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Humphries EM, Xavier D, Ashman K, Hains PG, Robinson PJ. High-Throughput Proteomics and Phosphoproteomics of Rat Tissues Using Microflow Zeno SWATH. J Proteome Res 2024. [PMID: 38819404 DOI: 10.1021/acs.jproteome.4c00010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2024]
Abstract
High-throughput tissue proteomics has great potential in the advancement of precision medicine. Here, we investigated the combined sensitivity of trap-elute microflow liquid chromatography with a ZenoTOF for DIA proteomics and phosphoproteomics. Method optimization was conducted on HEK293T cell lines to determine the optimal variable window size, MS2 accumulation time and gradient length. The ZenoTOF 7600 was then compared to the previous generation TripleTOF 6600 using eight rat organs, finding up to 23% more proteins using a fifth of the sample load and a third of the instrument time. Spectral reference libraries generated from Zeno SWATH data in FragPipe (MSFragger-DIA/DIA-NN) contained 4 times more fragment ions than the DIA-NN only library and quantified more proteins. Replicate single-shot phosphopeptide enrichments of 50-100 μg of rat tryptic peptide were analyzed by microflow HPLC using Zeno SWATH without fractionation. Using Spectronaut we quantified a shallow phosphoproteome containing 1000-3000 phosphoprecursors per organ. Promisingly, clear hierarchical clustering of organs was observed with high Pearson correlation coefficients >0.95 between replicate enrichments and median CV of 20%. The combined sensitivity of microflow HPLC with Zeno SWATH allows for the high-throughput quantitation of an extensive proteome and shallow phosphoproteome from small tissue samples.
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Affiliation(s)
- Erin M Humphries
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, New South Wales 2145, Australia
| | - Dylan Xavier
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, New South Wales 2145, Australia
| | - Keith Ashman
- Sciex, 96 Ricketts Road,Mount Waverley, Victoria 3149, Australia
| | - Peter G Hains
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, New South Wales 2145, Australia
| | - Phillip J Robinson
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, New South Wales 2145, Australia
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2
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Xavier D, Lucas N, Williams SG, Koh JMS, Ashman K, Loudon C, Reddel R, Hains PG, Robinson PJ. Heat 'n Beat: A Universal High-Throughput End-to-End Proteomics Sample Processing Platform in under an Hour. Anal Chem 2024; 96:4093-4102. [PMID: 38427620 DOI: 10.1021/acs.analchem.3c04708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2024]
Abstract
Proteomic analysis by mass spectrometry of small (≤2 mg) solid tissue samples from diverse formats requires high throughput and comprehensive proteome coverage. We developed a nearly universal, rapid, and robust protocol for sample preparation, suitable for high-throughput projects that encompass most cell or tissue types. This end-to-end workflow extends from original sample to loading the mass spectrometer and is centered on a one-tube homogenization and digestion method called Heat 'n Beat (HnB). It is applicable to most tissues, regardless of how they were fixed or embedded. Sample preparation was divided into separate challenges. The initial sample washing and final peptide cleanup steps were adapted to three tissue sources: fresh frozen (FF), optimal cutting temperature (OCT) compound embedded (FF-OCT), and formalin-fixed paraffin embedded (FFPE). Third, for core processing, tissue disruption and lysis were decreased to a 7 min heat and homogenization treatment, and reduction, alkylation, and proteolysis were optimized into a single step. The refinements produced near doubled peptide yield when compared to our earlier method ABLE delivered a consistently high digestion efficiency of 85-90%, reported by ProteinPilot, and required only 38 min for core processing in a single tube, with the total processing time being 53-63 min. The robustness of HnB was demonstrated on six organ types, a cell line, and a cancer biopsy. Its suitability for high-throughput applications was demonstrated on a set of 1171 FF-OCT human cancer biopsies, which were processed for end-to-end completion in 92 h, producing highly consistent peptide yield and quality for over 3513 MS runs.
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Affiliation(s)
- Dylan Xavier
- ProCan, Faculty of Medicine and Health, The University of Sydney, Children's Medical Research Institute, Westmead, NSW 2145, Australia
| | - Natasha Lucas
- ProCan, Faculty of Medicine and Health, The University of Sydney, Children's Medical Research Institute, Westmead, NSW 2145, Australia
| | - Steven G Williams
- ProCan, Faculty of Medicine and Health, The University of Sydney, Children's Medical Research Institute, Westmead, NSW 2145, Australia
| | - Jennifer M S Koh
- ProCan, Faculty of Medicine and Health, The University of Sydney, Children's Medical Research Institute, Westmead, NSW 2145, Australia
| | - Keith Ashman
- ProCan, Faculty of Medicine and Health, The University of Sydney, Children's Medical Research Institute, Westmead, NSW 2145, Australia
| | - Clare Loudon
- ProCan, Faculty of Medicine and Health, The University of Sydney, Children's Medical Research Institute, Westmead, NSW 2145, Australia
| | - Roger Reddel
- ProCan, Faculty of Medicine and Health, The University of Sydney, Children's Medical Research Institute, Westmead, NSW 2145, Australia
| | - Peter G Hains
- ProCan, Faculty of Medicine and Health, The University of Sydney, Children's Medical Research Institute, Westmead, NSW 2145, Australia
| | - Phillip J Robinson
- ProCan, Faculty of Medicine and Health, The University of Sydney, Children's Medical Research Institute, Westmead, NSW 2145, Australia
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3
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Punzalan C, Wang L, Bajrami B, Yao X. Measurement and utilization of the proteomic reactivity by mass spectrometry. MASS SPECTROMETRY REVIEWS 2024; 43:166-192. [PMID: 36924435 DOI: 10.1002/mas.21837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 02/17/2023] [Accepted: 02/20/2023] [Indexed: 06/18/2023]
Abstract
Chemical proteomics, which involves studying the covalent modifications of proteins by small molecules, has significantly contributed to our understanding of protein function and has become an essential tool in drug discovery. Mass spectrometry (MS) is the primary method for identifying and quantifying protein-small molecule adducts. In this review, we discuss various methods for measuring proteomic reactivity using MS and covalent proteomics probes that engage through reactivity-driven and proximity-driven mechanisms. We highlight the applications of these methods and probes in live-cell measurements, drug target identification and validation, and characterizing protein-small molecule interactions. We conclude the review with current developments and future opportunities in the field, providing our perspectives on analytical considerations for MS-based analysis of the proteomic reactivity landscape.
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Affiliation(s)
- Clodette Punzalan
- Department of Chemistry, University of Connecticut, Storrs, Connecticut, USA
| | - Lei Wang
- Department of Chemistry, University of Connecticut, Storrs, Connecticut, USA
- AD Bio US, Takeda, Lexington, Massachusetts, 02421, USA
| | - Bekim Bajrami
- Chemical Biology & Proteomics, Biogen, Cambridge, Massachusetts, USA
| | - Xudong Yao
- Department of Chemistry, University of Connecticut, Storrs, Connecticut, USA
- Institute for Systems Biology, University of Connecticut, Storrs, Connecticut, USA
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4
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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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5
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Wang SS, Pandey K, Watson KA, Abbott RC, Mifsud NA, Gracey FM, Ramarathinam SH, Cross RS, Purcell AW, Jenkins MR. Endogenous H3.3K27M derived peptide restricted to HLA-A∗02:01 is insufficient for immune-targeting in diffuse midline glioma. Mol Ther Oncolytics 2023; 30:167-180. [PMID: 37674626 PMCID: PMC10477804 DOI: 10.1016/j.omto.2023.08.005] [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: 05/01/2023] [Accepted: 08/11/2023] [Indexed: 09/08/2023] Open
Abstract
Diffuse midline glioma (DMG) is a childhood brain tumor with an extremely poor prognosis. Chimeric antigen receptor (CAR) T cell therapy has recently demonstrated some success in DMG, but there may a need to target multiple tumor-specific targets to avoid antigen escape. We developed a second-generation CAR targeting an HLA-A∗02:01 restricted histone 3K27M epitope in DMG, the target of previous peptide vaccination and T cell receptor-mimics. These CAR T cells demonstrated specific, titratable, binding to cells pulsed with the H3.3K27M peptide. However, we were unable to observe scFv binding, CAR T cell activation, or cytotoxic function against H3.3K27M+ patient-derived models. Despite using sensitive immunopeptidomics, we could not detect the H3.3K27M26-35-HLA-A∗02:01 peptide on these patient-derived models. Interestingly, other non-mutated peptides from DMG were detected bound to HLA-A∗02:01 and other class I molecules, including a novel HLA-A3-restricted peptide encompassing the K27M mutation and overlapping with the H3 K27M26-35-HLA-A∗02:01 peptide. These results suggest that targeting the H3 K27M26-35 mutation in context of HLA-A∗02:01 may not be a feasible immunotherapy strategy because of its lack of presentation. These findings should inform future investigations and clinical trials in DMG.
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Affiliation(s)
- Stacie S. Wang
- The Walter and Eliza Hall Institute of Medical Research, Immunology Division, Parkville, VIC 3052, Australia
- Murdoch Children’s Research Institute, Parkville, VIC 3052, Australia
- The University of Melbourne, Department of Medical Biology, Parkville, VIC 3052, Australia
| | - Kirti Pandey
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia
| | - Katherine A. Watson
- The Walter and Eliza Hall Institute of Medical Research, Immunology Division, Parkville, VIC 3052, Australia
| | - Rebecca C. Abbott
- The Walter and Eliza Hall Institute of Medical Research, Immunology Division, Parkville, VIC 3052, Australia
- The University of Melbourne, Department of Medical Biology, Parkville, VIC 3052, Australia
| | - Nicole A. Mifsud
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia
| | - Fiona M. Gracey
- Myrio Therapeutics, 6-16 Joseph St, Blackburn North, Melbourne, VIC 3130, Australia
| | - Sri H. Ramarathinam
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia
| | - Ryan S. Cross
- The Walter and Eliza Hall Institute of Medical Research, Immunology Division, Parkville, VIC 3052, Australia
| | - Anthony W. Purcell
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia
| | - Misty R. Jenkins
- The Walter and Eliza Hall Institute of Medical Research, Immunology Division, Parkville, VIC 3052, Australia
- The University of Melbourne, Department of Medical Biology, Parkville, VIC 3052, Australia
- La Trobe University, La Trobe Institute for Molecular Science, Bundoora, VIC, Australia
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6
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Midha MK, Kapil C, Maes M, Baxter DH, Morrone SR, Prokop TJ, Moritz RL. Vacuum Insulated Probe Heated Electrospray Ionization Source Enhances Microflow Rate Chromatography Signals in the Bruker timsTOF Mass Spectrometer. J Proteome Res 2023; 22:2525-2537. [PMID: 37294184 PMCID: PMC11060334 DOI: 10.1021/acs.jproteome.3c00305] [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] [Indexed: 06/10/2023]
Abstract
By far the largest contribution to ion detectability in liquid chromatography-driven mass spectrometry-based proteomics is the efficient generation of peptide molecular ions by the electrospray source. To maximize the transfer of peptides from the liquid to gaseous phase and allow molecular ions to enter the mass spectrometer at microspray flow rates, an efficient electrospray process is required. Here we describe the superior performance of newly design vacuum insulated probe heated electrospray ionization (VIP-HESI) source coupled to a Bruker timsTOF PRO mass spectrometer operated in microspray mode. VIP-HESI significantly improves chromatography signals in comparison to electrospray ionization (ESI) and nanospray ionization using the captivespray (CS) source and provides increased protein detection with higher quantitative precision, enhancing reproducibility of sample injection amounts. Protein quantitation of human K562 lymphoblast samples displayed excellent chromatographic retention time reproducibility (<10% coefficient of variation (CV)) with no signal degradation over extended periods of time, and a mouse plasma proteome analysis identified 12% more plasma protein groups allowing large-scale analysis to proceed with confidence (1,267 proteins at 0.4% CV). We show that the Slice-PASEF VIP-HESI mode is sensitive in identifying low amounts of peptide without losing quantitative precision. We demonstrate that VIP-HESI coupled with microflow rate chromatography achieves a higher depth of coverage and run-to-run reproducibility for a broad range of proteomic applications. Data and spectral libraries are available via ProteomeXchange (PXD040497).
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Affiliation(s)
- Mukul K Midha
- Institute for Systems Biology, 401 Terry Avenue N, Seattle, Washington 98109, United States
| | - Charu Kapil
- Institute for Systems Biology, 401 Terry Avenue N, Seattle, Washington 98109, United States
| | - Michal Maes
- Institute for Systems Biology, 401 Terry Avenue N, Seattle, Washington 98109, United States
| | - David H Baxter
- Institute for Systems Biology, 401 Terry Avenue N, Seattle, Washington 98109, United States
| | - Seamus R Morrone
- Institute for Systems Biology, 401 Terry Avenue N, Seattle, Washington 98109, United States
| | - Timothy J Prokop
- Institute for Systems Biology, 401 Terry Avenue N, Seattle, Washington 98109, United States
| | - Robert L Moritz
- Institute for Systems Biology, 401 Terry Avenue N, Seattle, Washington 98109, United States
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7
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Alvarez-Rivera E, Ortiz-Hernández EJ, Lugo E, Lozada-Reyes LM, Boukli NM. Oncogenic Proteomics Approaches for Translational Research and HIV-Associated Malignancy Mechanisms. Proteomes 2023; 11:22. [PMID: 37489388 PMCID: PMC10366845 DOI: 10.3390/proteomes11030022] [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: 03/30/2023] [Revised: 06/09/2023] [Accepted: 06/29/2023] [Indexed: 07/26/2023] Open
Abstract
Recent advances in the field of proteomics have allowed extensive insights into the molecular regulations of the cell proteome. Specifically, this allows researchers to dissect a multitude of signaling arrays while targeting for the discovery of novel protein signatures. These approaches based on data mining are becoming increasingly powerful for identifying both potential disease mechanisms as well as indicators for disease progression and overall survival predictive and prognostic molecular markers for cancer. Furthermore, mass spectrometry (MS) integrations satisfy the ongoing demand for in-depth biomarker validation. For the purpose of this review, we will highlight the current developments based on MS sensitivity, to place quantitative proteomics into clinical settings and provide a perspective to integrate proteomics data for future applications in cancer precision medicine. We will also discuss malignancies associated with oncogenic viruses such as Acquire Immunodeficiency Syndrome (AIDS) and suggest novel mechanisms behind this phenomenon. Human Immunodeficiency Virus type-1 (HIV-1) proteins are known to be oncogenic per se, to induce oxidative and endoplasmic reticulum stresses, and to be released from the infected or expressing cells. HIV-1 proteins can act alone or in collaboration with other known oncoproteins, which cause the bulk of malignancies in people living with HIV-1 on ART.
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Affiliation(s)
- Eduardo Alvarez-Rivera
- Biomedical Proteomics Facility, Department of Microbiology and Immunology, Universidad Central del Caribe, School of Medicine, Bayamón, PR 00960, USA
| | - Emanuel J. Ortiz-Hernández
- Biomedical Proteomics Facility, Department of Microbiology and Immunology, Universidad Central del Caribe, School of Medicine, Bayamón, PR 00960, USA
| | - Elyette Lugo
- Biomedical Proteomics Facility, Department of Microbiology and Immunology, Universidad Central del Caribe, School of Medicine, Bayamón, PR 00960, USA
| | | | - Nawal M. Boukli
- Biomedical Proteomics Facility, Department of Microbiology and Immunology, Universidad Central del Caribe, School of Medicine, Bayamón, PR 00960, USA
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8
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Sun W, Lin Y, Huang Y, Chan J, Terrillon S, Rosenbaum AI, Contrepois K. Robust and High-Throughput Analytical Flow Proteomics Analysis of Cynomolgus Monkey and Human Matrices with Zeno SWATH Data Independent Acquisition. Mol Cell Proteomics 2023:100562. [PMID: 37142056 DOI: 10.1016/j.mcpro.2023.100562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 04/17/2023] [Accepted: 04/26/2023] [Indexed: 05/06/2023] Open
Abstract
Modern mass spectrometers routinely allow deep proteome coverage in a single experiment. These methods are typically operated at nano and micro flow regimes, but they often lack throughput and chromatographic robustness, which is critical for large-scale studies. In this context, we have developed, optimized and benchmarked LC-MS methods combining the robustness and throughput of analytical flow chromatography with the added sensitivity provided by the Zeno trap across a wide range of cynomolgus monkey and human matrices of interest for toxicological studies and clinical biomarker discovery. SWATH data independent acquisition (DIA) experiments with Zeno trap activated (Zeno SWATH DIA) provided a clear advantage over conventional SWATH DIA in all sample types tested with improved sensitivity, quantitative robustness and signal linearity as well as increased protein coverage by up to 9-fold. Using a 10-min gradient chromatography, up to 3,300 proteins were identified in tissues at 2 μg peptide load. Importantly, the performance gains with Zeno SWATH translated into better biological pathway representation and improved the ability to identify dysregulated proteins and pathways associated with two metabolic diseases in human plasma. Finally, we demonstrate that this method is highly stable over time with the acquisition of reliable data over the injection of 1,000+ samples (14.2 days of uninterrupted acquisition) without the need for human intervention or normalization. Altogether, Zeno SWATH DIA methodology allows fast, sensitive and robust proteomic workflows using analytical flow and is amenable to large-scale studies. This work provides detailed method performance assessment on a variety of relevant biological matrices and serves as a valuable resource for the proteomics community.
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Affiliation(s)
- Weiwen Sun
- Integrated Bioanalysis, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, South San Francisco, CA 94080, USA
| | - Yuan Lin
- Integrated Bioanalysis, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, South San Francisco, CA 94080, USA
| | - Yue Huang
- Integrated Bioanalysis, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, South San Francisco, CA 94080, USA
| | - Josolyn Chan
- Integrated Bioanalysis, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, South San Francisco, CA 94080, USA
| | - Sonia Terrillon
- Integrated Bioanalysis, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, South San Francisco, CA 94080, USA
| | - Anton I Rosenbaum
- Integrated Bioanalysis, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, South San Francisco, CA 94080, USA.
| | - Kévin Contrepois
- Integrated Bioanalysis, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, South San Francisco, CA 94080, USA.
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9
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Messner CB, Demichev V, Wang Z, Hartl J, Kustatscher G, Mülleder M, Ralser M. Mass spectrometry-based high-throughput proteomics and its role in biomedical studies and systems biology. Proteomics 2023; 23:e2200013. [PMID: 36349817 DOI: 10.1002/pmic.202200013] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 10/13/2022] [Accepted: 10/13/2022] [Indexed: 11/11/2022]
Abstract
There are multiple reasons why the next generation of biological and medical studies require increasing numbers of samples. Biological systems are dynamic, and the effect of a perturbation depends on the genetic background and environment. As a consequence, many conditions need to be considered to reach generalizable conclusions. Moreover, human population and clinical studies only reach sufficient statistical power if conducted at scale and with precise measurement methods. Finally, many proteins remain without sufficient functional annotations, because they have not been systematically studied under a broad range of conditions. In this review, we discuss the latest technical developments in mass spectrometry (MS)-based proteomics that facilitate large-scale studies by fast and efficient chromatography, fast scanning mass spectrometers, data-independent acquisition (DIA), and new software. We further highlight recent studies which demonstrate how high-throughput (HT) proteomics can be applied to capture biological diversity, to annotate gene functions or to generate predictive and prognostic models for human diseases.
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Affiliation(s)
- Christoph B Messner
- Precision Proteomics Center, Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland
| | - Vadim Demichev
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Ziyue Wang
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Johannes Hartl
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Georg Kustatscher
- Wellcome Centre for Cell Biology, University of Edinburgh, Max Born Crescent, Edinburgh, Scotland, UK
| | - Michael Mülleder
- Core Facility High Throughput Mass Spectrometry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Markus Ralser
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
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10
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Obi EN, Tellock DA, Thomas GJ, Veenstra TD. Biomarker Analysis of Formalin-Fixed Paraffin-Embedded Clinical Tissues Using Proteomics. Biomolecules 2023; 13:biom13010096. [PMID: 36671481 PMCID: PMC9855471 DOI: 10.3390/biom13010096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 12/20/2022] [Accepted: 12/21/2022] [Indexed: 01/06/2023] Open
Abstract
The relatively recent developments in mass spectrometry (MS) have provided novel opportunities for this technology to impact modern medicine. One of those opportunities is in biomarker discovery and diagnostics. Key developments in sample preparation have enabled a greater range of clinical samples to be characterized at a deeper level using MS. While most of these developments have focused on blood, tissues have also been an important resource. Fresh tissues, however, are difficult to obtain for research purposes and require significant resources for long-term storage. There are millions of archived formalin-fixed paraffin-embedded (FFPE) tissues within pathology departments worldwide representing every possible tissue type including tumors that are rare or very small. Owing to the chemical technique used to preserve FFPE tissues, they were considered intractable to many newer proteomics techniques and primarily only useful for immunohistochemistry. In the past couple of decades, however, researchers have been able to develop methods to extract proteins from FFPE tissues in a form making them analyzable using state-of-the-art technologies such as MS and protein arrays. This review will discuss the history of these developments and provide examples of how they are currently being used to identify biomarkers and diagnose diseases such as cancer.
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11
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Gao H, Liu Y, Demichev V, Tate S, Chen C, Zhu J, Lu C, Ralser M, Guo T, Zhu Y. Optimization of Microflow LC Coupled with Scanning SWATH and Its Application in Hepatocellular Carcinoma Tissues. J Proteome Res 2022; 21:1686-1693. [PMID: 35653712 DOI: 10.1021/acs.jproteome.2c00078] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Scanning SWATH coupled with normal-flow LC has been recently introduced for high-content, high-throughput proteomics analysis, which requires a relatively large amount of sample injection. Here we established the microflow LC coupled with Scanning SWATH for samples with relatively small quantities. First, we optimized several key parameters of the LC and MS settings, including C18 particle size for the analytical column, LC gradient and flow rate, as well as effective ion accumulation time and isolation window width for MS acquisition. We then compared the optimized Scanning SWATH method with the conventional variable window SWATH (referred to as SWATH) method. Results showed that the total ion chromatogram signals in Scanning SWATH were 10 times higher than that of SWATH, and Scanning SWATH identified 12.2-22.2% more peptides than SWATH. Finally, we employed 120 min Scanning SWATH to acquire the proteomes of 62 formalin-fixed, paraffin-embedded (FFPE) tissue samples from 31 patients with hepatocellular carcinoma (HCC). Altogether, 92 334 peptides and 8516 proteins were quantified. Besides the reported biomarkers, including ANXA2, MCM7, SUOX, and AKR1B10, we identified new potential HCC biomarkers such as CST5, TP53, CEBPB, and E2F4. Taken together, we present an optimal workflow integrating microflow LC and Scanning SWATH that effectively improves the protein identification and quantitation.
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Affiliation(s)
- Huanhuan Gao
- Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, Zhejiang Province, China
| | - Youqi Liu
- Westlake Omics (Hangzhou) Biotechnology Co., Ltd., No. 1 Yunmeng Road, Cloud Town, Xihu District, Hangzhou 310024, Zhejiang Province, China
| | - Vadim Demichev
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London WC2N 5DU, U.K.,Department of Biochemistry, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin 10115, Germany
| | | | | | - Jiang Zhu
- Center for Stem Cell Research and Application, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, Hubei, China
| | - Cong Lu
- Center for Stem Cell Research and Application, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, Hubei, China
| | - Markus Ralser
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London WC2N 5DU, U.K.,Department of Biochemistry, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin 10115, Germany
| | - 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, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, Zhejiang Province, China
| | - Yi Zhu
- Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, Zhejiang Province, China
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12
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A Rapid LC-MS/MS-PRM Assay for Serologic Quantification of Sialylated O-HPX Glycoforms in Patients with Liver Fibrosis. Molecules 2022; 27:molecules27072213. [PMID: 35408612 PMCID: PMC9000230 DOI: 10.3390/molecules27072213] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 03/24/2022] [Accepted: 03/25/2022] [Indexed: 02/04/2023] Open
Abstract
Development of high throughput robust methods is a prerequisite for a successful clinical use of LC-MS/MS assays. In earlier studies, we reported that nLC-MS/MS measurement of the O-glycoforms of HPX is an indicator of liver fibrosis. In this study, we show that a microflow LC-MS/MS method using a single column setup for capture of the analytes, desalting, fast gradient elution, and on-line mass spectrometry measurements, is robust, substantially faster, and even more sensitive than our nLC setup. We demonstrate applicability of the workflow on the quantification of the O-HPX glycoforms in unfractionated serum samples of control and liver disease patients. The assay requires microliter volumes of serum samples, and the platform is amenable to one hundred sample injections per day, providing a valuable tool for biomarker validation and screening studies.
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13
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Abstract
INTRODUCTION Due to its excellent sensitivity, nano-flow liquid chromatography tandem mass spectrometry (LC-MS/MS) is the mainstay in proteome research; however, this comes at the expense of limited throughput and robustness. In contrast, micro-flow LC-MS/MS enables high-throughput, robustness, quantitative reproducibility, and precision while retaining a moderate degree of sensitivity. Such features make it an attractive technology for a wide range of proteomic applications. In particular, large-scale projects involving the analysis of hundreds to thousands of samples. AREAS COVERED This review summarizes the history of chromatographic separation in discovery proteomics with a focus on micro-flow LC-MS/MS, discusses the current state-of-the-art, highlights advances in column development and instrumentation, and provides guidance on which LC flow best supports different types of proteomic applications. EXPERT OPINION Micro-flow LC-MS/MS will replace nano-flow LC-MS/MS in many proteomic applications, particularly when sample quantities are not limited and sample cohorts are large. Examples include clinical analyses of body fluids, tissues, drug discovery and chemical biology investigations, plus systems biology projects across all kingdoms of life. When combined with rapid and sensitive MS, intelligent data acquisition, and informatics approaches, it will soon become possible to analyze large cohorts of more than 10,000 samples in a comprehensive and fully quantitative fashion.
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Affiliation(s)
- Yangyang Bian
- The College of Life Science, Northwest University, Xi'an, P.R. China
| | - Chunli Gao
- The College of Life Science, Northwest University, Xi'an, P.R. China
| | - Bernhard Kuster
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
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14
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Zhang Y, Cai X, Ge W, Wang D, Zhu G, Qian L, Xiang N, Yue L, Liang S, Zhang F, Wang J, Zhou K, Zheng Y, Lin M, Sun T, Lu R, Zhang C, Xu L, Sun Y, Zhou X, Yu J, Lyu M, Shen B, Zhu H, Xu J, Zhu Y, Guo T. Potential Use of Serum Proteomics for Monitoring COVID-19 Progression to Complement RT-PCR Detection. J Proteome Res 2022; 21:90-100. [PMID: 34783559 PMCID: PMC8610005 DOI: 10.1021/acs.jproteome.1c00525] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Indexed: 12/18/2022]
Abstract
RT-PCR is the primary method to diagnose COVID-19 and is also used to monitor the disease course. This approach, however, suffers from false negatives due to RNA instability and poses a high risk to medical practitioners. Here, we investigated the potential of using serum proteomics to predict viral nucleic acid positivity during COVID-19. We analyzed the proteome of 275 inactivated serum samples from 54 out of 144 COVID-19 patients and shortlisted 42 regulated proteins in the severe group and 12 in the non-severe group. Using these regulated proteins and several key clinical indexes, including days after symptoms onset, platelet counts, and magnesium, we developed two machine learning models to predict nucleic acid positivity, with an AUC of 0.94 in severe cases and 0.89 in non-severe cases, respectively. Our data suggest the potential of using a serum protein-based machine learning model to monitor COVID-19 progression, thus complementing swab RT-PCR tests. More efforts are required to promote this approach into clinical practice since mass spectrometry-based protein measurement is not currently widely accessible in clinic.
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Affiliation(s)
- Ying Zhang
- Taizhou Hospital of Zhejiang Province
Affiliated to Wenzhou Medical University, Linhai, Zhejiang 317000,
China
| | - Xue Cai
- Key Laboratory of Structural Biology of Zhejiang
Province, School of Life Sciences, Westlake University, Xihu
District, Hangzhou, Zhejiang 310000, China
- Center for Infectious Disease Research,
Westlake Laboratory of Life Sciences and Biomedicine, Xihu
District, Hangzhou, Zhejiang 310000, China
- Institute of Basic Medical Sciences,
Westlake Institute for Advanced Study, Xihu District,
Hangzhou, Zhejiang 310000, China
| | - Weigang Ge
- Key Laboratory of Structural Biology of Zhejiang
Province, School of Life Sciences, Westlake University, Xihu
District, Hangzhou, Zhejiang 310000, China
- Center for Infectious Disease Research,
Westlake Laboratory of Life Sciences and Biomedicine, Xihu
District, Hangzhou, Zhejiang 310000, China
- Institute of Basic Medical Sciences,
Westlake Institute for Advanced Study, Xihu District,
Hangzhou, Zhejiang 310000, China
- Westlake Omics (Hangzhou) Biotechnology
Co., Ltd., No.1, Yunmeng Road, Cloud Town, Xihu District, Hangzhou,
Zhejiang 310000, China
| | - Donglian Wang
- Taizhou Hospital of Zhejiang Province
Affiliated to Wenzhou Medical University, Linhai, Zhejiang 317000,
China
| | - Guangjun Zhu
- Taizhou Hospital of Zhejiang Province
Affiliated to Wenzhou Medical University, Linhai, Zhejiang 317000,
China
| | - Liujia Qian
- Key Laboratory of Structural Biology of Zhejiang
Province, School of Life Sciences, Westlake University, Xihu
District, Hangzhou, Zhejiang 310000, China
- Center for Infectious Disease Research,
Westlake Laboratory of Life Sciences and Biomedicine, Xihu
District, Hangzhou, Zhejiang 310000, China
- Institute of Basic Medical Sciences,
Westlake Institute for Advanced Study, Xihu District,
Hangzhou, Zhejiang 310000, China
| | - Nan Xiang
- Key Laboratory of Structural Biology of Zhejiang
Province, School of Life Sciences, Westlake University, Xihu
District, Hangzhou, Zhejiang 310000, China
- Center for Infectious Disease Research,
Westlake Laboratory of Life Sciences and Biomedicine, Xihu
District, Hangzhou, Zhejiang 310000, China
- Institute of Basic Medical Sciences,
Westlake Institute for Advanced Study, Xihu District,
Hangzhou, Zhejiang 310000, China
- Westlake Omics (Hangzhou) Biotechnology
Co., Ltd., No.1, Yunmeng Road, Cloud Town, Xihu District, Hangzhou,
Zhejiang 310000, China
| | - Liang Yue
- Key Laboratory of Structural Biology of Zhejiang
Province, School of Life Sciences, Westlake University, Xihu
District, Hangzhou, Zhejiang 310000, China
- Center for Infectious Disease Research,
Westlake Laboratory of Life Sciences and Biomedicine, Xihu
District, Hangzhou, Zhejiang 310000, China
- Institute of Basic Medical Sciences,
Westlake Institute for Advanced Study, Xihu District,
Hangzhou, Zhejiang 310000, China
| | - Shuang Liang
- Key Laboratory of Structural Biology of Zhejiang
Province, School of Life Sciences, Westlake University, Xihu
District, Hangzhou, Zhejiang 310000, China
- Center for Infectious Disease Research,
Westlake Laboratory of Life Sciences and Biomedicine, Xihu
District, Hangzhou, Zhejiang 310000, China
- Institute of Basic Medical Sciences,
Westlake Institute for Advanced Study, Xihu District,
Hangzhou, Zhejiang 310000, China
| | - Fangfei Zhang
- Key Laboratory of Structural Biology of Zhejiang
Province, School of Life Sciences, Westlake University, Xihu
District, Hangzhou, Zhejiang 310000, China
- Center for Infectious Disease Research,
Westlake Laboratory of Life Sciences and Biomedicine, Xihu
District, Hangzhou, Zhejiang 310000, China
- Institute of Basic Medical Sciences,
Westlake Institute for Advanced Study, Xihu District,
Hangzhou, Zhejiang 310000, China
| | - Jing Wang
- Taizhou Hospital of Zhejiang Province
Affiliated to Wenzhou Medical University, Linhai, Zhejiang 317000,
China
| | - Kai Zhou
- Taizhou Hospital of Zhejiang Province
Affiliated to Wenzhou Medical University, Linhai, Zhejiang 317000,
China
| | - Yufen Zheng
- Taizhou Hospital of Zhejiang Province
Affiliated to Wenzhou Medical University, Linhai, Zhejiang 317000,
China
| | - Minjie Lin
- Taizhou Hospital of Zhejiang Province
Affiliated to Wenzhou Medical University, Linhai, Zhejiang 317000,
China
| | - Tong Sun
- Taizhou Hospital of Zhejiang Province
Affiliated to Wenzhou Medical University, Linhai, Zhejiang 317000,
China
| | - Ruyue Lu
- Taizhou Hospital of Zhejiang Province
Affiliated to Wenzhou Medical University, Linhai, Zhejiang 317000,
China
| | - Chao Zhang
- Taizhou Hospital of Zhejiang Province
Affiliated to Wenzhou Medical University, Linhai, Zhejiang 317000,
China
| | - Luang Xu
- Key Laboratory of Structural Biology of Zhejiang
Province, School of Life Sciences, Westlake University, Xihu
District, Hangzhou, Zhejiang 310000, China
- Center for Infectious Disease Research,
Westlake Laboratory of Life Sciences and Biomedicine, Xihu
District, Hangzhou, Zhejiang 310000, China
- Institute of Basic Medical Sciences,
Westlake Institute for Advanced Study, Xihu District,
Hangzhou, Zhejiang 310000, China
| | - Yaoting Sun
- Key Laboratory of Structural Biology of Zhejiang
Province, School of Life Sciences, Westlake University, Xihu
District, Hangzhou, Zhejiang 310000, China
- Center for Infectious Disease Research,
Westlake Laboratory of Life Sciences and Biomedicine, Xihu
District, Hangzhou, Zhejiang 310000, China
- Institute of Basic Medical Sciences,
Westlake Institute for Advanced Study, Xihu District,
Hangzhou, Zhejiang 310000, China
| | - Xiaoxu Zhou
- Key Laboratory of Structural Biology of Zhejiang
Province, School of Life Sciences, Westlake University, Xihu
District, Hangzhou, Zhejiang 310000, China
- Institute of Basic Medical Sciences,
Westlake Institute for Advanced Study, Xihu District,
Hangzhou, Zhejiang 310000, China
| | - Jing Yu
- Key Laboratory of Structural Biology of Zhejiang
Province, School of Life Sciences, Westlake University, Xihu
District, Hangzhou, Zhejiang 310000, China
- Center for Infectious Disease Research,
Westlake Laboratory of Life Sciences and Biomedicine, Xihu
District, Hangzhou, Zhejiang 310000, China
- Institute of Basic Medical Sciences,
Westlake Institute for Advanced Study, Xihu District,
Hangzhou, Zhejiang 310000, China
| | - Mengge Lyu
- Key Laboratory of Structural Biology of Zhejiang
Province, School of Life Sciences, Westlake University, Xihu
District, Hangzhou, Zhejiang 310000, China
- Center for Infectious Disease Research,
Westlake Laboratory of Life Sciences and Biomedicine, Xihu
District, Hangzhou, Zhejiang 310000, China
- Institute of Basic Medical Sciences,
Westlake Institute for Advanced Study, Xihu District,
Hangzhou, Zhejiang 310000, China
| | - Bo Shen
- Taizhou Hospital of Zhejiang Province
Affiliated to Wenzhou Medical University, Linhai, Zhejiang 317000,
China
| | - Hongguo Zhu
- Taizhou Hospital of Zhejiang Province
Affiliated to Wenzhou Medical University, Linhai, Zhejiang 317000,
China
| | - Jiaqin Xu
- Taizhou Hospital of Zhejiang Province
Affiliated to Wenzhou Medical University, Linhai, Zhejiang 317000,
China
| | - Yi Zhu
- Key Laboratory of Structural Biology of Zhejiang
Province, School of Life Sciences, Westlake University, Xihu
District, Hangzhou, Zhejiang 310000, China
- Center for Infectious Disease Research,
Westlake Laboratory of Life Sciences and Biomedicine, Xihu
District, Hangzhou, Zhejiang 310000, China
- Institute of Basic Medical Sciences,
Westlake Institute for Advanced Study, Xihu District,
Hangzhou, Zhejiang 310000, China
| | - Tiannan Guo
- Key Laboratory of Structural Biology of Zhejiang
Province, School of Life Sciences, Westlake University, Xihu
District, Hangzhou, Zhejiang 310000, China
- Center for Infectious Disease Research,
Westlake Laboratory of Life Sciences and Biomedicine, Xihu
District, Hangzhou, Zhejiang 310000, China
- Institute of Basic Medical Sciences,
Westlake Institute for Advanced Study, Xihu District,
Hangzhou, Zhejiang 310000, China
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15
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Sun R, Lyu M, Liang S, Ge W, Wang Y, Ding X, Zhang C, Zhou Y, Chen S, Chen L, Guo T. A prostate cancer tissue specific spectral library for targeted proteomic analysis. Proteomics 2021; 22:e2100147. [PMID: 34799972 DOI: 10.1002/pmic.202100147] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 10/19/2021] [Accepted: 11/03/2021] [Indexed: 11/08/2022]
Abstract
Prostate cancer is the most common cancer in males worldwide. Mass spectrometry-based targeted proteomics has demonstrated great potential in quantifying proteins from formalin-fixed paraffin-embedded (FFPE) and (fresh) frozen biopsy tissues. Here we provide a comprehensive tissue-specific spectral library for targeted proteomic analysis of prostate tissue samples. Benign and malignant FFPE prostate tissue samples were processed into peptide samples by pressure cycling technology (PCT)-assisted sample preparation, and fractionated with high-pH reversed phase liquid chromatography (RPLC). Based on data-dependent acquisition (DDA) MS analysis using a TripleTOF 6600, we built a library containing 108,533 precursors, 84,198 peptides and 9384 unique proteins (1% FDR). The applicability of the library was demonstrated in prostate specimens.
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Affiliation(s)
- Rui Sun
- Zhejiang University, Hangzhou, Zhejiang, China.,Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Mengge Lyu
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Shuang Liang
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Weigang Ge
- Westlake Omics (Hangzhou) Biotechnology Co., Ltd., Hangzhou, China
| | - Yingrui Wang
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Xuan Ding
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Cheng Zhang
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Yan Zhou
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Shanjun Chen
- Westlake Omics (Hangzhou) Biotechnology Co., Ltd., Hangzhou, China
| | - Lirong Chen
- Department of Pathology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Tiannan Guo
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
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16
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Xiao Q, Zhang F, Xu L, Yue L, Kon OL, Zhu Y, Guo T. High-throughput proteomics and AI for cancer biomarker discovery. Adv Drug Deliv Rev 2021; 176:113844. [PMID: 34182017 DOI: 10.1016/j.addr.2021.113844] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 06/13/2021] [Accepted: 06/15/2021] [Indexed: 02/08/2023]
Abstract
Biomarkers are assayed to assess biological and pathological status. Recent advances in high-throughput proteomic technology provide opportunities for developing next generation biomarkers for clinical practice aided by artificial intelligence (AI) based techniques. We summarize the advances and limitations of cancer biomarkers based on genomic and transcriptomic analysis, as well as classical antibody-based methodologies. Then we review recent progresses in mass spectrometry (MS)-based proteomics in terms of sample preparation, peptide fractionation by liquid chromatography (LC) and mass spectrometric data acquisition. We highlight applications of AI techniques in high-throughput clinical studies as compared with clinical decisions based on singular features. This review sets out our approach for discovering clinical biomarkers in studies using proteomic big data technology conjoined with computational and statistical methods.
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17
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Lewandowska AE, Fel A, Thiel M, Czaplewska P, Łukaszuk K, Wiśniewski JR, Ołdziej S. Compatibility of Distinct Label-Free Proteomic Workflows in Absolute Quantification of Proteins Linked to the Oocyte Quality in Human Follicular Fluid. Int J Mol Sci 2021; 22:7415. [PMID: 34299044 PMCID: PMC8304916 DOI: 10.3390/ijms22147415] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 07/07/2021] [Accepted: 07/08/2021] [Indexed: 01/02/2023] Open
Abstract
We present two separate label-free quantitative workflows based on different high-resolution mass spectrometers and LC setups, which are termed after the utilized instrument: Quad-Orbitrap (nano-LC) and Triple Quad-TOF (micro-LC) and their directed adaptation toward the analysis of human follicular fluid proteome. We identified about 1000 proteins in each distinct workflow using various sample preparation methods. With assistance of the Total Protein Approach, we were able to obtain absolute protein concentrations for each workflow. In a pilot study of twenty samples linked to diverse oocyte quality status from four donors, 455 and 215 proteins were quantified by the Quad-Orbitrap and Triple Quad-TOF workflows, respectively. The concentration values obtained from both workflows correlated to a significant degree. We found reasonable agreement of both workflows in protein fold changes between tested groups, resulting in unified lists of 20 and 22 proteins linked to oocyte maturity and blastocyst development, respectively. The Quad-Orbitrap workflow was best suited for an in-depth analysis without the need of extensive fractionation, especially of low abundant proteome, whereas the Triple Quad-TOF workflow allowed a more robust approach with a greater potential to increase in effectiveness with the growing number of analyzed samples after the initial effort of building a comprehensive spectral library.
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Affiliation(s)
- Aleksandra E. Lewandowska
- Intercollegiate Faculty of Biotechnology UG&MUG, University of Gdańsk, Abrahama 58, 80-307 Gdańsk, Poland; (A.F.); (M.T.); (P.C.)
| | - Anna Fel
- Intercollegiate Faculty of Biotechnology UG&MUG, University of Gdańsk, Abrahama 58, 80-307 Gdańsk, Poland; (A.F.); (M.T.); (P.C.)
| | - Marcel Thiel
- Intercollegiate Faculty of Biotechnology UG&MUG, University of Gdańsk, Abrahama 58, 80-307 Gdańsk, Poland; (A.F.); (M.T.); (P.C.)
| | - Paulina Czaplewska
- Intercollegiate Faculty of Biotechnology UG&MUG, University of Gdańsk, Abrahama 58, 80-307 Gdańsk, Poland; (A.F.); (M.T.); (P.C.)
| | - Krzysztof Łukaszuk
- INVICTA Fertility and Reproductive Center, Polna 64, 81-740 Sopot, Poland;
- Department of Obstetrics and Gynecological Nursing, Faculty of Health Sciences, Medical University of Gdańsk, Dębinki 7, 80-211 Gdańsk, Poland
| | - Jacek R. Wiśniewski
- Department of Proteomics and Signal Transduction, Max-Planck-Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany;
| | - Stanisław Ołdziej
- Intercollegiate Faculty of Biotechnology UG&MUG, University of Gdańsk, Abrahama 58, 80-307 Gdańsk, Poland; (A.F.); (M.T.); (P.C.)
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18
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Tonry C, Finn S, Armstrong J, Pennington SR. Clinical proteomics for prostate cancer: understanding prostate cancer pathology and protein biomarkers for improved disease management. Clin Proteomics 2020; 17:41. [PMID: 33292167 PMCID: PMC7678104 DOI: 10.1186/s12014-020-09305-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 11/11/2020] [Indexed: 12/12/2022] Open
Abstract
Following the introduction of routine Prostate Specific Antigen (PSA) screening in the early 1990′s, Prostate Cancer (PCa) is often detected at an early stage. There are also a growing number of treatment options available and so the associated mortality rate is generally low. However, PCa is an extremely complex and heterogenous disease and many patients suffer disease recurrence following initial therapy. Disease recurrence commonly results in metastasis and metastatic PCa has an average survival rate of just 3–5 years. A significant problem in the clinical management of PCa is being able to differentiate between patients who will respond to standard therapies and those who may benefit from more aggressive intervention at an earlier stage. It is also acknowledged that for many men the disease is not life threatenting. Hence, there is a growing desire to identify patients who can be spared the significant side effects associated with PCa treatment until such time (if ever) their disease progresses to the point where treatment is required. To these important clinical needs, current biomarkers and clinical methods for patient stratification and personlised treatment are insufficient. This review provides a comprehensive overview of the complexities of PCa pathology and disease management. In this context it is possible to review current biomarkers and proteomic technologies that will support development of biomarker-driven decision tools to meet current important clinical needs. With such an in-depth understanding of disease pathology, the development of novel clinical biomarkers can proceed in an efficient and effective manner, such that they have a better chance of improving patient outcomes.
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Affiliation(s)
- Claire Tonry
- UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Stephen Finn
- Department of Histopathology and Morbid Anatomy, Trinity Translational Medicine Institute, Trinity College Dublin, Dublin 8, Ireland
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19
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Midha MK, Kusebauch U, Shteynberg D, Kapil C, Bader SL, Reddy PJ, Campbell DS, Baliga NS, Moritz RL. A comprehensive spectral assay library to quantify the Escherichia coli proteome by DIA/SWATH-MS. Sci Data 2020; 7:389. [PMID: 33184295 PMCID: PMC7665006 DOI: 10.1038/s41597-020-00724-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 10/05/2020] [Indexed: 02/06/2023] Open
Abstract
Data-Independent Acquisition (DIA) is a method to improve consistent identification and precise quantitation of peptides and proteins by mass spectrometry (MS). The targeted data analysis strategy in DIA relies on spectral assay libraries that are generally derived from a priori measurements of peptides for each species. Although Escherichia coli (E. coli) is among the best studied model organisms, so far there is no spectral assay library for the bacterium publicly available. Here, we generated a spectral assay library for 4,014 of the 4,389 annotated E. coli proteins using one- and two-dimensional fractionated samples, and ion mobility separation enabling deep proteome coverage. We demonstrate the utility of this high-quality library with robustness in quantitation of the E. coli proteome and with rapid-chromatography to enhance throughput by targeted DIA-MS. The spectral assay library supports the detection and quantification of 91.5% of all E. coli proteins at high-confidence with 56,182 proteotypic peptides, making it a valuable resource for the scientific community. Data and spectral libraries are available via ProteomeXchange (PXD020761, PXD020785) and SWATHAtlas (SAL00222-28).
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Affiliation(s)
- Mukul K Midha
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
| | - Ulrike Kusebauch
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
| | - David Shteynberg
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
| | - Charu Kapil
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
| | - Samuel L Bader
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
| | | | - David S Campbell
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
| | - Nitin S Baliga
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
- Departments of Biology and Microbiology, University of Washington, Seattle, WA, USA
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
- Lawrence Berkeley National Lab, Berkeley, CA, USA
| | - Robert L Moritz
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA.
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20
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Yue L, Zhang F, Sun R, Sun Y, Yuan C, Zhu Y, Guo T. Generating Proteomic Big Data for Precision Medicine. Proteomics 2020; 20:e1900358. [PMID: 32725921 DOI: 10.1002/pmic.201900358] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 07/13/2020] [Indexed: 12/23/2022]
Abstract
Here, the authors reason that the complexity of medical problems and proteome science might be tackled effectively with deep learning (DL) technology. However, deployment of DL for proteomics data requires the acquisition of data sets from a large number of samples. Based on the success of DL in medical imaging classification, proteome data from thousands of samples are arguably the minimal input for DL. Contemporary proteomics is turning high-throughput thanks to the rapid progresses of sample preparation and liquid chromatography mass spectrometry methods. In particular, data-independent acquisition now enables the generation of hundreds to thousands of quantitative proteome maps from clinical specimens in clinical cohorts with only limited sample amounts in clinical cohorts. Upheavals in the design of large-scale clinical proteomics studies might be required to generate proteomic big data and deploy DL to tackle complex medical problems.
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Affiliation(s)
- Liang Yue
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang Province, 310024, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, Zhejiang Province, 310024, China
| | - Fangfei Zhang
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang Province, 310024, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, Zhejiang Province, 310024, China
| | - Rui Sun
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang Province, 310024, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, Zhejiang Province, 310024, China
| | - Yaoting Sun
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang Province, 310024, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, Zhejiang Province, 310024, China
| | - Chunhui Yuan
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang Province, 310024, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, Zhejiang Province, 310024, China
| | - Yi Zhu
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang Province, 310024, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, Zhejiang Province, 310024, China
| | - Tiannan Guo
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang Province, 310024, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, Zhejiang Province, 310024, China
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21
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Gao H, Zhang F, Liang S, Zhang Q, Lyu M, Qian L, Liu W, Ge W, Chen C, Yi X, Zhu J, Lu C, Sun P, Liu K, Zhu Y, Guo T. Accelerated Lysis and Proteolytic Digestion of Biopsy-Level Fresh-Frozen and FFPE Tissue Samples Using Pressure Cycling Technology. J Proteome Res 2020; 19:1982-1990. [PMID: 32182071 DOI: 10.1021/acs.jproteome.9b00790] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Pressure cycling technology (PCT)-assisted tissue lysis and digestion have facilitated reproducible and high-throughput proteomic studies of both fresh-frozen (FF) and formalin-fixed paraffin-embedded (FFPE) tissue of biopsy scale for biomarker discovery. Here, we present an improved PCT method accelerating the conventional procedures by about two-fold without sacrificing peptide yield, digestion efficiency, peptide, and protein identification. The time required for processing 16 tissue samples from tissues to peptides is reduced from about 6 to about 3 h. We analyzed peptides prepared from FFPE hepatocellular carcinoma (HCC) tissue samples by the accelerated PCT method using multiple MS acquisition methods, including short-gradient SWATH-MS, PulseDIA-MS, and 10-plex TMT-based shotgun MS. The data showed that up to 8541 protein groups could be reliably quantified from the thus prepared peptide samples. We applied the accelerated sample preparation method to 25 pairs (tumorous and matched benign) of HCC samples followed by a single-shot, 15 min gradient SWATH-MS analysis. An average of 18 453 peptides from 2822 proteins were quantified in at least 20% samples in this cohort, while 1817 proteins were quantified in at least 50% samples. The data not only identified the previously known dysregulated proteins such as MCM7, MAPRE1, and SSRP1 but also discovered promising novel protein markers, including DRAP1 and PRMT5. In summary, we present an accelerated PCT protocol that effectively doubles the throughput of PCT-assisted sample preparation of biopsy-level FF and FFPE samples without compromising protein digestion efficiency, peptide yield, and protein identification.
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Affiliation(s)
- Huanhuan Gao
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China
| | - Fangfei Zhang
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China
| | - Shuang Liang
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China
| | - Qiushi Zhang
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China
| | - Mengge Lyu
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China
| | - Liujia Qian
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China
| | - Wei Liu
- Department of Clinical Pharmacology, College of Pharmacy, Dalian Medical University, Dalian 200335, Liaoning, China
| | - Weigang Ge
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China
| | | | - Xiao Yi
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China
| | - Jiang Zhu
- Center for Stem Cell Research and Application, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, Hubei, China
| | - Cong Lu
- Center for Stem Cell Research and Application, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, Hubei, China
| | - Ping Sun
- Department of Hepatobiliary Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, Hubei, China
| | - Kexin Liu
- Department of Clinical Pharmacology, College of Pharmacy, Dalian Medical University, Dalian 200335, Liaoning, China
| | - Yi Zhu
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China
| | - Tiannan Guo
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China
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22
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Fang X, Liu X, Weng C, Wu Y, Li B, Mao H, Guan M, Lu L, Liu G. Construction and Validation of a Protein Prognostic Model for Lung Squamous Cell Carcinoma. Int J Med Sci 2020; 17:2718-2727. [PMID: 33162799 PMCID: PMC7645351 DOI: 10.7150/ijms.47224] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 09/14/2020] [Indexed: 12/13/2022] Open
Abstract
Lung squamous cell carcinoma (LUSCC), as the major type of lung cancer, has high morbidity and mortality rates. The prognostic markers for LUSCC are much fewer than lung adenocarcinoma. Besides, protein biomarkers have advantages of economy, accuracy and stability. The aim of this study was to construct a protein prognostic model for LUSCC. The protein expression data of LUSCC were downloaded from The Cancer Protein Atlas (TCPA) database. Clinical data of LUSCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. A total of 237 proteins were identified from 325 cases of LUSCC patients based on the TCPA and TCGA database. According to Kaplan-Meier survival analysis, univariate and multivariate Cox analysis, a prognostic prediction model was established which was consisted of 6 proteins (CHK1_pS345, CHK2, IRS1, PAXILLIN, BRCA2 and BRAF_pS445). After calculating the risk values of each patient according to the coefficient of each protein in the risk model, the LUSCC patients were divided into high risk group and low risk group. The survival analysis demonstrated that there was significant difference between these two groups (p= 4.877e-05). The area under the curve (AUC) value of the receiver operating characteristic (ROC) curve was 0.699, which suggesting that the prognostic risk model could effectively predict the survival of LUSCC patients. Univariate and multivariate analysis indicated that this prognostic model could be used as independent prognosis factors for LUSCC patients. Proteins co-expression analysis showed that there were 21 proteins co-expressed with the proteins in the risk model. In conclusion, our study constructed a protein prognostic model, which could effectively predict the prognosis of LUSCC patients.
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Affiliation(s)
- Xisheng Fang
- Department of Medical Oncology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China, 510180.,Department of Medical Oncology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China, 510180
| | - Xia Liu
- Department of Medical Oncology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China, 510180.,Department of Medical Oncology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China, 510180
| | - Chengyin Weng
- Department of Medical Oncology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China, 510180.,Department of Medical Oncology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China, 510180
| | - Yong Wu
- Department of Medical Oncology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China, 510180.,Department of Medical Oncology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China, 510180
| | - Baoxiu Li
- Department of Medical Oncology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China, 510180.,Department of Medical Oncology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China, 510180
| | - Haibo Mao
- Department of Medical Oncology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China, 510180.,Department of Medical Oncology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China, 510180
| | - Mingmei Guan
- Department of Medical Oncology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China, 510180.,Department of Medical Oncology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China, 510180
| | - Lin Lu
- Department of Medical Oncology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China, 510180.,Department of Medical Oncology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China, 510180
| | - Guolong Liu
- Department of Medical Oncology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China, 510180.,Department of Medical Oncology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China, 510180
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