Yale University, West Haven, CT, USA
| | - Yansheng Liu
- Yale Cancer Biology Institute, Yale University, West Haven, CT, USA
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, USA
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