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Fröhlich K, Fahrner M, Brombacher E, Seredynska A, Maldacker M, Kreutz C, Schmidt A, Schilling O. Data-independent acquisition: A milestone and prospect in clinical mass spectrometry-based proteomics. Mol Cell Proteomics 2024:100800. [PMID: 38880244 DOI: 10.1016/j.mcpro.2024.100800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 06/08/2024] [Accepted: 06/13/2024] [Indexed: 06/18/2024] Open
Abstract
Data-independent acquisition (DIA) has revolutionized the field of mass spectrometry (MS)-based proteomics over the past few years. DIA stands out for its ability to systematically sample all peptides in a given mass-to-charge range, allowing an unbiased acquisition of proteomics data. This greatly mitigates the issue of missing values and significantly enhances quantitative accuracy, precision, and reproducibility compared to many traditional methods. This review focuses on the critical role of DIA analysis software tools, primarily focusing on their capabilities and the challenges they address in proteomic research. Advances in MS technology, such as trapped ion mobility spectrometry, or high field asymmetric waveform ion mobility spectrometry require sophisticated analysis software capable of handling the increased data complexity and exploiting the full potential of DIA. We identify and critically evaluate leading software tools in the DIA landscape, discussing their unique features, and the reliability of their quantitative and qualitative outputs. We present the biological and clinical relevance of DIA-MS and discuss crucial publications that paved the way for in-depth proteomic characterization in patient-derived specimens. Furthermore, we provide a perspective on emerging trends in clinical applications and present upcoming challenges including standardization and certification of MS-based acquisition strategies in molecular diagnostics. While we emphasize the need for continuous development of software tools to keep pace with evolving technologies, we advise researchers against uncritically accepting the results from DIA software tools. Each tool may have its own biases, and some may not be as sensitive or reliable as others. Our overarching recommendation for both researchers and clinicians is to employ multiple DIA analysis tools, utilizing orthogonal analysis approaches to enhance the robustness and reliability of their findings.
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Affiliation(s)
- Klemens Fröhlich
- Proteomics Core Facility, Biozentrum Basel, University of Basel, Basel, Switzerland
| | - Matthias Fahrner
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany; German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), Freiburg, Germany
| | - Eva Brombacher
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center-University of Freiburg, Germany; Centre for Integrative Biological Signaling Studies (CIBSS), University of Freiburg, Germany; Spemann Graduate School of Biology and Medicine (SGBM), University of Freiburg, Germany; Faculty of Biology, University of Freiburg, Germany
| | - Adrianna Seredynska
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany; German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), Freiburg, Germany; Faculty of Biology, University of Freiburg, Germany
| | - Maximilian Maldacker
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany; Faculty of Biology, University of Freiburg, Germany
| | - Clemens Kreutz
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center-University of Freiburg, Germany; Centre for Integrative Biological Signaling Studies (CIBSS), University of Freiburg, Germany
| | - Alexander Schmidt
- Proteomics Core Facility, Biozentrum Basel, University of Basel, Basel, Switzerland
| | - Oliver Schilling
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany; German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), Freiburg, Germany
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2
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Zhang C, Tang H, Li T, Wu H, Gu Y, Zhang J, Zhang Z, Zhao L, Li Y, Gu L, Zhang H. Integrating Physiological Features and Proteomic Analyses Provides New Insights in Blue/Red Light-Treated Moso Bamboo ( Phyllostachys edulis). JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024; 72:12859-12870. [PMID: 38780458 DOI: 10.1021/acs.jafc.4c00724] [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: 05/25/2024]
Abstract
Bamboo is one of the most important nontimber forestry products in the world. Light is not only the most critical source of energy for plant photosynthesis but also involved in regulating the biological processes of plants. However, there are few reports on how blue/red light affects Moso bamboo. This study investigated the growth status and physiological responses of Moso bamboo (Phyllostachys edulis) to blue/red light treatments. The growth status of the bamboo plants was evaluated, revealing that both blue- and red-light treatments promoted plant height and overall growth. Gas exchange parameters, chlorophyll fluorescence, and enzyme activity were measured to assess the photosystem response of Moso bamboo to light treatments. Additionally, the blue light treatment led to a higher chlorophyll content and enzyme activities compared to the red light treatment. A tandem mass tag quantitative proteomics approach identified significant changes in protein abundance under different light conditions with specific response proteins associated with distinct pathways, such as photosynthesis and starch metabolism. Overall, this study provides valuable insights into the physiological and proteomic responses of Moso bamboo to blue/red light treatments, highlighting their potential impact on growth and development.
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Affiliation(s)
- Chuanyu Zhang
- College of Forestry, Basic Forestry and Proteomics Research Center, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Haohao Tang
- College of Forestry, Basic Forestry and Proteomics Research Center, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Tuhe Li
- College of Forestry, Basic Forestry and Proteomics Research Center, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Hongwei Wu
- College of Forestry, Basic Forestry and Proteomics Research Center, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Yuying Gu
- School of Future Technology, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Jun Zhang
- College of Life Science, Basic Forestry and Proteomics Research Center, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Zeyu Zhang
- College of Forestry, Basic Forestry and Proteomics Research Center, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Liangzhen Zhao
- College of Forestry, Basic Forestry and Proteomics Research Center, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Yaxing Li
- College of Forestry, Basic Forestry and Proteomics Research Center, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Lianfeng Gu
- College of Forestry, Basic Forestry and Proteomics Research Center, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Hangxiao Zhang
- College of Forestry, Basic Forestry and Proteomics Research Center, Fujian Agriculture and Forestry University, Fuzhou 350002, China
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3
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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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4
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Padhye BD, Nawaz U, Hains PG, Reddel RR, Robinson PJ, Zhong Q, Poulos RC. Proteomic insights into paediatric cancer: Unravelling molecular signatures and therapeutic opportunities. Pediatr Blood Cancer 2024; 71:e30980. [PMID: 38556739 DOI: 10.1002/pbc.30980] [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: 11/28/2023] [Revised: 03/13/2024] [Accepted: 03/14/2024] [Indexed: 04/02/2024]
Abstract
Survival rates in some paediatric cancers have improved greatly over recent decades, in part due to the identification of diagnostic, prognostic and predictive molecular signatures, and the development of risk-directed therapies. However, other paediatric cancers have proved difficult to treat, and there is an urgent need to identify novel biomarkers that reveal therapeutic opportunities. The proteome is the total set of expressed proteins present in a cell or tissue at a point in time, and is vastly more dynamic than the genome. Proteomics holds significant promise for cancer research, as proteins are ultimately responsible for cellular phenotype and are the target of most anticancer drugs. Here, we review the discoveries, opportunities and challenges of proteomic analyses in paediatric cancer, with a focus on mass spectrometry (MS)-based approaches. Accelerating incorporation of proteomics into paediatric precision medicine has the potential to improve survival and quality of life for children with cancer.
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Affiliation(s)
- Bhavna D Padhye
- Cancer Centre for Children, The Children's Hospital at Westmead, Westmead, New South Wales, Australia
- Kids Research, Children's Cancer Research Unit, The Children's Hospital at Westmead, Westmead, New South Wales, Australia
| | - Urwah Nawaz
- ProCan, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, New South Wales, Australia
| | - Peter G Hains
- ProCan, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, New South Wales, Australia
| | - Roger R Reddel
- ProCan, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, New South Wales, Australia
| | - Phillip J Robinson
- ProCan, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, New South Wales, Australia
| | - Qing Zhong
- ProCan, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, New South Wales, Australia
| | - Rebecca C Poulos
- ProCan, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, New South Wales, Australia
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5
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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] [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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6
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Emery-Corbin SJ, Yousef JM, Adhikari S, Sumardy F, Nhu D, van Delft MF, Lessene G, Dziekan J, Webb AI, Dagley LF. Improved drug target deconvolution with PISA-DIA using an extended, overlapping temperature gradient. Proteomics 2024:e2300644. [PMID: 38766901 DOI: 10.1002/pmic.202300644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 05/03/2024] [Accepted: 05/07/2024] [Indexed: 05/22/2024]
Abstract
Thermal proteome profiling (TPP) is a powerful tool for drug target deconvolution. Recently, data-independent acquisition mass spectrometry (DIA-MS) approaches have demonstrated significant improvements to depth and missingness in proteome data, but traditional TPP (a.k.a. CEllular Thermal Shift Assay "CETSA") workflows typically employ multiplexing reagents reliant on data-dependent acquisition (DDA). Herein, we introduce a new experimental design for the Proteome Integral Solubility Alteration via label-free DIA approach (PISA-DIA). We highlight the proteome coverage and sensitivity achieved by using multiple overlapping thermal gradients alongside DIA-MS, which maximizes efficiencies in PISA sample concatenation and safeguards against missing protein targets that exist at high melting temperatures. We demonstrate our extended PISA-DIA design has superior proteome coverage as compared to using tandem-mass tags (TMT) necessitating DDA-MS analysis. Importantly, we demonstrate our PISA-DIA approach has the quantitative and statistical rigor using A-1331852, a specific inhibitor of BCL-xL. Due to the high melt temperature of this protein target, we utilized our extended multiple gradient PISA-DIA workflow to identify BCL-xL. We assert our novel overlapping gradient PISA-DIA-MS approach is ideal for unbiased drug target deconvolution, spanning a large temperature range whilst minimizing target dropout between gradients, increasing the likelihood of resolving the protein targets of novel compounds.
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Affiliation(s)
- Samantha J Emery-Corbin
- Advanced Technology and Biology Division, the Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia
- Department of Medical Biology, University of Melbourne, Melbourne, Victoria, Australia
| | - Jumana M Yousef
- Advanced Technology and Biology Division, the Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia
- Department of Medical Biology, University of Melbourne, Melbourne, Victoria, Australia
| | - Subash Adhikari
- Advanced Technology and Biology Division, the Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia
- Department of Medical Biology, University of Melbourne, Melbourne, Victoria, Australia
| | - Fransisca Sumardy
- Department of Medical Biology, University of Melbourne, Melbourne, Victoria, Australia
- ACRF Chemical Biology Division, the Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia
| | - Duong Nhu
- Department of Medical Biology, University of Melbourne, Melbourne, Victoria, Australia
- ACRF Chemical Biology Division, the Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia
| | - Mark F van Delft
- Department of Medical Biology, University of Melbourne, Melbourne, Victoria, Australia
- Blood Cells and Blood Cancer Division, the Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia
| | - Guillaume Lessene
- Department of Medical Biology, University of Melbourne, Melbourne, Victoria, Australia
- ACRF Chemical Biology Division, the Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia
- Department of Pharmacology and Therapeutics, University of Melbourne, Melbourne, Victoria, Australia
| | - Jerzy Dziekan
- Department of Medical Biology, University of Melbourne, Melbourne, Victoria, Australia
- Infection and Immunity Division, the Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia
| | - Andrew I Webb
- Advanced Technology and Biology Division, the Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia
- Department of Medical Biology, University of Melbourne, Melbourne, Victoria, Australia
| | - Laura F Dagley
- Advanced Technology and Biology Division, the Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia
- Department of Medical Biology, University of Melbourne, Melbourne, Victoria, Australia
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7
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Damianou A, Liang Z, Lassen F, Vendrell I, Vere G, Hester S, Charles PD, Pinto-Fernandez A, Santos A, Fischer R, Kessler BM. Oncogenic mutations of KRAS modulate its turnover by the CUL3/LZTR1 E3 ligase complex. Life Sci Alliance 2024; 7:e202302245. [PMID: 38453365 PMCID: PMC10921066 DOI: 10.26508/lsa.202302245] [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: 06/30/2023] [Revised: 02/27/2024] [Accepted: 02/27/2024] [Indexed: 03/09/2024] Open
Abstract
KRAS is a proto-oncogene encoding a small GTPase. Mutations contribute to ∼30% of human solid tumours, including lung adenocarcinoma, pancreatic, and colorectal carcinomas. Most KRAS activating mutations interfere with GTP hydrolysis, essential for its role as a molecular switch, leading to alterations in their molecular environment and oncogenic signalling. However, the precise signalling cascades these mutations affect are poorly understood. Here, APEX2 proximity labelling was used to profile the molecular environment of WT, G12D, G13D, and Q61H-activating KRAS mutants under starvation and stimulation conditions. Through quantitative proteomics, we demonstrate the presence of known KRAS interactors, including ARAF and LZTR1, which are differentially captured by WT and KRAS mutants. Notably, the KRAS mutations G12D, G13D, and Q61H abrogate their association with LZTR1, thereby affecting turnover. Elucidating the implications of LZTR1-mediated regulation of KRAS protein levels in cancer may offer insights into therapeutic strategies targeting KRAS-driven malignancies.
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Affiliation(s)
- Andreas Damianou
- https://ror.org/052gg0110 Target Discovery Institute, Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- https://ror.org/052gg0110 Chinese Academy for Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Zhu Liang
- https://ror.org/052gg0110 Target Discovery Institute, Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- https://ror.org/052gg0110 Chinese Academy for Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Frederik Lassen
- https://ror.org/052gg0110 Target Discovery Institute, Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- https://ror.org/052gg0110 Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Iolanda Vendrell
- https://ror.org/052gg0110 Target Discovery Institute, Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- https://ror.org/052gg0110 Chinese Academy for Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | | | - Svenja Hester
- https://ror.org/052gg0110 Target Discovery Institute, Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Philip D Charles
- https://ror.org/052gg0110 Target Discovery Institute, Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- https://ror.org/052gg0110 Chinese Academy for Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Adan Pinto-Fernandez
- https://ror.org/052gg0110 Target Discovery Institute, Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- https://ror.org/052gg0110 Chinese Academy for Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Alberto Santos
- https://ror.org/052gg0110 Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Center for Health Data Science, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
- NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Roman Fischer
- https://ror.org/052gg0110 Target Discovery Institute, Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- https://ror.org/052gg0110 Chinese Academy for Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Benedikt M Kessler
- https://ror.org/052gg0110 Target Discovery Institute, Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- https://ror.org/052gg0110 Chinese Academy for Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
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8
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Hamid MHBA, Cespedes PF, Jin C, Chen JL, Gileadi U, Antoun E, Liang Z, Gao F, Teague R, Manoharan N, Maldonado-Perez D, Khalid-Alham N, Cerundolo L, Ciaoca R, Hester SS, Pinto-Fernández A, Draganov SD, Vendrell I, Liu G, Yao X, Kvalvaag A, Dominey-Foy DCC, Nanayakkara C, Kanellakis N, Chen YL, Waugh C, Clark SA, Clark K, Sopp P, Rahman NM, Verrill C, Kessler BM, Ogg G, Fernandes RA, Fisher R, Peng Y, Dustin ML, Dong T. Unconventional human CD61 pairing with CD103 promotes TCR signaling and antigen-specific T cell cytotoxicity. Nat Immunol 2024; 25:834-846. [PMID: 38561495 PMCID: PMC11065694 DOI: 10.1038/s41590-024-01802-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 02/29/2024] [Indexed: 04/04/2024]
Abstract
Cancer remains one of the leading causes of mortality worldwide, leading to increased interest in utilizing immunotherapy strategies for better cancer treatments. In the past decade, CD103+ T cells have been associated with better clinical prognosis in patients with cancer. However, the specific immune mechanisms contributing toward CD103-mediated protective immunity remain unclear. Here, we show an unexpected and transient CD61 expression, which is paired with CD103 at the synaptic microclusters of T cells. CD61 colocalization with the T cell antigen receptor further modulates downstream T cell antigen receptor signaling, improving antitumor cytotoxicity and promoting physiological control of tumor growth. Clinically, the presence of CD61+ tumor-infiltrating T lymphocytes is associated with improved clinical outcomes, mediated through enhanced effector functions and phenotype with limited evidence of cellular exhaustion. In conclusion, this study identified an unconventional and transient CD61 expression and pairing with CD103 on human immune cells, which potentiates a new target for immune-based cellular therapies.
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MESH Headings
- Animals
- Humans
- Mice
- Antigens, CD/metabolism
- Antigens, CD/immunology
- Apyrase
- Cell Line, Tumor
- Cytotoxicity, Immunologic
- Integrin alpha Chains/metabolism
- Lymphocytes, Tumor-Infiltrating/immunology
- Lymphocytes, Tumor-Infiltrating/metabolism
- Neoplasms/immunology
- Neoplasms/therapy
- Receptors, Antigen, T-Cell/metabolism
- Receptors, Antigen, T-Cell/immunology
- Signal Transduction/immunology
- T-Lymphocytes, Cytotoxic/immunology
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Affiliation(s)
- Megat H B A Hamid
- CAMS Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Pablo F Cespedes
- CAMS Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
| | - Chen Jin
- CAMS Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Cardiothoracic Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Ji-Li Chen
- CAMS Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- MRC Translational Immune Discovery Unity, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Uzi Gileadi
- MRC Translational Immune Discovery Unity, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Elie Antoun
- CAMS Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Zhu Liang
- CAMS Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Target Discovery Institute, Centre for Medicines Discovery, University of Oxford, Oxford, UK
| | - Fei Gao
- CAMS Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Renuka Teague
- CAMS Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Nikita Manoharan
- CAMS Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - David Maldonado-Perez
- CAMS Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Nasullah Khalid-Alham
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
- Oxford National Institute of Health Research (NIHR) Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Lucia Cerundolo
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Raul Ciaoca
- CAMS Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Svenja S Hester
- Target Discovery Institute, Centre for Medicines Discovery, University of Oxford, Oxford, UK
| | - Adán Pinto-Fernández
- CAMS Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Target Discovery Institute, Centre for Medicines Discovery, University of Oxford, Oxford, UK
| | - Simeon D Draganov
- CAMS Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Target Discovery Institute, Centre for Medicines Discovery, University of Oxford, Oxford, UK
| | - Iolanda Vendrell
- CAMS Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Target Discovery Institute, Centre for Medicines Discovery, University of Oxford, Oxford, UK
| | - Guihai Liu
- CAMS Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Xuan Yao
- CAMS Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Audun Kvalvaag
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
- Department of Molecular Cell Biology, Institute of Cancer Research, Oslo University Hospital, Oslo, Norway
| | | | - Charunya Nanayakkara
- CAMS Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Nikolaos Kanellakis
- CAMS Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford National Institute of Health Research (NIHR) Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
- Laboratory of Pleural and Lung Cancer Translational Research, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Respiratory Medicine, Churchill Hospital, Oxford University Hospitals, Oxford, UK
| | - Yi-Ling Chen
- CAMS Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- MRC Translational Immune Discovery Unity, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Craig Waugh
- Flow Cytometry Facility, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Sally-Ann Clark
- Flow Cytometry Facility, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Kevin Clark
- Flow Cytometry Facility, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Paul Sopp
- Flow Cytometry Facility, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Najib M Rahman
- CAMS Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford National Institute of Health Research (NIHR) Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
- Laboratory of Pleural and Lung Cancer Translational Research, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Respiratory Medicine, Churchill Hospital, Oxford University Hospitals, Oxford, UK
| | - Clare Verrill
- CAMS Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
- Oxford National Institute of Health Research (NIHR) Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Benedikt M Kessler
- CAMS Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Target Discovery Institute, Centre for Medicines Discovery, University of Oxford, Oxford, UK
| | - Graham Ogg
- CAMS Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- MRC Translational Immune Discovery Unity, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Ricardo A Fernandes
- CAMS Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Roman Fisher
- CAMS Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Target Discovery Institute, Centre for Medicines Discovery, University of Oxford, Oxford, UK
| | - Yanchun Peng
- CAMS Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- MRC Translational Immune Discovery Unity, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Michael L Dustin
- CAMS Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
| | - Tao Dong
- CAMS Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- MRC Translational Immune Discovery Unity, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
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9
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He Q, Guo H, Li Y, He G, Li X, Shuai J. SeFilter-DIA: Squeeze-and-Excitation Network for Filtering High-Confidence Peptides of Data-Independent Acquisition Proteomics. Interdiscip Sci 2024:10.1007/s12539-024-00611-4. [PMID: 38472692 DOI: 10.1007/s12539-024-00611-4] [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: 07/17/2023] [Revised: 01/12/2024] [Accepted: 01/21/2024] [Indexed: 03/14/2024]
Abstract
Mass spectrometry is crucial in proteomics analysis, particularly using Data Independent Acquisition (DIA) for reliable and reproducible mass spectrometry data acquisition, enabling broad mass-to-charge ratio coverage and high throughput. DIA-NN, a prominent deep learning software in DIA proteome analysis, generates peptide results but may include low-confidence peptides. Conventionally, biologists have to manually screen peptide fragment ion chromatogram peaks (XIC) for identifying high-confidence peptides, a time-consuming and subjective process prone to variability. In this study, we introduce SeFilter-DIA, a deep learning algorithm, aiming at automating the identification of high-confidence peptides. Leveraging compressed excitation neural network and residual network models, SeFilter-DIA extracts XIC features and effectively discerns between high and low-confidence peptides. Evaluation of the benchmark datasets demonstrates SeFilter-DIA achieving 99.6% AUC on the test set and 97% for other performance indicators. Furthermore, SeFilter-DIA is applicable for screening peptides with phosphorylation modifications. These results demonstrate the potential of SeFilter-DIA to replace manual screening, providing an efficient and objective approach for high-confidence peptide identification while mitigating associated limitations.
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Affiliation(s)
- Qingzu He
- Department of Physics, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361005, China
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, 325001, China
| | - Huan Guo
- Department of Physics, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361005, China
| | - Yulin Li
- Department of Physics, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361005, China
| | - Guoqiang He
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, 325001, China
| | - Xiang Li
- Department of Physics, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361005, China.
| | - Jianwei Shuai
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, 325001, China.
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou, 325001, China.
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10
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Fröhlich K, Furrer R, Schori C, Handschin C, Schmidt A. Robust, Precise, and Deep Proteome Profiling Using a Small Mass Range and Narrow Window Data-Independent-Acquisition Scheme. J Proteome Res 2024; 23:1028-1038. [PMID: 38275131 PMCID: PMC10913089 DOI: 10.1021/acs.jproteome.3c00736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 12/20/2023] [Accepted: 12/26/2023] [Indexed: 01/27/2024]
Abstract
In recent years, a plethora of different data-independent acquisition methods have been developed for proteomics to cover a wide range of requirements. Current deep proteome profiling methods rely on fractionations, elaborate chromatography, and mass spectrometry setups or display suboptimal quantitative precision. We set out to develop an easy-to-use one shot DIA method that achieves high quantitative precision and high proteome coverage. We achieve this by focusing on a small mass range of 430-670 m/z using small isolation windows without overlap. With this new method, we were able to quantify >9200 protein groups in HEK lysates with an average coefficient of variance of 3.2%. To demonstrate the power of our newly developed narrow mass range method, we applied it to investigate the effect of PGC-1α knockout on the skeletal muscle proteome in mice. Compared to a standard data-dependent acquisition method, we could double proteome coverage and, most importantly, achieve a significantly higher quantitative precision, as compared to a previously proposed DIA method. We believe that our method will be especially helpful in quantifying low abundant proteins in samples with a high dynamic range. All raw and result files are available at massive.ucsd.edu (MSV000092186).
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Affiliation(s)
- Klemens Fröhlich
- Proteomics
Core Facility, Biozentrum Basel, University
of Basel, 4056 Basel, Switzerland
| | - Regula Furrer
- Biozentrum
Basel, University of Basel, 4056 Basel, Switzerland
| | - Christian Schori
- Proteomics
Core Facility, Biozentrum Basel, University
of Basel, 4056 Basel, Switzerland
| | | | - Alexander Schmidt
- Proteomics
Core Facility, Biozentrum Basel, University
of Basel, 4056 Basel, Switzerland
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11
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Haroon H, Ho AMC, Gupta VK, Dasari S, Sellgren CM, Cervenka S, Engberg G, Eren F, Erhardt S, Sung J, Choi DS. Cerebrospinal fluid proteomic signatures are associated with symptom severity of first-episode psychosis. J Psychiatr Res 2024; 171:306-315. [PMID: 38340697 PMCID: PMC10995989 DOI: 10.1016/j.jpsychires.2024.02.002] [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: 09/18/2023] [Revised: 01/04/2024] [Accepted: 02/01/2024] [Indexed: 02/12/2024]
Abstract
Apart from their diagnostic, monitoring, or prognostic utility in clinical settings, molecular biomarkers may be instrumental in understanding the pathophysiology of psychiatric disorders, including schizophrenia. Using untargeted metabolomics, we recently identified eight cerebrospinal fluid (CSF) metabolites unique to first-episode psychosis (FEP) subjects compared to healthy controls (HC). In this study, we sought to investigate the CSF proteomic signatures associated with FEP. We employed 16-plex tandem mass tag (TMT) mass spectrometry (MS) to examine the relative protein abundance in CSF samples of 15 individuals diagnosed with FEP and 15 age-and-sex-matched healthy controls (HC). Multiple linear regression model (MLRM) identified 16 differentially abundant CSF proteins between FEP and HC at p < 0.01. Among them, the two most significant CSF proteins were collagen alpha-2 (IV) chain (COL4A2: standard mean difference [SMD] = -1.12, p = 1.64 × 10-4) and neuron-derived neurotrophic factor (NDNF: SMD = -1.03, p = 4.52 × 10-4) both of which were down-regulated in FEP subjects compared to HC. We also identified several potential CSF proteins associated with the pathophysiology and the symptom profile and severity in FEP subjects, including COL4A2, NDNF, hornerin (HRNR), contactin-6 (CNTN6), voltage-dependent calcium channel subunit alpha-2/delta-3 (CACNA2D3), tropomyosin alpha-3 chain (TPM3 and TPM4). Moreover, several protein signatures were associated with cognitive performance. Although the results need replication, our exploratory study suggests that CSF protein signatures can be used to increase the understanding of the pathophysiology of psychosis.
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Affiliation(s)
- Humza Haroon
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science, Rochester, MN, USA
| | - Ada Man-Choi Ho
- Department of Psychiatry and Psychology, Mayo Clinic College of Medicine and Science, Rochester, MN, USA
| | - Vinod K Gupta
- Division of Surgery Research, Department of Surgery, Rochester, MN, USA; Microbiome Program, Center for Individualized Medicine, Rochester, MN, USA
| | - Surendra Dasari
- Division of Biomedical Statistics and Informatics, Mayo Clinic College of Medicine and Science, Rochester, MN, USA
| | - Carl M Sellgren
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden; Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden
| | - Simon Cervenka
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden; Department of Medical Sciences, Psychiatry, Uppsala University, Uppsala, Sweden
| | - Göran Engberg
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Feride Eren
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Sophie Erhardt
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Jaeyun Sung
- Division of Surgery Research, Department of Surgery, Rochester, MN, USA; Microbiome Program, Center for Individualized Medicine, Rochester, MN, USA; Division of Rheumatology, Department of Internal Medicine, Rochester, MN, USA
| | - Doo-Sup Choi
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science, Rochester, MN, USA; Department of Psychiatry and Psychology, Mayo Clinic College of Medicine and Science, Rochester, MN, USA; Neuroscience Program, Mayo Clinic College of Medicine and Science, Rochester, MN, USA.
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12
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Wu Q, Zheng J, Sui X, Fu C, Cui X, Liao B, Ji H, Luo Y, He A, Lu X, Xue X, Tan CSH, Tian R. High-throughput drug target discovery using a fully automated proteomics sample preparation platform. Chem Sci 2024; 15:2833-2847. [PMID: 38404368 PMCID: PMC10882491 DOI: 10.1039/d3sc05937e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 12/19/2023] [Indexed: 02/27/2024] Open
Abstract
Drug development is plagued by inefficiency and high costs due to issues such as inadequate drug efficacy and unexpected toxicity. Mass spectrometry (MS)-based proteomics, particularly isobaric quantitative proteomics, offers a solution to unveil resistance mechanisms and unforeseen side effects related to off-targeting pathways. Thermal proteome profiling (TPP) has gained popularity for drug target identification at the proteome scale. However, it involves experiments with multiple temperature points, resulting in numerous samples and considerable variability in large-scale TPP analysis. We propose a high-throughput drug target discovery workflow that integrates single-temperature TPP, a fully automated proteomics sample preparation platform (autoSISPROT), and data independent acquisition (DIA) quantification. The autoSISPROT platform enables the simultaneous processing of 96 samples in less than 2.5 hours, achieving protein digestion, desalting, and optional TMT labeling (requires an additional 1 hour) with 96-channel all-in-tip operations. The results demonstrated excellent sample preparation performance with >94% digestion efficiency, >98% TMT labeling efficiency, and >0.9 intra- and inter-batch Pearson correlation coefficients. By automatically processing 87 samples, we identified both known targets and potential off-targets of 20 kinase inhibitors, affording over a 10-fold improvement in throughput compared to classical TPP. This fully automated workflow offers a high-throughput solution for proteomics sample preparation and drug target/off-target identification.
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Affiliation(s)
- Qiong Wu
- Department of Chemistry, School of Science, Southern University of Science and Technology Shenzhen 518055 China
| | - Jiangnan Zheng
- Department of Chemistry, School of Science, Southern University of Science and Technology Shenzhen 518055 China
- Southern University of Science and Technology, Guangming Advanced Research Institute Shenzhen 518055 China
| | - Xintong Sui
- Department of Chemistry, School of Science, Southern University of Science and Technology Shenzhen 518055 China
| | - Changying Fu
- Department of Chemistry, School of Science, Southern University of Science and Technology Shenzhen 518055 China
| | - Xiaozhen Cui
- Department of Chemistry, School of Science, Southern University of Science and Technology Shenzhen 518055 China
| | - Bin Liao
- Department of Chemistry, School of Science, Southern University of Science and Technology Shenzhen 518055 China
| | - Hongchao Ji
- Department of Chemistry, School of Science, Southern University of Science and Technology Shenzhen 518055 China
| | - Yang Luo
- Department of Chemistry, School of Science, Southern University of Science and Technology Shenzhen 518055 China
| | - An He
- Department of Chemistry, School of Science, Southern University of Science and Technology Shenzhen 518055 China
| | - Xue Lu
- Department of Chemistry, School of Science, Southern University of Science and Technology Shenzhen 518055 China
| | - Xinyue Xue
- Department of Chemistry, School of Science, Southern University of Science and Technology Shenzhen 518055 China
| | - Chris Soon Heng Tan
- Department of Chemistry, School of Science, Southern University of Science and Technology Shenzhen 518055 China
- Research Center for Chemical Biology and Omics Analysis, School of Science, Southern University of Science and Technology 1088 Xueyuan Road Shenzhen 518055 China
- Southern University of Science and Technology, Guangming Advanced Research Institute Shenzhen 518055 China
| | - Ruijun Tian
- Department of Chemistry, School of Science, Southern University of Science and Technology Shenzhen 518055 China
- Research Center for Chemical Biology and Omics Analysis, School of Science, Southern University of Science and Technology 1088 Xueyuan Road Shenzhen 518055 China
- Southern University of Science and Technology, Guangming Advanced Research Institute Shenzhen 518055 China
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13
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Dellar ER, Vendrell I, Talbot K, Kessler BM, Fischer R, Turner MR, Thompson AG. Data-independent acquisition proteomics of cerebrospinal fluid implicates endoplasmic reticulum and inflammatory mechanisms in amyotrophic lateral sclerosis. J Neurochem 2024; 168:115-127. [PMID: 38087504 PMCID: PMC10952667 DOI: 10.1111/jnc.16030] [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: 08/10/2023] [Revised: 11/29/2023] [Accepted: 12/02/2023] [Indexed: 01/26/2024]
Abstract
While unbiased proteomics of human cerebrospinal fluid (CSF) has been used successfully to identify biomarkers of amyotrophic lateral sclerosis (ALS), high-abundance proteins mask the presence of lower abundance proteins that may have diagnostic and prognostic value. However, developments in mass spectrometry (MS) proteomic data acquisition methods offer improved protein depth. In this study, MS with library-free data-independent acquisition (DIA) was used to compare the CSF proteome of people with ALS (n = 40), healthy (n = 15) and disease (n = 8) controls. Quantified protein groups were subsequently correlated with clinical variables. Univariate analysis identified 7 proteins, all significantly upregulated in ALS versus healthy controls, and 9 with altered abundance in ALS versus disease controls (FDR < 0.1). Elevated chitotriosidase-1 (CHIT1) was common to both comparisons and was proportional to ALS disability progression rate (Pearson r = 0.41, FDR-adjusted p = 0.035) but not overall survival. Ubiquitin carboxyl-terminal hydrolase isozyme L1 (UCHL1; upregulated in ALS versus healthy controls) was proportional to disability progression rate (Pearson r = 0.53, FDR-adjusted p = 0.003) and survival (Kaplan Meier log-rank p = 0.013) but not independently in multivariate proportional hazards models. Weighted correlation network analysis was used to identify functionally relevant modules of proteins. One module, enriched for inflammatory functions, was associated with age at symptom onset (Pearson r = 0.58, FDR-adjusted p = 0.005) and survival (Hazard Ratio = 1.78, FDR = 0.065), and a second module, enriched for endoplasmic reticulum proteins, was negatively correlated with disability progression rate (r = -0.42, FDR-adjusted p = 0.109). DIA acquisition methodology therefore strengthened the biomarker candidacy of CHIT1 and UCHL1 in ALS, while additionally highlighted inflammatory and endoplasmic reticulum proteins as novel sources of prognostic biomarkers.
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Affiliation(s)
| | - Iolanda Vendrell
- Centre for Medicines Discovery, Nuffield Department of Medicine, Target Discovery InstituteUniversity of OxfordOxfordUK
- Nuffield Department of Medicine, Chinese Academy of Medical Sciences Oxford InstituteUniversity of OxfordOxfordUK
| | - Kevin Talbot
- Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
- Kavli Institute for Nanoscience DiscoveryUniversity of OxfordOxfordUK
| | - Benedikt M. Kessler
- Centre for Medicines Discovery, Nuffield Department of Medicine, Target Discovery InstituteUniversity of OxfordOxfordUK
- Nuffield Department of Medicine, Chinese Academy of Medical Sciences Oxford InstituteUniversity of OxfordOxfordUK
| | - Roman Fischer
- Centre for Medicines Discovery, Nuffield Department of Medicine, Target Discovery InstituteUniversity of OxfordOxfordUK
- Nuffield Department of Medicine, Chinese Academy of Medical Sciences Oxford InstituteUniversity of OxfordOxfordUK
| | - Martin R. Turner
- Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
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14
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O'Brien JJ, Raj A, Gaun A, Waite A, Li W, Hendrickson DG, Olsson N, McAllister FE. A data analysis framework for combining multiple batches increases the power of isobaric proteomics experiments. Nat Methods 2024; 21:290-300. [PMID: 38110636 DOI: 10.1038/s41592-023-02120-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 10/31/2023] [Indexed: 12/20/2023]
Abstract
We present a framework for the analysis of multiplexed mass spectrometry proteomics data that reduces estimation error when combining multiple isobaric batches. Variations in the number and quality of observations have long complicated the analysis of isobaric proteomics data. Here we show that the power to detect statistical associations is substantially improved by utilizing models that directly account for known sources of variation in the number and quality of observations that occur across batches.In a multibatch benchmarking experiment, our open-source software (msTrawler) increases the power to detect changes, especially in the range of less than twofold changes, while simultaneously increasing quantitative proteome coverage by utilizing more low-signal observations. Further analyses of previously published multiplexed datasets of 4 and 23 batches highlight both increased power and the ability to navigate complex missing data patterns without relying on unverifiable imputations or discarding reliable measurements.
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Affiliation(s)
| | - Anil Raj
- Calico Life Sciences LLC, South San Francisco, CA, USA
| | | | - Adam Waite
- Calico Life Sciences LLC, South San Francisco, CA, USA
| | - Wenzhou Li
- Calico Life Sciences LLC, South San Francisco, CA, USA
| | | | - Niclas Olsson
- Calico Life Sciences LLC, South San Francisco, CA, USA
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15
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van Oostrum M, Blok TM, Giandomenico SL, Tom Dieck S, Tushev G, Fürst N, Langer JD, Schuman EM. The proteomic landscape of synaptic diversity across brain regions and cell types. Cell 2023; 186:5411-5427.e23. [PMID: 37918396 PMCID: PMC10686415 DOI: 10.1016/j.cell.2023.09.028] [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: 02/09/2023] [Revised: 08/18/2023] [Accepted: 09/28/2023] [Indexed: 11/04/2023]
Abstract
Neurons build synaptic contacts using different protein combinations that define the specificity, function, and plasticity potential of synapses; however, the diversity of synaptic proteomes remains largely unexplored. We prepared synaptosomes from 7 different transgenic mouse lines with fluorescently labeled presynaptic terminals. Combining microdissection of 5 different brain regions with fluorescent-activated synaptosome sorting (FASS), we isolated and analyzed the proteomes of 18 different synapse types. We discovered ∼1,800 unique synapse-type-enriched proteins and allocated thousands of proteins to different types of synapses (https://syndive.org/). We identify shared synaptic protein modules and highlight the proteomic hotspots for synapse specialization. We reveal unique and common features of the striatal dopaminergic proteome and discover the proteome signatures that relate to the functional properties of different interneuron classes. This study provides a molecular systems-biology analysis of synapses and a framework to integrate proteomic information for synapse subtypes of interest with cellular or circuit-level experiments.
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Affiliation(s)
- Marc van Oostrum
- Max Planck Institute for Brain Research, Frankfurt am Main, Germany
| | - Thomas M Blok
- Max Planck Institute for Brain Research, Frankfurt am Main, Germany
| | | | | | - Georgi Tushev
- Max Planck Institute for Brain Research, Frankfurt am Main, Germany
| | - Nicole Fürst
- Max Planck Institute for Brain Research, Frankfurt am Main, Germany
| | - Julian D Langer
- Max Planck Institute for Brain Research, Frankfurt am Main, Germany; Max Planck Institute of Biophysics, Frankfurt am Main, Germany
| | - Erin M Schuman
- Max Planck Institute for Brain Research, Frankfurt am Main, Germany.
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16
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Li R, Wang C, Gou L, Zhou Y, Peng L, Liu F, Zhang Y. Potential mechanism of the AgNCs-hydrogel in promoting the regeneration of diabetic infectious wounds. Analyst 2023; 148:5873-5881. [PMID: 37908193 DOI: 10.1039/d3an01569f] [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
Diabetic infectious wound treatment is challenging due to insistent wound infections. To treat such complicated pathological diabetic infectious wounds, multifunctional materials need to be developed, and their mechanisms need to be understood. Here, we developed a material termed AgNCs-hydrogel, which is a multifunctional DNA hydrogel used as dressings by integrating it with antibacterial silver nanoclusters. The AgNCs-hydrogel was applied to promote the regeneration of diabetic infectious wounds in mice because it exhibited superior antibacterial activity and effective ROS-scavenging properties. Based on skin proteomics, we explored the potential mechanism of the AgNCs-hydrogel in treating mouse skin wounds. We found that the AgNCs-hydrogel can regulate some key proteins located primarily in the extracellular exosomes, involved in the negative regulation of the apoptotic process, and perform ATP binding to accelerate diabetic infected wound closure. Therefore, this study provided a multifunctional AgNCs-hydrogel and revealed its potential mechanism in promoting the regeneration of diabetic infectious wounds.
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Affiliation(s)
- Ruoqing Li
- Department of General Medicine, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing Key Laboratory of Emergency Medicine, Chongqing, 400014, China
| | - Chengshi Wang
- Department of General Medicine, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing Key Laboratory of Emergency Medicine, Chongqing, 400014, China
- Department of Endocrinology and Metabolism, Center for Diabetes and Metabolism Research, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Liping Gou
- Department of Endocrinology and Metabolism, Center for Diabetes and Metabolism Research, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Ye Zhou
- Department of Endocrinology and Metabolism, Center for Diabetes and Metabolism Research, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Linrui Peng
- Department of Endocrinology and Metabolism, Center for Diabetes and Metabolism Research, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Fang Liu
- Department of Nephrology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China.
| | - Yong Zhang
- Department of General Medicine, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing Key Laboratory of Emergency Medicine, Chongqing, 400014, China
- Department of Nephrology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China.
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17
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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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18
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Rodriguez Gallo MC, Li Q, Talasila M, Uhrig RG. Quantitative Time-Course Analysis of Osmotic and Salt Stress in Arabidopsis thaliana Using Short Gradient Multi-CV FAIMSpro BoxCar DIA. Mol Cell Proteomics 2023; 22:100638. [PMID: 37704098 PMCID: PMC10663867 DOI: 10.1016/j.mcpro.2023.100638] [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: 02/16/2023] [Revised: 08/22/2023] [Accepted: 08/27/2023] [Indexed: 09/15/2023] Open
Abstract
A major limitation when undertaking quantitative proteomic time-course experimentation is the tradeoff between depth-of-analysis and speed-of-analysis. In high complexity and high dynamic range sample types, such as plant extracts, balance between resolution and time is especially apparent. To address this, we evaluate multiple compensation voltage (CV) high field asymmetric waveform ion mobility spectrometry (FAIMSpro) settings using the latest label-free single-shot Orbitrap-based DIA acquisition workflows for their ability to deeply quantify the Arabidopsis thaliana seedling proteome. Using a BoxCarDIA acquisition workflow with a -30 -50 -70 CV FAIMSpro setting, we were able to consistently quantify >5000 Arabidopsis seedling proteins over a 21-min gradient, facilitating the analysis of ∼42 samples per day. Utilizing this acquisition approach, we then quantified proteome-level changes occurring in Arabidopsis seedling shoots and roots over 24 h of salt and osmotic stress, to identify early and late stress response proteins and reveal stress response overlaps. Here, we successfully quantify >6400 shoot and >8500 root protein groups, respectively, quantifying nearly ∼9700 unique protein groups in total across the study. Collectively, we pioneer a short gradient, multi-CV FAIMSpro BoxCarDIA acquisition workflow that represents an exciting new analysis approach for undertaking quantitative proteomic time-course experimentation in plants.
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Affiliation(s)
- M C Rodriguez Gallo
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Q Li
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - M Talasila
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - R G Uhrig
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada; Department of Biochemistry, University of Alberta, Edmonton, Alberta, Canada.
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19
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Hay BN, Akinlaja MO, Baker TC, Houfani AA, Stacey RG, Foster LJ. Integration of data-independent acquisition (DIA) with co-fractionation mass spectrometry (CF-MS) to enhance interactome mapping capabilities. Proteomics 2023; 23:e2200278. [PMID: 37144656 DOI: 10.1002/pmic.202200278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 04/03/2023] [Accepted: 04/14/2023] [Indexed: 05/06/2023]
Abstract
Proteomics technologies are continually advancing, providing opportunities to develop stronger and more robust protein interaction networks (PINs). In part, this is due to the ever-growing number of high-throughput proteomics methods that are available. This review discusses how data-independent acquisition (DIA) and co-fractionation mass spectrometry (CF-MS) can be integrated to enhance interactome mapping abilities. Furthermore, integrating these two techniques can improve data quality and network generation through extended protein coverage, less missing data, and reduced noise. CF-DIA-MS shows promise in expanding our knowledge of interactomes, notably for non-model organisms (NMOs). CF-MS is a valuable technique on its own, but upon the integration of DIA, the potential to develop robust PINs increases, offering a unique approach for researchers to gain an in-depth understanding into the dynamics of numerous biological processes.
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Affiliation(s)
- Brenna N Hay
- Michael Smith Laboratories and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Mopelola O Akinlaja
- Michael Smith Laboratories and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Teesha C Baker
- Michael Smith Laboratories and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Aicha Asma Houfani
- Michael Smith Laboratories and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - R Greg Stacey
- Michael Smith Laboratories and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Leonard J Foster
- Michael Smith Laboratories and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC, Canada
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20
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Juliano BR, Ruotolo BT. Collision Induced Unfolding Enables the Quantitation of Isomass Biotherapeutics in Complex Biological Matrices. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023; 34:2350-2357. [PMID: 37584234 PMCID: PMC11081006 DOI: 10.1021/jasms.3c00234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2023]
Abstract
Quantitative mass spectrometry has been widely used to evaluate the concentrations of molecules within a variety of biological matrices. Typically, such quantitative mass spectrometry analyses are predicated upon the production of mass-resolved precursor or fragment ions, leading to challenges surrounding the quantification of isomeric or conformationally distinct analytes. As such, new approaches are required for the label-free quantification of isomass proteins. Native ion-mobility MS (nIM-MS) in combination with collision induced unfolding (CIU) is a potentially enabling approach for such quantitative mass spectrometry methods as the technique can rapidly separate and detect many biomacromolecule isoforms. CIU uses collisional activation to capture the unfolding trajectory of ions in the gas phase, producing different intermediate structures that can be leveraged to distinguish protein structures that exhibit identical sizes at lower energies. Here we describe the deployment of quantitative CIU methodology to measure the concentrations of isomass pairs of biotherapeutics and sequence homologues in both standard and biological matrices. Our results cover three antibody pairs and include examples of mixed therapies where multiple biologics are commonly provided to patients. In all cases, CIU enables the production of resolved features for each antibody mixture probed, producing calibration curves with correlation coefficients ranging from 0.92 to 0.99, limits of detection ranging from 300 to 5000 nM and sensitivities ranging from 8.7 × 10-5 nM-1 to 6 × 10-3 μM-1. We conclude our report by projecting the future utility of CIU-enabled quantitative MS methods.
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Affiliation(s)
- Brock R Juliano
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Brandon T Ruotolo
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
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21
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Filandrova R, Douglas P, Zhan X, Verhey TB, Morrissy S, Turner RW, Schriemer DC. Mouse Model of Fragile X Syndrome Analyzed by Quantitative Proteomics: A Comparison of Methods. J Proteome Res 2023; 22:3054-3067. [PMID: 37595185 DOI: 10.1021/acs.jproteome.3c00363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/20/2023]
Abstract
Multiple methods for quantitative proteomics are available for proteome profiling. It is unclear which methods are most useful in situations involving deep proteome profiling and the detection of subtle distortions in the proteome. Here, we compared the performance of seven different strategies in the analysis of a mouse model of Fragile X Syndrome, involving the knockout of the fmr1 gene that is the leading cause of autism spectrum disorder. Focusing on the cerebellum, we show that data-independent acquisition (DIA) and the tandem mass tag (TMT)-based real-time search method (RTS) generated the most informative profiles, generating 334 and 329 significantly altered proteins, respectively, although the latter still suffered from ratio compression. Label-free methods such as BoxCar and a conventional data-dependent acquisition were too noisy to generate a reliable profile, while TMT methods that do not invoke RTS showed a suppressed dynamic range. The TMT method using the TMTpro reagents together with complementary ion quantification (ProC) overcomes ratio compression, but current limitations in ion detection reduce sensitivity. Overall, both DIA and RTS uncovered known regulators of the syndrome and detected alterations in calcium signaling pathways that are consistent with calcium deregulation recently observed in imaging studies. Data are available via ProteomeXchange with the identifier PXD039885.
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Affiliation(s)
- Ruzena Filandrova
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta T2N 4N1, Canada
- Arnie Charbonneau Cancer Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta T2N 4N1, Canada
| | - Pauline Douglas
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta T2N 4N1, Canada
- Arnie Charbonneau Cancer Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta T2N 4N1, Canada
| | - Xiaoqin Zhan
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta T2N 4N1, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta T2N 4N1, Canada
| | - Theodore B Verhey
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta T2N 4N1, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta T2N 4N1, Canada
- Arnie Charbonneau Cancer Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta T2N 4N1, Canada
| | - Sorana Morrissy
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta T2N 4N1, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta T2N 4N1, Canada
- Arnie Charbonneau Cancer Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta T2N 4N1, Canada
| | - Raymond W Turner
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta T2N 4N1, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta T2N 4N1, Canada
| | - David C Schriemer
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta T2N 4N1, Canada
- Department of Chemistry, University of Calgary, Calgary, Alberta T2N 4N1, Canada
- Arnie Charbonneau Cancer Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta T2N 4N1, Canada
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22
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George AL, Sidgwick FR, Watt JE, Martin MP, Trost M, Marín-Rubio JL, Dueñas ME. Comparison of Quantitative Mass Spectrometric Methods for Drug Target Identification by Thermal Proteome Profiling. J Proteome Res 2023; 22:2629-2640. [PMID: 37439223 PMCID: PMC10407934 DOI: 10.1021/acs.jproteome.3c00111] [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: 02/17/2023] [Indexed: 07/14/2023]
Abstract
Thermal proteome profiling (TPP) provides a powerful approach to studying proteome-wide interactions of small therapeutic molecules and their target and off-target proteins, complementing phenotypic-based drug screens. Detecting differences in thermal stability due to target engagement requires high quantitative accuracy and consistent detection. Isobaric tandem mass tags (TMTs) are used to multiplex samples and increase quantification precision in TPP analysis by data-dependent acquisition (DDA). However, advances in data-independent acquisition (DIA) can provide higher sensitivity and protein coverage with reduced costs and sample preparation steps. Herein, we explored the performance of different DIA-based label-free quantification approaches compared to TMT-DDA for thermal shift quantitation. Acute myeloid leukemia cells were treated with losmapimod, a known inhibitor of MAPK14 (p38α). Label-free DIA approaches, and particularly the library-free mode in DIA-NN, were comparable of TMT-DDA in their ability to detect target engagement of losmapimod with MAPK14 and one of its downstream targets, MAPKAPK3. Using DIA for thermal shift quantitation is a cost-effective alternative to labeled quantitation in the TPP pipeline.
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Affiliation(s)
- Amy L. George
- Laboratory
for Biological Mass Spectrometry, Biosciences Institute, Newcastle University, Newcastle-upon-Tyne NE2 4HH, U.K.
| | - Frances R. Sidgwick
- Laboratory
for Biological Mass Spectrometry, Biosciences Institute, Newcastle University, Newcastle-upon-Tyne NE2 4HH, U.K.
| | - Jessica E. Watt
- Newcastle
Cancer Centre, Northern Institute for Cancer Research, Medical School, Newcastle University, Paul O’Gorman Building, Framlington Place, Newcastle upon Tyne NE2 4HH, U.K.
| | - Mathew P. Martin
- Newcastle
Cancer Centre, Northern Institute for Cancer Research, Medical School, Newcastle University, Paul O’Gorman Building, Framlington Place, Newcastle upon Tyne NE2 4HH, U.K.
| | - Matthias Trost
- Laboratory
for Biological Mass Spectrometry, Biosciences Institute, Newcastle University, Newcastle-upon-Tyne NE2 4HH, U.K.
| | - José Luis Marín-Rubio
- Laboratory
for Biological Mass Spectrometry, Biosciences Institute, Newcastle University, Newcastle-upon-Tyne NE2 4HH, U.K.
| | - Maria Emilia Dueñas
- Laboratory
for Biological Mass Spectrometry, Biosciences Institute, Newcastle University, Newcastle-upon-Tyne NE2 4HH, U.K.
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23
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Huang T, Staniak M, da Veiga Leprevost F, Figueroa-Navedo AM, Ivanov AR, Nesvizhskii AI, Choi M, Vitek O. Statistical Detection of Differentially Abundant Proteins in Experiments with Repeated Measures Designs and Isobaric Labeling. J Proteome Res 2023; 22:2641-2659. [PMID: 37467362 PMCID: PMC11090052 DOI: 10.1021/acs.jproteome.3c00155] [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: 07/21/2023]
Abstract
Repeated measures experimental designs, which quantify proteins in biological subjects repeatedly over multiple experimental conditions or times, are commonly used in mass spectrometry-based proteomics. Such designs distinguish the biological variation within and between the subjects and increase the statistical power of detecting within-subject changes in protein abundance. Meanwhile, proteomics experiments increasingly incorporate tandem mass tag (TMT) labeling, a multiplexing strategy that gains both relative protein quantification accuracy and sample throughput. However, combining repeated measures and TMT multiplexing in a large-scale investigation presents statistical challenges due to unique interplays of between-mixture, within-mixture, between-subject, and within-subject variation. This manuscript proposes a family of linear mixed-effects models for differential analysis of proteomics experiments with repeated measures and TMT multiplexing. These models decompose the variation in the data into the contributions from its sources as appropriate for the specifics of each experiment, enable statistical inference of differential protein abundance, and recognize a difference in the uncertainty of between-subject versus within-subject comparisons. The proposed family of models is implemented in the R/Bioconductor package MSstatsTMT v2.2.0. Evaluations of four simulated datasets and four investigations answering diverse biological questions demonstrated the value of this approach as compared to the existing general-purpose approaches and implementations.
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Affiliation(s)
- Ting Huang
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Mateusz Staniak
- Institute of Mathematics, University of Wrocław, Wrocław, Poland
| | | | - Amanda M. Figueroa-Navedo
- Department of Chemistry and Chemical Biology, Barnett Institute of Biological and Chemical Analysis, Northeastern University, Boston, MA, USA
| | - Alexander R. Ivanov
- Department of Chemistry and Chemical Biology, Barnett Institute of Biological and Chemical Analysis, Northeastern University, Boston, MA, USA
| | | | - Meena Choi
- Departments of Microchemistry, Proteomics & Lipidomics, Genentech, South San Francisco, CA, USA
| | - Olga Vitek
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
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24
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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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25
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O'Brien DP, Jones HBL, Guenther F, Murphy EJ, England KS, Vendrell I, Anderson M, Brennan PE, Davis JB, Pinto-Fernández A, Turnbull AP, Kessler BM. Structural Premise of Selective Deubiquitinase USP30 Inhibition by Small-Molecule Benzosulfonamides. Mol Cell Proteomics 2023; 22:100609. [PMID: 37385347 PMCID: PMC10400906 DOI: 10.1016/j.mcpro.2023.100609] [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: 10/31/2022] [Revised: 06/07/2023] [Accepted: 06/25/2023] [Indexed: 07/01/2023] Open
Abstract
Dampening functional levels of the mitochondrial deubiquitylating enzyme Ubiquitin-specific protease 30 (USP30) has been suggested as an effective therapeutic strategy against neurodegenerative disorders such as Parkinson's Disease. USP30 inhibition may counteract the deleterious effects of impaired turnover of damaged mitochondria, which is inherent to both familial and sporadic forms of the disease. Small-molecule inhibitors targeting USP30 are currently in development, but little is known about their precise nature of binding to the protein. We have integrated biochemical and structural approaches to gain novel mechanistic insights into USP30 inhibition by a small-molecule benzosulfonamide-containing compound, USP30inh. Activity-based protein profiling mass spectrometry confirmed target engagement, high selectivity, and potency of USP30inh for USP30 against 49 other deubiquitylating enzymes in a neuroblastoma cell line. In vitro characterization of USP30inh enzyme kinetics inferred slow and tight binding behavior, which is comparable with features of covalent modification of USP30. Finally, we blended hydrogen-deuterium exchange mass spectrometry and computational docking to elucidate the molecular architecture and geometry of USP30 complex formation with USP30inh, identifying structural rearrangements at the cleft of the USP30 thumb and palm subdomains. These studies suggest that USP30inh binds to this thumb-palm cleft, which guides the ubiquitin C terminus into the active site, thereby preventing ubiquitin binding and isopeptide bond cleavage, and confirming its importance in the inhibitory process. Our data will pave the way for the design and development of next-generation inhibitors targeting USP30 and associated deubiquitinylases.
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Affiliation(s)
- Darragh P O'Brien
- Target Discovery Institute, Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, Oxfordshire, UK.
| | - Hannah B L Jones
- Target Discovery Institute, Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, Oxfordshire, UK
| | - Franziska Guenther
- ARUK-Oxford Drug Discovery Institute, Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, Oxfordshire, UK
| | - Emma J Murphy
- ARUK-Oxford Drug Discovery Institute, Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, Oxfordshire, UK
| | - Katherine S England
- ARUK-Oxford Drug Discovery Institute, Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, Oxfordshire, UK
| | - Iolanda Vendrell
- Target Discovery Institute, Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, Oxfordshire, UK
| | | | - Paul E Brennan
- ARUK-Oxford Drug Discovery Institute, Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, Oxfordshire, UK
| | - John B Davis
- ARUK-Oxford Drug Discovery Institute, Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, Oxfordshire, UK
| | - Adán Pinto-Fernández
- Target Discovery Institute, Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, Oxfordshire, UK; Chinese Academy of Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, Oxfordshire, UK
| | | | - Benedikt M Kessler
- Target Discovery Institute, Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, Oxfordshire, UK; Chinese Academy of Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, Oxfordshire, UK.
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26
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Ismail NH, Mussa A, Al-Khreisat MJ, Mohamed Yusoff S, Husin A, Johan MF. Proteomic Alteration in the Progression of Multiple Myeloma: A Comprehensive Review. Diagnostics (Basel) 2023; 13:2328. [PMID: 37510072 PMCID: PMC10378430 DOI: 10.3390/diagnostics13142328] [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: 05/16/2023] [Revised: 06/18/2023] [Accepted: 06/30/2023] [Indexed: 07/30/2023] Open
Abstract
Multiple myeloma (MM) is an incurable hematologic malignancy. Most MM patients are diagnosed at a late stage because the early symptoms of the disease can be uncertain and nonspecific, often resembling other, more common conditions. Additionally, MM patients are commonly associated with rapid relapse and an inevitable refractory phase. MM is characterized by the abnormal proliferation of monoclonal plasma cells in the bone marrow. During the progression of MM, massive genomic alterations occur that target multiple signaling pathways and are accompanied by a multistep process involving differentiation, proliferation, and invasion. Moreover, the transformation of healthy plasma cell biology into genetically heterogeneous MM clones is driven by a variety of post-translational protein modifications (PTMs), which has complicated the discovery of effective treatments. PTMs have been identified as the most promising candidates for biomarker detection, and further research has been recommended to develop promising surrogate markers. Proteomics research has begun in MM, and a comprehensive literature review is available. However, proteomics applications in MM have yet to make significant progress. Exploration of proteomic alterations in MM is worthwhile to improve understanding of the pathophysiology of MM and to search for new treatment targets. Proteomics studies using mass spectrometry (MS) in conjunction with robust bioinformatics tools are an excellent way to learn more about protein changes and modifications during disease progression MM. This article addresses in depth the proteomic changes associated with MM disease transformation.
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Affiliation(s)
- Nor Hayati Ismail
- Department of Haematology, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia
| | - Ali Mussa
- Department of Haematology, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia
- Department of Biology, Faculty of Education, Omdurman Islamic University, Omdurman P.O. Box 382, Sudan
| | - Mutaz Jamal Al-Khreisat
- Department of Haematology, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia
| | - Shafini Mohamed Yusoff
- Department of Haematology, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia
| | - Azlan Husin
- Department of Internal Medicine, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia
| | - Muhammad Farid Johan
- Department of Haematology, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia
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27
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He Q, Zhong CQ, Li X, Guo H, Li Y, Gao M, Yu R, Liu X, Zhang F, Guo D, Ye F, Guo T, Shuai J, Han J. Dear-DIA XMBD: Deep Autoencoder Enables Deconvolution of Data-Independent Acquisition Proteomics. RESEARCH (WASHINGTON, D.C.) 2023; 6:0179. [PMID: 37377457 PMCID: PMC10292580 DOI: 10.34133/research.0179] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 06/01/2023] [Indexed: 06/29/2023]
Abstract
Data-independent acquisition (DIA) technology for protein identification from mass spectrometry and related algorithms is developing rapidly. The spectrum-centric analysis of DIA data without the use of spectra library from data-dependent acquisition data represents a promising direction. In this paper, we proposed an untargeted analysis method, Dear-DIAXMBD, for direct analysis of DIA data. Dear-DIAXMBD first integrates the deep variational autoencoder and triplet loss to learn the representations of the extracted fragment ion chromatograms, then uses the k-means clustering algorithm to aggregate fragments with similar representations into the same classes, and finally establishes the inverted index tables to determine the precursors of fragment clusters between precursors and peptides and between fragments and peptides. We show that Dear-DIAXMBD performs superiorly with the highly complicated DIA data of different species obtained by different instrument platforms. Dear-DIAXMBD is publicly available at https://github.com/jianweishuai/Dear-DIA-XMBD.
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Affiliation(s)
- Qingzu He
- Department of Physics, and Fujian Provincial Key Laboratory for Soft Functional Materials Research,
Xiamen University, Xiamen 361005, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health) and Wenzhou Institute,
University of Chinese Academy of Sciences, Wenzhou, Zhejiang 325001, China
| | - Chuan-Qi Zhong
- School of Life Sciences,
Xiamen University, Xiamen 361102, China
- State Key Laboratory of Cellular Stress Biology,
Innovation Center for Cell Signaling Network, Xiamen 361102, China
| | - Xiang Li
- Department of Physics, and Fujian Provincial Key Laboratory for Soft Functional Materials Research,
Xiamen University, Xiamen 361005, China
- State Key Laboratory of Cellular Stress Biology,
Innovation Center for Cell Signaling Network, Xiamen 361102, China
| | - Huan Guo
- Department of Physics, and Fujian Provincial Key Laboratory for Soft Functional Materials Research,
Xiamen University, Xiamen 361005, China
| | - Yiming Li
- Department of Physics, and Fujian Provincial Key Laboratory for Soft Functional Materials Research,
Xiamen University, Xiamen 361005, China
| | - Mingxuan Gao
- Department of Computer Science,
Xiamen University, Xiamen 361005, China
| | - Rongshan Yu
- Department of Computer Science,
Xiamen University, Xiamen 361005, China
- National Institute for Data Science in Health and Medicine, School of Medicine,
Xiamen University, Xiamen 361102, China
| | - Xianming Liu
- Bruker (Beijing) Scientific Technology Co. Ltd., Beijing, China
| | - Fangfei Zhang
- Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences,
Westlake University, 18 Shilongshan Road, Hangzhou 310024, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, China
| | - Donghui Guo
- Department of Electronic Engineering,
Xiamen University, Xiamen 361005, China
| | - Fangfu Ye
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health) and Wenzhou Institute,
University of Chinese Academy of Sciences, Wenzhou, Zhejiang 325001, China
| | - Tiannan Guo
- Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences,
Westlake University, 18 Shilongshan Road, Hangzhou 310024, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, China
- Westlake Omics Ltd., Yunmeng Road 1, Hangzhou, China
| | - Jianwei Shuai
- Department of Physics, and Fujian Provincial Key Laboratory for Soft Functional Materials Research,
Xiamen University, Xiamen 361005, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health) and Wenzhou Institute,
University of Chinese Academy of Sciences, Wenzhou, Zhejiang 325001, China
- State Key Laboratory of Cellular Stress Biology,
Innovation Center for Cell Signaling Network, Xiamen 361102, China
- National Institute for Data Science in Health and Medicine, School of Medicine,
Xiamen University, Xiamen 361102, China
| | - Jiahuai Han
- School of Life Sciences,
Xiamen University, Xiamen 361102, China
- State Key Laboratory of Cellular Stress Biology,
Innovation Center for Cell Signaling Network, Xiamen 361102, China
- National Institute for Data Science in Health and Medicine, School of Medicine,
Xiamen University, Xiamen 361102, China
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28
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Cao Q, Han M, Zhang Z, Yu C, Xu L, Shi T, Zheng P, Sun J. Novel 15N Metabolic Labeling-Based Large-Scale Absolute Quantitative Proteomics Method for Corynebacterium glutamicum. Anal Chem 2023; 95:4829-4833. [PMID: 36897266 DOI: 10.1021/acs.analchem.2c05524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/11/2023]
Abstract
With fast growth, synthetic biology powers us with the capability to produce high commercial value products in an efficient resource/energy-consuming manner. Comprehensive knowledge of the protein regulatory network of a bacterial host chassis, e.g., the actual amount of the given proteins, is the key to building cell factories for certain target hyperproduction. Many talent methods have been introduced for absolute quantitative proteomics. However, for most cases, a set of reference peptides with isotopic labeling (e.g., SIL, AQUA, QconCAT) or a set of reference proteins (e.g., commercial UPS2 kit) needs to be prepared. The higher cost hinders these methods for large sample research. In this work, we proposed a novel metabolic labeling-based absolute quantification approach (termed nMAQ). The reference Corynebacterium glutamicum strain is metabolically labeled with 15N, and a set of endogenous anchor proteins of the reference proteome is quantified by chemically synthesized light (14N) peptides. The prequantified reference proteome was then utilized as an internal standard (IS) and spiked into the target (14N) samples. SWATH-MS analysis is performed to obtain the absolute expression levels of the proteins from the target cells. The cost for nMAQ is estimated to be less than 10 dollars per sample. We have benchmarked the quantitative performance of the novel method. We believe this method will help with the deep understanding of the intrinsic regulatory mechanism of C. glutamicum during bioengineering and will promote the process of building cell factories for synthetic biology.
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Affiliation(s)
- Qichen Cao
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Systems Biology Centre, Technical Support Core Facilities, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Manman Han
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Systems Biology Centre, Technical Support Core Facilities, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Zuoqing Zhang
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Systems Biology Centre, Technical Support Core Facilities, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- College of Biotechnology, Tianjin University of Science and Technology, Tianjin 300457, China
| | - Chang Yu
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Systems Biology Centre, Technical Support Core Facilities, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- College of Life Sciences, Nankai University, Tianjin 300350, China
| | - Lida Xu
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Systems Biology Centre, Technical Support Core Facilities, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- College of Biotechnology, Tianjin University of Science and Technology, Tianjin 300457, China
| | - Tuo Shi
- Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Ping Zheng
- Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Jibin Sun
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Systems Biology Centre, Technical Support Core Facilities, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
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29
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Schwab K, Coronel L, Riege K, Sacramento EK, Rahnis N, Häckes D, Cirri E, Groth M, Hoffmann S, Fischer M. Multi-omics analysis identifies RFX7 targets involved in tumor suppression and neuronal processes. Cell Death Discov 2023; 9:80. [PMID: 36864036 PMCID: PMC9981735 DOI: 10.1038/s41420-023-01378-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 02/19/2023] [Accepted: 02/22/2023] [Indexed: 03/04/2023] Open
Abstract
Recurrently mutated in lymphoid neoplasms, the transcription factor RFX7 is emerging as a tumor suppressor. Previous reports suggested that RFX7 may also have a role in neurological and metabolic disorders. We recently reported that RFX7 responds to p53 signaling and cellular stress. Furthermore, we found RFX7 target genes to be dysregulated in numerous cancer types also beyond the hematological system. However, our understanding of RFX7's target gene network and its role in health and disease remains limited. Here, we generated RFX7 knock-out cells and employed a multi-omics approach integrating transcriptome, cistrome, and proteome data to obtain a more comprehensive picture of RFX7 targets. We identify novel target genes linked to RFX7's tumor suppressor function and underscoring its potential role in neurological disorders. Importantly, our data reveal RFX7 as a mechanistic link that enables the activation of these genes in response to p53 signaling.
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Affiliation(s)
- Katjana Schwab
- grid.418245.e0000 0000 9999 5706Computational Biology Group, Leibniz Institute on Aging—Fritz Lipmann Institute (FLI), Beutenbergstraße 11, 07745 Jena, Germany
| | - Luis Coronel
- grid.418245.e0000 0000 9999 5706Computational Biology Group, Leibniz Institute on Aging—Fritz Lipmann Institute (FLI), Beutenbergstraße 11, 07745 Jena, Germany
| | - Konstantin Riege
- grid.418245.e0000 0000 9999 5706Computational Biology Group, Leibniz Institute on Aging—Fritz Lipmann Institute (FLI), Beutenbergstraße 11, 07745 Jena, Germany
| | - Erika K. Sacramento
- grid.418245.e0000 0000 9999 5706Core Facility for Proteomics, Leibniz Institute on Aging—Fritz Lipmann Institute (FLI), Beutenbergstraße 11, 07745 Jena, Germany
| | - Norman Rahnis
- grid.418245.e0000 0000 9999 5706Core Facility for Proteomics, Leibniz Institute on Aging—Fritz Lipmann Institute (FLI), Beutenbergstraße 11, 07745 Jena, Germany
| | - David Häckes
- grid.418245.e0000 0000 9999 5706Computational Biology Group, Leibniz Institute on Aging—Fritz Lipmann Institute (FLI), Beutenbergstraße 11, 07745 Jena, Germany
| | - Emilio Cirri
- grid.418245.e0000 0000 9999 5706Core Facility for Proteomics, Leibniz Institute on Aging—Fritz Lipmann Institute (FLI), Beutenbergstraße 11, 07745 Jena, Germany
| | - Marco Groth
- grid.418245.e0000 0000 9999 5706Core Facility for Next-Generation Sequencing, Leibniz Institute on Aging—Fritz Lipmann Institute (FLI), Beutenbergstraße 11, 07745 Jena, Germany
| | - Steve Hoffmann
- grid.418245.e0000 0000 9999 5706Computational Biology Group, Leibniz Institute on Aging—Fritz Lipmann Institute (FLI), Beutenbergstraße 11, 07745 Jena, Germany
| | - Martin Fischer
- Computational Biology Group, Leibniz Institute on Aging-Fritz Lipmann Institute (FLI), Beutenbergstraße 11, 07745, Jena, Germany.
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30
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Phetsanthad A, Vu NQ, Yu Q, Buchberger AR, Chen Z, Keller C, Li L. Recent advances in mass spectrometry analysis of neuropeptides. MASS SPECTROMETRY REVIEWS 2023; 42:706-750. [PMID: 34558119 PMCID: PMC9067165 DOI: 10.1002/mas.21734] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 08/22/2021] [Accepted: 08/28/2021] [Indexed: 05/08/2023]
Abstract
Due to their involvement in numerous biochemical pathways, neuropeptides have been the focus of many recent research studies. Unfortunately, classic analytical methods, such as western blots and enzyme-linked immunosorbent assays, are extremely limited in terms of global investigations, leading researchers to search for more advanced techniques capable of probing the entire neuropeptidome of an organism. With recent technological advances, mass spectrometry (MS) has provided methodology to gain global knowledge of a neuropeptidome on a spatial, temporal, and quantitative level. This review will cover key considerations for the analysis of neuropeptides by MS, including sample preparation strategies, instrumental advances for identification, structural characterization, and imaging; insightful functional studies; and newly developed absolute and relative quantitation strategies. While many discoveries have been made with MS, the methodology is still in its infancy. Many of the current challenges and areas that need development will also be highlighted in this review.
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Affiliation(s)
- Ashley Phetsanthad
- Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, WI 53706, USA
| | - Nhu Q. Vu
- Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, WI 53706, USA
| | - Qing Yu
- School of Pharmacy, University of Wisconsin-Madison, 777 Highland Avenue, Madison, WI 53705, USA
| | - Amanda R. Buchberger
- Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, WI 53706, USA
| | - Zhengwei Chen
- Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, WI 53706, USA
| | - Caitlin Keller
- Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, WI 53706, USA
| | - Lingjun Li
- Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, WI 53706, USA
- School of Pharmacy, University of Wisconsin-Madison, 777 Highland Avenue, Madison, WI 53705, USA
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31
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Zhang J, Gaowa N, Wang Y, Li H, Cao Z, Yang H, Zhang X, Li S. Complementary hepatic metabolomics and proteomics reveal the adaptive mechanisms of dairy cows to the transition period. J Dairy Sci 2023; 106:2071-2088. [PMID: 36567250 DOI: 10.3168/jds.2022-22224] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Accepted: 09/06/2022] [Indexed: 12/24/2022]
Abstract
The transition period from late pregnancy to early lactation is a vital time of the lifecycle of dairy cows due to the marked metabolic challenges. Besides, the liver is the pivot point of metabolism in cattle. Nevertheless, the hepatic physiological molecular adaptation during the transition period has not been elucidated, especially from the metabolomics and proteomics view. Therefore, the present study aims to investigate the hepatic metabolic alterations in transition cows by using integrative metabolomics and proteomics methods. Gas chromatography quadrupole-time-of-flight mass spectrometry-based metabolomics and data-independent acquisition-based quantitative proteomics methods were used to analyze liver tissues collected from 8 healthy multiparous Holstein dairy cows 21 d before and after calving. In total, 44 metabolites and 250 proteins were identified as differentially expressed from 233 metabolites and 3,539 proteins detected from the liver biopsies during the transition period. Complementary functional analysis of different metabolites and proteins indicated the upregulated gluconeogenesis, tricarboxylic acid cycles, AA degradation, fatty acid oxidation, AMP-activated protein kinase signaling pathway, peroxisome proliferator-activated receptor signaling pathway, and ribosome proteins in postpartum dairy cows. In terms of the metabolites and proteins, glucose-6-phosphate, fructose-6-phosphate, carnitine palmitoyltransferase 1A, and phosphoenolpyruvate carboxykinase played a significant role in these pathways. The upregulated oxidative status may be accompanied by the pathways mentioned above. In addition, the upregulated glucagon and insulin signaling pathways also indicated the significant requirement for glucose in postpartum dairy cows. These outcomes, from the view of global metabolites and proteins, may present a better comprehension of the biology of the transition period, which can be helpful in further developing nutritional regulation strategies targeting the liver to help cows overcome this metabolically challenging time.
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Affiliation(s)
- Jun Zhang
- College of Animal Science and Technology, Northwest A&F University, Yangling 712100 China; State Key Laboratory of Animal Nutrition, Beijing Engineering Technology Research Center of Raw Milk Quality and Safety Control, College of Animal Science and Technology, China Agricultural University, Beijing 100193 China
| | - Naren Gaowa
- State Key Laboratory of Animal Nutrition, Beijing Engineering Technology Research Center of Raw Milk Quality and Safety Control, College of Animal Science and Technology, China Agricultural University, Beijing 100193 China
| | - Yajing Wang
- State Key Laboratory of Animal Nutrition, Beijing Engineering Technology Research Center of Raw Milk Quality and Safety Control, College of Animal Science and Technology, China Agricultural University, Beijing 100193 China
| | - Huanxu Li
- Beijing Oriental Kingherd Biotechnology Company, Beijing 100193, China
| | - Zhijun Cao
- State Key Laboratory of Animal Nutrition, Beijing Engineering Technology Research Center of Raw Milk Quality and Safety Control, College of Animal Science and Technology, China Agricultural University, Beijing 100193 China
| | - Hongjian Yang
- State Key Laboratory of Animal Nutrition, Beijing Engineering Technology Research Center of Raw Milk Quality and Safety Control, College of Animal Science and Technology, China Agricultural University, Beijing 100193 China
| | - Xiaoming Zhang
- State Key Laboratory of Animal Nutrition, Beijing Engineering Technology Research Center of Raw Milk Quality and Safety Control, College of Animal Science and Technology, China Agricultural University, Beijing 100193 China
| | - Shengli Li
- State Key Laboratory of Animal Nutrition, Beijing Engineering Technology Research Center of Raw Milk Quality and Safety Control, College of Animal Science and Technology, China Agricultural University, Beijing 100193 China.
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32
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Huang YZ, Xie YS, Li YX, Zhao MY, Sun N, Qi H, Dong XP. Quality assessment of variable collagen tissues of sea cucumber (Stichopus japonicus) body wall under different heat treatment durations by label-Free proteomics analysis. Food Res Int 2023; 165:112540. [PMID: 36869547 DOI: 10.1016/j.foodres.2023.112540] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 11/28/2022] [Accepted: 01/22/2023] [Indexed: 02/05/2023]
Abstract
The microstructure of the body wall, body wall composition, and collagen fibers of sea cucumber (Stichopus japonicus) under different heating times (1 h, 4 h, 12 h, and 24 h) was investigated based on heat treatment at 80 °C. A Label-Free proteomics technique was applied to study the proteomic changes in the body wall of sea cucumbers under 4 and 12 h of heat treatment. Compared with the fresh group, 981 proteins were found to be differentially expressed proteins (DEPs) after heat treatment at 80 °C (4 h), and 1110 DEPs were observed after heat treatment at the same temperature for 12 h. There were 69 DEPs associated with mutable collagenous tissues (MCTs) structures. The results of correlation analysis showed that 55 DEPs were correlated with sensory properties, among which A0A2G8KRV2 was significantly correlated with hardness and SEM image texture features (SEM_Energy, SEM_Correlation, SEM_Homogeneity, and SEM_Contrast). These findings could be conducive to further comprehension of the structural changes and mechanisms of quality loss in the body wall of sea cucumbers at different heat treatment times.
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Affiliation(s)
- Yi-Zhen Huang
- Academy of Food Interdisciplinary Science, School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, Liaoning, China; National Engineering Research Center of Seafood, Collaborative Innovation Center of Seafood Deep Processing, Liaoning Province Collaborative Innovation Center for Marine Food Deep Processing, Dalian 116034, Liaoning, China
| | - Yi-Sha Xie
- Academy of Food Interdisciplinary Science, School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, Liaoning, China; National Engineering Research Center of Seafood, Collaborative Innovation Center of Seafood Deep Processing, Liaoning Province Collaborative Innovation Center for Marine Food Deep Processing, Dalian 116034, Liaoning, China
| | - Yan-Xin Li
- Academy of Food Interdisciplinary Science, School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, Liaoning, China; National Engineering Research Center of Seafood, Collaborative Innovation Center of Seafood Deep Processing, Liaoning Province Collaborative Innovation Center for Marine Food Deep Processing, Dalian 116034, Liaoning, China
| | - Mei-Yu Zhao
- Academy of Food Interdisciplinary Science, School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, Liaoning, China; National Engineering Research Center of Seafood, Collaborative Innovation Center of Seafood Deep Processing, Liaoning Province Collaborative Innovation Center for Marine Food Deep Processing, Dalian 116034, Liaoning, China
| | - Na Sun
- Academy of Food Interdisciplinary Science, School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, Liaoning, China; National Engineering Research Center of Seafood, Collaborative Innovation Center of Seafood Deep Processing, Liaoning Province Collaborative Innovation Center for Marine Food Deep Processing, Dalian 116034, Liaoning, China
| | - Hang Qi
- Academy of Food Interdisciplinary Science, School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, Liaoning, China; National Engineering Research Center of Seafood, Collaborative Innovation Center of Seafood Deep Processing, Liaoning Province Collaborative Innovation Center for Marine Food Deep Processing, Dalian 116034, Liaoning, China
| | - Xiu-Ping Dong
- Academy of Food Interdisciplinary Science, School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, Liaoning, China; National Engineering Research Center of Seafood, Collaborative Innovation Center of Seafood Deep Processing, Liaoning Province Collaborative Innovation Center for Marine Food Deep Processing, Dalian 116034, Liaoning, China.
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33
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Samant RS, Batista S, Larance M, Ozer B, Milton CI, Bludau I, Wu E, Biggins L, Andrews S, Hervieu A, Johnston HE, Al-Lazikhani B, Lamond AI, Clarke PA, Workman P. Native Size-Exclusion Chromatography-Based Mass Spectrometry Reveals New Components of the Early Heat Shock Protein 90 Inhibition Response Among Limited Global Changes. Mol Cell Proteomics 2023; 22:100485. [PMID: 36549590 PMCID: PMC9898794 DOI: 10.1016/j.mcpro.2022.100485] [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: 06/27/2022] [Revised: 11/16/2022] [Accepted: 12/07/2022] [Indexed: 12/24/2022] Open
Abstract
The molecular chaperone heat shock protein 90 (HSP90) works in concert with co-chaperones to stabilize its client proteins, which include multiple drivers of oncogenesis and malignant progression. Pharmacologic inhibitors of HSP90 have been observed to exert a wide range of effects on the proteome, including depletion of client proteins, induction of heat shock proteins, dissociation of co-chaperones from HSP90, disruption of client protein signaling networks, and recruitment of the protein ubiquitylation and degradation machinery-suggesting widespread remodeling of cellular protein complexes. However, proteomics studies to date have focused on inhibitor-induced changes in total protein levels, often overlooking protein complex alterations. Here, we use size-exclusion chromatography in combination with mass spectrometry (SEC-MS) to characterize the early changes in native protein complexes following treatment with the HSP90 inhibitor tanespimycin (17-AAG) for 8 h in the HT29 colon adenocarcinoma cell line. After confirming the signature cellular response to HSP90 inhibition (e.g., induction of heat shock proteins, decreased total levels of client proteins), we were surprised to find only modest perturbations to the global distribution of protein elution profiles in inhibitor-treated HT29 cells at this relatively early time-point. Similarly, co-chaperones that co-eluted with HSP90 displayed no clear difference between control and treated conditions. However, two distinct analysis strategies identified multiple inhibitor-induced changes, including known and unknown components of the HSP90-dependent proteome. We validate two of these-the actin-binding protein Anillin and the mitochondrial isocitrate dehydrogenase 3 complex-as novel HSP90 inhibitor-modulated proteins. We present this dataset as a resource for the HSP90, proteostasis, and cancer communities (https://www.bioinformatics.babraham.ac.uk/shiny/HSP90/SEC-MS/), laying the groundwork for future mechanistic and therapeutic studies related to HSP90 pharmacology. Data are available via ProteomeXchange with identifier PXD033459.
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Affiliation(s)
- Rahul S Samant
- Centre for Cancer Drug Discovery, The Institute of Cancer Research, London, United Kingdom; Signalling Programme, The Babraham Institute, Cambridge, United Kingdom.
| | - Silvia Batista
- Centre for Cancer Drug Discovery, The Institute of Cancer Research, London, United Kingdom
| | - Mark Larance
- Centre for Gene Regulation & Expression, University of Dundee, Dundee, United Kingdom
| | - Bugra Ozer
- Centre for Cancer Drug Discovery, The Institute of Cancer Research, London, United Kingdom
| | - Christopher I Milton
- Centre for Cancer Drug Discovery, The Institute of Cancer Research, London, United Kingdom
| | - Isabell Bludau
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Estelle Wu
- Signalling Programme, The Babraham Institute, Cambridge, United Kingdom
| | - Laura Biggins
- Bioinformatics Group, The Babraham Institute, Cambridge, United Kingdom
| | - Simon Andrews
- Bioinformatics Group, The Babraham Institute, Cambridge, United Kingdom
| | - Alexia Hervieu
- Centre for Cancer Drug Discovery, The Institute of Cancer Research, London, United Kingdom
| | - Harvey E Johnston
- Signalling Programme, The Babraham Institute, Cambridge, United Kingdom
| | - Bissan Al-Lazikhani
- Centre for Cancer Drug Discovery, The Institute of Cancer Research, London, United Kingdom; Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Angus I Lamond
- Centre for Gene Regulation & Expression, University of Dundee, Dundee, United Kingdom
| | - Paul A Clarke
- Centre for Cancer Drug Discovery, The Institute of Cancer Research, London, United Kingdom
| | - Paul Workman
- Centre for Cancer Drug Discovery, The Institute of Cancer Research, London, United Kingdom.
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34
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Zhao J, Yang Y, Xu H, Zheng J, Shen C, Chen T, Wang T, Wang B, Yi J, Zhao D, Wu E, Qin Q, Xia L, Qiao L. Data-independent acquisition boosts quantitative metaproteomics for deep characterization of gut microbiota. NPJ Biofilms Microbiomes 2023; 9:4. [PMID: 36693863 PMCID: PMC9873935 DOI: 10.1038/s41522-023-00373-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 01/11/2023] [Indexed: 01/26/2023] Open
Abstract
Metaproteomics can provide valuable insights into the functions of human gut microbiota (GM), but is challenging due to the extreme complexity and heterogeneity of GM. Data-independent acquisition (DIA) mass spectrometry (MS) has been an emerging quantitative technique in conventional proteomics, but is still at the early stage of development in the field of metaproteomics. Herein, we applied library-free DIA (directDIA)-based metaproteomics and compared the directDIA with other MS-based quantification techniques for metaproteomics on simulated microbial communities and feces samples spiked with bacteria with known ratios, demonstrating the superior performance of directDIA by a comprehensive consideration of proteome coverage in identification as well as accuracy and precision in quantification. We characterized human GM in two cohorts of clinical fecal samples of pancreatic cancer (PC) and mild cognitive impairment (MCI). About 70,000 microbial proteins were quantified in each cohort and annotated to profile the taxonomic and functional characteristics of GM in different diseases. Our work demonstrated the utility of directDIA in quantitative metaproteomics for investigating intestinal microbiota and its related disease pathogenesis.
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Affiliation(s)
- Jinzhi Zhao
- Department of Chemistry, Shanghai Stomatological Hospital, Fudan University, 200000, Shanghai, China
| | - Yi Yang
- Department of Chemistry, Shanghai Stomatological Hospital, Fudan University, 200000, Shanghai, China.,ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, 311200, Hangzhou, China
| | - Hua Xu
- Department of Core Facility of Basic Medical Sciences, and Department of Psychiatry of Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 200000, Shanghai, China
| | - Jianxujie Zheng
- Department of Chemistry, Shanghai Stomatological Hospital, Fudan University, 200000, Shanghai, China
| | - Chengpin Shen
- Shanghai Omicsolution Co., Ltd, 201100, Shanghai, China
| | - Tian Chen
- Changhai Hospital, The Naval Military Medical University, 200433, Shanghai, China
| | - Tao Wang
- Department of Core Facility of Basic Medical Sciences, and Department of Psychiatry of Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 200000, Shanghai, China
| | - Bing Wang
- College of Food Science and Technology, Shanghai Ocean University, 201306, Shanghai, China
| | - Jia Yi
- Department of Chemistry, Shanghai Stomatological Hospital, Fudan University, 200000, Shanghai, China
| | - Dan Zhao
- Department of Chemistry, Shanghai Stomatological Hospital, Fudan University, 200000, Shanghai, China
| | - Enhui Wu
- Department of Chemistry, Shanghai Stomatological Hospital, Fudan University, 200000, Shanghai, China
| | - Qin Qin
- Changhai Hospital, The Naval Military Medical University, 200433, Shanghai, China.
| | - Li Xia
- Department of Core Facility of Basic Medical Sciences, and Department of Psychiatry of Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 200000, Shanghai, China.
| | - Liang Qiao
- Department of Chemistry, Shanghai Stomatological Hospital, Fudan University, 200000, Shanghai, China.
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35
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Derks J, Leduc A, Wallmann G, Huffman RG, Willetts M, Khan S, Specht H, Ralser M, Demichev V, Slavov N. Increasing the throughput of sensitive proteomics by plexDIA. Nat Biotechnol 2023; 41:50-59. [PMID: 35835881 PMCID: PMC9839897 DOI: 10.1038/s41587-022-01389-w] [Citation(s) in RCA: 82] [Impact Index Per Article: 82.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 06/13/2022] [Indexed: 01/22/2023]
Abstract
Current mass spectrometry methods enable high-throughput proteomics of large sample amounts, but proteomics of low sample amounts remains limited in depth and throughput. To increase the throughput of sensitive proteomics, we developed an experimental and computational framework, called plexDIA, for simultaneously multiplexing the analysis of peptides and samples. Multiplexed analysis with plexDIA increases throughput multiplicatively with the number of labels without reducing proteome coverage or quantitative accuracy. By using three-plex non-isobaric mass tags, plexDIA enables quantification of threefold more protein ratios among nanogram-level samples. Using 1-hour active gradients, plexDIA quantified ~8,000 proteins in each sample of labeled three-plex sets and increased data completeness, reducing missing data more than twofold across samples. Applied to single human cells, plexDIA quantified ~1,000 proteins per cell and achieved 98% data completeness within a plexDIA set while using ~5 minutes of active chromatography per cell. These results establish a general framework for increasing the throughput of sensitive and quantitative protein analysis.
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Affiliation(s)
- Jason Derks
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA, USA.
| | - Andrew Leduc
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA, USA
| | - Georg Wallmann
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA, USA
| | - R Gray Huffman
- 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
| | - Harrison Specht
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA, USA
| | - Markus Ralser
- Charité - Universitätsmedizin Berlin, Berlin, Germany
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
| | | | - Nikolai Slavov
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA, USA.
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36
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Recent advance in the investigation of aquatic “blue foods” at a molecular level: A proteomics strategy. Trends Food Sci Technol 2023. [DOI: 10.1016/j.tifs.2022.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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37
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Tear Proteome Revealed Association of S100A Family Proteins and Mesothelin with Thrombosis in Elderly Patients with Retinal Vein Occlusion. Int J Mol Sci 2022; 23:ijms232314653. [PMID: 36498980 PMCID: PMC9736253 DOI: 10.3390/ijms232314653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 11/03/2022] [Accepted: 11/21/2022] [Indexed: 11/25/2022] Open
Abstract
Tear samples collected from patients with central retinal vein occlusion (CRVO; n = 28) and healthy volunteers (n = 29) were analyzed using a proteomic label-free absolute quantitative approach. A large proportion (458 proteins with a frequency > 0.6) of tear proteomes was found to be shared between the study groups. Comparative proteomic analysis revealed 29 proteins (p < 0.05) significantly differed between CRVO patients and the control group. Among them, S100A6 (log (2) FC = 1.11, p < 0.001), S100A8 (log (2) FC = 2.45, p < 0.001), S100A9 (log2 (FC) = 2.08, p < 0.001), and mesothelin ((log2 (FC) = 0.82, p < 0.001) were the most abundantly represented upregulated proteins, and β2-microglobulin was the most downregulated protein (log2 (FC) = −2.13, p < 0.001). The selected up- and downregulated proteins were gathered to customize a map of CRVO-related critical protein interactions with quantitative properties. The customized map (FDR < 0.01) revealed inflammation, impairment of retinal hemostasis, and immune response as the main set of processes associated with CRVO ischemic condition. The semantic analysis displayed the prevalence of core biological processes covering dysregulation of mitochondrial organization and utilization of improperly or topologically incorrect folded proteins as a consequence of oxidative stress, and escalating of the ischemic condition caused by the local retinal hemostasis dysregulation. The most significantly different proteins (S100A6, S100A8, S100A9, MSLN, and β2-microglobulin) were applied for the ROC analysis, and their AUC varied from 0.772 to 0.952, suggesting probable association with the CRVO.
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38
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Baker CP, Phair IR, Brenes AJ, Atrih A, Ryan DG, Bruderer R, Dinkova-Kostova AT, Lamont DJ, Arthur JSC, Howden AJ. DIA label-free proteomic analysis of murine bone-marrow-derived macrophages. STAR Protoc 2022; 3:101725. [PMID: 36166358 PMCID: PMC9519785 DOI: 10.1016/j.xpro.2022.101725] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 08/09/2022] [Accepted: 08/31/2022] [Indexed: 01/26/2023] Open
Abstract
Here, we describe an optimized protocol to analyze murine bone-marrow-derived macrophages using label-free data-independent acquisition (DIA) proteomics. We provide a complete step-by-step protocol describing sample preparation utilizing the S-Trap approach for on-column digestion and peptide purification. We then detail mass spectrometry data acquisition and approaches for data analysis. Single-shot DIA protocols achieve comparable proteomic depth with data-dependent MS approaches without the need for fractionation. This allows for better scaling for large sample numbers with high inter-experimental reproducibility. For complete details on the use and execution of this protocol, please refer to Ryan et al. (2022).
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Affiliation(s)
- Christa P. Baker
- School of Life Sciences, University of Dundee, Dow Street, Dundee DD1 5EH, Scotland
| | - Iain R. Phair
- Division of Cellular and Systems Medicine, School of Medicine, University of Dundee, Ninewells Hospital and Medical School, James Arrott Drive, Dundee DD1 9SY, Scotland
| | - Alejandro J. Brenes
- School of Life Sciences, University of Dundee, Dow Street, Dundee DD1 5EH, Scotland
| | - Abdelmadjid Atrih
- School of Life Sciences, University of Dundee, Dow Street, Dundee DD1 5EH, Scotland
| | - Dylan G. Ryan
- MRC Mitochondrial Biology Unit, University of Cambridge, Cambridge CB2 0XY, Scotland
| | | | - Albena T. Dinkova-Kostova
- Division of Cellular and Systems Medicine, School of Medicine, University of Dundee, Ninewells Hospital and Medical School, James Arrott Drive, Dundee DD1 9SY, Scotland
| | - Douglas J. Lamont
- School of Life Sciences, University of Dundee, Dow Street, Dundee DD1 5EH, Scotland
| | - J. Simon C. Arthur
- School of Life Sciences, University of Dundee, Dow Street, Dundee DD1 5EH, Scotland,Corresponding author
| | - Andrew J.M. Howden
- School of Life Sciences, University of Dundee, Dow Street, Dundee DD1 5EH, Scotland,Corresponding author
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39
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Ebinezer LB, Battisti I, Sharma N, Ravazzolo L, Ravi L, Trentin AR, Barion G, Panozzo A, Dall'Acqua S, Vamerali T, Quaggiotti S, Arrigoni G, Masi A. Perfluorinated alkyl substances affect the growth, physiology and root proteome of hydroponically grown maize plants. JOURNAL OF HAZARDOUS MATERIALS 2022; 438:129512. [PMID: 35999737 DOI: 10.1016/j.jhazmat.2022.129512] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 06/14/2022] [Accepted: 06/29/2022] [Indexed: 06/15/2023]
Abstract
Poly- and perfluorinated alkyl substances (PFAS) are a group of persistent organic pollutants causing serious global concern. Plants can accumulate PFAS but their effect on plant physiology, especially at the molecular level is not very well understood. Hence, we used hydroponically-grown maize plants treated with a combination of eleven different PFAS (each at 100 μg L-1) to investigate their bioaccumulation and effects on the growth, physiology and their impact on the root proteome. A dose-dependent decrease in root growth parameters was evidenced with a significant reduction in the relative growth rate, fresh weight of leaves and roots and altered photosynthetic parameters in PFAS-treated plants. Higher concentration of shorter PFAS (C < 8) was detected in the leaves, while long-chain PFAS (C ≥ 8) were more retained in roots. From the root proteome analysis, we identified 75 differentially abundant proteins, mostly involved in cellular metabolic and biosynthetic processes, translation and cytoskeletal reorganization. Validating the altered protein abundance using quantitative real-time PCR, the results were further substantiated using amino acid and fatty acid profiling, thus, providing first insight into the altered metabolic state of plants exposed to PFAS from a proteomics perspective.
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Affiliation(s)
- Leonard Barnabas Ebinezer
- Department of Agronomy, Food, Natural Resources, Animals, and Environment, University of Padova, Padua, Italy
| | - Ilaria Battisti
- Department of Agronomy, Food, Natural Resources, Animals, and Environment, University of Padova, Padua, Italy; Proteomics Center, University of Padova and Azienda Ospedaliera di Padova, via G. Orus 2/B, 35129 Padova, Italy
| | - Nisha Sharma
- Department of Agronomy, Food, Natural Resources, Animals, and Environment, University of Padova, Padua, Italy
| | - Laura Ravazzolo
- Department of Agronomy, Food, Natural Resources, Animals, and Environment, University of Padova, Padua, Italy
| | - Lokesh Ravi
- Department of Botany, St. Joseph's College (Autonomous), Bengaluru, India
| | - Anna Rita Trentin
- Department of Agronomy, Food, Natural Resources, Animals, and Environment, University of Padova, Padua, Italy
| | - Giuseppe Barion
- Department of Agronomy, Food, Natural Resources, Animals, and Environment, University of Padova, Padua, Italy
| | - Anna Panozzo
- Department of Agronomy, Food, Natural Resources, Animals, and Environment, University of Padova, Padua, Italy
| | - Stefano Dall'Acqua
- Department of Pharmaceutical Sciences, University of Padova, Via Marzolo 5, 35131 PD, Italy
| | - Teofilo Vamerali
- Department of Agronomy, Food, Natural Resources, Animals, and Environment, University of Padova, Padua, Italy
| | - Silvia Quaggiotti
- Department of Agronomy, Food, Natural Resources, Animals, and Environment, University of Padova, Padua, Italy
| | - Giorgio Arrigoni
- Proteomics Center, University of Padova and Azienda Ospedaliera di Padova, via G. Orus 2/B, 35129 Padova, Italy; Department of Biomedical Sciences, University of Padova, via U. Bassi 58/B, 35131 Padova, Italy; CRIBI Biotechnology Center, University of Padova, via U. Bassi 58/B, 35131 Padova, Italy.
| | - Antonio Masi
- Department of Agronomy, Food, Natural Resources, Animals, and Environment, University of Padova, Padua, Italy
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40
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Su T, Liang L, Zhang L, Wang J, Chen L, Su C, Cao J, Yu Q, Deng S, Chan HF, Tang S, Guo Y, Chen J. Retinal organoids and microfluidic chip-based approaches to explore the retinitis pigmentosa with USH2A mutations. Front Bioeng Biotechnol 2022; 10:939774. [PMID: 36185441 PMCID: PMC9524156 DOI: 10.3389/fbioe.2022.939774] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 08/23/2022] [Indexed: 11/19/2022] Open
Abstract
Retinitis pigmentosa (RP) is a leading cause of vision impairment and blindness worldwide, with limited medical treatment options. USH2A mutations are one of the most common causes of non-syndromic RP. In this study, we developed retinal organoids (ROs) and retinal pigment epithelium (RPE) cells from induced pluripotent stem cells (iPSCs) of RP patient to establish a sustainable in vitro RP disease model. RT-qPCR, western blot, and immunofluorescent staining assessments showed that USH2A mutations induced apoptosis of iPSCs and ROs, and deficiency of the extracellular matrix (ECM) components. Transcriptomics and proteomics findings suggested that abnormal ECM-receptor interactions could result in apoptosis of ROs with USH2A mutations via the PI3K-Akt pathway. To optimize the culture conditions of ROs, we fabricated a microfluidic chip to co-culture the ROs with RPE cells. Our results showed that this perfusion system could efficiently improve the survival rate of ROs. Further, ECM components such as laminin and collagen IV of ROs in the RP group were upregulated compared with those maintained in static culture. These findings illustrate the potential of microfluidic chip combined with ROs technology in disease modelling for RP.
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Affiliation(s)
- Ting Su
- Department of Ophthalmology, First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, China
| | - Liying Liang
- Department of Ophthalmology, First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, China
| | - Lan Zhang
- Department of Ophthalmology, First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, China
| | - Jianing Wang
- Key Laboratory for Regenerative Medicine, Ministry of Education, Jinan University, Guangzhou, China
| | - Luyin Chen
- Key Laboratory for Regenerative Medicine, Ministry of Education, Jinan University, Guangzhou, China
| | - Caiying Su
- Department of Ophthalmology, First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, China
| | - Jixing Cao
- Department of Ophthalmology, First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, China
| | - Quan Yu
- Centric Laboratory, Medical College, Jinan University, Guangzhou, China
| | - Shuai Deng
- Institute for Tissue Engineering and Regenerative Medicine, Chinese University of Hong Kong, Hong Kong, China
- Key Laboratory for Regenerative Medicine of the Ministry of Education of China, Ministry of Education of China, School of Biomedical Sciences, Faculty of Medicine, Chinese University of Hong Kong, Hong Kong, China
| | - Hon Fai Chan
- Institute for Tissue Engineering and Regenerative Medicine, Chinese University of Hong Kong, Hong Kong, China
- Key Laboratory for Regenerative Medicine of the Ministry of Education of China, Ministry of Education of China, School of Biomedical Sciences, Faculty of Medicine, Chinese University of Hong Kong, Hong Kong, China
| | | | - Yonglong Guo
- Department of Ophthalmology, First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, China
- College of Veterinary Medicine, South China Agricultural University, Guangzhou, China
- *Correspondence: Jiansu Chen, ; Yonglong Guo,
| | - Jiansu Chen
- Key Laboratory for Regenerative Medicine, Ministry of Education, Jinan University, Guangzhou, China
- Aier Eye Institute, Changsha, China
- Institute of Ophthalmology, Medical College, Jinan University, Guangzhou, China
- *Correspondence: Jiansu Chen, ; Yonglong Guo,
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41
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Frankenfield AM, Ni J, Ahmed M, Hao L. Protein Contaminants Matter: Building Universal Protein Contaminant Libraries for DDA and DIA Proteomics. J Proteome Res 2022; 21:2104-2113. [PMID: 35793413 PMCID: PMC10040255 DOI: 10.1021/acs.jproteome.2c00145] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Mass spectrometry-based proteomics is constantly challenged by the presence of contaminant background signals. In particular, protein contaminants from reagents and sample handling are almost impossible to avoid. For data-dependent acquisition (DDA) proteomics, an exclusion list can be used to reduce the influence of protein contaminants. However, protein contamination has not been evaluated and is rarely addressed in data-independent acquisition (DIA). How protein contaminants influence proteomic data is also unclear. In this study, we established new protein contaminant FASTA and spectral libraries that are applicable to all proteomic workflows and evaluated the impact of protein contaminants on both DDA and DIA proteomics. We demonstrated that including our contaminant libraries can reduce false discoveries and increase protein identifications, without influencing the quantification accuracy in various proteomic software platforms. With the pressing need to standardize proteomic workflow in the research community, we highly recommend including our contaminant FASTA and spectral libraries in all bottom-up proteomic data analysis. Our contaminant libraries and a step-by-step tutorial to incorporate these libraries in various DDA and DIA data analysis platforms can be valuable resources for proteomic researchers, freely accessible at https://github.com/HaoGroup-ProtContLib.
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Affiliation(s)
- Ashley M Frankenfield
- Department of Chemistry, The George Washington University, Science and Engineering Hall 4000, 800, 22nd St., Northwest, Washington, DC 20052, United States
| | - Jiawei Ni
- Department of Chemistry, The George Washington University, Science and Engineering Hall 4000, 800, 22nd St., Northwest, Washington, DC 20052, United States
| | - Mustafa Ahmed
- Department of Chemistry, The George Washington University, Science and Engineering Hall 4000, 800, 22nd St., Northwest, Washington, DC 20052, United States
| | - Ling Hao
- Department of Chemistry, The George Washington University, Science and Engineering Hall 4000, 800, 22nd St., Northwest, Washington, DC 20052, United States
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42
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Multi-omics study identifies novel signatures of DNA/RNA, amino acid, peptide, and lipid metabolism by simulated diabetes on coronary endothelial cells. Sci Rep 2022; 12:12027. [PMID: 35835939 PMCID: PMC9283518 DOI: 10.1038/s41598-022-16300-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 07/07/2022] [Indexed: 12/14/2022] Open
Abstract
Coronary artery endothelial cells (CAEC) exert an important role in the development of cardiovascular disease. Dysfunction of CAEC is associated with cardiovascular disease in subjects with type 2 diabetes mellitus (T2DM). However, comprehensive studies of the effects that a diabetic environment exerts on this cellular type are scarce. The present study characterized the molecular perturbations occurring on cultured bovine CAEC subjected to a prolonged diabetic environment (high glucose and high insulin). Changes at the metabolite and peptide level were assessed by Liquid Chromatography–Mass Spectrometry (LC–MS2) and chemoinformatics. The results were integrated with published LC–MS2-based quantitative proteomics on the same in vitro model. Our findings were consistent with reports on other endothelial cell types and identified novel signatures of DNA/RNA, amino acid, peptide, and lipid metabolism in cells under a diabetic environment. Manual data inspection revealed disturbances on tryptophan catabolism and biosynthesis of phenylalanine-based, glutathione-based, and proline-based peptide metabolites. Fluorescence microscopy detected an increase in binucleation in cells under treatment that also occurred when human CAEC were used. This multi-omics study identified particular molecular perturbations in an induced diabetic environment that could help unravel the mechanisms underlying the development of cardiovascular disease in subjects with T2DM.
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43
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Wang Y, Lih TSM, Chen L, Xu Y, Kuczler MD, Cao L, Pienta KJ, Amend SR, Zhang H. Optimized data-independent acquisition approach for proteomic analysis at single-cell level. Clin Proteomics 2022; 19:24. [PMID: 35810282 PMCID: PMC9270744 DOI: 10.1186/s12014-022-09359-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 05/26/2022] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND Single-cell proteomic analysis provides valuable insights into cellular heterogeneity allowing the characterization of the cellular microenvironment which is difficult to accomplish in bulk proteomic analysis. Currently, single-cell proteomic studies utilize data-dependent acquisition (DDA) mass spectrometry (MS) coupled with a TMT labelled carrier channel. Due to the extremely imbalanced MS signals among the carrier channel and other TMT reporter ions, the quantification is compromised. Thus, data-independent acquisition (DIA)-MS should be considered as an alternative approach towards single-cell proteomic study since it generates reproducible quantitative data. However, there are limited reports on the optimal workflow for DIA-MS-based single-cell analysis. METHODS We report an optimized DIA workflow for single-cell proteomics using Orbitrap Lumos Tribrid instrument. We utilized a breast cancer cell line (MDA-MB-231) and induced drug resistant polyaneuploid cancer cells (PACCs) to evaluate our established workflow. RESULTS We found that a short LC gradient was preferable for peptides extracted from single cell level with less than 2 ng sample amount. The total number of co-searching peptide precursors was also critical for protein and peptide identifications at nano- and sub-nano-gram levels. Post-translationally modified peptides could be identified from a nano-gram level of peptides. Using the optimized workflow, up to 1500 protein groups were identified from a single PACC corresponding to 0.2 ng of peptides. Furthermore, about 200 peptides with phosphorylation, acetylation, and ubiquitination were identified from global DIA analysis of 100 cisplatin resistant PACCs (20 ng). Finally, we used this optimized DIA approach to compare the whole proteome of MDA-MB-231 parental cells and induced PACCs at a single-cell level. We found the single-cell level comparison could reflect real protein expression changes and identify the protein copy number. CONCLUSIONS Our results demonstrate that the optimized DIA pipeline can serve as a reliable quantitative tool for single-cell as well as sub-nano-gram proteomic analysis.
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Affiliation(s)
- Yuefan Wang
- Department of Pathology, Johns Hopkins University, Baltimore, MD, 21287, USA
| | | | - Lijun Chen
- Department of Pathology, Johns Hopkins University, Baltimore, MD, 21287, USA
| | - Yuanwei Xu
- Department of Pathology, Johns Hopkins University, Baltimore, MD, 21287, USA
| | - Morgan D Kuczler
- Cancer Ecology Center, The Brady Urological Institute, Johns Hopkins School of Medicine, 600 N. Wolfe Street, Baltimore, MD, 21287, USA
| | - Liwei Cao
- Department of Pathology, Johns Hopkins University, Baltimore, MD, 21287, USA
| | - Kenneth J Pienta
- Cancer Ecology Center, The Brady Urological Institute, Johns Hopkins School of Medicine, 600 N. Wolfe Street, Baltimore, MD, 21287, USA
| | - Sarah R Amend
- Cancer Ecology Center, The Brady Urological Institute, Johns Hopkins School of Medicine, 600 N. Wolfe Street, Baltimore, MD, 21287, USA
| | - Hui Zhang
- Department of Pathology, Johns Hopkins University, Baltimore, MD, 21287, USA.
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Abstract
There are probably no biological samples that did more to spur interest in proteomics than serum and plasma. The belief was that comparing the proteomes of these samples obtained from healthy and disease-affected individuals would lead to biomarkers that could be used to diagnose conditions such as cancer. While the continuing development of mass spectrometers with greater sensitivity and resolution has been invaluable, the invention of strategic strategies to separate circulatory proteins has been just as critical. Novel and creative separation techniques were required because serum and plasma probably have the greatest dynamic range of protein concentration of any biological sample. The concentrations of circulating proteins can range over twelve orders of magnitude, making it a challenge to identify low-abundance proteins where the bulk of the useful biomarkers are believed to exist. The major goals of this article are to (i) provide an historical perspective on the rapid development of serum and plasma proteomics; (ii) describe various separation techniques that have made obtaining an in-depth view of the proteome of these biological samples possible; and (iii) describe applications where serum and plasma proteomics have been employed to discover potential biomarkers for pathological conditions.
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45
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Fahrner M, Föll MC, Grüning BA, Bernt M, Röst H, Schilling O. Democratizing data-independent acquisition proteomics analysis on public cloud infrastructures via the Galaxy framework. Gigascience 2022; 11:6528772. [PMID: 35166338 PMCID: PMC8848309 DOI: 10.1093/gigascience/giac005] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 11/26/2021] [Accepted: 01/12/2022] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Data-independent acquisition (DIA) has become an important approach in global, mass spectrometric proteomic studies because it provides in-depth insights into the molecular variety of biological systems. However, DIA data analysis remains challenging owing to the high complexity and large data and sample size, which require specialized software and vast computing infrastructures. Most available open-source DIA software necessitates basic programming skills and covers only a fraction of a complete DIA data analysis. In consequence, DIA data analysis often requires usage of multiple software tools and compatibility thereof, severely limiting the usability and reproducibility. FINDINGS To overcome this hurdle, we have integrated a suite of open-source DIA tools in the Galaxy framework for reproducible and version-controlled data processing. The DIA suite includes OpenSwath, PyProphet, diapysef, and swath2stats. We have compiled functional Galaxy pipelines for DIA processing, which provide a web-based graphical user interface to these pre-installed and pre-configured tools for their use on freely accessible, powerful computational resources of the Galaxy framework. This approach also enables seamless sharing workflows with full configuration in addition to sharing raw data and results. We demonstrate the usability of an all-in-one DIA pipeline in Galaxy by the analysis of a spike-in case study dataset. Additionally, extensive training material is provided to further increase access for the proteomics community. CONCLUSION The integration of an open-source DIA analysis suite in the web-based and user-friendly Galaxy framework in combination with extensive training material empowers a broad community of researches to perform reproducible and transparent DIA data analysis.
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Affiliation(s)
- Matthias Fahrner
- Institute for Surgical Pathology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Breisacher Straße 115a, D-79106 Freiburg, Germany.,Faculty of Biology, Albert-Ludwigs-University Freiburg, Schänzlestraße 1, D-79104 Freiburg, Freiburg, Germany.,Spemann Graduate School of Biology and Medicine (SGBM), University of Freiburg, Albertstraße 19A, D-79104 Freiburg, Germany
| | - Melanie Christine Föll
- Institute for Surgical Pathology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Breisacher Straße 115a, D-79106 Freiburg, Germany.,Khoury College of Computer Sciences, Northeastern University, 440 Huntington Ave, Boston, MA 02115, USA
| | - Björn Andreas Grüning
- Department of Computer Science, University of Freiburg, Georges-Köhler-Allee 106, 79110 Freiburg, Germany
| | - Matthias Bernt
- Young Investigators Group Bioinformatics and Transcriptomics, Helmholtz Centre for Environmental Research-UFZ, Permoserstraße 15, D-04318 Leipzig, Germany
| | - Hannes Röst
- Donnelly Centre,University of Toronto, 160 College St, Toronto, ON M5S 3E1, Toronto, ON, Canada
| | - Oliver Schilling
- Institute for Surgical Pathology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Breisacher Straße 115a, D-79106 Freiburg, Germany.,German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Hugstetter Straße 55, D-79106 Freiburg, Heidelberg, Germany.,BIOSS Centre for Biological Signaling Studies,University of Freiburg, Schänzlestraße 18, D-79104 Freiburg, D-79104 Freiburg, Germany
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46
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MSSort-DIAXMBD: A deep learning classification tool of the peptide precursors quantified by OpenSWATH. J Proteomics 2022; 259:104542. [DOI: 10.1016/j.jprot.2022.104542] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 02/17/2022] [Accepted: 02/18/2022] [Indexed: 11/21/2022]
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47
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Isaksson M, Karlsson C, Laurell T, Kirkeby A, Heusel M. MSLibrarian: Optimized Predicted Spectral Libraries for Data-Independent Acquisition Proteomics. J Proteome Res 2022; 21:535-546. [PMID: 35042333 PMCID: PMC8822486 DOI: 10.1021/acs.jproteome.1c00796] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
![]()
Data-independent
acquisition-mass spectrometry (DIA-MS) is the
method of choice for deep, consistent, and accurate single-shot profiling
in bottom-up proteomics. While classic workflows for targeted quantification
from DIA-MS data require auxiliary data-dependent acquisition (DDA)
MS analysis of subject samples to derive prior-knowledge spectral
libraries, library-free approaches based on in silico prediction promise deep DIA-MS profiling with reduced experimental
effort and cost. Coverage and sensitivity in such analyses are however
limited, in part, by the large library size and persistent deviations
from the experimental data. We present MSLibrarian, a new workflow
and tool to obtain optimized predicted spectral libraries by the integrated
usage of spectrum-centric DIA data interpretation via the DIA-Umpire
approach to inform and calibrate the in silico predicted
library and analysis approach. Predicted-vs-observed comparisons enabled
optimization of intensity prediction parameters, calibration of retention
time prediction for deviating chromatographic setups, and optimization
of the library scope and sample representativeness. Benchmarking via
a dedicated ground-truth-embedded experiment of species-mixed proteins
and quantitative ratio-validation confirmed gains of up to 13% on
peptide and 8% on protein level at equivalent FDR control and validation
criteria. MSLibrarian is made available as an open-source R software
package, including step-by-step user instructions, at https://github.com/MarcIsak/MSLibrarian.
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Affiliation(s)
- Marc Isaksson
- Department of Biomedical Engineering, Lund University, 22100 Lund, Sweden.,Department of Experimental Medical Science and Wallenberg Center for Molecular Medicine, Lund University, 22100 Lund, Sweden
| | - Christofer Karlsson
- Infection Medicine Proteomics Lab, Division of Infection Medicine (BMC), Faculty of Medicine, Lund University, 22100 Lund, Sweden
| | - Thomas Laurell
- Department of Biomedical Engineering, Lund University, 22100 Lund, Sweden
| | - Agnete Kirkeby
- Department of Experimental Medical Science and Wallenberg Center for Molecular Medicine, Lund University, 22100 Lund, Sweden.,Department of Neuroscience, University of Copenhagen, DK-2200 Copenhagen, Denmark.,The Novo Nordisk Foundation Center for Stem Cell Biology (DanStem), Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark
| | - Moritz Heusel
- Infection Medicine Proteomics Lab, Division of Infection Medicine (BMC), Faculty of Medicine, Lund University, 22100 Lund, Sweden
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48
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Petelski AA, Emmott E, Leduc A, Huffman RG, Specht H, Perlman DH, Slavov N. Multiplexed single-cell proteomics using SCoPE2. Nat Protoc 2021; 16:5398-5425. [PMID: 34716448 PMCID: PMC8643348 DOI: 10.1038/s41596-021-00616-z] [Citation(s) in RCA: 94] [Impact Index Per Article: 31.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 08/12/2021] [Indexed: 11/09/2022]
Abstract
Many biological systems are composed of diverse single cells. This diversity necessitates functional and molecular single-cell analysis. Single-cell protein analysis has long relied on affinity reagents, but emerging mass-spectrometry methods (either label-free or multiplexed) have enabled quantifying >1,000 proteins per cell while simultaneously increasing the specificity of protein quantification. Here we describe the Single Cell ProtEomics (SCoPE2) protocol, which uses an isobaric carrier to enhance peptide sequence identification. Single cells are isolated by FACS or CellenONE into multiwell plates and lysed by Minimal ProteOmic sample Preparation (mPOP), and their peptides labeled by isobaric mass tags (TMT or TMTpro) for multiplexed analysis. SCoPE2 affords a cost-effective single-cell protein quantification that can be fully automated using widely available equipment and scaled to thousands of single cells. SCoPE2 uses inexpensive reagents and is applicable to any sample that can be processed to a single-cell suspension. The SCoPE2 workflow allows analyzing ~200 single cells per 24 h using only standard commercial equipment. We emphasize experimental steps and benchmarks required for achieving quantitative protein analysis.
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Affiliation(s)
- Aleksandra A Petelski
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Barnett Institute, Northeastern University, Boston, MA, USA
| | - Edward Emmott
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Barnett Institute, Northeastern University, Boston, MA, USA
- Centre for Proteome Research, Department of Biochemistry & Systems Biology, University of Liverpool, Liverpool, UK
| | - Andrew Leduc
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Barnett Institute, Northeastern University, Boston, MA, USA
| | - R Gray Huffman
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Barnett Institute, Northeastern University, Boston, MA, USA
| | - Harrison Specht
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Barnett Institute, Northeastern University, Boston, MA, USA
| | - David H Perlman
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Merck Exploratory Sciences Center, Merck Sharp & Dohme Corp., Cambridge, MA, USA
| | - Nikolai Slavov
- Department of Bioengineering, Northeastern University, Boston, MA, USA.
- Barnett Institute, Northeastern University, Boston, MA, USA.
- Department of Biology, Northeastern University, Boston, MA, USA.
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Deep representation features from DreamDIA XMBD improve the analysis of data-independent acquisition proteomics. Commun Biol 2021; 4:1190. [PMID: 34650228 PMCID: PMC8517002 DOI: 10.1038/s42003-021-02726-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Accepted: 09/27/2021] [Indexed: 12/24/2022] Open
Abstract
We developed DreamDIAXMBD (denoted as DreamDIA), a software suite based on a deep representation model for data-independent acquisition (DIA) data analysis. DreamDIA adopts a data-driven strategy to capture comprehensive information from elution patterns of peptides in DIA data and achieves considerable improvements on both identification and quantification performance compared with other state-of-the-art methods such as OpenSWATH, Skyline and DIA-NN. Specifically, in contrast to existing methods which use only 6 to 10 selected fragment ions from spectral libraries, DreamDIA extracts additional features from hundreds of theoretical elution profiles originated from different ions of each precursor using a deep representation network. To achieve higher coverage of target peptides without sacrificing specificity, the extracted features are further processed by nonlinear discriminative models under the framework of positive-unlabeled learning with decoy peptides as affirmative negative controls. DreamDIA is publicly available at https://github.com/xmuyulab/DreamDIA-XMBD for high coverage and accuracy DIA data analysis.
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50
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Cho KC, Oh S, Wang Y, Rosenthal LS, Na CH, Zhang H. Evaluation of the Sensitivity and Reproducibility of Targeted Proteomic Analysis Using Data-Independent Acquisition for Serum and Cerebrospinal Fluid Proteins. J Proteome Res 2021; 20:4284-4291. [PMID: 34384221 PMCID: PMC8631582 DOI: 10.1021/acs.jproteome.1c00238] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
There is a need for targeted analysis of biological fluids for diagnosis, prognosis, or monitoring the progression of diseases. Cerebrospinal fluid (CSF) and serum have been widely used for the development of protein analysis for neurodegenerative diseases and other diseases, respectively. Recently, data-independent acquisition (DIA) mass spectrometry (MS) has been developed to increase the throughput over data-dependent acquisition (DDA) on screening of a large number of samples and discovery of candidate targets. When it comes to target validation, the analytical performance of targeted analysis is critical. However, the inter- and intralaboratory analytical performances of the DIA-MS for targeted proteomic analysis of CSF and serum samples have not yet been investigated. In this study, we showed that the DIA-MS approach allowed us to identify and quantify 1732 CSF and 424 serum proteins, with 90% of proteins identified and quantified in at least 50% of DIA-MS runs. To evaluate the sensitivity, linearity, and dynamic range of the DIA approach, we included the stable isotope-labeled (SI) peptides into CSF and serum samples with serial dilutions. The lower limit of quantification (LLOQ) of peptides was 0.1-0.5 fmol, and the dynamic range was over 3.53 orders of magnitude, with excellent linearity (r2 < 0.978) in CSF and serum samples. Finally, the reproducibility of the DIA-MS approach was evaluated using entire proteins identified in CSF and serum samples. The intralaboratory three replicate results showed reliable reproducibility with 12.5 and 17.3% of the median coefficient of variation (CV) in both CSF and serum matrices, whereas the median CVs of interlaboratory three replicates were 23.8 and 32.0% in CSF and serum samples, respectively. The comparison of the quantitative result between replicates showed close similarity at intra- and interlaboratories with a median Pearson correlation value of >0.98 in CSF and serum, respectively. In conclusion, we demonstrate the capability of the DIA approach as a targeted proteomic analysis for candidate proteins from CSF and serum samples.
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Affiliation(s)
- Kyung-Cho Cho
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
- These authors contributed equally
| | - Sungtaek Oh
- Departments of Neurology, Johns Hopkins University, Baltimore, MD 21205, USA
- Institute for Cell Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
- These authors contributed equally
| | - Yuefan Wang
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
- These authors contributed equally
| | - Liana S. Rosenthal
- Institute for Cell Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Chan Hyun Na
- Departments of Neurology, Johns Hopkins University, Baltimore, MD 21205, USA
- Institute for Cell Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Hui Zhang
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
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