1
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Chen S, Chen K, Lin Y, Wang S, Yu H, Chang C, Cheng T, Hsieh C, Li J, Lai H, Chen D, Huang C. Ganoderic acid T, a Ganoderma triterpenoid, modulates the tumor microenvironment and enhances the chemotherapy and immunotherapy efficacy through downregulating galectin-1 levels. Toxicol Appl Pharmacol 2024; 491:117069. [PMID: 39142358 DOI: 10.1016/j.taap.2024.117069] [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: 05/14/2024] [Revised: 07/29/2024] [Accepted: 08/10/2024] [Indexed: 08/16/2024]
Abstract
Ganoderic acid T (GAT), a triterpenoid molecule of Ganoderma lucidum, exhibits anti-cancer activity; however, the underlying mechanisms remain unclear. Therefore, in this study, we aimed to investigate the anti-cancer molecular mechanisms of GAT and explore its therapeutic applications for cancer treatment. GAT exhibited potent anti-cancer activity in an ES-2 orthotopic ovarian cancer model in a humanized mouse model, leading to significant alterations in the tumor microenvironment (TME). Specifically, GAT reduced the proportion of α-SMA+ cells and enhanced the infiltration of tumor-infiltrating lymphocytes (TILs) in tumor tissues. After conducting proteomic analysis, it was revealed that GAT downregulates galectin-1 (Gal-1), a key molecule in the TME. This downregulation has been confirmed in multiple cancer cell lines and xenograft tumors. Molecular docking suggested a theoretical direct interaction between GAT and Gal-1. Further research revealed that GAT induces ubiquitination of Gal-1. Moreover, GAT significantly augmented the anti-cancer effects of paclitaxel, thereby increasing intratumoral drug concentrations and reducing tumor size. Combined with immunotherapy, GAT enhanced the tumor-suppressive effects of the anti-programmed death-ligand 1 antibody and increased the proportion of CD8+ cells in the EMT6 syngeneic mammary cancer model. In conclusion, GAT inhibited tumor growth, downregulated Gal-1, modulated the TME, and promoted chemotherapy and immunotherapy efficacy. Our findings highlight the potential of GAT as an effective therapeutic agent for cancer.
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Affiliation(s)
- Suyu Chen
- Trineo Biotechnology Co., Ltd, 20F, No.81, Sec.1, Xintai 5th Rd, Xizhi Dist., New Taipei City 221, Taiwan
| | - Kuangdee Chen
- Trineo Biotechnology Co., Ltd, 20F, No.81, Sec.1, Xintai 5th Rd, Xizhi Dist., New Taipei City 221, Taiwan
| | - Yihsiu Lin
- Trineo Biotechnology Co., Ltd, 20F, No.81, Sec.1, Xintai 5th Rd, Xizhi Dist., New Taipei City 221, Taiwan
| | - Ssuchia Wang
- Trineo Biotechnology Co., Ltd, 20F, No.81, Sec.1, Xintai 5th Rd, Xizhi Dist., New Taipei City 221, Taiwan
| | - Huichuan Yu
- Trineo Biotechnology Co., Ltd, 20F, No.81, Sec.1, Xintai 5th Rd, Xizhi Dist., New Taipei City 221, Taiwan
| | - Chaohsuan Chang
- Trineo Biotechnology Co., Ltd, 20F, No.81, Sec.1, Xintai 5th Rd, Xizhi Dist., New Taipei City 221, Taiwan
| | - Tingchun Cheng
- Trineo Biotechnology Co., Ltd, 20F, No.81, Sec.1, Xintai 5th Rd, Xizhi Dist., New Taipei City 221, Taiwan
| | - Chiaoyun Hsieh
- Trineo Biotechnology Co., Ltd, 20F, No.81, Sec.1, Xintai 5th Rd, Xizhi Dist., New Taipei City 221, Taiwan
| | - Jiayi Li
- Trineo Biotechnology Co., Ltd, 20F, No.81, Sec.1, Xintai 5th Rd, Xizhi Dist., New Taipei City 221, Taiwan
| | - Hsiaohsuan Lai
- Trineo Biotechnology Co., Ltd, 20F, No.81, Sec.1, Xintai 5th Rd, Xizhi Dist., New Taipei City 221, Taiwan
| | - Denghai Chen
- Trineo Biotechnology Co., Ltd, 20F, No.81, Sec.1, Xintai 5th Rd, Xizhi Dist., New Taipei City 221, Taiwan.
| | - Chengpo Huang
- Trineo Biotechnology Co., Ltd, 20F, No.81, Sec.1, Xintai 5th Rd, Xizhi Dist., New Taipei City 221, Taiwan.
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2
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Liu Q, Zhu H, Fang Z, Dong M, Qin H, Ye M. GP-Marker facilitates the analysis of intact glycopeptide quantitative data at different levels. Anal Bioanal Chem 2024:10.1007/s00216-024-05499-z. [PMID: 39207492 DOI: 10.1007/s00216-024-05499-z] [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: 05/29/2024] [Revised: 07/24/2024] [Accepted: 08/13/2024] [Indexed: 09/04/2024]
Abstract
Protein glycosylation is a highly heterogeneous post-translational modification that has been demonstrated to exhibit significant variations in various diseases. Due to the differential patterns observed in disease and healthy populations, the glycosylated proteins hold promise as early indicators for multiple diseases. With the continuous development of liquid chromatography-mass spectrometry (LC-MS) technology and spectrum analysis software, the sensitivity for the decipher of the tandem mass spectra of the glycopeptides carrying intact glycans, i.e., intact glycopeptides, enzymatic hydrolyzed from glycoproteins has been significantly improved. From quantified intact glycopeptides, the difference of protein glycosylation at multiple levels, e.g., glycoprotein, glycan, glycosite, and site-specific glycans, could be obtained for different samples. However, the manual analysis of the intact glycopeptide quantitative data at multiple levels is tedious and time consuming. In this study, we have developed a software tool named "GP-Marker" to facilitate large-scale data mining of spectra dataset of intact N-glycopeptide at multiple levels. This software provides a user-friendly and interactive interface, offering operational tools for machine learning to researchers without programming backgrounds. It includes a range of visualization plots displaying differential glycosylation and provides the ability to extract multi-level data analysis from intact glycopeptide data quantified by Glyco-Decipher.
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Affiliation(s)
- Qi Liu
- State Key Laboratory of Medical Proteomics, National Chromatographic R. & A. Center, CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - He Zhu
- State Key Laboratory of Medical Proteomics, National Chromatographic R. & A. Center, CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zheng Fang
- State Key Laboratory of Medical Proteomics, National Chromatographic R. & A. Center, CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Mingming Dong
- School of Bioengineering, Dalian University of Technology, Dalian, 116024, China
| | - Hongqiang Qin
- State Key Laboratory of Medical Proteomics, National Chromatographic R. & A. Center, CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Mingliang Ye
- State Key Laboratory of Medical Proteomics, National Chromatographic R. & A. Center, CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
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3
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Wang Z, Liu PK, Li L. A Tutorial Review of Labeling Methods in Mass Spectrometry-Based Quantitative Proteomics. ACS MEASUREMENT SCIENCE AU 2024; 4:315-337. [PMID: 39184361 PMCID: PMC11342459 DOI: 10.1021/acsmeasuresciau.4c00007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 04/01/2024] [Accepted: 04/03/2024] [Indexed: 08/27/2024]
Abstract
Recent advancements in mass spectrometry (MS) have revolutionized quantitative proteomics, with multiplex isotope labeling emerging as a key strategy for enhancing accuracy, precision, and throughput. This tutorial review offers a comprehensive overview of multiplex isotope labeling techniques, including precursor-based, mass defect-based, reporter ion-based, and hybrid labeling methods. It details their fundamental principles, advantages, and inherent limitations along with strategies to mitigate the limitation of ratio-distortion. This review will also cover the applications and latest progress in these labeling techniques across various domains, including cancer biomarker discovery, neuroproteomics, post-translational modification analysis, cross-linking MS, and single-cell proteomics. This Review aims to provide guidance for researchers on selecting appropriate methods for their specific goals while also highlighting the potential future directions in this rapidly evolving field.
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Affiliation(s)
- Zicong Wang
- School
of Pharmacy, University of Wisconsin—Madison, Madison, Wisconsin 53705, United States
| | - Peng-Kai Liu
- Biophysics
Graduate program, University of Wisconsin—Madison, Madison, Wisconsin 53705, United States
| | - Lingjun Li
- School
of Pharmacy, University of Wisconsin—Madison, Madison, Wisconsin 53705, United States
- Biophysics
Graduate program, University of Wisconsin—Madison, Madison, Wisconsin 53705, United States
- Department
of Chemistry, University of Wisconsin—Madison, Madison, Wisconsin 53706, United States
- Lachman
Institute for Pharmaceutical Development, School of Pharmacy, University of Wisconsin—Madison, Madison, Wisconsin 53705, United States
- Wisconsin
Center for NanoBioSystems, School of Pharmacy, University of Wisconsin—Madison, Madison, Wisconsin 53705, United States
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4
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Leduc A, Khoury L, Cantlon J, Khan S, Slavov N. Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP. Nat Protoc 2024:10.1038/s41596-024-01033-8. [PMID: 39117766 DOI: 10.1038/s41596-024-01033-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 05/27/2024] [Indexed: 08/10/2024]
Abstract
Single-cell proteomics by mass spectrometry (MS) allows the quantification of proteins with high specificity and sensitivity. To increase its throughput, we developed nano-proteomic sample preparation (nPOP), a method for parallel preparation of thousands of single cells in nanoliter-volume droplets deposited on glass slides. Here, we describe its protocol with emphasis on its flexibility to prepare samples for different multiplexed MS methods. An implementation using the plexDIA MS multiplexing method, which uses non-isobaric mass tags to barcode peptides from different samples for data-independent acquisition, demonstrates accurate quantification of ~3,000-3,700 proteins per human cell. A separate implementation with isobaric mass tags and prioritized data acquisition demonstrates analysis of 1,827 single cells at a rate of >1,000 single cells per day at a depth of 800-1,200 proteins per human cell. The protocol is implemented by using a cell-dispensing and liquid-handling robot-the CellenONE instrument-and uses readily available consumables, which should facilitate broad adoption. nPOP can be applied to all samples that can be processed to a single-cell suspension. It takes 1 or 2 d to prepare >3,000 single cells. We provide metrics and software (the QuantQC R package) for quality control and data exploration. QuantQC supports the robust scaling of nPOP to higher plex reagents for achieving reliable and scalable single-cell proteomics.
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Affiliation(s)
- Andrew Leduc
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA, USA.
| | - Luke Khoury
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA, USA
| | | | - Saad Khan
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA, USA
| | - Nikolai Slavov
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA, USA.
- Parallel Squared Technology Institute, Watertown, MA, USA.
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5
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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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6
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Fedorov II, Protasov SA, Tarasova IA, Gorshkov MV. Ultrafast Proteomics. BIOCHEMISTRY. BIOKHIMIIA 2024; 89:1349-1361. [PMID: 39245450 DOI: 10.1134/s0006297924080017] [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: 05/23/2024] [Revised: 06/21/2024] [Accepted: 06/24/2024] [Indexed: 09/10/2024]
Abstract
Current stage of proteomic research in the field of biology, medicine, development of new drugs, population screening, or personalized approaches to therapy dictates the need to analyze large sets of samples within the reasonable experimental time. Until recently, mass spectrometry measurements in proteomics were characterized as unique in identifying and quantifying cellular protein composition, but low throughput, requiring many hours to analyze a single sample. This was in conflict with the dynamics of changes in biological systems at the whole cellular proteome level upon the influence of external and internal factors. Thus, low speed of the whole proteome analysis has become the main factor limiting developments in functional proteomics, where it is necessary to annotate intracellular processes not only in a wide range of conditions, but also over a long period of time. Enormous level of heterogeneity of tissue cells or tumors, even of the same type, dictates the need to analyze biological systems at the level of individual cells. These studies involve obtaining molecular characteristics for tens, if not hundreds of thousands of individual cells, including their whole proteome profiles. Development of mass spectrometry technologies providing high resolution and mass measurement accuracy, predictive chromatography, new methods for peptide separation by ion mobility and processing of proteomic data based on artificial intelligence algorithms have opened a way for significant, if not radical, increase in the throughput of whole proteome analysis and led to implementation of the novel concept of ultrafast proteomics. Work done just in the last few years has demonstrated the proteome-wide analysis throughput of several hundred samples per day at a depth of several thousand proteins, levels unimaginable three or four years ago. The review examines background of these developments, as well as modern methods and approaches that implement ultrafast analysis of the entire proteome.
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Affiliation(s)
- Ivan I Fedorov
- Moscow Institute of Physics and Technology (National University), Dolgoprudny, Moscow Region, 141700, Russia
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, Moscow, 119334, Russia
| | - Sergey A Protasov
- Moscow Institute of Physics and Technology (National University), Dolgoprudny, Moscow Region, 141700, Russia
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, Moscow, 119334, Russia
| | - Irina A Tarasova
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, Moscow, 119334, Russia
| | - Mikhail V Gorshkov
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, Moscow, 119334, Russia.
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7
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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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8
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Chen Y, Gu M, Peng J, Li Y, Ren D. Capturing the phosphorylation-linked protein-complex landscape in plants. TRENDS IN PLANT SCIENCE 2024; 29:823-824. [PMID: 38862367 DOI: 10.1016/j.tplants.2024.05.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2024] [Revised: 05/24/2024] [Accepted: 05/24/2024] [Indexed: 06/13/2024]
Affiliation(s)
- Yanmei Chen
- State Key Laboratory of Plant Environmental Resilience, College of Biological Sciences, China Agricultural University, Beijing, 100193, China.
| | - Mingyang Gu
- State Key Laboratory of Plant Environmental Resilience, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Jing Peng
- State Key Laboratory of Plant Environmental Resilience, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Yuan Li
- State Key Laboratory of Plant Environmental Resilience, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Dongtao Ren
- State Key Laboratory of Plant Environmental Resilience, College of Biological Sciences, China Agricultural University, Beijing, 100193, China.
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9
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Van Puyvelde B, Hunter CL, Zhgamadze M, Savant S, Wang YO, Hoedt E, Raedschelders K, Pope M, Huynh CA, Ramanujan VK, Tourtellotte W, Razavi M, Anderson NL, Martens G, Deforce D, Fu Q, Dhaenens M, Van Eyk JE. Acoustic ejection mass spectrometry empowers ultra-fast protein biomarker quantification. Nat Commun 2024; 15:5114. [PMID: 38879593 PMCID: PMC11180209 DOI: 10.1038/s41467-024-48563-z] [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] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 05/07/2024] [Indexed: 06/19/2024] Open
Abstract
The global scientific response to COVID 19 highlighted the urgent need for increased throughput and capacity in bioanalytical laboratories, especially for the precise quantification of proteins that pertain to health and disease. Acoustic ejection mass spectrometry (AEMS) represents a much-needed paradigm shift for ultra-fast biomarker screening. Here, a quantitative AEMS assays is presented, employing peptide immunocapture to enrich (i) 10 acute phase response (APR) protein markers from plasma, and (ii) SARS-CoV-2 NCAP peptides from nasopharyngeal swabs. The APR proteins were quantified in 267 plasma samples, in triplicate in 4.8 h, with %CV from 4.2% to 10.5%. SARS-CoV-2 peptides were quantified in triplicate from 145 viral swabs in 10 min. This assay represents a 15-fold speed improvement over LC-MS, with instrument stability demonstrated across 10,000 peptide measurements. The combination of speed from AEMS and selectivity from peptide immunocapture enables ultra-high throughput, reproducible quantitative biomarker screening in very large cohorts.
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Affiliation(s)
- Bart Van Puyvelde
- ProGenTomics, Laboratory of Pharmaceutical Biotechnology, Ghent University, 9000, Ghent, Belgium
- Advanced Clinical Biosystems Research Institute, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | | | - Maxim Zhgamadze
- Advanced Clinical Biosystems Research Institute, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | | | - Y Oliver Wang
- Advanced Clinical Biosystems Research Institute, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Esthelle Hoedt
- Advanced Clinical Biosystems Research Institute, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Koen Raedschelders
- Advanced Clinical Biosystems Research Institute, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Matt Pope
- SISCAPA Assay Technologies Inc., Box 53309, Washington, DC, 20009, USA
| | - Carissa A Huynh
- Cedars Sinai Biobank & Research Pathology Resource, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - V Krishnan Ramanujan
- Cedars Sinai Biobank & Research Pathology Resource, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Warren Tourtellotte
- Cedars Sinai Biobank & Research Pathology Resource, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Morteza Razavi
- SISCAPA Assay Technologies Inc., Box 53309, Washington, DC, 20009, USA
| | - N Leigh Anderson
- SISCAPA Assay Technologies Inc., Box 53309, Washington, DC, 20009, USA
| | - Geert Martens
- AZ Delta Medical Laboratories, AZ Delta General Hospital, 8800, Roeselare, Belgium
| | - Dieter Deforce
- ProGenTomics, Laboratory of Pharmaceutical Biotechnology, Ghent University, 9000, Ghent, Belgium
| | - Qin Fu
- Advanced Clinical Biosystems Research Institute, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Maarten Dhaenens
- ProGenTomics, Laboratory of Pharmaceutical Biotechnology, Ghent University, 9000, Ghent, Belgium.
| | - Jennifer E Van Eyk
- Advanced Clinical Biosystems Research Institute, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA.
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10
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Jiang Y, DeBord D, Vitrac H, Stewart J, Haghani A, Van Eyk JE, Fert-Bober J, Meyer JG. The Future of Proteomics is Up in the Air: Can Ion Mobility Replace Liquid Chromatography for High Throughput Proteomics? J Proteome Res 2024; 23:1871-1882. [PMID: 38713528 PMCID: PMC11161313 DOI: 10.1021/acs.jproteome.4c00248] [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: 05/09/2024]
Abstract
The coevolution of liquid chromatography (LC) with mass spectrometry (MS) has shaped contemporary proteomics. LC hyphenated to MS now enables quantification of more than 10,000 proteins in a single injection, a number that likely represents most proteins in specific human cells or tissues. Separations by ion mobility spectrometry (IMS) have recently emerged to complement LC and further improve the depth of proteomics. Given the theoretical advantages in speed and robustness of IMS in comparison to LC, we envision that ongoing improvements to IMS paired with MS may eventually make LC obsolete, especially when combined with targeted or simplified analyses, such as rapid clinical proteomics analysis of defined biomarker panels. In this perspective, we describe the need for faster analysis that might drive this transition, the current state of direct infusion proteomics, and discuss some technical challenges that must be overcome to fully complete the transition to entirely gas phase proteomics.
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Affiliation(s)
- Yuming Jiang
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
- The Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Daniel DeBord
- MOBILion Systems Inc., Chadds Ford, Pennsylvania 19317, United States
| | - Heidi Vitrac
- MOBILion Systems Inc., Chadds Ford, Pennsylvania 19317, United States
| | - Jordan Stewart
- MOBILion Systems Inc., Chadds Ford, Pennsylvania 19317, United States
| | - Ali Haghani
- The Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
- Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Jennifer E Van Eyk
- The Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
- Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Justyna Fert-Bober
- The Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
- Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Jesse G Meyer
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
- The Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
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11
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Wang J, Tan H, Fu Y, Mishra A, Sun H, Wang Z, Wu Z, Wang X, Serrano GE, Beach TG, Peng J, High AA. Evaluation of Protein Identification and Quantification by the diaPASEF Method on timsTOF SCP. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2024; 35:1253-1260. [PMID: 38754071 DOI: 10.1021/jasms.4c00067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2024]
Abstract
Accurate and precise quantification is crucial in modern proteomics, particularly in the context of exploring low-amount samples. While the innovative 4D-data-independent acquisition (DIA) quantitative proteomics facilitated by timsTOF mass spectrometers gives enhanced sensitivity and selectivity for protein identification, the diaPASEF (parallel accumulation-serial fragmentation combined with data-independent acquisition) parameters have not been systematically optimized, and a comprehensive evaluation of the quantification is currently lacking. In this study, we conducted a thorough optimization of key parameters on a timsTOF SCP instrument, including sample loading amount (50 ng), ramp/accumulation time (140 ms), isolation window width (20 m/z), and gradient time (60 min). To further improve the identification of proteins in low-amount samples, we utilized different column settings and introduced 0.02% n-dodecyl-β-d-maltoside (DDM) in the sample reconstitution solution, resulting in a remarkable 19-fold increase in protein identification at the single-cell-equivalent level. Moreover, a comprehensive comparison of protein quantification using a tandem mass tag reporter (TMT-reporter), complement TMT ions (TMTc), and diaPASEF revealed a strong correlation between these methods. Both diaPASEF and TMTc have effectively addressed the issue of ratio compression, highlighting the diaPASEF method's effectiveness in achieving accurate quantification data compared to TMT reporter quantification. Additionally, an in-depth analysis of in-group variation positioned diaPASEF between the TMT-reporter and TMTc methods. Therefore, diaPASEF quantification on the timsTOF SCP instrument emerges as a precise and accurate methodology for quantitative proteomics, especially for samples with small amounts.
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Affiliation(s)
- Ju Wang
- Departments of Structural Biology and Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, United States
| | - Haiyan Tan
- Center for Proteomics and Metabolomics, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, United States
| | - Yingxue Fu
- Center for Proteomics and Metabolomics, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, United States
| | - Ashutosh Mishra
- Center for Proteomics and Metabolomics, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, United States
| | - Huan Sun
- Departments of Structural Biology and Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, United States
| | - Zhen Wang
- Departments of Structural Biology and Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, United States
| | - Zhiping Wu
- Departments of Structural Biology and Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, United States
| | - Xusheng Wang
- Center for Proteomics and Metabolomics, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, United States
| | - Geidy E Serrano
- Banner Sun Health Research Institute, Sun City, Arizona 85351, United States
| | - Thomas G Beach
- Banner Sun Health Research Institute, Sun City, Arizona 85351, United States
| | - Junmin Peng
- Departments of Structural Biology and Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, United States
| | - Anthony A High
- Center for Proteomics and Metabolomics, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, United States
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12
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Cross J, Rai A, Fang H, Claridge B, Greening DW. Rapid and in-depth proteomic profiling of small extracellular vesicles for ultralow samples. Proteomics 2024; 24:e2300211. [PMID: 37786918 DOI: 10.1002/pmic.202300211] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 09/20/2023] [Accepted: 09/20/2023] [Indexed: 10/04/2023]
Abstract
The integration of robust single-pot, solid-phase-enhanced sample preparation with powerful liquid chromatography-tandem mass spectrometry (LC-MS/MS) is routinely used to define the extracellular vesicle (EV) proteome landscape and underlying biology. However, EV proteome studies are often limited by sample availability, requiring upscaling cell cultures or larger volumes of biofluids to generate sufficient materials. Here, we have refined data independent acquisition (DIA)-based MS analysis of EV proteome by optimizing both protein enzymatic digestion and chromatography gradient length (ranging from 15 to 44 min). Our short 15 min gradient length can reproducibly quantify 1168 (from as little as 500 pg of EV peptides) to 3882 proteins groups (from 50 ng peptides), including robust quantification of 22 core EV marker proteins. Compared to data-dependent acquisition, DIA achieved significantly greater EV proteome coverage and quantification of low abundant protein species. Moreover, we have achieved optimal magnetic bead-based sample preparation tailored to low quantities of EVs (0.5 to 1 µg protein) to obtain sufficient peptides for MS quantification of 1908-2340 protein groups. We demonstrate the power and robustness of our pipeline in obtaining sufficient EV proteomes granularity of different cell sources to ascertain known EV biology. This underscores the capacity of our optimised workflow to capture precise and comprehensive proteome of EVs, especially from ultra-low sample quantities (sub-nanogram), an important challenge in the field where obtaining in-depth proteome information is essential.
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Affiliation(s)
- Jonathon Cross
- Baker Heart and Diabetes Institute, Melbourne, Australia
| | - Alin Rai
- Baker Heart and Diabetes Institute, Melbourne, Australia
- Department of Cardiovascular Research, Translation and Implementation (CaRTI), School of Agriculture, Biomedicine and Environment, La Trobe University, Bundoora, Australia
| | - Haoyun Fang
- Baker Heart and Diabetes Institute, Melbourne, Australia
- Department of Cardiometabolic Health, University of Melbourne, Melbourne, Australia
| | - Bethany Claridge
- Baker Heart and Diabetes Institute, Melbourne, Australia
- Department of Cardiovascular Research, Translation and Implementation (CaRTI), School of Agriculture, Biomedicine and Environment, La Trobe University, Bundoora, Australia
| | - David W Greening
- Baker Heart and Diabetes Institute, Melbourne, Australia
- Department of Cardiovascular Research, Translation and Implementation (CaRTI), School of Agriculture, Biomedicine and Environment, La Trobe University, Bundoora, Australia
- Department of Cardiometabolic Health, University of Melbourne, Melbourne, Australia
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13
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Muenzner J, Trébulle P, Agostini F, Zauber H, Messner CB, Steger M, Kilian C, Lau K, Barthel N, Lehmann A, Textoris-Taube K, Caudal E, Egger AS, Amari F, De Chiara M, Demichev V, Gossmann TI, Mülleder M, Liti G, Schacherer J, Selbach M, Berman J, Ralser M. Natural proteome diversity links aneuploidy tolerance to protein turnover. Nature 2024; 630:149-157. [PMID: 38778096 PMCID: PMC11153158 DOI: 10.1038/s41586-024-07442-9] [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: 04/07/2022] [Accepted: 04/19/2024] [Indexed: 05/25/2024]
Abstract
Accessing the natural genetic diversity of species unveils hidden genetic traits, clarifies gene functions and allows the generalizability of laboratory findings to be assessed. One notable discovery made in natural isolates of Saccharomyces cerevisiae is that aneuploidy-an imbalance in chromosome copy numbers-is frequent1,2 (around 20%), which seems to contradict the substantial fitness costs and transient nature of aneuploidy when it is engineered in the laboratory3-5. Here we generate a proteomic resource and merge it with genomic1 and transcriptomic6 data for 796 euploid and aneuploid natural isolates. We find that natural and lab-generated aneuploids differ specifically at the proteome. In lab-generated aneuploids, some proteins-especially subunits of protein complexes-show reduced expression, but the overall protein levels correspond to the aneuploid gene dosage. By contrast, in natural isolates, more than 70% of proteins encoded on aneuploid chromosomes are dosage compensated, and average protein levels are shifted towards the euploid state chromosome-wide. At the molecular level, we detect an induction of structural components of the proteasome, increased levels of ubiquitination, and reveal an interdependency of protein turnover rates and attenuation. Our study thus highlights the role of protein turnover in mediating aneuploidy tolerance, and shows the utility of exploiting the natural diversity of species to attain generalizable molecular insights into complex biological processes.
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Affiliation(s)
- Julia Muenzner
- Department of Biochemistry, Charité Universitätsmedizin, Berlin, Germany
| | - Pauline Trébulle
- Molecular Biology of Metabolism Laboratory, Francis Crick Institute, London, UK
- Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Federica Agostini
- Department of Biochemistry, Charité Universitätsmedizin, Berlin, Germany
| | - Henrik Zauber
- Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Christoph B Messner
- Molecular Biology of Metabolism Laboratory, Francis Crick Institute, London, UK
- Precision Proteomics Center, Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland
| | - Martin Steger
- Evotec (München), Martinsried, Germany
- NEOsphere Biotechnologies, Martinsried, Germany
| | - Christiane Kilian
- Department of Biochemistry, Charité Universitätsmedizin, Berlin, Germany
| | - Kate Lau
- Department of Biochemistry, Charité Universitätsmedizin, Berlin, Germany
| | - Natalie Barthel
- Department of Biochemistry, Charité Universitätsmedizin, Berlin, Germany
| | - Andrea Lehmann
- Department of Biochemistry, Charité Universitätsmedizin, Berlin, Germany
| | - Kathrin Textoris-Taube
- Department of Biochemistry, Charité Universitätsmedizin, Berlin, Germany
- Core Facility High-Throughput Mass Spectrometry, Charité Universitätsmedizin, Berlin, Germany
| | - Elodie Caudal
- Université de Strasbourg, CNRS GMGM UMR 7156, Strasbourg, France
| | - Anna-Sophia Egger
- Molecular Biology of Metabolism Laboratory, Francis Crick Institute, London, UK
| | - Fatma Amari
- Department of Biochemistry, Charité Universitätsmedizin, Berlin, Germany
- Core Facility High-Throughput Mass Spectrometry, Charité Universitätsmedizin, Berlin, Germany
| | | | - Vadim Demichev
- Department of Biochemistry, Charité Universitätsmedizin, Berlin, Germany
- Molecular Biology of Metabolism Laboratory, Francis Crick Institute, London, UK
| | - Toni I Gossmann
- Computational Systems Biology, Faculty of Biochemical and Chemical Engineering, TU Dortmund University, Dortmund, Germany
| | - Michael Mülleder
- Core Facility High-Throughput Mass Spectrometry, Charité Universitätsmedizin, Berlin, Germany
| | - Gianni Liti
- Université Côte d'Azur, CNRS, INSERM, IRCAN, Nice, France
| | - Joseph Schacherer
- Université de Strasbourg, CNRS GMGM UMR 7156, Strasbourg, France
- Institut Universitaire de France (IUF), Paris, France
| | | | - Judith Berman
- Shmunis School of Biomedical and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Ramat Aviv, Israel.
| | - Markus Ralser
- Department of Biochemistry, Charité Universitätsmedizin, Berlin, Germany.
- Molecular Biology of Metabolism Laboratory, Francis Crick Institute, London, UK.
- Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Max Planck Institute for Molecular Genetics, Berlin, Germany.
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14
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Brunet TA, Clément Y, Calabrese V, Lemoine J, Geffard O, Chaumot A, Degli-Esposti D, Salvador A, Ayciriex S. Concomitant investigation of crustacean amphipods lipidome and metabolome during the molting cycle by Zeno SWATH data-independent acquisition coupled with electron activated dissociation and machine learning. Anal Chim Acta 2024; 1304:342533. [PMID: 38637034 DOI: 10.1016/j.aca.2024.342533] [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: 01/03/2024] [Revised: 03/13/2024] [Accepted: 03/25/2024] [Indexed: 04/20/2024]
Abstract
BACKGROUND DIA (Data-Independent Acquisition) is a powerful technique in Liquid Chromatography coupled with high-resolution tandem Mass Spectrometry (LC-MS/MS) initially developed for proteomics studies and recently emerging in metabolomics and lipidomics. It provides a comprehensive and unbiased coverage of molecules with improved reproducibility and quantitative accuracy compared to Data-Dependent Acquisition (DDA). Combined with the Zeno trap and Electron-Activated Dissociation (EAD), DIA enhances data quality and structural elucidation compared to conventional fragmentation under CID. These tools were applied to study the lipidome and metabolome of the freshwater amphipod Gammarus fossarum, successfully discriminating stages and highlighting significant biological features. Despite being underused, DIA, along with the Zeno trap and EAD, holds great potential for advancing research in the omics field. RESULTS DIA combined with the Zeno trap enhances detection reproducibility compared to conventional DDA, improving fragmentation spectra quality and putative identifications. LC coupled with Zeno-SWATH-DIA methods were used to characterize molecular changes in reproductive cycle of female gammarids. Multivariate data analysis including Principal Component Analysis and Partial Least Square Discriminant Analysis successfully identified significant features. EAD fragmentation helped to identify unknown features and to confirm their molecular structure using fragmentation spectra database annotation or machine learning. EAD database matching accurately annotated five glycerophospholipids, including the position of double bonds on fatty acid chain moieties. SIRIUS database predicted structures of unknown features based on experimental fragmentation spectra to compensate for database incompleteness. SIGNIFICANCE Reproducible detection of features and confident identification of putative compounds are pivotal stages within analytical pipelines. The DIA approach combined with Zeno pulsing enhances detection sensitivity and targeted fragmentation with EAD in positive polarity provides orthogonal fragmentation information. In our study, Zeno-DIA and EAD thereby facilitated a comprehensive and insightful exploration of pertinent biological molecules associated with the reproductive cycle of gammarids. The developed methodology holds great promises for identifying informative biomarkers on the health status of an environmental sentinel species.
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Affiliation(s)
- Thomas Alexandre Brunet
- Univ Lyon, CNRS, Université Claude Bernard Lyon 1, Institut des Sciences Analytiques, UMR 5280, 5 rue de la Doua, F-69100, Villeurbanne, France
| | - Yohann Clément
- Univ Lyon, CNRS, Université Claude Bernard Lyon 1, Institut des Sciences Analytiques, UMR 5280, 5 rue de la Doua, F-69100, Villeurbanne, France
| | - Valentina Calabrese
- Univ Lyon, CNRS, Université Claude Bernard Lyon 1, Institut des Sciences Analytiques, UMR 5280, 5 rue de la Doua, F-69100, Villeurbanne, France
| | - Jérôme Lemoine
- Univ Lyon, CNRS, Université Claude Bernard Lyon 1, Institut des Sciences Analytiques, UMR 5280, 5 rue de la Doua, F-69100, Villeurbanne, France
| | - Olivier Geffard
- INRAE, UR RiverLy, Ecotoxicology Team, F-69625, Villeurbanne, France
| | - Arnaud Chaumot
- INRAE, UR RiverLy, Ecotoxicology Team, F-69625, Villeurbanne, France
| | | | - Arnaud Salvador
- Univ Lyon, CNRS, Université Claude Bernard Lyon 1, Institut des Sciences Analytiques, UMR 5280, 5 rue de la Doua, F-69100, Villeurbanne, France
| | - Sophie Ayciriex
- Univ Lyon, CNRS, Université Claude Bernard Lyon 1, Institut des Sciences Analytiques, UMR 5280, 5 rue de la Doua, F-69100, Villeurbanne, France.
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15
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Peters-Clarke TM, Coon JJ, Riley NM. Instrumentation at the Leading Edge of Proteomics. Anal Chem 2024; 96:7976-8010. [PMID: 38738990 DOI: 10.1021/acs.analchem.3c04497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2024]
Affiliation(s)
- Trenton M Peters-Clarke
- Department of Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Department of Biomolecular Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
| | - Joshua J Coon
- Department of Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Department of Biomolecular Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Morgridge Institute for Research, Madison, Wisconsin 53715, United States
| | - Nicholas M Riley
- Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
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16
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Rosario-Rodríguez LJ, Cantres-Rosario YM, Carrasquillo-Carrión K, Rosa-Díaz A, Rodríguez-De Jesús AE, Rivera-Nieves V, Tosado-Rodríguez EL, Méndez LB, Roche-Lima A, Bertrán J, Meléndez LM. Plasma Proteins Associated with COVID-19 Severity in Puerto Rico. Int J Mol Sci 2024; 25:5426. [PMID: 38791465 PMCID: PMC11121485 DOI: 10.3390/ijms25105426] [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: 04/07/2024] [Revised: 05/10/2024] [Accepted: 05/12/2024] [Indexed: 05/26/2024] Open
Abstract
Viral strains, age, and host factors are associated with variable immune responses against SARS-CoV-2 and disease severity. Puerto Ricans have a genetic mixture of races: European, African, and Native American. We hypothesized that unique host proteins/pathways are associated with COVID-19 disease severity in Puerto Rico. Following IRB approval, a total of 95 unvaccinated men and women aged 21-71 years old were recruited in Puerto Rico from 2020-2021. Plasma samples were collected from COVID-19-positive subjects (n = 39) and COVID-19-negative individuals (n = 56) during acute disease. COVID-19-positive individuals were stratified based on symptomatology as follows: mild (n = 18), moderate (n = 13), and severe (n = 8). Quantitative proteomics was performed in plasma samples using tandem mass tag (TMT) labeling. Labeled peptides were subjected to LC/MS/MS and analyzed by Proteome Discoverer (version 2.5), Limma software (version 3.41.15), and Ingenuity Pathways Analysis (IPA, version 22.0.2). Cytokines were quantified using a human cytokine array. Proteomics analyses of severely affected COVID-19-positive individuals revealed 58 differentially expressed proteins. Cadherin-13, which participates in synaptogenesis, was downregulated in severe patients and validated by ELISA. Cytokine immunoassay showed that TNF-α levels decreased with disease severity. This study uncovers potential host predictors of COVID-19 severity and new avenues for treatment in Puerto Ricans.
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Affiliation(s)
- Lester J. Rosario-Rodríguez
- Department of Microbiology and Medical Zoology, University of Puerto Rico, Medical Sciences Campus, San Juan 00935, Puerto Rico;
| | - Yadira M. Cantres-Rosario
- Translational Proteomics Center, Research Capacity Core, Center for Collaborative Research in Health Disparities, University of Puerto Rico, Medical Sciences Campus, San Juan 00935, Puerto Rico; (Y.M.C.-R.); (A.E.R.-D.J.)
| | - Kelvin Carrasquillo-Carrión
- Integrated Informatics, Research Capacity Core, Center for Collaborative Research in Health Disparities, University of Puerto Rico, Medical Sciences Campus, San Juan 00935, Puerto Rico; (K.C.-C.); (E.L.T.-R.); (A.R.-L.)
| | - Alexandra Rosa-Díaz
- Interdisciplinary Studies, Natural Sciences, University of Puerto Rico, Río Piedras Campus, San Juan 00925, Puerto Rico; (A.R.-D.); (V.R.-N.)
| | - Ana E. Rodríguez-De Jesús
- Translational Proteomics Center, Research Capacity Core, Center for Collaborative Research in Health Disparities, University of Puerto Rico, Medical Sciences Campus, San Juan 00935, Puerto Rico; (Y.M.C.-R.); (A.E.R.-D.J.)
| | - Verónica Rivera-Nieves
- Interdisciplinary Studies, Natural Sciences, University of Puerto Rico, Río Piedras Campus, San Juan 00925, Puerto Rico; (A.R.-D.); (V.R.-N.)
| | - Eduardo L. Tosado-Rodríguez
- Integrated Informatics, Research Capacity Core, Center for Collaborative Research in Health Disparities, University of Puerto Rico, Medical Sciences Campus, San Juan 00935, Puerto Rico; (K.C.-C.); (E.L.T.-R.); (A.R.-L.)
| | - Loyda B. Méndez
- Department of Science & Technology, Ana G. Mendez University, Carolina 00928, Puerto Rico;
| | - Abiel Roche-Lima
- Integrated Informatics, Research Capacity Core, Center for Collaborative Research in Health Disparities, University of Puerto Rico, Medical Sciences Campus, San Juan 00935, Puerto Rico; (K.C.-C.); (E.L.T.-R.); (A.R.-L.)
| | - Jorge Bertrán
- Infectious Diseases, Auxilio Mutuo Hospital, San Juan 00919, Puerto Rico;
| | - Loyda M. Meléndez
- Department of Microbiology and Medical Zoology, University of Puerto Rico, Medical Sciences Campus, San Juan 00935, Puerto Rico;
- Translational Proteomics Center, Research Capacity Core, Center for Collaborative Research in Health Disparities, University of Puerto Rico, Medical Sciences Campus, San Juan 00935, Puerto Rico; (Y.M.C.-R.); (A.E.R.-D.J.)
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17
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Teyssonnière EM, Trébulle P, Muenzner J, Loegler V, Ludwig D, Amari F, Mülleder M, Friedrich A, Hou J, Ralser M, Schacherer J. Species-wide quantitative transcriptomes and proteomes reveal distinct genetic control of gene expression variation in yeast. Proc Natl Acad Sci U S A 2024; 121:e2319211121. [PMID: 38696467 PMCID: PMC11087752 DOI: 10.1073/pnas.2319211121] [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: 11/02/2023] [Accepted: 03/25/2024] [Indexed: 05/04/2024] Open
Abstract
Gene expression varies between individuals and corresponds to a key step linking genotypes to phenotypes. However, our knowledge regarding the species-wide genetic control of protein abundance, including its dependency on transcript levels, is very limited. Here, we have determined quantitative proteomes of a large population of 942 diverse natural Saccharomyces cerevisiae yeast isolates. We found that mRNA and protein abundances are weakly correlated at the population gene level. While the protein coexpression network recapitulates major biological functions, differential expression patterns reveal proteomic signatures related to specific populations. Comprehensive genetic association analyses highlight that genetic variants associated with variation in protein (pQTL) and transcript (eQTL) levels poorly overlap (3%). Our results demonstrate that transcriptome and proteome are governed by distinct genetic bases, likely explained by protein turnover. It also highlights the importance of integrating these different levels of gene expression to better understand the genotype-phenotype relationship.
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Affiliation(s)
- Elie Marcel Teyssonnière
- UMR 7156 Génétique Moléculaire, Génomique et Microbiologie, Université de Strasbourg, CNRS, Strasbourg67000, France
| | - Pauline Trébulle
- The Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, OxfordOX3 7BN, United Kingdom
| | - Julia Muenzner
- Department of Biochemistry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
| | - Victor Loegler
- UMR 7156 Génétique Moléculaire, Génomique et Microbiologie, Université de Strasbourg, CNRS, Strasbourg67000, France
| | - Daniela Ludwig
- Department of Biochemistry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
- Core Facility High-Throughput Mass Spectrometry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
| | - Fatma Amari
- Department of Biochemistry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
- Core Facility High-Throughput Mass Spectrometry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
| | - Michael Mülleder
- Core Facility High-Throughput Mass Spectrometry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
| | - Anne Friedrich
- UMR 7156 Génétique Moléculaire, Génomique et Microbiologie, Université de Strasbourg, CNRS, Strasbourg67000, France
| | - Jing Hou
- UMR 7156 Génétique Moléculaire, Génomique et Microbiologie, Université de Strasbourg, CNRS, Strasbourg67000, France
| | - Markus Ralser
- The Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, OxfordOX3 7BN, United Kingdom
- Department of Biochemistry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
- Max Planck Institute for Molecular Genetics, Berlin14195, Germany
| | - Joseph Schacherer
- UMR 7156 Génétique Moléculaire, Génomique et Microbiologie, Université de Strasbourg, CNRS, Strasbourg67000, France
- Institut Universitaire de France, Paris75000, France
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18
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Reder A, Hentschker C, Steil L, Gesell Salazar M, Hammer E, Dhople VM, Sura T, Lissner U, Wolfgramm H, Dittmar D, Harms M, Surmann K, Völker U, Michalik S. MassSpecPreppy-An end-to-end solution for automated protein concentration determination and flexible sample digestion for proteomics applications. Proteomics 2024; 24:e2300294. [PMID: 37772677 DOI: 10.1002/pmic.202300294] [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: 07/31/2023] [Revised: 09/13/2023] [Accepted: 09/14/2023] [Indexed: 09/30/2023]
Abstract
In proteomics, fast, efficient, and highly reproducible sample preparation is of utmost importance, particularly in view of fast scanning mass spectrometers enabling analyses of large sample series. To address this need, we have developed the web application MassSpecPreppy that operates on the open science OT-2 liquid handling robot from Opentrons. This platform can prepare up to 96 samples at once, performing tasks like BCA protein concentration determination, sample digestion with normalization, reduction/alkylation and peptide elution into vials or loading specified peptide amounts onto Evotips in an automated and flexible manner. The performance of the developed workflows using MassSpecPreppy was compared with standard manual sample preparation workflows. The BCA assay experiments revealed an average recovery of 101.3% (SD: ± 7.82%) for the MassSpecPreppy workflow, while the manual workflow had a recovery of 96.3% (SD: ± 9.73%). The species mix used in the evaluation experiments showed that 94.5% of protein groups for OT-2 digestion and 95% for manual digestion passed the significance thresholds with comparable peptide level coefficient of variations. These results demonstrate that MassSpecPreppy is a versatile and scalable platform for automated sample preparation, producing injection-ready samples for proteomics research.
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Affiliation(s)
- Alexander Reder
- Interfaculty Institute of Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Christian Hentschker
- Interfaculty Institute of Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Leif Steil
- Interfaculty Institute of Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Manuela Gesell Salazar
- Interfaculty Institute of Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Elke Hammer
- Interfaculty Institute of Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Vishnu M Dhople
- Interfaculty Institute of Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Thomas Sura
- Interfaculty Institute of Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Ulrike Lissner
- Interfaculty Institute of Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Hannes Wolfgramm
- Interfaculty Institute of Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Denise Dittmar
- Interfaculty Institute of Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Marco Harms
- Interfaculty Institute of Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Kristin Surmann
- Interfaculty Institute of Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Uwe Völker
- Interfaculty Institute of Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Stephan Michalik
- Interfaculty Institute of Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany
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19
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Kurgan N, Kjærgaard Larsen J, Deshmukh AS. Harnessing the power of proteomics in precision diabetes medicine. Diabetologia 2024; 67:783-797. [PMID: 38345659 DOI: 10.1007/s00125-024-06097-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 12/20/2023] [Indexed: 03/21/2024]
Abstract
Precision diabetes medicine (PDM) aims to reduce errors in prevention programmes, diagnosis thresholds, prognosis prediction and treatment strategies. However, its advancement and implementation are difficult due to the heterogeneity of complex molecular processes and environmental exposures that influence an individual's disease trajectory. To address this challenge, it is imperative to develop robust screening methods for all areas of PDM. Innovative proteomic technologies, alongside genomics, have proven effective in precision cancer medicine and are showing promise in diabetes research for potential translation. This narrative review highlights how proteomics is well-positioned to help improve PDM. Specifically, a critical assessment of widely adopted affinity-based proteomic technologies in large-scale clinical studies and evidence of the benefits and feasibility of using MS-based plasma proteomics is presented. We also present a case for the use of proteomics to identify predictive protein panels for type 2 diabetes subtyping and the development of clinical prediction models for prevention, diagnosis, prognosis and treatment strategies. Lastly, we discuss the importance of plasma and tissue proteomics and its integration with genomics (proteogenomics) for identifying unique type 2 diabetes intra- and inter-subtype aetiology. We conclude with a call for action formed on advancing proteomics technologies, benchmarking their performance and standardisation across sites, with an emphasis on data sharing and the inclusion of diverse ancestries in large cohort studies. These efforts should foster collaboration with key stakeholders and align with ongoing academic programmes such as the Precision Medicine in Diabetes Initiative consortium.
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Affiliation(s)
- Nigel Kurgan
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Jeppe Kjærgaard Larsen
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Atul S Deshmukh
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark.
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20
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Serrano LR, Peters-Clarke TM, Arrey TN, Damoc E, Robinson ML, Lancaster NM, Shishkova E, Moss C, Pashkova A, Sinitcyn P, Brademan DR, Quarmby ST, Peterson AC, Zeller M, Hermanson D, Stewart H, Hock C, Makarov A, Zabrouskov V, Coon JJ. The One Hour Human Proteome. Mol Cell Proteomics 2024; 23:100760. [PMID: 38579929 PMCID: PMC11103439 DOI: 10.1016/j.mcpro.2024.100760] [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: 02/06/2024] [Revised: 03/23/2024] [Accepted: 03/29/2024] [Indexed: 04/07/2024] Open
Abstract
We describe deep analysis of the human proteome in less than 1 h. We achieve this expedited proteome characterization by leveraging state-of-the-art sample preparation, chromatographic separations, and data analysis tools, and by using the new Orbitrap Astral mass spectrometer equipped with a quadrupole mass filter, a high-field Orbitrap mass analyzer, and an asymmetric track lossless (Astral) mass analyzer. The system offers high tandem mass spectrometry acquisition speed of 200 Hz and detects hundreds of peptide sequences per second within data-independent acquisition or data-dependent acquisition modes of operation. The fast-switching capabilities of the new quadrupole complement the sensitivity and fast ion scanning of the Astral analyzer to enable narrow-bin data-independent analysis methods. Over a 30-min active chromatographic method consuming a total analysis time of 56 min, the Q-Orbitrap-Astral hybrid MS collects an average of 4319 MS1 scans and 438,062 tandem mass spectrometry scans per run, producing 235,916 peptide sequences (1% false discovery rate). On average, each 30-min analysis achieved detection of 10,411 protein groups (1% false discovery rate). We conclude, with these results and alongside other recent reports, that the 1-h human proteome is within reach.
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Affiliation(s)
- Lia R Serrano
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA; Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Trenton M Peters-Clarke
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA; Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | | | - Eugen Damoc
- Thermo Fisher Scientific GmbH, Bremen, Germany
| | - Margaret Lea Robinson
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Noah M Lancaster
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA; Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Evgenia Shishkova
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA; National Center for Quantitative Biology of Complex Systems, Madison, Wisconsin, USA
| | - Corinne Moss
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | | | - Pavel Sinitcyn
- Morgridge Institute for Research, Madison, Wisconsin, USA
| | | | - Scott T Quarmby
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA; National Center for Quantitative Biology of Complex Systems, Madison, Wisconsin, USA
| | | | | | | | | | | | | | | | - Joshua J Coon
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA; Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA; National Center for Quantitative Biology of Complex Systems, Madison, Wisconsin, USA; Morgridge Institute for Research, Madison, Wisconsin, USA.
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21
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Coorssen JR, Padula MP. Proteomics-The State of the Field: The Definition and Analysis of Proteomes Should Be Based in Reality, Not Convenience. Proteomes 2024; 12:14. [PMID: 38651373 PMCID: PMC11036260 DOI: 10.3390/proteomes12020014] [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: 03/17/2024] [Revised: 04/17/2024] [Accepted: 04/17/2024] [Indexed: 04/25/2024] Open
Abstract
With growing recognition and acknowledgement of the genuine complexity of proteomes, we are finally entering the post-proteogenomic era. Routine assessment of proteomes as inferred correlates of gene sequences (i.e., canonical 'proteins') cannot provide the necessary critical analysis of systems-level biology that is needed to understand underlying molecular mechanisms and pathways or identify the most selective biomarkers and therapeutic targets. These critical requirements demand the analysis of proteomes at the level of proteoforms/protein species, the actual active molecular players. Currently, only highly refined integrated or integrative top-down proteomics (iTDP) enables the analytical depth necessary to provide routine, comprehensive, and quantitative proteome assessments across the widest range of proteoforms inherent to native systems. Here we provide a broad perspective of the field, taking in historical and current realities, to establish a more balanced understanding of where the field has come from (in particular during the ten years since Proteomes was launched), current issues, and how things likely need to proceed if necessary deep proteome analyses are to succeed. We base this in our firm belief that the best proteomic analyses reflect, as closely as possible, the native sample at the moment of sampling. We also seek to emphasise that this and future analytical approaches are likely best based on the broad recognition and exploitation of the complementarity of currently successful approaches. This also emphasises the need to continuously evaluate and further optimize established approaches, to avoid complacency in thinking and expectations but also to promote the critical and careful development and introduction of new approaches, most notably those that address proteoforms. Above all, we wish to emphasise that a rigorous focus on analytical quality must override current thinking that largely values analytical speed; the latter would certainly be nice, if only proteoforms could thus be effectively, routinely, and quantitatively assessed. Alas, proteomes are composed of proteoforms, not molecular species that can be amplified or that directly mirror genes (i.e., 'canonical'). The problem is hard, and we must accept and address it as such, but the payoff in playing this longer game of rigorous deep proteome analyses is the promise of far more selective biomarkers, drug targets, and truly personalised or even individualised medicine.
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Affiliation(s)
- Jens R. Coorssen
- Department of Biological Sciences, Faculty of Mathematics and Science, Brock University, St. Catharines, ON L2S 3A1, Canada
- Institute for Globally Distributed Open Research and Education (IGDORE), St. Catharines, ON L2N 4X2, Canada
| | - Matthew P. Padula
- School of Life Sciences and Proteomics, Lipidomics and Metabolomics Core Facility, Faculty of Science, University of Technology Sydney, Sydney, NSW 2007, Australia
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22
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Rrustemi T, Meyer K, Roske Y, Uyar B, Akalin A, Imami K, Ishihama Y, Daumke O, Selbach M. Pathogenic mutations of human phosphorylation sites affect protein-protein interactions. Nat Commun 2024; 15:3146. [PMID: 38605029 PMCID: PMC11009412 DOI: 10.1038/s41467-024-46794-8] [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: 08/09/2023] [Accepted: 03/11/2024] [Indexed: 04/13/2024] Open
Abstract
Despite their lack of a defined 3D structure, intrinsically disordered regions (IDRs) of proteins play important biological roles. Many IDRs contain short linear motifs (SLiMs) that mediate protein-protein interactions (PPIs), which can be regulated by post-translational modifications like phosphorylation. 20% of pathogenic missense mutations are found in IDRs, and understanding how such mutations affect PPIs is essential for unraveling disease mechanisms. Here, we employ peptide-based interaction proteomics to investigate 36 disease-associated mutations affecting phosphorylation sites. Our results unveil significant differences in interactomes between phosphorylated and non-phosphorylated peptides, often due to disrupted phosphorylation-dependent SLiMs. We focused on a mutation of a serine phosphorylation site in the transcription factor GATAD1, which causes dilated cardiomyopathy. We find that this phosphorylation site mediates interaction with 14-3-3 family proteins. Follow-up experiments reveal the structural basis of this interaction and suggest that 14-3-3 binding affects GATAD1 nucleocytoplasmic transport by masking a nuclear localisation signal. Our results demonstrate that pathogenic mutations of human phosphorylation sites can significantly impact protein-protein interactions, offering insights into potential molecular mechanisms underlying pathogenesis.
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Affiliation(s)
| | - Katrina Meyer
- Max Delbrück Center (MDC), Robert-Rössle-Str. 10, 13125, Berlin, Germany
- Max Planck Institute for Molecular Genetics, Ihnestraße 63, 14195, Berlin, Germany
| | - Yvette Roske
- Max Delbrück Center (MDC), Robert-Rössle-Str. 10, 13125, Berlin, Germany
| | - Bora Uyar
- Max Delbrück Center (MDC), Robert-Rössle-Str. 10, 13125, Berlin, Germany
| | - Altuna Akalin
- Max Delbrück Center (MDC), Robert-Rössle-Str. 10, 13125, Berlin, Germany
| | - Koshi Imami
- Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, 606-8501, Japan
- RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Kanagawa, Japan
| | - Yasushi Ishihama
- Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, 606-8501, Japan
| | - Oliver Daumke
- Max Delbrück Center (MDC), Robert-Rössle-Str. 10, 13125, Berlin, Germany
- Freie Universität Berlin, Institute of Chemistry and Biochemistry, Takustraße 6, Berlin, Germany
| | - Matthias Selbach
- Max Delbrück Center (MDC), Robert-Rössle-Str. 10, 13125, Berlin, Germany.
- Charité-Universitätsmedizin Berlin, 10117, Berlin, Germany.
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23
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Strauss MT, Bludau I, Zeng WF, Voytik E, Ammar C, Schessner JP, Ilango R, Gill M, Meier F, Willems S, Mann M. AlphaPept: a modern and open framework for MS-based proteomics. Nat Commun 2024; 15:2168. [PMID: 38461149 PMCID: PMC10924963 DOI: 10.1038/s41467-024-46485-4] [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: 12/20/2022] [Accepted: 02/20/2024] [Indexed: 03/11/2024] Open
Abstract
In common with other omics technologies, mass spectrometry (MS)-based proteomics produces ever-increasing amounts of raw data, making efficient analysis a principal challenge. A plethora of different computational tools can process the MS data to derive peptide and protein identification and quantification. However, during the last years there has been dramatic progress in computer science, including collaboration tools that have transformed research and industry. To leverage these advances, we develop AlphaPept, a Python-based open-source framework for efficient processing of large high-resolution MS data sets. Numba for just-in-time compilation on CPU and GPU achieves hundred-fold speed improvements. AlphaPept uses the Python scientific stack of highly optimized packages, reducing the code base to domain-specific tasks while accessing the latest advances. We provide an easy on-ramp for community contributions through the concept of literate programming, implemented in Jupyter Notebooks. Large datasets can rapidly be processed as shown by the analysis of hundreds of proteomes in minutes per file, many-fold faster than acquisition. AlphaPept can be used to build automated processing pipelines with web-serving functionality and compatibility with downstream analysis tools. It provides easy access via one-click installation, a modular Python library for advanced users, and via an open GitHub repository for developers.
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Affiliation(s)
- Maximilian T Strauss
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.
- NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Isabell Bludau
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Wen-Feng Zeng
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Eugenia Voytik
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Constantin Ammar
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Julia P Schessner
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | | | | | - Florian Meier
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
- Functional Proteomics, Jena University Hospital, Jena, Germany
| | - Sander Willems
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Matthias Mann
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.
- NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark.
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24
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A high-throughput workflow for comprehensive glycopeptide mapping. Nat Biomed Eng 2024; 8:212-213. [PMID: 37474613 DOI: 10.1038/s41551-023-01068-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/22/2023]
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25
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White MEH, Sinn LR, Jones DM, de Folter J, Aulakh SK, Wang Z, Flynn HR, Krüger L, Tober-Lau P, Demichev V, Kurth F, Mülleder M, Blanchard V, Messner CB, Ralser M. Oxonium ion scanning mass spectrometry for large-scale plasma glycoproteomics. Nat Biomed Eng 2024; 8:233-247. [PMID: 37474612 DOI: 10.1038/s41551-023-01067-5] [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: 07/19/2022] [Accepted: 06/15/2023] [Indexed: 07/22/2023]
Abstract
Protein glycosylation, a complex and heterogeneous post-translational modification that is frequently dysregulated in disease, has been difficult to analyse at scale. Here we report a data-independent acquisition technique for the large-scale mass-spectrometric quantification of glycopeptides in plasma samples. The technique, which we named 'OxoScan-MS', identifies oxonium ions as glycopeptide fragments and exploits a sliding-quadrupole dimension to generate comprehensive and untargeted oxonium ion maps of precursor masses assigned to fragment ions from non-enriched plasma samples. By applying OxoScan-MS to quantify 1,002 glycopeptide features in the plasma glycoproteomes from patients with COVID-19 and healthy controls, we found that severe COVID-19 induces differential glycosylation in IgA, haptoglobin, transferrin and other disease-relevant plasma glycoproteins. OxoScan-MS may allow for the quantitative mapping of glycoproteomes at the scale of hundreds to thousands of samples.
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Affiliation(s)
- Matthew E H White
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
| | - Ludwig R Sinn
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - D Marc Jones
- Bioinformatics and Computational Biology Laboratory, The Francis Crick Institute, London, UK
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, London, UK
| | - Joost de Folter
- Software Engineering and Artificial Intelligence Technology Platform, The Francis Crick Institute, London, UK
| | - Simran Kaur Aulakh
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
| | - Ziyue Wang
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Helen R Flynn
- Mass Spectrometry Proteomics Science Technology Platform, The Francis Crick Institute, London, UK
| | - Lynn Krüger
- Institute of Diagnostic Laboratory Medicine, Charité - Universitätsmedizin Berlin Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Human Medicine, Medical School Berlin, Berlin, Germany
| | - Pinkus Tober-Lau
- Department of Infectious Diseases and Critical Care Medicine, Charité - Universitätsmedizin Berlin Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Vadim Demichev
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Florian Kurth
- Department of Infectious Diseases and Critical Care Medicine, Charité - Universitätsmedizin Berlin Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Michael Mülleder
- Core Facility High-throughput Mass Spectrometry, Charité - Universitätsmedizin Berlin Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Véronique Blanchard
- Institute of Diagnostic Laboratory Medicine, Charité - Universitätsmedizin Berlin Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Human Medicine, Medical School Berlin, Berlin, Germany
| | - Christoph B Messner
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK.
- Precision Proteomic Center, Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland.
| | - Markus Ralser
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK.
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
- Max Planck Institute for Molecular Genetics, Berlin, Germany.
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26
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Steigerwald S, Sinha A, Fort KL, Zeng WF, Niu L, Wichmann C, Kreutzmann A, Mourad D, Aizikov K, Grinfeld D, Makarov A, Mann M, Meier F. Full Mass Range ΦSDM Orbitrap Mass Spectrometry for DIA Proteome Analysis. Mol Cell Proteomics 2024; 23:100713. [PMID: 38184013 PMCID: PMC10851225 DOI: 10.1016/j.mcpro.2024.100713] [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: 08/30/2023] [Revised: 12/21/2023] [Accepted: 01/03/2024] [Indexed: 01/08/2024] Open
Abstract
Optimizing data-independent acquisition methods for proteomics applications often requires balancing spectral resolution and acquisition speed. Here, we describe a real-time full mass range implementation of the phase-constrained spectrum deconvolution method (ΦSDM) for Orbitrap mass spectrometry that increases mass resolving power without increasing scan time. Comparing its performance to the standard enhanced Fourier transformation signal processing revealed that the increased resolving power of ΦSDM is beneficial in areas of high peptide density and comes with a greater ability to resolve low-abundance signals. In a standard 2 h analysis of a 200 ng HeLa digest, this resulted in an increase of 16% in the number of quantified peptides. As the acquisition speed becomes even more important when using fast chromatographic gradients, we further applied ΦSDM methods to a range of shorter gradient lengths (21, 12, and 5 min). While ΦSDM improved identification rates and spectral quality in all tested gradients, it proved particularly advantageous for the 5 min gradient. Here, the number of identified protein groups and peptides increased by >15% in comparison to enhanced Fourier transformation processing. In conclusion, ΦSDM is an alternative signal processing algorithm for processing Orbitrap data that can improve spectral quality and benefit quantitative accuracy in typical proteomics experiments, especially when using short gradients.
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Affiliation(s)
- Sophia Steigerwald
- Department Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Ankit Sinha
- Department Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Kyle L Fort
- Thermo Fisher Scientific (GmbH), Bremen, Germany
| | - Wen-Feng Zeng
- Department Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Lili Niu
- Department Clinical Proteomics, NNF Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Christoph Wichmann
- Department Computational Systems Biochemistry, Max Planck Institute of Biochemistry, Martinsried, Germany
| | | | | | | | | | | | - Matthias Mann
- Department Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany; Department Clinical Proteomics, NNF Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Florian Meier
- Department Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany; Functional Proteomics, Jena University Hospital, Jena, Germany.
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27
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Guzman UH, Martinez-Val A, Ye Z, Damoc E, Arrey TN, Pashkova A, Renuse S, Denisov E, Petzoldt J, Peterson AC, Harking F, Østergaard O, Rydbirk R, Aznar S, Stewart H, Xuan Y, Hermanson D, Horning S, Hock C, Makarov A, Zabrouskov V, Olsen JV. Ultra-fast label-free quantification and comprehensive proteome coverage with narrow-window data-independent acquisition. Nat Biotechnol 2024:10.1038/s41587-023-02099-7. [PMID: 38302753 DOI: 10.1038/s41587-023-02099-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 12/13/2023] [Indexed: 02/03/2024]
Abstract
Mass spectrometry (MS)-based proteomics aims to characterize comprehensive proteomes in a fast and reproducible manner. Here we present the narrow-window data-independent acquisition (nDIA) strategy consisting of high-resolution MS1 scans with parallel tandem MS (MS/MS) scans of ~200 Hz using 2-Th isolation windows, dissolving the differences between data-dependent and -independent methods. This is achieved by pairing a quadrupole Orbitrap mass spectrometer with the asymmetric track lossless (Astral) analyzer which provides >200-Hz MS/MS scanning speed, high resolving power and sensitivity, and low-ppm mass accuracy. The nDIA strategy enables profiling of >100 full yeast proteomes per day, or 48 human proteomes per day at the depth of ~10,000 human protein groups in half-an-hour or ~7,000 proteins in 5 min, representing 3× higher coverage compared with current state-of-the-art MS. Multi-shot acquisition of offline fractionated samples provides comprehensive coverage of human proteomes in ~3 h. High quantitative precision and accuracy are demonstrated in a three-species proteome mixture, quantifying 14,000+ protein groups in a single half-an-hour run.
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Affiliation(s)
- Ulises H Guzman
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Ana Martinez-Val
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Zilu Ye
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou, China
| | - Eugen Damoc
- Thermo Fisher Scientific (Bremen) GmbH, Bremen, Germany
| | | | - Anna Pashkova
- Thermo Fisher Scientific (Bremen) GmbH, Bremen, Germany
| | | | | | | | | | - Florian Harking
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Ole Østergaard
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Rasmus Rydbirk
- Center for Functional Genomics and Tissue Plasticity (ATLAS), Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark
| | - Susana Aznar
- Centre for Neuroscience and Stereology, Copenhagen University Hospital, Copenhagen, Denmark
| | | | - Yue Xuan
- Thermo Fisher Scientific (Bremen) GmbH, Bremen, Germany
| | | | | | | | | | | | - Jesper V Olsen
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark.
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28
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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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29
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Kardell O, von Toerne C, Merl-Pham J, König AC, Blindert M, Barth TK, Mergner J, Ludwig C, Tüshaus J, Eckert S, Müller SA, Breimann S, Giesbertz P, Bernhardt AM, Schweizer L, Albrecht V, Teupser D, Imhof A, Kuster B, Lichtenthaler SF, Mann M, Cox J, Hauck SM. Multicenter Collaborative Study to Optimize Mass Spectrometry Workflows of Clinical Specimens. J Proteome Res 2024; 23:117-129. [PMID: 38015820 PMCID: PMC10775142 DOI: 10.1021/acs.jproteome.3c00473] [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: 07/31/2023] [Revised: 11/02/2023] [Accepted: 11/14/2023] [Indexed: 11/30/2023]
Abstract
The foundation for integrating mass spectrometry (MS)-based proteomics into systems medicine is the development of standardized start-to-finish and fit-for-purpose workflows for clinical specimens. An essential step in this pursuit is to highlight the common ground in a diverse landscape of different sample preparation techniques and liquid chromatography-mass spectrometry (LC-MS) setups. With the aim to benchmark and improve the current best practices among the proteomics MS laboratories of the CLINSPECT-M consortium, we performed two consecutive round-robin studies with full freedom to operate in terms of sample preparation and MS measurements. The six study partners were provided with two clinically relevant sample matrices: plasma and cerebrospinal fluid (CSF). In the first round, each laboratory applied their current best practice protocol for the respective matrix. Based on the achieved results and following a transparent exchange of all lab-specific protocols within the consortium, each laboratory could advance their methods before measuring the same samples in the second acquisition round. Both time points are compared with respect to identifications (IDs), data completeness, and precision, as well as reproducibility. As a result, the individual performances of participating study centers were improved in the second measurement, emphasizing the effect and importance of the expert-driven exchange of best practices for direct practical improvements.
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Affiliation(s)
- Oliver Kardell
- Metabolomics
and Proteomics Core (MPC), Helmholtz Zentrum
München,German Research Center for Environmental Health (GmbH), Munich 80939, Germany
| | - Christine von Toerne
- Metabolomics
and Proteomics Core (MPC), Helmholtz Zentrum
München,German Research Center for Environmental Health (GmbH), Munich 80939, Germany
| | - Juliane Merl-Pham
- Metabolomics
and Proteomics Core (MPC), Helmholtz Zentrum
München,German Research Center for Environmental Health (GmbH), Munich 80939, Germany
| | - Ann-Christine König
- Metabolomics
and Proteomics Core (MPC), Helmholtz Zentrum
München,German Research Center for Environmental Health (GmbH), Munich 80939, Germany
| | - Marcel Blindert
- Metabolomics
and Proteomics Core (MPC), Helmholtz Zentrum
München,German Research Center for Environmental Health (GmbH), Munich 80939, Germany
| | - Teresa K. Barth
- Clinical
Protein Analysis Unit (ClinZfP), Biomedical Center (BMC), Faculty
of Medicine, Ludwig-Maximilians-University
(LMU) Munich, Großhaderner Straße 9, Martinsried 82152, Germany
| | - Julia Mergner
- Bavarian
Center for Biomolecular Mass Spectrometry at Klinikum Rechts der Isar
(BayBioMS@MRI), Technical University of
Munich, Munich 80333, Germany
| | - Christina Ludwig
- Bavarian
Center for Biomolecular Mass Spectrometry (BayBioMS), TUM School of
Life Sciences, Technical University of Munich, Freising 85354, Germany
| | - Johanna Tüshaus
- Chair
of Proteomics and Bioanalytics, Technical
University of Munich, Freising 85354, Germany
| | - Stephan Eckert
- Chair
of Proteomics and Bioanalytics, Technical
University of Munich, Freising 85354, Germany
| | - Stephan A. Müller
- German
Center
for Neurodegenerative Diseases (DZNE) Munich, DZNE, Munich 81377, Germany
- Neuroproteomics,
School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich 81675, Germany
| | - Stephan Breimann
- German
Center
for Neurodegenerative Diseases (DZNE) Munich, DZNE, Munich 81377, Germany
- Neuroproteomics,
School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich 81675, Germany
| | - Pieter Giesbertz
- German
Center
for Neurodegenerative Diseases (DZNE) Munich, DZNE, Munich 81377, Germany
- Neuroproteomics,
School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich 81675, Germany
| | - Alexander M. Bernhardt
- German
Center
for Neurodegenerative Diseases (DZNE) Munich, DZNE, Munich 81377, Germany
- Department
of Neurology, Ludwig-Maximilians-Universität
München, Munich 80539, Germany
| | - Lisa Schweizer
- Department
of Proteomics and Signal Transduction, Max-Planck
Institute of Biochemistry, Martinsried 82152, Germany
| | - Vincent Albrecht
- Department
of Proteomics and Signal Transduction, Max-Planck
Institute of Biochemistry, Martinsried 82152, Germany
| | - Daniel Teupser
- Institute
of Laboratory Medicine, University Hospital,
LMU Munich, Munich 81377, Germany
| | - Axel Imhof
- Clinical
Protein Analysis Unit (ClinZfP), Biomedical Center (BMC), Faculty
of Medicine, Ludwig-Maximilians-University
(LMU) Munich, Großhaderner Straße 9, Martinsried 82152, Germany
| | - Bernhard Kuster
- Bavarian
Center for Biomolecular Mass Spectrometry (BayBioMS), TUM School of
Life Sciences, Technical University of Munich, Freising 85354, Germany
- Chair
of Proteomics and Bioanalytics, Technical
University of Munich, Freising 85354, Germany
| | - Stefan F. Lichtenthaler
- German
Center
for Neurodegenerative Diseases (DZNE) Munich, DZNE, Munich 81377, Germany
- Neuroproteomics,
School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich 81675, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich 81377, Germany
| | - Matthias Mann
- Department
of Proteomics and Signal Transduction, Max-Planck
Institute of Biochemistry, Martinsried 82152, Germany
| | - Jürgen Cox
- Computational Systems
Biochemistry Research Group, Max-Planck
Institute of Biochemistry, Martinsried 82152, Germany
| | - Stefanie M. Hauck
- Metabolomics
and Proteomics Core (MPC), Helmholtz Zentrum
München,German Research Center for Environmental Health (GmbH), Munich 80939, Germany
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30
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Szyrwiel L, Gille C, Mülleder M, Demichev V, Ralser M. Fast proteomics with dia-PASEF and analytical flow-rate chromatography. Proteomics 2024; 24:e2300100. [PMID: 37287406 DOI: 10.1002/pmic.202300100] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 05/22/2023] [Accepted: 05/22/2023] [Indexed: 06/09/2023]
Abstract
Increased throughput in proteomic experiments can improve accessibility of proteomic platforms, reduce costs, and facilitate new approaches in systems biology and biomedical research. Here we propose combination of analytical flow rate chromatography with ion mobility separation of peptide ions, data-independent acquisition, and data analysis with the DIA-NN software suite, to achieve high-quality proteomic experiments from limited sample amounts, at a throughput of up to 400 samples per day. For instance, when benchmarking our workflow using a 500-μL/min flow rate and 3-min chromatographic gradients, we report the quantification of 5211 proteins from 2 μg of a mammalian cell-line standard at high quantitative accuracy and precision. We further used this platform to analyze blood plasma samples from a cohort of COVID-19 inpatients, using a 3-min chromatographic gradient and alternating column regeneration on a dual pump system. The method delivered a comprehensive view of the COVID-19 plasma proteome, allowing classification of the patients according to disease severity and revealing plasma biomarker candidates.
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Affiliation(s)
- Lukasz Szyrwiel
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Christoph Gille
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Core Facility High-Throughput Mass Spectrometry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Michael Mülleder
- Core Facility High-Throughput Mass Spectrometry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Vadim Demichev
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Markus Ralser
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
- The Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Max Planck Institute for Molecular Genetics, Berlin, Germany
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31
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Shi J, Zhao J, Zhang Y, Wang Y, Tan CP, Xu YJ, Liu Y. Windows Scanning Multiomics: Integrated Metabolomics and Proteomics. Anal Chem 2023; 95:18793-18802. [PMID: 38095040 DOI: 10.1021/acs.analchem.3c03785] [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: 12/27/2023]
Abstract
Metabolomics and proteomics offer significant advantages in understanding biological mechanisms at two hierarchical levels. However, conventional single omics analysis faces challenges due to the high demand for specimens and the complexity of intrinsic associations. To obtain comprehensive and accurate system biological information, we developed a multiomics analytical method called Windows Scanning Multiomics (WSM). In this method, we performed simultaneous extraction of metabolites and proteins from the same sample, resulting in a 10% increase in the coverage of the identified biomolecules. Both metabolomics and proteomics analyses were conducted by using ultrahigh-performance liquid chromatography mass spectrometry (UPLC-MS), eliminating the need for instrument conversions. Additionally, we designed an R-based program (WSM.R) to integrate mathematical and biological correlations between metabolites and proteins into a correlation network. The network created from simultaneously extracted biomolecules was more focused and comprehensive compared to those from separate extractions. Notably, we excluded six pairs of false-positive relationships between metabolites and proteins in the network established using simultaneously extracted biomolecules. In conclusion, this study introduces a novel approach for multiomics analysis and data processing that greatly aids in bioinformation mining from multiomics results. This method is poised to play an indispensable role in systems biology research.
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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
| | - Jialiang Zhao
- 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
| | - Yu Zhang
- 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
| | - Yanan Wang
- 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
| | - Chin Ping Tan
- Department of Food Technology, Faculty of Food Science and Technology, Universiti Putra Malaysia, Serdang, Selangor 43400, Malaysia
| | - 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
| | - 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
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32
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Tüshaus J, Sakhteman A, Lechner S, The M, Mucha E, Krisp C, Schlegel J, Delbridge C, Kuster B. A region-resolved proteomic map of the human brain enabled by high-throughput proteomics. EMBO J 2023; 42:e114665. [PMID: 37916885 PMCID: PMC10690467 DOI: 10.15252/embj.2023114665] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 10/16/2023] [Accepted: 10/17/2023] [Indexed: 11/03/2023] Open
Abstract
Substantial efforts are underway to deepen our understanding of human brain morphology, structure, and function using high-resolution imaging as well as high-content molecular profiling technologies. The current work adds to these approaches by providing a comprehensive and quantitative protein expression map of 13 anatomically distinct brain regions covering more than 11,000 proteins. This was enabled by the optimization, characterization, and implementation of a high-sensitivity and high-throughput microflow liquid chromatography timsTOF tandem mass spectrometry system (LC-MS/MS) capable of analyzing more than 2,000 consecutive samples prepared from formalin-fixed paraffin embedded (FFPE) material. Analysis of this proteomic resource highlighted brain region-enriched protein expression patterns and functional protein classes, protein localization differences between brain regions and individual markers for specific areas. To facilitate access to and ease further mining of the data by the scientific community, all data can be explored online in a purpose-built R Shiny app (https://brain-region-atlas.proteomics.ls.tum.de).
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Affiliation(s)
- Johanna Tüshaus
- Proteomics and Bioanalytics, Department of Molecular Life Sciences, School of Life SciencesTechnical University of MunichMunichGermany
| | - Amirhossein Sakhteman
- Proteomics and Bioanalytics, Department of Molecular Life Sciences, School of Life SciencesTechnical University of MunichMunichGermany
| | - Severin Lechner
- Proteomics and Bioanalytics, Department of Molecular Life Sciences, School of Life SciencesTechnical University of MunichMunichGermany
| | - Matthew The
- Proteomics and Bioanalytics, Department of Molecular Life Sciences, School of Life SciencesTechnical University of MunichMunichGermany
| | - Eike Mucha
- Bruker Daltonics GmbH & Co. KGBremenGermany
| | | | - Jürgen Schlegel
- Department of Neuropathology, Klinikum Rechts der ISAR, School of MedicineTechnical University MunichMunichGermany
| | - Claire Delbridge
- Department of Neuropathology, Klinikum Rechts der ISAR, School of MedicineTechnical University MunichMunichGermany
| | - Bernhard Kuster
- Proteomics and Bioanalytics, Department of Molecular Life Sciences, School of Life SciencesTechnical University of MunichMunichGermany
- German Cancer Consortium (DKTK), Munich SiteHeidelbergGermany
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33
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Wevers D, Ramautar R, Clark C, Hankemeier T, Ali A. Opportunities and challenges for sample preparation and enrichment in mass spectrometry for single-cell metabolomics. Electrophoresis 2023; 44:2000-2024. [PMID: 37667867 DOI: 10.1002/elps.202300105] [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: 05/11/2023] [Revised: 08/08/2023] [Accepted: 08/19/2023] [Indexed: 09/06/2023]
Abstract
Single-cell heterogeneity in metabolism, drug resistance and disease type poses the need for analytical techniques for single-cell analysis. As the metabolome provides the closest view of the status quo in the cell, studying the metabolome at single-cell resolution may unravel said heterogeneity. A challenge in single-cell metabolome analysis is that metabolites cannot be amplified, so one needs to deal with picolitre volumes and a wide range of analyte concentrations. Due to high sensitivity and resolution, MS is preferred in single-cell metabolomics. Large numbers of cells need to be analysed for proper statistics; this requires high-throughput analysis, and hence automation of the analytical workflow. Significant advances in (micro)sampling methods, CE and ion mobility spectrometry have been made, some of which have been applied in high-throughput analyses. Microfluidics has enabled an automation of cell picking and metabolite extraction; image recognition has enabled automated cell identification. Many techniques have been used for data analysis, varying from conventional techniques to novel combinations of advanced chemometric approaches. Steps have been set in making data more findable, accessible, interoperable and reusable, but significant opportunities for improvement remain. Herein, advances in single-cell analysis workflows and data analysis are discussed, and recommendations are made based on the experimental goal.
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Affiliation(s)
- Dirk Wevers
- Wageningen University and Research, Wageningen, The Netherlands
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden, The Netherlands
| | - Rawi Ramautar
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden, The Netherlands
| | - Charlie Clark
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden, The Netherlands
| | - Thomas Hankemeier
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden, The Netherlands
| | - Ahmed Ali
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden, The Netherlands
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34
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Leduc A, Koury L, Cantlon J, Slavov N. Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.27.568927. [PMID: 38076795 PMCID: PMC10705290 DOI: 10.1101/2023.11.27.568927] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Single-cell proteomics by mass spectrometry (MS) allows quantifying proteins with high specificity and sensitivity. To increase its throughput, we developed nPOP, a method for parallel preparation of thousands of single cells in nanoliter volume droplets deposited on glass slides. Here, we describe its protocol with emphasis on its flexibility to prepare samples for different multiplexed MS methods. An implementation with plexDIA demonstrates accurate quantification of about 3,000 - 3,700 proteins per human cell. The protocol is implemented on the CellenONE instrument and uses readily available consumables, which should facilitate broad adoption. nPOP can be applied to all samples that can be processed to a single-cell suspension. It takes 1 or 2 days to prepare over 3,000 single cells. We provide metrics and software for quality control that can support the robust scaling of nPOP to higher plex reagents for achieving reliable high-throughput single-cell protein analysis.
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Affiliation(s)
- Andrew Leduc
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA 02115, USA
| | - Luke Koury
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA 02115, USA
| | | | - Nikolai Slavov
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA 02115, USA
- Parallel Squared Technology Institute, Watertown, MA 02472, USA
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35
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Davis S, Scott C, Oetjen J, Charles PD, Kessler BM, Ansorge O, Fischer R. Deep topographic proteomics of a human brain tumour. Nat Commun 2023; 14:7710. [PMID: 38001067 PMCID: PMC10673928 DOI: 10.1038/s41467-023-43520-8] [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: 03/01/2023] [Accepted: 11/13/2023] [Indexed: 11/26/2023] Open
Abstract
The spatial organisation of cellular protein expression profiles within tissue determines cellular function and is key to understanding disease pathology. To define molecular phenotypes in the spatial context of tissue, there is a need for unbiased, quantitative technology capable of mapping proteomes within tissue structures. Here, we present a workflow for spatially-resolved, quantitative proteomics of tissue that generates maps of protein abundance across tissue slices derived from a human atypical teratoid-rhabdoid tumour at three spatial resolutions, the highest being 40 µm, to reveal distinct abundance patterns of thousands of proteins. We employ spatially-aware algorithms that do not require prior knowledge of the fine tissue structure to detect proteins and pathways with spatial abundance patterns and correlate proteins in the context of tissue heterogeneity and cellular features such as extracellular matrix or proximity to blood vessels. We identify PYGL, ASPH and CD45 as spatial markers for tumour boundary and reveal immune response-driven, spatially-organised protein networks of the extracellular tumour matrix. Overall, we demonstrate spatially-aware deep proteo-phenotyping of tissue heterogeneity, to re-define understanding tissue biology and pathology at the molecular level.
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Affiliation(s)
- Simon Davis
- Target Discovery Institute, Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FZ, UK
- Chinese Academy for Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FZ, UK
| | - Connor Scott
- Academic Unit of Neuropathology, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DU, UK
| | - Janina Oetjen
- Bruker Daltonics GmbH & Co. KG, Fahrenheitstraße 4, 28359, Bremen, Germany
| | - Philip D Charles
- Target Discovery Institute, Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FZ, UK
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FZ, UK
| | - Benedikt M Kessler
- Target Discovery Institute, Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FZ, UK
- Chinese Academy for Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FZ, UK
| | - Olaf Ansorge
- Academic Unit of Neuropathology, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DU, UK
| | - Roman Fischer
- Target Discovery Institute, Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FZ, UK.
- Chinese Academy for Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FZ, UK.
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36
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Yan B, Shi M, Cai S, Su Y, Chen R, Huang C, Chen DDY. Data-Driven Tool for Cross-Run Ion Selection and Peak-Picking in Quantitative Proteomics with Data-Independent Acquisition LC-MS/MS. Anal Chem 2023; 95:16558-16566. [PMID: 37906674 DOI: 10.1021/acs.analchem.3c02689] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Proteomics provides molecular bases of biology and disease, and liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a platform widely used for bottom-up proteomics. Data-independent acquisition (DIA) improves the run-to-run reproducibility of LC-MS/MS in proteomics research. However, the existing DIA data processing tools sometimes produce large deviations from true values for the peptides and proteins in quantification. Peak-picking error and incorrect ion selection are the two main causes of the deviations. We present a cross-run ion selection and peak-picking (CRISP) tool that utilizes the important advantage of run-to-run consistency of DIA and simultaneously examines the DIA data from the whole set of runs to filter out the interfering signals, instead of only looking at a single run at a time. Eight datasets acquired by mass spectrometers from different vendors with different types of mass analyzers were used to benchmark our CRISP-DIA against other currently available DIA tools. In the benchmark datasets, for analytes with large content variation among samples, CRISP-DIA generally resulted in 20 to 50% relative decrease in error rates compared to other DIA tools, at both the peptide precursor level and the protein level. CRISP-DIA detected differentially expressed proteins more efficiently, with 3.3 to 90.3% increases in the numbers of true positives and 12.3 to 35.3% decreases in the false positive rates, in some cases. In the real biological datasets, CRISP-DIA showed better consistencies of the quantification results. The advantages of assimilating DIA data in multiple runs for quantitative proteomics were demonstrated, which can significantly improve the quantification accuracy.
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Affiliation(s)
- Binjun Yan
- Key Laboratory of Systems Biology, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China
| | - Mengtian Shi
- Key Laboratory of Systems Biology, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China
- College of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Siyu Cai
- Key Laboratory of Systems Biology, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China
- College of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Yuan Su
- Key Laboratory of Systems Biology, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China
- College of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Renhui Chen
- Key Laboratory of Systems Biology, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China
| | - Chiyuan Huang
- Key Laboratory of Systems Biology, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China
| | - David Da Yong Chen
- Department of Chemistry, University of British Columbia, Vancouver, British Columbia V6T 1Z1, Canada
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37
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Mahoney SA, Dey AK, Basisty N, Herman AB. Identification and functional analysis of senescent cells in the cardiovascular system using omics approaches. Am J Physiol Heart Circ Physiol 2023; 325:H1039-H1058. [PMID: 37656130 PMCID: PMC10908411 DOI: 10.1152/ajpheart.00352.2023] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 08/28/2023] [Accepted: 08/28/2023] [Indexed: 09/02/2023]
Abstract
Cardiovascular disease (CVD) is a leading cause of morbidity and mortality worldwide, and senescent cells have emerged as key contributors to its pathogenesis. Senescent cells exhibit cell cycle arrest and secrete a range of proinflammatory factors, termed the senescence-associated secretory phenotype (SASP), which promotes tissue dysfunction and exacerbates CVD progression. Omics technologies, specifically transcriptomics and proteomics, offer powerful tools to uncover and define the molecular signatures of senescent cells in cardiovascular tissue. By analyzing the comprehensive molecular profiles of senescent cells, omics approaches can identify specific genetic alterations, gene expression patterns, protein abundances, and metabolite levels associated with senescence in CVD. These omics-based discoveries provide insights into the mechanisms underlying senescence-induced cardiovascular damage, facilitating the development of novel diagnostic biomarkers and therapeutic targets. Furthermore, integration of multiple omics data sets enables a systems-level understanding of senescence in CVD, paving the way for precision medicine approaches to prevent or treat cardiovascular aging and its associated complications.
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Affiliation(s)
- Sophia A Mahoney
- Department of Integrative Physiology, University of Colorado at Boulder, Boulder, Colorado, United States
| | - Amit K Dey
- Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States
| | - Nathan Basisty
- Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States
| | - Allison B Herman
- Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States
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38
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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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39
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Basak I, Harfoot R, Palmer JE, Kumar A, Quiñones-Mateu ME, Schweitzer L, Hughes SM. Neuroproteomic Analysis after SARS-CoV-2 Infection Reveals Overrepresented Neurodegeneration Pathways and Disrupted Metabolic Pathways. Biomolecules 2023; 13:1597. [PMID: 38002279 PMCID: PMC10669333 DOI: 10.3390/biom13111597] [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: 09/20/2023] [Revised: 10/19/2023] [Accepted: 10/26/2023] [Indexed: 11/26/2023] Open
Abstract
Besides respiratory illness, SARS-CoV-2, the causative agent of COVID-19, leads to neurological symptoms. The molecular mechanisms leading to neuropathology after SARS-CoV-2 infection are sparsely explored. SARS-CoV-2 enters human cells via different receptors, including ACE-2, TMPRSS2, and TMEM106B. In this study, we used a human-induced pluripotent stem cell-derived neuronal model, which expresses ACE-2, TMPRSS2, TMEM106B, and other possible SARS-CoV-2 receptors, to evaluate its susceptibility to SARS-CoV-2 infection. The neurons were exposed to SARS-CoV-2, followed by RT-qPCR, immunocytochemistry, and proteomic analyses of the infected neurons. Our findings showed that SARS-CoV-2 infects neurons at a lower rate than other human cells; however, the virus could not replicate or produce infectious virions in this neuronal model. Despite the aborted SARS-CoV-2 replication, the infected neuronal nuclei showed irregular morphology compared to other human cells. Since cytokine storm is a significant effect of SARS-CoV-2 infection in COVID-19 patients, in addition to the direct neuronal infection, the neurons were treated with pre-conditioned media from SARS-CoV-2-infected lung cells, and the neuroproteomic changes were investigated. The limited SARS-CoV-2 infection in the neurons and the neurons treated with the pre-conditioned media showed changes in the neuroproteomic profile, particularly affecting mitochondrial proteins and apoptotic and metabolic pathways, which may lead to the development of neurological complications. The findings from our study uncover a possible mechanism behind SARS-CoV-2-mediated neuropathology that might contribute to the lingering effects of the virus on the human brain.
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Affiliation(s)
- Indranil Basak
- Brain Health Research Centre, Department of Biochemistry, University of Otago, Dunedin 9016, New Zealand
| | - Rhodri Harfoot
- Department of Microbiology and Immunology, University of Otago, Dunedin 9016, New Zealand (M.E.Q.-M.)
| | - Jennifer E. Palmer
- Brain Health Research Centre, Department of Biochemistry, University of Otago, Dunedin 9016, New Zealand
| | - Abhishek Kumar
- Centre for Protein Research, University of Otago, Dunedin 9016, New Zealand
| | - Miguel E. Quiñones-Mateu
- Department of Microbiology and Immunology, University of Otago, Dunedin 9016, New Zealand (M.E.Q.-M.)
| | - Lucia Schweitzer
- Brain Health Research Centre, Department of Biochemistry, University of Otago, Dunedin 9016, New Zealand
| | - Stephanie M. Hughes
- Brain Health Research Centre, Department of Biochemistry, University of Otago, Dunedin 9016, New Zealand
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40
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Jin L, Wang F, Wang X, Harvey BP, Bi Y, Hu C, Cui B, Darcy AT, Maull JW, Phillips BR, Kim Y, Jenkins GJ, Sornasse TR, Tian Y. Identification of Plasma Biomarkers from Rheumatoid Arthritis Patients Using an Optimized Sequential Window Acquisition of All THeoretical Mass Spectra (SWATH) Proteomics Workflow. Proteomes 2023; 11:32. [PMID: 37873874 PMCID: PMC10594463 DOI: 10.3390/proteomes11040032] [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: 08/16/2023] [Revised: 09/28/2023] [Accepted: 10/02/2023] [Indexed: 10/25/2023] Open
Abstract
Rheumatoid arthritis (RA) is a systemic autoimmune and inflammatory disease. Plasma biomarkers are critical for understanding disease mechanisms, treatment effects, and diagnosis. Mass spectrometry-based proteomics is a powerful tool for unbiased biomarker discovery. However, plasma proteomics is significantly hampered by signal interference from high-abundance proteins, low overall protein coverage, and high levels of missing data from data-dependent acquisition (DDA). To achieve quantitative proteomics analysis for plasma samples with a balance of throughput, performance, and cost, we developed a workflow incorporating plate-based high abundance protein depletion and sample preparation, comprehensive peptide spectral library building, and data-independent acquisition (DIA) SWATH mass spectrometry-based methodology. In this study, we analyzed plasma samples from both RA patients and healthy donors. The results showed that the new workflow performance exceeded that of the current state-of-the-art depletion-based plasma proteomic platforms in terms of both data quality and proteome coverage. Proteins from biological processes related to the activation of systemic inflammation, suppression of platelet function, and loss of muscle mass were enriched and differentially expressed in RA. Some plasma proteins, particularly acute-phase reactant proteins, showed great power to distinguish between RA patients and healthy donors. Moreover, protein isoforms in the plasma were also analyzed, providing even deeper proteome coverage. This workflow can serve as a basis for further application in discovering plasma biomarkers of other diseases.
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Affiliation(s)
- Liang Jin
- Research & Development, AbbVie, North Chicago, IL 60064, USA; (L.J.); (B.P.H.); (B.C.); (A.T.D.); (J.W.M.); (T.R.S.)
| | - Fei Wang
- Research & Development, AbbVie, North Chicago, IL 60064, USA; (L.J.); (B.P.H.); (B.C.); (A.T.D.); (J.W.M.); (T.R.S.)
| | - Xue Wang
- Research & Development, AbbVie, North Chicago, IL 60064, USA; (L.J.); (B.P.H.); (B.C.); (A.T.D.); (J.W.M.); (T.R.S.)
| | - Bohdan P. Harvey
- Research & Development, AbbVie, North Chicago, IL 60064, USA; (L.J.); (B.P.H.); (B.C.); (A.T.D.); (J.W.M.); (T.R.S.)
| | - Yingtao Bi
- Research & Development, AbbVie, North Chicago, IL 60064, USA; (L.J.); (B.P.H.); (B.C.); (A.T.D.); (J.W.M.); (T.R.S.)
| | - Chenqi Hu
- DMPK, Takeda Development Center Americas Inc., Cambridge, MA 02142, USA; (C.H.)
| | - Baoliang Cui
- Research & Development, AbbVie, North Chicago, IL 60064, USA; (L.J.); (B.P.H.); (B.C.); (A.T.D.); (J.W.M.); (T.R.S.)
| | - Anhdao T. Darcy
- Research & Development, AbbVie, North Chicago, IL 60064, USA; (L.J.); (B.P.H.); (B.C.); (A.T.D.); (J.W.M.); (T.R.S.)
| | - John W. Maull
- Research & Development, AbbVie, North Chicago, IL 60064, USA; (L.J.); (B.P.H.); (B.C.); (A.T.D.); (J.W.M.); (T.R.S.)
| | - Ben R. Phillips
- Research & Development, AbbVie, North Chicago, IL 60064, USA; (L.J.); (B.P.H.); (B.C.); (A.T.D.); (J.W.M.); (T.R.S.)
| | - Youngjae Kim
- DMPK, Takeda Development Center Americas Inc., Cambridge, MA 02142, USA; (C.H.)
| | - Gary J. Jenkins
- Research & Development, AbbVie, North Chicago, IL 60064, USA; (L.J.); (B.P.H.); (B.C.); (A.T.D.); (J.W.M.); (T.R.S.)
| | - Thierry R. Sornasse
- Research & Development, AbbVie, North Chicago, IL 60064, USA; (L.J.); (B.P.H.); (B.C.); (A.T.D.); (J.W.M.); (T.R.S.)
| | - Yu Tian
- Research & Development, AbbVie, North Chicago, IL 60064, USA; (L.J.); (B.P.H.); (B.C.); (A.T.D.); (J.W.M.); (T.R.S.)
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41
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Teyssonnière E, Trébulle P, Muenzner J, Loegler V, Ludwig D, Amari F, Mülleder M, Friedrich A, Hou J, Ralser M, Schacherer J. Species-wide quantitative transcriptomes and proteomes reveal distinct genetic control of gene expression variation in yeast. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.18.558197. [PMID: 37781592 PMCID: PMC10541136 DOI: 10.1101/2023.09.18.558197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
Abstract
Gene expression varies between individuals and corresponds to a key step linking genotypes to phenotypes. However, our knowledge regarding the species-wide genetic control of protein abundance, including its dependency on transcript levels, is very limited. Here, we have determined quantitative proteomes of a large population of 942 diverse natural Saccharomyces cerevisiae yeast isolates. We found that mRNA and protein abundances are weakly correlated at the population gene level. While the protein co-expression network recapitulates major biological functions, differential expression patterns reveal proteomic signatures related to specific populations. Comprehensive genetic association analyses highlight that genetic variants associated with variation in protein (pQTL) and transcript (eQTL) levels poorly overlap (3.6%). Our results demonstrate that transcriptome and proteome are governed by distinct genetic bases, likely explained by protein turnover. It also highlights the importance of integrating these different levels of gene expression to better understand the genotype-phenotype relationship. Highlights At the level of individual genes, the abundance of transcripts and proteins is weakly correlated within a species ( ρ = 0.165). While the proteome is not imprinted by population structure, co-expression patterns recapitulate the cellular functional landscapeWild populations exhibit a higher abundance of respiration-related proteins compared to domesticated populationsLoci that influence protein abundance differ from those that impact transcript levels, likely because of protein turnover.
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42
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Schweizer L, Schaller T, Zwiebel M, Karayel Ö, Müller‐Reif JB, Zeng W, Dintner S, Nordmann TM, Hirschbühl K, Märkl B, Claus R, Mann M. Quantitative multiorgan proteomics of fatal COVID-19 uncovers tissue-specific effects beyond inflammation. EMBO Mol Med 2023; 15:e17459. [PMID: 37519267 PMCID: PMC10493576 DOI: 10.15252/emmm.202317459] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 07/11/2023] [Accepted: 07/14/2023] [Indexed: 08/01/2023] Open
Abstract
SARS-CoV-2 may directly and indirectly damage lung tissue and other host organs, but there are few system-wide, untargeted studies of these effects on the human body. Here, we developed a parallelized mass spectrometry (MS) proteomics workflow enabling the rapid, quantitative analysis of hundreds of virus-infected FFPE tissues. The first layer of response to SARS-CoV-2 in all tissues was dominated by circulating inflammatory molecules. Beyond systemic inflammation, we differentiated between systemic and true tissue-specific effects to reflect distinct COVID-19-associated damage patterns. Proteomic changes in the lungs resembled those of diffuse alveolar damage (DAD) in non-COVID-19 patients. Extensive organ-specific changes were also evident in the kidneys, liver, and lymphatic and vascular systems. Secondary inflammatory effects in the brain were related to rearrangements in neurotransmitter receptors and myelin degradation. These MS-proteomics-derived results contribute substantially to our understanding of COVID-19 pathomechanisms and suggest strategies for organ-specific therapeutic interventions.
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Affiliation(s)
- Lisa Schweizer
- Department of Proteomics and Signal TransductionMax Planck Institute of BiochemistryMartinsriedGermany
| | - Tina Schaller
- Pathology, Medical FacultyUniversity of AugsburgAugsburgGermany
| | - Maximilian Zwiebel
- Department of Proteomics and Signal TransductionMax Planck Institute of BiochemistryMartinsriedGermany
| | - Özge Karayel
- Department of Proteomics and Signal TransductionMax Planck Institute of BiochemistryMartinsriedGermany
- Present address:
Department of Physiological ChemistryGenentechSouth San FranciscoUSA
| | | | - Wen‐Feng Zeng
- Department of Proteomics and Signal TransductionMax Planck Institute of BiochemistryMartinsriedGermany
| | | | - Thierry M Nordmann
- Department of Proteomics and Signal TransductionMax Planck Institute of BiochemistryMartinsriedGermany
| | - Klaus Hirschbühl
- Hematology and Oncology, Medical FacultyUniversity of AugsburgAugsburgGermany
| | - Bruno Märkl
- Pathology, Medical FacultyUniversity of AugsburgAugsburgGermany
| | - Rainer Claus
- Pathology, Medical FacultyUniversity of AugsburgAugsburgGermany
- Hematology and Oncology, Medical FacultyUniversity of AugsburgAugsburgGermany
| | - Matthias Mann
- Department of Proteomics and Signal TransductionMax Planck Institute of BiochemistryMartinsriedGermany
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43
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Zhang F, Luna A, Tan T, Chen Y, Sander C, Guo T. COVIDpro: Database for Mining Protein Dysregulation in Patients with COVID-19. J Proteome Res 2023; 22:2847-2859. [PMID: 37555633 DOI: 10.1021/acs.jproteome.3c00092] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/10/2023]
Abstract
The ongoing pandemic of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 still has limited treatment options. Our understanding of the molecular dysregulations that occur in response to infection remains incomplete. We developed a web application COVIDpro (https://www.guomics.com/covidPro/) that includes proteomics data obtained from 41 original studies conducted in 32 hospitals worldwide, involving 3077 patients and covering 19 types of clinical specimens, predominantly plasma and serum. The data set encompasses 53 protein expression matrices, comprising a total of 5434 samples and 14,403 unique proteins. We identified a panel of proteins that exhibit significant dysregulation, enabling the classification of COVID-19 patients into severe and non-severe disease categories. The proteomic signatures achieved promising results in distinguishing severe cases, with a mean area under the curve of 0.87 and accuracy of 0.80 across five independent test sets. COVIDpro serves as a valuable resource for testing hypotheses and exploring potential targets for novel treatments in COVID-19 patients.
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Affiliation(s)
- Fangfei Zhang
- Fudan University, 220 Handan Road, Shanghai 200433, China
- Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province 310024, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang Province 310024, China
- Research Center for Industries of the Future, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030, China
| | - Augustin Luna
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts 02115, United States
- Broad Institute of MIT and Harvard, Cambridge, Cambridge, Massachusetts 02142, United States
| | - Tingting Tan
- Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province 310024, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang Province 310024, China
- Research Center for Industries of the Future, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030, China
| | - Yingdan Chen
- Westlake Omics (Hangzhou) Biotechnology Company Limited, Hangzhou, Zhejiang Province 310024, China
| | - Chris Sander
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts 02115, United States
- Broad Institute of MIT and Harvard, Cambridge, Cambridge, Massachusetts 02142, United States
| | - Tiannan Guo
- Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province 310024, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang Province 310024, China
- Research Center for Industries of the Future, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030, China
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44
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Chen R, Yang J, Wu M, Zhao D, Yuan Z, Zeng L, Hu J, Zhang X, Wang T, Xu J, Zhang J. M2 Macrophage Hybrid Membrane-Camouflaged Targeted Biomimetic Nanosomes to Reprogram Inflammatory Microenvironment for Enhanced Enzyme-Thermo-Immunotherapy. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2304123. [PMID: 37339776 DOI: 10.1002/adma.202304123] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 06/16/2023] [Indexed: 06/22/2023]
Abstract
Excessive inflammatory reactions caused by uric acid deposition are the key factor leading to gout. However, clinical medications cannot simultaneously remove uric acid and eliminate inflammation. An M2 macrophage-erythrocyte hybrid membrane-camouflaged biomimetic nanosized liposome (USM[H]L) is engineered to deliver targeted self-cascading bienzymes and immunomodulators to reprogram the inflammatory microenvironment in gouty rats. The cell-membrane-coating endow nanosomes with good immune escape and lysosomal escape to achieve long circulation time and intracellular retention times. After being uptaken by inflammatory cells, synergistic enzyme-thermo-immunotherapies are achieved: uricase and nanozyme degraded uric acid and hydrogen peroxide, respectively; bienzymes improved the catalytic abilities of each other; nanozyme produced photothermal effects; and methotrexate has immunomodulatory and anti-inflammatory effects. The uric acid levels markedly decrease, and ankle swelling and claw curling are effectively alleviated. The levels of inflammatory cytokines and ROS decrease, while the anti-inflammatory cytokine levels increase. Proinflammatory M1 macrophages are reprogrammed to the anti-inflammatory M2 phenotype. Notably, the IgG and IgM levels in USM[H]L-treated rats decrease substantially, while uricase-treated rats show high immunogenicity. Proteomic analysis show that there are 898 downregulated and 725 upregulated differentially expressed proteins in USM[H]L-treated rats. The protein-protein interaction network indicates that the signaling pathways include the spliceosome, ribosome, purine metabolism, etc.
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Affiliation(s)
- Ran Chen
- College of Pharmacy, Chongqing Medical University, Chongqing, 400016, China
| | - Jie Yang
- College of Pharmacy, Chongqing Medical University, Chongqing, 400016, China
| | - Mingjun Wu
- Institute of Life Sciences, Chongqing Medical University, Chongqing, 400016, China
| | - Dezhang Zhao
- College of Pharmacy, Chongqing Medical University, Chongqing, 400016, China
| | - Ziyi Yuan
- College of Pharmacy, Chongqing Medical University, Chongqing, 400016, China
| | - Linggao Zeng
- NMPA Key Laboratory for Quality Monitoring of Narcotic Drugs and Psychotropic Substances, Chongqing Institute for Food and Drug Control, Chongqing, 401121, China
| | - Juan Hu
- College of Pharmacy, Chongqing Medical University, Chongqing, 400016, China
| | - Xinping Zhang
- College of Pharmacy, Chongqing Medical University, Chongqing, 400016, China
| | - Tingting Wang
- Biochemistry and Molecular Biology Laboratory, Experimental Teaching and Management Center, Chongqing Medical University, Chongqing, 400016, China
| | - Jingxin Xu
- College of Pharmacy, Chongqing Medical University, Chongqing, 400016, China
| | - Jingqing Zhang
- College of Pharmacy, Chongqing Medical University, Chongqing, 400016, China
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Naplekov D, Jadeja S, Fučíková AM, Švec F, Sklenářová H, Lenčo J. Easy, Robust, and Repeatable Online Acid Cleavage of Proteins in Mobile Phase for Fast Quantitative LC-MS Bottom-Up Protein Analysis─Application for Ricin Detection. Anal Chem 2023; 95:12339-12348. [PMID: 37565982 PMCID: PMC10448442 DOI: 10.1021/acs.analchem.3c01772] [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: 04/24/2023] [Accepted: 07/31/2023] [Indexed: 08/12/2023]
Abstract
Sample preparation involving the cleavage of proteins into peptides is the first critical step for successful bottom-up proteomics and protein analyses. Time- and labor-intensiveness are among the bottlenecks of the commonly used methods for protein sample preparation. Here, we report a fast online method for postinjection acid cleavage of proteins directly in the mobile phase typically used for LC-MS analyses in proteomics. The chemical cleavage is achieved in 0.1% formic acid within 35 s in a capillary heated to 195 °C installed upstream of the analytical column, enabling the generated peptides to be separated. The peptides generated by the optimized method covered the entire sequence except for one amino acid of trastuzumab used for the method development. The qualitative results are extraordinarily stable, even over a long period of time. Moreover, the method is also suitable for accurate and repeatable quantification. The procedure requires only one manual step, significantly decreasing sample transfer losses. To demonstrate its practical utility, we tested the method for the fast detection of ricin. Ricin can be unambiguously identified from an injection of 10 ng, and the results can be obtained within 7-8 min after receiving a suspicious sample. Because no sophisticated accessories and no additional reagents are needed, the method can be seamlessly transferred to any laboratory for high-throughput proteomic workflows.
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Affiliation(s)
- Denis
K. Naplekov
- Department
of Analytical Chemistry, Faculty of Pharmacy in Hradec Králové, Charles University, Heyrovského 1203/8, 500 05 Hradec Králové, Czech Republic
| | - Siddharth Jadeja
- Department
of Analytical Chemistry, Faculty of Pharmacy in Hradec Králové, Charles University, Heyrovského 1203/8, 500 05 Hradec Králové, Czech Republic
| | - Alena Myslivcová Fučíková
- Department
of Biology, Faculty of Science, University
of Hradec Králové, Hradecká 1285, 500 03 Hradec Králové, Czech Republic
| | - František Švec
- Department
of Analytical Chemistry, Faculty of Pharmacy in Hradec Králové, Charles University, Heyrovského 1203/8, 500 05 Hradec Králové, Czech Republic
| | - Hana Sklenářová
- Department
of Analytical Chemistry, Faculty of Pharmacy in Hradec Králové, Charles University, Heyrovského 1203/8, 500 05 Hradec Králové, Czech Republic
| | - Juraj Lenčo
- Department
of Analytical Chemistry, Faculty of Pharmacy in Hradec Králové, Charles University, Heyrovského 1203/8, 500 05 Hradec Králové, Czech Republic
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Gu L, Li X, Zhu W, Shen Y, Wang Q, Liu W, Zhang J, Zhang H, Li J, Li Z, Liu Z, Li C, Wang H. Ultrasensitive proteomics depicted an in-depth landscape for the very early stage of mouse maternal-to-zygotic transition. J Pharm Anal 2023; 13:942-954. [PMID: 37719194 PMCID: PMC10499587 DOI: 10.1016/j.jpha.2023.05.003] [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: 12/29/2022] [Revised: 05/05/2023] [Accepted: 05/08/2023] [Indexed: 09/19/2023] Open
Abstract
Single-cell or low-input multi-omics techniques have revolutionized the study of pre-implantation embryo development. However, the single-cell or low-input proteomic research in this field is relatively underdeveloped because of the higher threshold of the starting material for mammalian embryo samples and the lack of hypersensitive proteome technology. In this study, a comprehensive solution of ultrasensitive proteome technology (CS-UPT) was developed for single-cell or low-input mouse oocyte/embryo samples. The deep coverage and high-throughput routes significantly reduced the starting material and were selected by investigators based on their demands. Using the deep coverage route, we provided the first large-scale snapshot of the very early stage of mouse maternal-to-zygotic transition, including almost 5,500 protein groups from 20 mouse oocytes or zygotes for each sample. Moreover, significant protein regulatory networks centered on transcription factors and kinases between the MII oocyte and 1-cell embryo provided rich insights into minor zygotic genome activation.
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Affiliation(s)
- Lei Gu
- Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xumiao Li
- Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Wencheng Zhu
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, CAS Key Laboratory of Primate Neurobiology, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai, 200031, China
- Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai, 200031, China
| | - Yi Shen
- Shanghai Applied Protein Technology Co., Ltd., Shanghai, 201100, China
| | - Qinqin Wang
- Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Wenjun Liu
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, CAS Key Laboratory of Primate Neurobiology, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing, 101408, China
| | - Junfeng Zhang
- Shanghai Applied Protein Technology Co., Ltd., Shanghai, 201100, China
| | - Huiping Zhang
- Shanghai Applied Protein Technology Co., Ltd., Shanghai, 201100, China
| | - Jingquan Li
- Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Ziyi Li
- Shanghai Applied Protein Technology Co., Ltd., Shanghai, 201100, China
| | - Zhen Liu
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, CAS Key Laboratory of Primate Neurobiology, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai, 200031, China
- Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai, 200031, China
| | - Chen Li
- Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Hui Wang
- Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
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47
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Yang KL, Yu F, Teo GC, Li K, Demichev V, Ralser M, Nesvizhskii AI. MSBooster: improving peptide identification rates using deep learning-based features. Nat Commun 2023; 14:4539. [PMID: 37500632 PMCID: PMC10374903 DOI: 10.1038/s41467-023-40129-9] [Citation(s) in RCA: 34] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 07/06/2023] [Indexed: 07/29/2023] Open
Abstract
Peptide identification in liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments relies on computational algorithms for matching acquired MS/MS spectra against sequences of candidate peptides using database search tools, such as MSFragger. Here, we present a new tool, MSBooster, for rescoring peptide-to-spectrum matches using additional features incorporating deep learning-based predictions of peptide properties, such as LC retention time, ion mobility, and MS/MS spectra. We demonstrate the utility of MSBooster, in tandem with MSFragger and Percolator, in several different workflows, including nonspecific searches (immunopeptidomics), direct identification of peptides from data independent acquisition data, single-cell proteomics, and data generated on an ion mobility separation-enabled timsTOF MS platform. MSBooster is fast, robust, and fully integrated into the widely used FragPipe computational platform.
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Affiliation(s)
- Kevin L Yang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
| | - Guo Ci Teo
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Kai Li
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Vadim Demichev
- Department of Biochemistry, Charité Universitätsmedizin, Berlin, Germany
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Markus Ralser
- Department of Biochemistry, Charité Universitätsmedizin, Berlin, Germany
- Nuffield Department of Medicine, The Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Alexey I Nesvizhskii
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
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White MEH, Gil J, Tate EW. Proteome-wide structural analysis identifies warhead- and coverage-specific biases in cysteine-focused chemoproteomics. Cell Chem Biol 2023; 30:828-838.e4. [PMID: 37451266 DOI: 10.1016/j.chembiol.2023.06.021] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 03/20/2023] [Accepted: 06/23/2023] [Indexed: 07/18/2023]
Abstract
Covalent drug discovery has undergone a resurgence over the past two decades and reactive cysteine profiling has emerged in parallel as a platform for ligand discovery through on- and off-target profiling; however, the scope of this approach has not been fully explored at the whole-proteome level. We combined AlphaFold2-predicted side-chain accessibilities for >95% of the human proteome with a meta-analysis of eighteen public cysteine profiling datasets, totaling 44,187 unique cysteine residues, revealing accessibility biases in sampled cysteines primarily dictated by warhead chemistry. Analysis of >3.5 million cysteine-fragment interactions further showed that hit elaboration and optimization drives increased bias against buried cysteine residues. Based on these data, we suggest that current profiling approaches cover a small proportion of potential ligandable cysteine residues and propose future directions for increasing coverage, focusing on high-priority residues and depth. All analysis and produced resources are freely available and extendable to other reactive amino acids.
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Affiliation(s)
- Matthew E H White
- Department of Chemistry, Molecular Sciences Research Hub, Imperial College London, London W12 0BZ, UK; MRC London Institute of Medical Sciences (LMS), London W12 0NN, UK
| | - Jesús Gil
- MRC London Institute of Medical Sciences (LMS), London W12 0NN, UK; Institute of Clinical Sciences (ICS), Faculty of Medicine, Imperial College London, London W12 0NN, UK
| | - Edward W Tate
- Department of Chemistry, Molecular Sciences Research Hub, Imperial College London, London W12 0BZ, UK; The Francis Crick Institute, London NW1 1AT, UK.
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49
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Vernardis SI, Demichev V, Lemke O, Grüning NM, Messner C, White M, Pietzner M, Peluso A, Collet TH, Henning E, Gille C, Campbell A, Hayward C, Porteous DJ, Marioni RE, Mülleder M, Zelezniak A, Wareham NJ, Langenberg C, Farooqi IS, Ralser M. The Impact of Acute Nutritional Interventions on the Plasma Proteome. J Clin Endocrinol Metab 2023; 108:2087-2098. [PMID: 36658456 PMCID: PMC10348471 DOI: 10.1210/clinem/dgad031] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 01/14/2023] [Accepted: 01/17/2023] [Indexed: 01/21/2023]
Abstract
CONTEXT Humans respond profoundly to changes in diet, while nutrition and environment have a great impact on population health. It is therefore important to deeply characterize the human nutritional responses. OBJECTIVE Endocrine parameters and the metabolome of human plasma are rapidly responding to acute nutritional interventions such as caloric restriction or a glucose challenge. It is less well understood whether the plasma proteome would be equally dynamic, and whether it could be a source of corresponding biomarkers. METHODS We used high-throughput mass spectrometry to determine changes in the plasma proteome of i) 10 healthy, young, male individuals in response to 2 days of acute caloric restriction followed by refeeding; ii) 200 individuals of the Ely epidemiological study before and after a glucose tolerance test at 4 time points (0, 30, 60, 120 minutes); and iii) 200 random individuals from the Generation Scotland study. We compared the proteomic changes detected with metabolome data and endocrine parameters. RESULTS Both caloric restriction and the glucose challenge substantially impacted the plasma proteome. Proteins responded across individuals or in an individual-specific manner. We identified nutrient-responsive plasma proteins that correlate with changes in the metabolome, as well as with endocrine parameters. In particular, our study highlights the role of apolipoprotein C1 (APOC1), a small, understudied apolipoprotein that was affected by caloric restriction and dominated the response to glucose consumption and differed in abundance between individuals with and without type 2 diabetes. CONCLUSION Our study identifies APOC1 as a dominant nutritional responder in humans and highlights the interdependency of acute nutritional response proteins and the endocrine system.
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Affiliation(s)
- Spyros I Vernardis
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, NW1 1HT, UK
| | - Vadim Demichev
- Department of Biochemistry, Charité—Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Oliver Lemke
- Department of Biochemistry, Charité—Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Nana-Maria Grüning
- Department of Biochemistry, Charité—Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Christoph Messner
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, NW1 1HT, UK
| | - Matt White
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, NW1 1HT, UK
| | - Maik Pietzner
- MRC Epidemiology Unit, University of Cambridge, Cambridge, CB2 0SL, UK
- Computational Medicine, Berlin Institute of Health at Charité—Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Alina Peluso
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, NW1 1HT, UK
| | - Tinh-Hai Collet
- Metabolic Research Laboratories and National Institute for Health Research Cambridge Biomedical Research Centre, Wellcome-Medical Research Council Institute of Metabolic Science, Addenbrooke's Hospital, University of Cambridge, Cambridge, CB2 0QQ, UK
- Service of Endocrinology, Diabetology, Nutrition and Therapeutic Education, Department of Medicine, Geneva University Hospitals, 1211 Geneva, Switzerland
| | - Elana Henning
- Metabolic Research Laboratories and National Institute for Health Research Cambridge Biomedical Research Centre, Wellcome-Medical Research Council Institute of Metabolic Science, Addenbrooke's Hospital, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Christoph Gille
- Department of Biochemistry, Charité—Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - David J Porteous
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Michael Mülleder
- Core Facility High Throughput Mass Spectrometry, Charité—Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Aleksej Zelezniak
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, NW1 1HT, UK
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius SE-412 96, Lithuania
- Randall Centre for Cell & Molecular Biophysics, King's College London, New Hunt's House, Guy's Campus, SE1 1UL London, UK
| | | | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, Cambridge, CB2 0SL, UK
- Computational Medicine, Berlin Institute of Health at Charité—Universitätsmedizin Berlin, 10117 Berlin, Germany
- Precision Healthcare University Research Institute, Queen Mary University of London, London, E1 1HH, UK
| | - I Sadaf Farooqi
- Metabolic Research Laboratories and National Institute for Health Research Cambridge Biomedical Research Centre, Wellcome-Medical Research Council Institute of Metabolic Science, Addenbrooke's Hospital, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Markus Ralser
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, NW1 1HT, UK
- Department of Biochemistry, Charité—Universitätsmedizin Berlin, 10117 Berlin, Germany
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50
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Jiang Y, Salladay-Perez I, Momenzadeh A, Covarrubias AJ, Meyer JG. Simultaneous Multi-Omics Analysis by Direct Infusion Mass Spectrometry (SMAD-MS). BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.26.546628. [PMID: 37425781 PMCID: PMC10326973 DOI: 10.1101/2023.06.26.546628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Combined multi-omics analysis of proteomics, polar metabolomics, and lipidomics requires separate liquid chromatography-mass spectrometry (LC-MS) platforms for each omics layer. This requirement for different platforms limits throughput and increases costs, preventing the application of mass spectrometry-based multi-omics to large scale drug discovery or clinical cohorts. Here, we present an innovative strategy for simultaneous multi-omics analysis by direct infusion (SMAD) using one single injection without liquid chromatography. SMAD allows quantification of over 9,000 metabolite m/z features and over 1,300 proteins from the same sample in less than five minutes. We validated the efficiency and reliability of this method and then present two practical applications: mouse macrophage M1/M2 polarization and high throughput drug screening in human 293T cells. Finally, we demonstrate relationships between proteomic and metabolomic data are discovered by machine learning.
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Affiliation(s)
- Yuming Jiang
- Department of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
- Advanced Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
- Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Ivan Salladay-Perez
- Department of Molecular Biology, Immunology, and Molecular Genetics, University of California, Los Angeles, 90095, USA
| | - Amanda Momenzadeh
- Department of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
- Advanced Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
- Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Anthony J. Covarrubias
- Department of Molecular Biology, Immunology, and Molecular Genetics, University of California, Los Angeles, 90095, USA
| | - Jesse G. Meyer
- Department of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
- Advanced Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
- Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
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