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Roberts DS, Loo JA, Tsybin YO, Liu X, Wu S, Chamot-Rooke J, Agar JN, Paša-Tolić L, Smith LM, Ge Y. Top-down proteomics. NATURE REVIEWS. METHODS PRIMERS 2024; 4:38. [PMID: 39006170 PMCID: PMC11242913 DOI: 10.1038/s43586-024-00318-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/24/2024] [Indexed: 07/16/2024]
Abstract
Proteoforms, which arise from post-translational modifications, genetic polymorphisms and RNA splice variants, play a pivotal role as drivers in biology. Understanding proteoforms is essential to unravel the intricacies of biological systems and bridge the gap between genotypes and phenotypes. By analysing whole proteins without digestion, top-down proteomics (TDP) provides a holistic view of the proteome and can decipher protein function, uncover disease mechanisms and advance precision medicine. This Primer explores TDP, including the underlying principles, recent advances and an outlook on the future. The experimental section discusses instrumentation, sample preparation, intact protein separation, tandem mass spectrometry techniques and data collection. The results section looks at how to decipher raw data, visualize intact protein spectra and unravel data analysis. Additionally, proteoform identification, characterization and quantification are summarized, alongside approaches for statistical analysis. Various applications are described, including the human proteoform project and biomedical, biopharmaceutical and clinical sciences. These are complemented by discussions on measurement reproducibility, limitations and a forward-looking perspective that outlines areas where the field can advance, including potential future applications.
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Affiliation(s)
- David S Roberts
- Department of Chemistry, Stanford University, Stanford, CA, USA
- Sarafan ChEM-H, Stanford University, Stanford, CA, USA
| | - Joseph A Loo
- Department of Chemistry and Biochemistry, Department of Biological Chemistry, University of California - Los Angeles, Los Angeles, CA, USA
| | | | - Xiaowen Liu
- Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, USA
| | - Si Wu
- Department of Chemistry and Biochemistry, The University of Alabama, Tuscaloosa, AL, USA
| | | | - Jeffrey N Agar
- Departments of Chemistry and Chemical Biology and Pharmaceutical Sciences, Northeastern University, Boston, MA, USA
| | - Ljiljana Paša-Tolić
- Environmental and Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Lloyd M Smith
- Department of Chemistry, University of Wisconsin, Madison, WI, USA
| | - Ying Ge
- Department of Chemistry, University of Wisconsin, Madison, WI, USA
- Department of Cell and Regenerative Biology, Human Proteomics Program, University of Wisconsin - Madison, Madison, WI, USA
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2
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Cai YH, Wang CC, Hsiao CH, Wang YS. Experimental Validation of Comprehensive Calculation for High-Resolution Linear MALDI-TOF Mass Spectrometry. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2024; 35:992-998. [PMID: 38634762 PMCID: PMC11066958 DOI: 10.1021/jasms.4c00018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 03/25/2024] [Accepted: 04/04/2024] [Indexed: 04/19/2024]
Abstract
This work discusses the effectiveness of the previously developed comprehensive calculation model to optimize linear MALDI-TOF mass spectrometers. The model couples space- and velocity-focusing to precisely analyze the flight-time distribution of ions and predict optimal experimental parameters for the highest mass resolving power. Experimental validation was conducted using a laboratory-made instrument to analyze CsI3 and angiotensin I ions in low to medium m/z range. The results indicate that the predicted optimal extraction voltage and delay were reasonably accurate and effective. In the low m/z range, the peak width obtained using optimal parameters reached the sub nanosecond range, corresponding to a mass resolving power of 10 000-17 000, or 20 000-34 000 if shot-to-shot random fluctuations were minimized by the dynamic data correction method. The observed optimal mass resolving power in the current experiment is 4.8-7.8 times that of commercial instruments. Practical limitations resulting in the gap between the observed and theoretical ultimate mass resolving power are discussed.
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Affiliation(s)
- Yi-Hong Cai
- Genomics
Research Center, Academia Sinica, Taipei 115, Taiwan ROC
| | - Chia-Chen Wang
- Instrumentation
Center, National Taiwan Normal University, Taipei 106, Taiwan ROC
| | - Chih-Hao Hsiao
- Genomics
Research Center, Academia Sinica, Taipei 115, Taiwan ROC
| | - Yi-Sheng Wang
- Genomics
Research Center, Academia Sinica, Taipei 115, Taiwan ROC
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Zhou P, Xiao Y, Zhou X, Fang J, Zhang J, Liu J, Guo L, Zhang J, Zhang N, Chen K, Zhao C. Mapping Spatiotemporal Heterogeneity in Multifocal Breast Tumor Progression by Noninvasive Ultrasound Elastography-Guided Mass Spectrometry Imaging Strategy. JACS AU 2024; 4:465-475. [PMID: 38425919 PMCID: PMC10900218 DOI: 10.1021/jacsau.3c00589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 01/10/2024] [Accepted: 01/24/2024] [Indexed: 03/02/2024]
Abstract
Spatiotemporal heterogeneity of tumors provides an escape mechanism for breast cancer cells, which can obstruct the investigation of tumor progression. While molecular profiling obtained from mass spectrometry imaging (MSI) is rich in biochemical information, it lacks the capacity for in vivo analysis. Ultrasound diagnosis has a high diagnostic accuracy but low chemical specificity. Here, we describe a noninvasive ultrasound elastography (UE)-guided MSI strategy (UEg-MSI) that integrates physical and biochemical characteristics of tumors acquired from both in vivo and in vitro imaging. Using UEg-MSI, both elasticity histopathology metabolism "fingerprints" and reciprocal crosstalk are revealed, indicating the intact, multifocal spatiotemporal heterogeneity of spontaneous tumorigenesis of the breast from early, middle, and late stages. Our results demonstrate a gradual increase in malignant degree of primary focus in cervical and thoracic mammary glands. This progression is characterized by increased stiffness according to elasticity scores, histopathological changes from hyperplasia to increased nests of neoplastic cells and necrotic areas, and regional metabolic heterogeneity and reprogramming at the spatiotemporal level. De novo fatty acid (FA) synthesis focused on independent (such as ω-9 FAs) and dependent (such as ω-6 FAs) dietary FA intake in the core cancerous nest areas in the middle and late stages of tumor or in the peripheral microareas in the early stage of the tumor. SM-Cer signaling pathway and GPs biosynthesis and degradation, as well as glycerophosphoinositol intensity, changed in multiple characteristic microareas. The UEg-MSI strategy holds the potential to expand MSI applications and enhance ultrasound-mediated cancer diagnosis. It offers new insight into early cancer discovery and the occurrence of metastasis.
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Affiliation(s)
- Peng Zhou
- Bionic
Sensing and Intelligence Center, Institute of Biomedical and Health
Engineering, Shenzhen Institute of Advanced
Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- Department
of Ultrasound, First Affiliated Hospital of Shenzhen University Health
Science Center, Shenzhen Second People’s
Hospital, Shenzhen 518009, China
| | - Yu Xiao
- Department
of Thyroid and Breast department, First Affiliated Hospital of Shenzhen
University, Shenzhen Second People’s
Hospital, Shenzhen 518009, China
| | - Xin Zhou
- Bionic
Sensing and Intelligence Center, Institute of Biomedical and Health
Engineering, Shenzhen Institute of Advanced
Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Jinghui Fang
- Department
of Ultrasound, First Affiliated Hospital of Shenzhen University Health
Science Center, Shenzhen Second People’s
Hospital, Shenzhen 518009, China
| | - Jingwen Zhang
- Department
of Ultrasound, First Affiliated Hospital of Shenzhen University Health
Science Center, Shenzhen Second People’s
Hospital, Shenzhen 518009, China
| | - Jianjun Liu
- Shenzhen
Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline
of Health Toxicology (2020-2024), Shenzhen
Center for Disease Control and Prevention, 518054, Shenzhen, China
| | - Ling Guo
- Shenzhen
Key Laboratory of Epigenetics and Precision Medicine for Cancers,
National Cancer Center/National Clinical Research Center for Cancer/Cancer
Hospital & Shenzhen Hospital, Chinese
Academic of Medical Sciences & Peking Union Medical College, Shenzhen 518172, China
| | - Jiuhong Zhang
- Shenzhen
Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline
of Health Toxicology (2020-2024), Shenzhen
Center for Disease Control and Prevention, 518054, Shenzhen, China
| | - Ning Zhang
- College
of Chemistry and Chemical Engineering, Dezhou
University, Dezhou 253026, Shandong, China
| | - Ke Chen
- Key
Laboratory of Resources Conversion and Pollution Control of the State
Ethnic Affairs Commission, College of Resources and Environmental
Science, South-Central Minzu University, Wuhan 430074, China
| | - Chao Zhao
- Bionic
Sensing and Intelligence Center, Institute of Biomedical and Health
Engineering, Shenzhen Institute of Advanced
Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- Department
of Ultrasound, First Affiliated Hospital of Shenzhen University Health
Science Center, Shenzhen Second People’s
Hospital, Shenzhen 518009, China
- Shenzhen
Key Laboratory of Precision Diagnosis and Treatment of Depression, Shenzhen Institute of Advanced Technology, Chinese
Academy of Sciences, Shenzhen 518055, China
- University
of Chinese Academy of Sciences, Beijing 100049, China
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Dowling P, Swandulla D, Ohlendieck K. Mass Spectrometry-Based Proteomic Technology and Its Application to Study Skeletal Muscle Cell Biology. Cells 2023; 12:2560. [PMID: 37947638 PMCID: PMC10649384 DOI: 10.3390/cells12212560] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 10/27/2023] [Accepted: 10/31/2023] [Indexed: 11/12/2023] Open
Abstract
Voluntary striated muscles are characterized by a highly complex and dynamic proteome that efficiently adapts to changed physiological demands or alters considerably during pathophysiological dysfunction. The skeletal muscle proteome has been extensively studied in relation to myogenesis, fiber type specification, muscle transitions, the effects of physical exercise, disuse atrophy, neuromuscular disorders, muscle co-morbidities and sarcopenia of old age. Since muscle tissue accounts for approximately 40% of body mass in humans, alterations in the skeletal muscle proteome have considerable influence on whole-body physiology. This review outlines the main bioanalytical avenues taken in the proteomic characterization of skeletal muscle tissues, including top-down proteomics focusing on the characterization of intact proteoforms and their post-translational modifications, bottom-up proteomics, which is a peptide-centric method concerned with the large-scale detection of proteins in complex mixtures, and subproteomics that examines the protein composition of distinct subcellular fractions. Mass spectrometric studies over the last two decades have decisively improved our general cell biological understanding of protein diversity and the heterogeneous composition of individual myofibers in skeletal muscles. This detailed proteomic knowledge can now be integrated with findings from other omics-type methodologies to establish a systems biological view of skeletal muscle function.
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Affiliation(s)
- Paul Dowling
- Department of Biology, Maynooth University, National University of Ireland, W23 F2H6 Maynooth, Co. Kildare, Ireland;
- Kathleen Lonsdale Institute for Human Health Research, Maynooth University, W23 F2H6 Maynooth, Co. Kildare, Ireland
| | - Dieter Swandulla
- Institute of Physiology, Faculty of Medicine, University of Bonn, D53115 Bonn, Germany;
| | - Kay Ohlendieck
- Department of Biology, Maynooth University, National University of Ireland, W23 F2H6 Maynooth, Co. Kildare, Ireland;
- Kathleen Lonsdale Institute for Human Health Research, Maynooth University, W23 F2H6 Maynooth, Co. Kildare, Ireland
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Sheng Y, Xue Y, Wang J, Liu S, Jiang Y. Fast screening and identification of illegal adulteration in dietary supplements and herbal medicines using molecular networking with deep-learning-based similarity algorithms. Anal Bioanal Chem 2023:10.1007/s00216-023-04708-5. [PMID: 37119358 DOI: 10.1007/s00216-023-04708-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 04/18/2023] [Accepted: 04/19/2023] [Indexed: 05/01/2023]
Abstract
Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is a powerful analytical tool used for adulteration inspection. Nevertheless, it is a challenging task to identify illegal adulterants that are not included in the library or are unexpected from large MS data. Molecular networking is a good tool for exploring, visualizing, and organizing MS/MS spectra, and moreover, it employs shifted peak match to calculate spectral similarity, making it capable of identifying adulteration that is not included in the library. The key of molecular networking is spectral similarity algorithms, and therefore, in this study, we compared the performance of four cutting-edge similarity algorithms, modified cosine similarity (shifted peak match), entropy similarity, and two deep-learning-based algorithms, MS2DeepScore and Spec2Vec, in building molecular networking for identification of adulteration that is not included in the library. We conducted an analysis of excluded-query-compound on all MS/MS spectra in test library and performed a large-scale false discovery rate estimation to investigate whether the spectral similarity calculated by each algorithm could represent the actual structural similarity well. The obtained results demonstrated Spec2Vec exhibited good performance in both detection capability and false discovery rate. Further comprehensive evaluation of the performance of Spec2Vec in the identification of adulteration that is not included in the library or is unexpected in different matrices and in application to real samples proved the approach studied here is a promising and powerful tool for adulterant inspection and improved the capability of analyzing large MS data.
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Affiliation(s)
- Yanghao Sheng
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, 410008, China
- Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Ying Xue
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, 410008, China
- Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Jue Wang
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, 410008, China
- Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Shao Liu
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, 410008, China.
- Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China.
| | - Yueping Jiang
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, 410008, China.
- Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China.
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Bottom-Up Proteomics: Advancements in Sample Preparation. Int J Mol Sci 2023; 24:ijms24065350. [PMID: 36982423 PMCID: PMC10049050 DOI: 10.3390/ijms24065350] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 02/28/2023] [Accepted: 03/09/2023] [Indexed: 03/14/2023] Open
Abstract
Liquid chromatography–tandem mass spectrometry (LC–MS/MS)-based proteomics is a powerful technique for profiling proteomes of cells, tissues, and body fluids. Typical bottom-up proteomic workflows consist of the following three major steps: sample preparation, LC–MS/MS analysis, and data analysis. LC–MS/MS and data analysis techniques have been intensively developed, whereas sample preparation, a laborious process, remains a difficult task and the main challenge in different applications. Sample preparation is a crucial stage that affects the overall efficiency of a proteomic study; however, it is prone to errors and has low reproducibility and throughput. In-solution digestion and filter-aided sample preparation are the typical and widely used methods. In the past decade, novel methods to improve and facilitate the entire sample preparation process or integrate sample preparation and fractionation have been reported to reduce time, increase throughput, and improve reproducibility. In this review, we have outlined the current methods used for sample preparation in proteomics, including on-membrane digestion, bead-based digestion, immobilized enzymatic digestion, and suspension trapping. Additionally, we have summarized and discussed current devices and methods for integrating different steps of sample preparation and peptide fractionation.
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Punetha A, Kotiya D. Advancements in Oncoproteomics Technologies: Treading toward Translation into Clinical Practice. Proteomes 2023; 11:2. [PMID: 36648960 PMCID: PMC9844371 DOI: 10.3390/proteomes11010002] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 01/03/2023] [Accepted: 01/04/2023] [Indexed: 01/12/2023] Open
Abstract
Proteomics continues to forge significant strides in the discovery of essential biological processes, uncovering valuable information on the identity, global protein abundance, protein modifications, proteoform levels, and signal transduction pathways. Cancer is a complicated and heterogeneous disease, and the onset and progression involve multiple dysregulated proteoforms and their downstream signaling pathways. These are modulated by various factors such as molecular, genetic, tissue, cellular, ethnic/racial, socioeconomic status, environmental, and demographic differences that vary with time. The knowledge of cancer has improved the treatment and clinical management; however, the survival rates have not increased significantly, and cancer remains a major cause of mortality. Oncoproteomics studies help to develop and validate proteomics technologies for routine application in clinical laboratories for (1) diagnostic and prognostic categorization of cancer, (2) real-time monitoring of treatment, (3) assessing drug efficacy and toxicity, (4) therapeutic modulations based on the changes with prognosis and drug resistance, and (5) personalized medication. Investigation of tumor-specific proteomic profiles in conjunction with healthy controls provides crucial information in mechanistic studies on tumorigenesis, metastasis, and drug resistance. This review provides an overview of proteomics technologies that assist the discovery of novel drug targets, biomarkers for early detection, surveillance, prognosis, drug monitoring, and tailoring therapy to the cancer patient. The information gained from such technologies has drastically improved cancer research. We further provide exemplars from recent oncoproteomics applications in the discovery of biomarkers in various cancers, drug discovery, and clinical treatment. Overall, the future of oncoproteomics holds enormous potential for translating technologies from the bench to the bedside.
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Affiliation(s)
- Ankita Punetha
- Department of Microbiology, Biochemistry and Molecular Genetics, Rutgers New Jersey Medical School, Rutgers University, 225 Warren St., Newark, NJ 07103, USA
| | - Deepak Kotiya
- Department of Pharmacology and Nutritional Sciences, University of Kentucky, 900 South Limestone St., Lexington, KY 40536, USA
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