1
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Huang CF, Hollas MA, Sanchez A, Bhattacharya M, Ho G, Sundaresan A, Caldwell MA, Zhao X, Benz R, Siddiqui A, Kelleher NL. Deep Profiling of Plasma Proteoforms with Engineered Nanoparticles for Top-Down Proteomics. J Proteome Res 2024. [PMID: 39312774 DOI: 10.1021/acs.jproteome.4c00621] [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: 09/25/2024]
Abstract
The dynamic range challenge for the detection of proteins and their proteoforms in human plasma has been well documented. Here, we use the nanoparticle protein corona approach to enrich low-abundance proteins selectively and reproducibly from human plasma and use top-down proteomics to quantify differential enrichment for the 2841 detected proteoforms from 114 proteins. Furthermore, nanoparticle enrichment allowed top-down detection of proteoforms between ∼1 μg/mL and ∼10 pg/mL in absolute abundance, providing up to a 105-fold increase in proteome depth over neat plasma in which only proteoforms from abundant proteins (>1 μg/mL) were detected. The ability to monitor medium and some low-abundant proteoforms through reproducible enrichment significantly extends the applicability of proteoform research by adding depth beyond albumin, immunoglobins, and apolipoproteins to uncover many involved in immunity and cell signaling. As proteoforms carry unique information content relative to peptides, this report opens the door to deeper proteoform sequencing in clinical proteomics of disease or aging cohorts.
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Affiliation(s)
- Che-Fan Huang
- Proteomics Center of Excellence, Northwestern University, Evanston, Illinois 60208, United States
| | - Michael A Hollas
- Proteomics Center of Excellence, Northwestern University, Evanston, Illinois 60208, United States
| | - Aniel Sanchez
- Proteomics Center of Excellence, Northwestern University, Evanston, Illinois 60208, United States
| | | | - Giang Ho
- Seer Inc., Redwood City, California 94065, United States
| | | | - Michael A Caldwell
- Proteomics Center of Excellence, Northwestern University, Evanston, Illinois 60208, United States
| | - Xiaoyan Zhao
- Seer Inc., Redwood City, California 94065, United States
| | - Ryan Benz
- Seer Inc., Redwood City, California 94065, United States
| | - Asim Siddiqui
- Seer Inc., Redwood City, California 94065, United States
| | - Neil L Kelleher
- Proteomics Center of Excellence, Northwestern University, Evanston, Illinois 60208, United States
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2
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Wang L, Yang X, Xie Y, Xu C, Dai X, Wang M, Liu Y. Nanoparticle-Protein Corona-Based Tissue Proteomics for the Aging Mouse Proteome Atlas. Anal Chem 2024; 96:14363-14371. [PMID: 39192740 DOI: 10.1021/acs.analchem.4c00932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/29/2024]
Abstract
Highly abundant proteins present in biological fluids and tissues significantly interfere with low-abundance protein identification by mass spectrometry (MS), limiting proteomic depth and hindering protein biomarker discovery. Herein, to enhance the coverage of tissue proteomics, we developed a nanoparticle-protein corona (NP-PC)-based method for the aging mouse proteome atlas. Based on this method, we investigated the complexity of life process of 5 major organs, including the heart, liver, spleen, lungs, and kidneys, from 4 groups of mice at different ages. Compared with the conventional strategy, NP-PC-based proteomics significantly increased the number of identified protein groups in the heart (from 3007 to 3927; increase of 30.6%), liver (from 2982 to 4610; increase of 54.6%), spleen (from 5047 to 7351; increase of 45.7%), lungs (from 4984 to 6903; increase of 38.5%), and kidneys (from 3550 to 5739; increase of 61.7%), and we identified a total of 10 104 protein groups. The overall data indicated that 3-week-old mice showed more differences compared with the other three age groups. The proteins of amino acid-related metabolism were increased in aged mice compared with those in the 3-week-old mice. Protein-related infections were increased in the spleen of the aged mice. Interestingly, the spliceosome-related pathway significantly changed from youth to elders in the liver, spleen, and lungs, indicating the vital role of the spliceosome during the aging process. Our established aging mouse organ proteome atlas provides comprehensive insights into understanding the aging process, and it may help in prevention and treatment of age-related diseases.
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Affiliation(s)
- Lichao Wang
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
| | - Xu Yang
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
| | - Yueli Xie
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
- School of Life Sciences, Tianjin University, Tianjin 300072, China
| | - Chenlu Xu
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
- Academy of Medical Engineering and Translational Medicine, Medical College, Tianjin University, Tianjin 300072, China
| | - Xin Dai
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
- Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
| | - Mengjie Wang
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
- Molecular Science and Biomedicine Laboratory (MBL), State Key Laboratory of Chemo/Bio-Sensing and Chemometrics, College of Chemistry and Chemical Engineering, Aptamer Engineering Center of Hunan Province, Hunan University, Changsha, Hunan 410082, China
| | - Yuan Liu
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
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3
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Hendricks NG, Bhosale SD, Keoseyan AJ, Ortiz J, Stotland A, Seyedmohammad S, Nguyen CDL, Bui JT, Moradian A, Mockus SM, Van Eyk JE. An Inflection Point in High-Throughput Proteomics with Orbitrap Astral: Analysis of Biofluids, Cells, and Tissues. J Proteome Res 2024; 23:4163-4169. [PMID: 39163279 PMCID: PMC11385373 DOI: 10.1021/acs.jproteome.4c00384] [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: 08/22/2024]
Abstract
This Technical Note presents a comprehensive proteomics workflow for the new combination of Orbitrap and Astral mass analyzers across biofluids, cells, and tissues. Central to our workflow is the integration of Adaptive Focused Acoustics (AFA) technology for cells and tissue lysis to ensure robust and reproducible sample preparation in a high-throughput manner. Furthermore, we automated the detergent-compatible single-pot, solid-phase-enhanced sample Preparation (SP3) method for protein digestion. The synergy of these advanced methodologies facilitates a robust and high-throughput approach for cell and tissue analysis, an important consideration in translational research. This work disseminates our platform workflow, analyzes the effectiveness, demonstrates the reproducibility of the results, and highlights the potential of these technologies in biomarker discovery and disease pathology. For cells and tissues (heart, liver, lung, and intestine) proteomics analysis by data-independent acquisition mode, identifications exceeding 10,000 proteins can be achieved with a 24 min active gradient. In 200 ng injections of HeLa digest across multiple gradients, an average of more than 80% of proteins have a CV less than 20%, and a 45 min run covers ∼90% of the expressed proteome. This complete workflow allows for large swaths of the proteome to be identified and is compatible with diverse sample types.
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Affiliation(s)
- Nathan G Hendricks
- Precision Biomarker Laboratories, Cedars-Sinai Medical Center, Beverly Hills, California 90211, United States
| | - Santosh D Bhosale
- Precision Biomarker Laboratories, Cedars-Sinai Medical Center, Beverly Hills, California 90211, United States
| | - Angel J Keoseyan
- Precision Biomarker Laboratories, Cedars-Sinai Medical Center, Beverly Hills, California 90211, United States
| | - Josselin Ortiz
- Precision Biomarker Laboratories, Cedars-Sinai Medical Center, Beverly Hills, California 90211, United States
| | - Aleksandr Stotland
- Smidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Saeed Seyedmohammad
- Smidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Chi D L Nguyen
- Precision Biomarker Laboratories, Cedars-Sinai Medical Center, Beverly Hills, California 90211, United States
| | - Jonathan T Bui
- Precision Biomarker Laboratories, Cedars-Sinai Medical Center, Beverly Hills, California 90211, United States
| | - Annie Moradian
- Precision Biomarker Laboratories, Cedars-Sinai Medical Center, Beverly Hills, California 90211, United States
| | - Susan M Mockus
- Precision Biomarker Laboratories, Cedars-Sinai Medical Center, Beverly Hills, California 90211, United States
| | - Jennifer E Van Eyk
- Precision Biomarker Laboratories, Cedars-Sinai Medical Center, Beverly Hills, California 90211, United States
- Smidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
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4
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Jiang Y, Meyer JG. Rapid Plasma Proteome Profiling via Nanoparticle Protein Corona and Direct Infusion Mass Spectrometry. J Proteome Res 2024; 23:3649-3658. [PMID: 39007500 DOI: 10.1021/acs.jproteome.4c00302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Noninvasive detection of protein biomarkers in plasma is crucial for clinical purposes. Liquid chromatography-mass spectrometry (LC-MS) is the gold standard technique for plasma proteome analysis, but despite recent advances, it remains limited by throughput, cost, and coverage. Here, we introduce a new hybrid method that integrates direct infusion shotgun proteome analysis (DISPA) with nanoparticle (NP) protein corona enrichment for high-throughput and efficient plasma proteomic profiling. We realized over 280 protein identifications in 1.4 min collection time, which enables a potential throughput of approximately 1000 samples daily. The identified proteins are involved in valuable pathways, and 44 of the proteins are FDA-approved biomarkers. The robustness and quantitative accuracy of this method were evaluated across multiple NPs and concentrations with a mean coefficient of variation of 17%. Moreover, different protein corona profiles were observed among various NPs based on their distinct surface modifications, and all NP protein profiles exhibited deeper coverage and better quantification than neat plasma. Our streamlined workflow merges coverage and throughput with precise quantification, leveraging both DISPA and NP protein corona enrichment. This underscores the significant potential of DISPA when paired with NP sample preparation techniques for plasma proteome studies.
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Affiliation(s)
- Yuming Jiang
- Department of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
- Advanced Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
- Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
| | - Jesse G Meyer
- Department of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
- Advanced Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
- Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
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5
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Zhang S, Ghalandari B, Chen Y, Wang Q, Liu K, Sun X, Ding X, Song S, Jiang L, Ding X. Boronic Acid-Rich Lanthanide Metal-Organic Frameworks Enable Deep Proteomics with Ultratrace Biological Samples. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2401559. [PMID: 38958107 DOI: 10.1002/adma.202401559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 06/21/2024] [Indexed: 07/04/2024]
Abstract
Label-free proteomics is widely used to identify disease mechanism and potential therapeutic targets. However, deep proteomics with ultratrace clinical specimen remains a major technical challenge due to extensive contact loss during complex sample pretreatment. Here, a hybrid of four boronic acid-rich lanthanide metal-organic frameworks (MOFs) with high protein affinity is introduced to capture proteins in ultratrace samples jointly by nitrogen-boronate complexation, cation-π and ionic interactions. A MOFs Aided Sample Preparation (MASP) workflow that shrinks sample volume and integrates lysis, protein capture, protein digestion and peptide collection steps into a single PCR tube to minimize sample loss caused by non-specific absorption, is proposed further. MASP is validated to quantify ≈1800 proteins in 10 HEK-293T cells. MASP is applied to profile cerebrospinal fluid (CSF) proteome from cerebral stroke and brain damaged patients, and identified ≈3700 proteins in 1 µL CSF. MASP is further demonstrated to detect ≈9600 proteins in as few as 50 µg mouse brain tissues. MASP thus enables deep, scalable, and reproducible proteome on precious clinical samples with low abundant proteins.
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Affiliation(s)
- Shuang Zhang
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Behafarid Ghalandari
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Youming Chen
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Qingwen Wang
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Kun Liu
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Xinyi Sun
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Xinwen Ding
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Sunfengda Song
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Lai Jiang
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Xianting Ding
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
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6
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Li S, Jin B, Ma Y, Yang X, Fan J, Xie Y, Xu C, Dai X, Wang M, Liu Q, Fu T, Liu Y, Tan W. Proteome Fishing for CRISPR/Cas12a-Based Orthogonal Multiplex Aptamer Sensing. J Am Chem Soc 2024; 146:19874-19885. [PMID: 39007743 DOI: 10.1021/jacs.4c03061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Detection of serum protein biomarkers is extremely challenging owing to the superior complexity of serum. Here, we report a method of proteome fishing from the serum. It uses a magnetic nanoparticle-protein corona and a multiplexed aptamer panel, which we incubated with the nanoparticle-protein corona for biomarker recognition. To transfer protein biomarker detection to aptamer detection, we established a CRISPR/Cas12a-based orthogonal multiplex aptamer sensing (COMPASS) platform by profiling the aptamers of protein corona with clinical nonsmall cell lung cancer (NSCLC) serum samples. Furthermore, we determined the four out of nine (FOON) panel (including HE4, NSE, AFP, and VEGF165) to be the most cost-effective and accurate panel for COMPASS in NSCLC diagnosis. The diagnostic accuracy of NSCLC by the FOON panel with internal and external cohorts was 95.56% (ROC-AUC = 99.40%) and 89.58% (ROC-AUC = 95.41%), respectively. Our developed COMPASS technology circumvents the otherwise challenging multiplexed serum protein amplification problem and avoids aptamer degradation in serum. Therefore, this novel COMPASS could lead to the development of a facile, cost-effective, intelligent, and high-throughput diagnostic platform for large-cohort cancer screening.
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Affiliation(s)
- Shuangqin Li
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
| | - Baichuan Jin
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
| | - Yintao Ma
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, China
| | - Xu Yang
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
| | - Jinlong Fan
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
| | - Yueli Xie
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
- School of Life Sciences, Tianjin University, Tianjin 300072, China
| | - Chenlu Xu
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
- Academy of Medical Engineering and Translational Medicine, Medical College, Tianjin University, Tianjin 300072, China
| | - Xin Dai
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
- Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, Zhejiang 310024, China
| | - Mengjie Wang
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
- Molecular Science and Biomedicine Laboratory (MBL), State Key Laboratory of Chemo/Bio-Sensing and Chemometrics, College of Chemistry and Chemical Engineering, Aptamer Engineering Center of Hunan Province, Hunan University, Changsha, Hunan 410082, China
| | - Qiqi Liu
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
- Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, Zhejiang 310024, China
| | - Ting Fu
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
- Molecular Science and Biomedicine Laboratory (MBL), State Key Laboratory of Chemo/Bio-Sensing and Chemometrics, College of Chemistry and Chemical Engineering, Aptamer Engineering Center of Hunan Province, Hunan University, Changsha, Hunan 410082, China
| | - Yuan Liu
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
| | - Weihong Tan
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
- Institute of Molecular Medicine (IMM), Renji Hospital, Shanghai Jiao Tong University School of Medicine, and College of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
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7
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Chang MEK, Lange J, Cartier JM, Moore TW, Soriano SM, Albracht B, Krawitzky M, Guturu H, Alavi A, Stukalov A, Zhou X, Elgierari EM, Chu J, Benz R, Cuevas JC, Ferdosi S, Hornburg D, Farokhzad O, Siddiqui A, Batzoglou S, Leach RJ, Liss MA, Kopp RP, Flory MR. A Scaled Proteomic Discovery Study for Prostate Cancer Diagnostic Markers Using Proteograph TM and Trapped Ion Mobility Mass Spectrometry. Int J Mol Sci 2024; 25:8010. [PMID: 39125581 PMCID: PMC11311733 DOI: 10.3390/ijms25158010] [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: 06/12/2024] [Revised: 07/03/2024] [Accepted: 07/09/2024] [Indexed: 08/12/2024] Open
Abstract
There is a significant unmet need for clinical reflex tests that increase the specificity of prostate-specific antigen blood testing, the longstanding but imperfect tool for prostate cancer diagnosis. Towards this endpoint, we present the results from a discovery study that identifies new prostate-specific antigen reflex markers in a large-scale patient serum cohort using differentiating technologies for deep proteomic interrogation. We detect known prostate cancer blood markers as well as novel candidates. Through bioinformatic pathway enrichment and network analysis, we reveal associations of differentially abundant proteins with cytoskeletal, metabolic, and ribosomal activities, all of which have been previously associated with prostate cancer progression. Additionally, optimized machine learning classifier analysis reveals proteomic signatures capable of detecting the disease prior to biopsy, performing on par with an accepted clinical risk calculator benchmark.
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Affiliation(s)
- Matthew E. K. Chang
- Cancer Early Detection Advanced Research Center, Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97201, USA; (M.E.K.C.); (S.M.S.)
| | - Jane Lange
- Cancer Early Detection Advanced Research Center, Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97201, USA; (M.E.K.C.); (S.M.S.)
| | - Jessie May Cartier
- Cancer Early Detection Advanced Research Center, Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97201, USA; (M.E.K.C.); (S.M.S.)
| | - Travis W. Moore
- Cancer Early Detection Advanced Research Center, Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97201, USA; (M.E.K.C.); (S.M.S.)
| | - Sophia M. Soriano
- Cancer Early Detection Advanced Research Center, Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97201, USA; (M.E.K.C.); (S.M.S.)
| | - Brenna Albracht
- Department of Cell Systems and Anatomy, University of Texas Health San Antonio, San Antonio, TX 78229, USA
| | | | | | | | | | | | | | | | - Ryan Benz
- Seer Inc., Redwood City, CA 94065, USA
| | | | | | | | | | | | | | - Robin J. Leach
- Department of Cell Systems and Anatomy, University of Texas Health San Antonio, San Antonio, TX 78229, USA
| | - Michael A. Liss
- Roger L. & Laura D. Zeller Charitable Foundation in Urologic Oncology, University of Texas Health San Antonio, San Antonio, TX 78229, USA
| | - Ryan P. Kopp
- Cancer Early Detection Advanced Research Center, Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97201, USA; (M.E.K.C.); (S.M.S.)
| | - Mark R. Flory
- Cancer Early Detection Advanced Research Center, Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97201, USA; (M.E.K.C.); (S.M.S.)
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8
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Huang CF, Hollas MA, Sanchez A, Bhattacharya M, Ho G, Sundaresan A, Caldwell MA, Zhao X, Benz R, Siddiqui A, Kelleher NL. Deep Profiling of Plasma Proteoforms with Engineered Nanoparticles for Top-down Proteomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.20.604425. [PMID: 39071411 PMCID: PMC11275834 DOI: 10.1101/2024.07.20.604425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
The dynamic range challenge for detection of proteins and their proteoforms in human plasma has been well documented. Here, we use the nanoparticle protein corona approach to enrich low-abundant proteins selectively and reproducibly from human plasma and use top-down proteomics to quantify differential enrichment for the 2841 detected proteoforms from 114 proteins. Furthermore, nanoparticle enrichment allowed top-down detection of proteoforms between ∼1 µg/mL and ∼10 pg/mL in absolute abundance, providing up to 10 5 -fold increase in proteome depth over neat plasma in which only proteoforms from abundant proteins (>1 µg/mL) were detected. The ability to monitor medium and some low abundant proteoforms through reproducible enrichment significantly extends the applicability of proteoform research by adding depth beyond albumin, immunoglobins and apolipoproteins to uncover many involved in immunity and cell signaling. As proteoforms carry unique information content relative to peptides, this report opens the door to deeper proteoform sequencing in clinical proteomics of disease or aging cohorts.
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9
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Carrasco-Zanini J, Pietzner M, Koprulu M, Wheeler E, Kerrison ND, Wareham NJ, Langenberg C. Proteomic prediction of diverse incident diseases: a machine learning-guided biomarker discovery study using data from a prospective cohort study. Lancet Digit Health 2024; 6:e470-e479. [PMID: 38906612 DOI: 10.1016/s2589-7500(24)00087-6] [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: 03/31/2023] [Revised: 04/03/2024] [Accepted: 04/19/2024] [Indexed: 06/23/2024]
Abstract
BACKGROUND Broad-capture proteomic technologies have the potential to improve disease prediction, enabling targeted prevention and management, but studies have so far been limited to very few selected diseases and have not evaluated predictive performance across multiple conditions. We aimed to evaluate the potential of serum proteins to improve risk prediction over and above health-derived information and polygenic risk scores across a diverse set of 24 outcomes. METHODS We designed multiple case-cohorts nested in the EPIC-Norfolk prospective study, from participants with available serum samples and genome-wide genotype data, with more than 32 974 person-years of follow-up. Participants were middle-aged individuals (aged 40-79 years at baseline) of European ancestry who were recruited from the general population of Norfolk, England, between March, 1993 and December, 1997. We selected participants who developed one of ten less common diseases within 10 years of follow-up; we also subsampled a randomly drawn control subcohort, which also served to investigate 14 more common outcomes (n>70), including all-cause premature mortality (death before the age of 75 years; case numbers 71-437; controls 608-1556). Individuals were excluded from the current study owing to failed genotyping or proteomic quality control, relatedness, or missing information on age, sex, BMI, or smoking status. We used a machine learning framework to derive sparse predictive protein models for the onset of the the 23 individual diseases and all-cause premature mortality, and to derive a single common sparse multimorbidity signature that was predictive across multiple diseases from 2923 serum proteins. FINDINGS Participants who developed one of ten less common diseases within 10 years of follow-up included 482 women and 507 men, with a mean age at baseline of 64·56 years (8·08). The random subcohort included 990 women and 769 men, with a mean age of 58·79 years (9·31). As few as five proteins alone outperformed polygenic risk scores for 17 of 23 outcomes (median dfference in concordance index [C-index] 0·13 [0·10-0·17]) and improved predictive performance when added over basic patient-derived information models for seven outcomes, achieving a median C-index of 0·82 (IQR 0·77-0·82). This included diseases with poor prognosis such as lung cancer (C-index 0·85 [+/- cross-validation error 0·83-0·87]), for which we identified unreported biomarkers such as C-X-C motif chemokine ligand 17. A sparse multimorbidity signature of ten proteins improved prediction across seven outcomes over patient-derived information models, achieving performances (median C-index 0·81 [IQR 0·80-0·82]) similar to those of disease-specific signatures. INTERPRETATION We show the value of broad-capture proteomic biomarker discovery studies across multiple diseases of diverse causes, pointing to those that might benefit the most from proteomic approaches, and the potential to derive common sparse biomarker panels for prediction of multiple diseases at once. This framework could enable follow-up studies to explore the generalisability of proteomic models and to benchmark these against clinical assays, which are required to understand the translational potential of these findings. FUNDING Medical Research Council, Health Data Research UK, UK Research and Innovation-National Institute for Health and Care Research, Cancer Research UK, and Wellcome Trust.
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Affiliation(s)
- Julia Carrasco-Zanini
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK; Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - Maik Pietzner
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK; Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany; Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - Mine Koprulu
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Eleanor Wheeler
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Nicola D Kerrison
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK; Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany; Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.
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10
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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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11
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Hendricks NG, Bhosale SD, Keoseyan AJ, Ortiz J, Stotland A, Seyedmohammad S, Nguyen CDL, Bui J, Moradian A, Mockus SM, Van Eyk JE. An inflection point in high-throughput proteomics with Orbitrap Astral: analysis of biofluids, cells, and tissues. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.26.591396. [PMID: 38712179 PMCID: PMC11071456 DOI: 10.1101/2024.04.26.591396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
This technical note presents a comprehensive proteomics workflow for the new combination of Orbitrap and Astral mass analyzers across biofluids, cells, and tissues. Central to our workflow is the integration of Adaptive Focused Acoustics (AFA) technology for cells and tissue lysis, to ensure robust and reproducible sample preparation in a high-throughput manner. Furthermore, we automated the detergent-compatible single-pot, solid-phase-enhanced sample Preparation (SP3) method for protein digestion, a technique that streamlines the process by combining purification and digestion steps, thereby reducing sample loss and improving efficiency. The synergy of these advanced methodologies facilitates a robust and high-throughput approach for cells and tissue analysis, an important consideration in translational research. This work disseminates our platform workflow, analyzes the effectiveness, demonstrates reproducibility of the results, and highlights the potential of these technologies in biomarker discovery and disease pathology. For cells and tissues (heart, liver, lung, and intestine) proteomics analysis by data-independent acquisition mode, identifications exceeding 10,000 proteins can be achieved with a 24-minute active gradient. In 200ng injections of HeLa digest across multiple gradients, an average of more than 80% of proteins have a CV less than 20%, and a 45-minute run covers ~90% of the expressed proteome. In plasma samples including naive, depleted, perchloric acid precipitated, and Seer nanoparticle captured, all with a 24-minute gradient length, we identified 87, 108, 96 and 137 out of 216 FDA approved circulating protein biomarkers, respectively. This complete workflow allows for large swaths of the proteome to be identified and is compatible across diverse sample types.
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Affiliation(s)
- Nathan G. Hendricks
- Precision Biomarker Laboratories, Cedars-Sinai Medical Center, Beverly Hills, California 90210, United States
| | - Santosh D. Bhosale
- Precision Biomarker Laboratories, Cedars-Sinai Medical Center, Beverly Hills, California 90210, United States
| | - Angel J. Keoseyan
- Precision Biomarker Laboratories, Cedars-Sinai Medical Center, Beverly Hills, California 90210, United States
| | - Josselin Ortiz
- Precision Biomarker Laboratories, Cedars-Sinai Medical Center, Beverly Hills, California 90210, United States
| | - Aleksandr Stotland
- Smidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Saeed Seyedmohammad
- Smidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Chi D. L. Nguyen
- Precision Biomarker Laboratories, Cedars-Sinai Medical Center, Beverly Hills, California 90210, United States
| | - Jonathan Bui
- Precision Biomarker Laboratories, Cedars-Sinai Medical Center, Beverly Hills, California 90210, United States
| | - Annie Moradian
- Precision Biomarker Laboratories, Cedars-Sinai Medical Center, Beverly Hills, California 90210, United States
| | - Susan M. Mockus
- Precision Biomarker Laboratories, Cedars-Sinai Medical Center, Beverly Hills, California 90210, United States
| | - Jennifer E Van Eyk
- Precision Biomarker Laboratories, Cedars-Sinai Medical Center, Beverly Hills, California 90210, United States
- Smidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
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12
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Li N, Huang J, He S, Zheng Q, Ye F, Qin Z, Wang D, Xiao T, Mao M, Zhou Z, Tang T, Zhang L, Wang X, Wang Y, Lyu Y, Liu L, Dai L, Wang J, Guan J. The development of a novel zeolite-based assay for efficient and deep plasma proteomic profiling. J Nanobiotechnology 2024; 22:164. [PMID: 38600601 PMCID: PMC11007927 DOI: 10.1186/s12951-024-02404-9] [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: 08/20/2023] [Accepted: 03/18/2024] [Indexed: 04/12/2024] Open
Abstract
Plasma proteins are considered the most informative source of biomarkers for disease diagnosis and monitoring. Mass spectrometry (MS)-based proteomics has been applied to identify biomarkers in plasma, but the complexity of the plasma proteome and the extremely large dynamic range of protein abundances in plasma make the clinical application of plasma proteomics highly challenging. We designed and synthesized zeolite-based nanoparticles to deplete high-abundance plasma proteins. The resulting novel plasma proteomic assay can measure approximately 3000 plasma proteins in a 45 min chromatographic gradient. Compared to those in neat and depleted plasma, the plasma proteins identified by our assay exhibited distinct biological profiles, as validated in several public datasets. A pilot investigation of the proteomic profile of a hepatocellular carcinoma (HCC) cohort identified 15 promising protein features, highlighting the diagnostic value of the plasma proteome in distinguishing individuals with and without HCC. Furthermore, this assay can be easily integrated with all current downstream protein profiling methods and potentially extended to other biofluids. In conclusion, we established a robust and efficient plasma proteomic assay with unprecedented identification depth, paving the way for the translation of plasma proteomics into clinical applications.
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Affiliation(s)
- Nan Li
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Jingnan Huang
- Department of Nephrology, Shenzhen Key Laboratory of Kidney Diseases, Shenzhen People's Hospital, (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, 518020, Guangdong, China
- School of Medicine, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China
| | - Shangwen He
- Chronic Airways Diseases Laboratory, Department of Respiratory and Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Qiaocong Zheng
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
- Department of Oncology, People's Hospital of YangJiang, Yangjiang, 529500, Guangdong, China
| | - Feng Ye
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Zhengxing Qin
- State Key Laboratory of Heavy Oil Processing, College of Chemical Engineering, China University of Petroleum (East China), Qingdao, 266580, Shandong, China
| | - Dong Wang
- College of Chemistry and Chemical Engineering, China University of Petroleum (East China), Qingdao, 266580, Shandong, China
| | - Ting Xiao
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Mengyuan Mao
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Zhenhua Zhou
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Tingxi Tang
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Longshan Zhang
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Xiaoqing Wang
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Yingqiao Wang
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Ying Lyu
- Department of Traditional Chinese Medicine, Nanfang Hospital,, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Laiyu Liu
- Chronic Airways Diseases Laboratory, Department of Respiratory and Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China.
| | - Lingyun Dai
- Department of Nephrology, Shenzhen Key Laboratory of Kidney Diseases, Shenzhen People's Hospital, (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, 518020, Guangdong, China.
- School of Medicine, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China.
| | - Jigang Wang
- Department of Nephrology, Shenzhen Key Laboratory of Kidney Diseases, Shenzhen People's Hospital, (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, 518020, Guangdong, China.
- School of Medicine, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China.
| | - Jian Guan
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China.
- Guangdong Province Key Laboratory of Molecular Tumor Pathology, Guangzhou, 510515, Guangdong, China.
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13
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Wu CC, Tsantilas KA, Park J, Plubell D, Sanders JA, Naicker P, Govender I, Buthelezi S, Stoychev S, Jordaan J, Merrihew G, Huang E, Parker ED, Riffle M, Hoofnagle AN, Noble WS, Poston KL, Montine TJ, MacCoss MJ. Mag-Net: Rapid enrichment of membrane-bound particles enables high coverage quantitative analysis of the plasma proteome. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.06.10.544439. [PMID: 38617345 PMCID: PMC11014469 DOI: 10.1101/2023.06.10.544439] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Membrane-bound particles in plasma are composed of exosomes, microvesicles, and apoptotic bodies and represent ~1-2% of the total protein composition. Proteomic interrogation of this subset of plasma proteins augments the representation of tissue-specific proteins, representing a "liquid biopsy," while enabling the detection of proteins that would otherwise be beyond the dynamic range of liquid chromatography-tandem mass spectrometry of unfractionated plasma. We have developed an enrichment strategy (Mag-Net) using hyper-porous strong-anion exchange magnetic microparticles to sieve membrane-bound particles from plasma. The Mag-Net method is robust, reproducible, inexpensive, and requires <100 μL plasma input. Coupled to a quantitative data-independent mass spectrometry analytical strategy, we demonstrate that we can collect results for >37,000 peptides from >4,000 plasma proteins with high precision. Using this analytical pipeline on a small cohort of patients with neurodegenerative disease and healthy age-matched controls, we discovered 204 proteins that differentiate (q-value < 0.05) patients with Alzheimer's disease dementia (ADD) from those without ADD. Our method also discovered 310 proteins that were different between Parkinson's disease and those with either ADD or healthy cognitively normal individuals. Using machine learning we were able to distinguish between ADD and not ADD with a mean ROC AUC = 0.98 ± 0.06.
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Affiliation(s)
- Christine C. Wu
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | | | - Jea Park
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Deanna Plubell
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Justin A. Sanders
- Department of Computer Science, University of Washington, Seattle, WA, USA
| | | | | | | | | | | | - Gennifer Merrihew
- Department of Computer Science, University of Washington, Seattle, WA, USA
| | - Eric Huang
- Department of Computer Science, University of Washington, Seattle, WA, USA
| | - Edward D. Parker
- Vision Core Lab, Department of Ophthalmology, University of Washington, Seattle, WA, USA
| | - Michael Riffle
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Andrew N. Hoofnagle
- Department of Lab Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - William S. Noble
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Department of Computer Science, University of Washington, Seattle, WA, USA
| | - Kathleen L. Poston
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto CA, USA
| | | | - Michael J. MacCoss
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
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14
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Metatla I, Roger K, Chhuon C, Ceccacci S, Chapelle M, Pierre-Olivier Schmit, Demichev V, Guerrera IC. Neat plasma proteomics: getting the best out of the worst. Clin Proteomics 2024; 21:22. [PMID: 38475715 DOI: 10.1186/s12014-024-09477-6] [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: 01/11/2024] [Accepted: 02/29/2024] [Indexed: 03/14/2024] Open
Abstract
Plasma proteomics holds immense potential for clinical research and biomarker discovery, serving as a non-invasive "liquid biopsy" for tissue sampling. Mass spectrometry (MS)-based proteomics, thanks to improvement in speed and robustness, emerges as an ideal technology for exploring the plasma proteome for its unbiased and highly specific protein identification and quantification. Despite its potential, plasma proteomics is still a challenge due to the vast dynamic range of protein abundance, hindering the detection of less abundant proteins. Different approaches can help overcome this challenge. Conventional depletion methods face limitations in cost, throughput, accuracy, and off-target depletion. Nanoparticle-based enrichment shows promise in compressing dynamic range, but cost remains a constraint. Enrichment strategies for extracellular vesicles (EVs) can enhance plasma proteome coverage dramatically, but current methods are still too laborious for large series. Neat plasma remains popular for its cost-effectiveness, time efficiency, and low volume requirement. We used a test set of 33 plasma samples for all evaluations. Samples were digested using S-Trap and analyzed on Evosep One and nanoElute coupled to a timsTOF Pro using different elution gradients and ion mobility ranges. Data were mainly analyzed using library-free searches using DIA-NN. This study explores ways to improve proteome coverage in neat plasma both in MS data acquisition and MS data analysis. We demonstrate the value of sampling smaller hydrophilic peptides, increasing chromatographic separation, and using library-free searches. Additionally, we introduce the EV boost approach, that leverages on the extracellular vesicle fraction to enhance protein identification in neat plasma samples. Globally, our optimized analysis workflow allows the quantification of over 1000 proteins in neat plasma with a 24SPD throughput. We believe that these considerations can be of help independently of the LC-MS platform used.
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Affiliation(s)
- Ines Metatla
- Proteomics Platform Necker, Université Paris Cité-Structure Fédérative de Recherche Necker, INSERM US24/CNRS UAR3633, 75015, Paris, France
| | - Kevin Roger
- Proteomics Platform Necker, Université Paris Cité-Structure Fédérative de Recherche Necker, INSERM US24/CNRS UAR3633, 75015, Paris, France
| | - Cerina Chhuon
- Proteomics Platform Necker, Université Paris Cité-Structure Fédérative de Recherche Necker, INSERM US24/CNRS UAR3633, 75015, Paris, France
| | - Sara Ceccacci
- Proteomics Platform Necker, Université Paris Cité-Structure Fédérative de Recherche Necker, INSERM US24/CNRS UAR3633, 75015, Paris, France
| | - Manuel Chapelle
- Bruker Daltonique SA, 34 Rue de l'Industrie, 67166, Wissembourg Cedex, France
| | | | - Vadim Demichev
- Department of Biochemistry, Charité-Universitätsmedizin Berlin, 10117, Berlin, Germany
| | - Ida Chiara Guerrera
- Proteomics Platform Necker, Université Paris Cité-Structure Fédérative de Recherche Necker, INSERM US24/CNRS UAR3633, 75015, Paris, France.
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15
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Zhou Y, Zheng H, Tan Z, Kang E, Xue P, Li X, Guan F. Optimizing and integrating depletion and precipitation methods for plasma proteomics through data-independent acquisition-mass spectrometry. J Chromatogr B Analyt Technol Biomed Life Sci 2024; 1235:124046. [PMID: 38382157 DOI: 10.1016/j.jchromb.2024.124046] [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: 12/09/2023] [Revised: 01/29/2024] [Accepted: 02/10/2024] [Indexed: 02/23/2024]
Abstract
The application of plasma proteomics is a reliable approach for the discovery of biomarkers. However, the utilization of mass spectrometry-based proteomics in plasma encounters limitations due to the presence of high-abundant proteins (HAPs) and the vast dynamic range. To address this issue, we conducted an optimization and integration of depletion and precipitation strategies eliminating interference from HAPs. The optimized procedure involved utilizing 40 µL of beads for the removal of 1 µL of plasma, and maintaining a ratio of 1:1:1 between plasma, urea, and trichloroacetic acid for the precipitation of 50 µL of plasma. To facilitate high-throughput processing, experimental procedures were carried out utilizing 96-well plates. The depletion method identified a total of 1510 proteins, whereas the precipitated method yielded a total of 802 proteins. The integration of these methods yielded a total of 1794 proteins, including a wide concentration range spanning over 8 orders of magnitude. Furthermore, these approaches exhibited a commendable level of reproducibility, as indicated by median coefficients of variation of 14.7 % and 21.1 % for protein intensities, respectively. The integrative method was found to be effective in precisely quantifying yeast proteins that were intentionally spiked in plasma at predetermined rations of 5, 2, 0.5, and 0.2 with a high genuine positive recovery with a range of 71 % to 91 % of all yeast proteins. The use of a complementary and finely tuned approach involving depletion and precipitation demonstrates tremendous potential in the field of discovering protein biomarkers from large-scale cohort studies.
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Affiliation(s)
- Yue Zhou
- College of Life Science, Northwest University, Xi'an, Shaanxi, China
| | - Helong Zheng
- College of Life Science, Northwest University, Xi'an, Shaanxi, China
| | - Zengqi Tan
- College of Life Science, Northwest University, Xi'an, Shaanxi, China
| | - Enci Kang
- Xi'an Gaoxin No.1 High School International Division, Xi'an, Shaanxi, China
| | - Peng Xue
- Guangzhou National Laboratory, Guangzhou, Guangdong, China
| | - Xiang Li
- College of Life Science, Northwest University, Xi'an, Shaanxi, China
| | - Feng Guan
- College of Life Science, Northwest University, Xi'an, Shaanxi, China.
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16
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Lattmann E, Räss L, Tognetti M, Gómez JMM, Lapaire V, Bruderer R, Reiter L, Feng Y, Steinmetz LM, Levesque MP. Size-exclusion chromatography combined with DIA-MS enables deep proteome profiling of extracellular vesicles from melanoma plasma and serum. Cell Mol Life Sci 2024; 81:90. [PMID: 38353833 PMCID: PMC10867102 DOI: 10.1007/s00018-024-05137-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 01/18/2024] [Accepted: 01/19/2024] [Indexed: 02/16/2024]
Abstract
Extracellular vesicles (EVs) are important players in melanoma progression, but their use as clinical biomarkers has been limited by the difficulty of profiling blood-derived EV proteins with high depth of coverage, the requirement for large input amounts, and complex protocols. Here, we provide a streamlined and reproducible experimental workflow to identify plasma- and serum- derived EV proteins of healthy donors and melanoma patients using minimal amounts of sample input. SEC-DIA-MS couples size-exclusion chromatography to EV concentration and deep-proteomic profiling using data-independent acquisition. From as little as 200 µL of plasma per patient in a cohort of three healthy donors and six melanoma patients, we identified and quantified 2896 EV-associated proteins, achieving a 3.5-fold increase in depth compared to previously published melanoma studies. To compare the EV-proteome to unenriched blood, we employed an automated workflow to deplete the 14 most abundant proteins from plasma and serum and thereby approximately doubled protein group identifications versus native blood. The EV proteome diverged from corresponding unenriched plasma and serum, and unlike the latter, separated healthy donor and melanoma patient samples. Furthermore, known melanoma markers, such as MCAM, TNC, and TGFBI, were upregulated in melanoma EVs but not in depleted melanoma plasma, highlighting the specific information contained in EVs. Overall, EVs were significantly enriched in intact membrane proteins and proteins related to SNARE protein interactions and T-cell biology. Taken together, we demonstrated the increased sensitivity of an EV-based proteomic workflow that can be easily applied to larger melanoma cohorts and other indications.
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Affiliation(s)
- Evelyn Lattmann
- Department of Dermatology, University Hospital Zurich, University of Zurich, Schlieren, Switzerland
| | - Luca Räss
- Biognosys AG, Schlieren, Switzerland
| | | | - Julia M Martínez Gómez
- Department of Dermatology, University Hospital Zurich, University of Zurich, Schlieren, Switzerland
| | - Valérie Lapaire
- Department of Dermatology, University Hospital Zurich, University of Zurich, Schlieren, Switzerland
| | | | | | | | - Lars M Steinmetz
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA.
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.
| | - Mitchell P Levesque
- Department of Dermatology, University Hospital Zurich, University of Zurich, Schlieren, Switzerland.
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17
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Jiang Y, Meyer JG. 1.4 min Plasma Proteome Profiling via Nanoparticle Protein Corona and Direct Infusion Mass Spectrometry. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.06.579213. [PMID: 38370692 PMCID: PMC10871276 DOI: 10.1101/2024.02.06.579213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Non-invasive detection of protein biomarkers in plasma is crucial for clinical purposes. Liquid chromatography mass spectrometry (LC-MS) is the gold standard technique for plasma proteome analysis, but despite recent advances, it remains limited by throughput, cost, and coverage. Here, we introduce a new hybrid method, which integrates direct infusion shotgun proteome analysis (DISPA) with nanoparticle (NP) protein coronas enrichment for high throughput and efficient plasma proteomic profiling. We realized over 280 protein identifications in 1.4 minutes collection time, which enables a potential throughput of approximately 1,000 samples daily. The identified proteins are involved in valuable pathways and 44 of the proteins are FDA approved biomarkers. The robustness and quantitative accuracy of this method were evaluated across multiple NPs and concentrations with a mean coefficient of variation at 17%. Moreover, different protein corona profiles were observed among various nanoparticles based on their distinct surface modifications, and all NP protein profiles exhibited deeper coverage and better quantification than neat plasma. Our streamlined workflow merges coverage and throughput with precise quantification, leveraging both DISPA and NP protein corona enrichments. This underscores the significant potential of DISPA when paired with NP sample preparation techniques for plasma proteome studies.
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Affiliation(s)
- Yuming Jiang
- Department of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
- Advanced Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
- Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Jesse G. Meyer
- Department of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
- Advanced Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
- Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
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18
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Fu L, Guldiken N, Remih K, Karl AS, Preisinger C, Strnad P. Serum/Plasma Proteome in Non-Malignant Liver Disease. Int J Mol Sci 2024; 25:2008. [PMID: 38396688 PMCID: PMC10889128 DOI: 10.3390/ijms25042008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 01/31/2024] [Accepted: 02/03/2024] [Indexed: 02/25/2024] Open
Abstract
The liver is the central metabolic organ and produces 85-90% of the proteins found in plasma. Accordingly, the plasma proteome is an attractive source of liver disease biomarkers that reflects the different cell types present in this organ, as well as the processes such as responses to acute and chronic injury or the formation of an extracellular matrix. In the first part, we summarize the biomarkers routinely used in clinical evaluations and their biological relevance in the different stages of non-malignant liver disease. Later, we describe the current proteomic approaches, including mass spectrometry and affinity-based techniques, that allow a more comprehensive assessment of the liver function but also require complex data processing. The many approaches of analysis and interpretation and their potential caveats are delineated. While these advances hold the promise to transform our understanding of liver diseases and support the development and validation of new liver-related drugs, an interdisciplinary collaboration is needed.
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Affiliation(s)
- Lei Fu
- Department of Internal Medicine III, Gastroenterology, Metabolic Diseases and Intensive Care, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074 Aachen, Germany; (L.F.); (N.G.); (K.R.); (A.S.K.)
| | - Nurdan Guldiken
- Department of Internal Medicine III, Gastroenterology, Metabolic Diseases and Intensive Care, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074 Aachen, Germany; (L.F.); (N.G.); (K.R.); (A.S.K.)
| | - Katharina Remih
- Department of Internal Medicine III, Gastroenterology, Metabolic Diseases and Intensive Care, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074 Aachen, Germany; (L.F.); (N.G.); (K.R.); (A.S.K.)
| | - Anna Sophie Karl
- Department of Internal Medicine III, Gastroenterology, Metabolic Diseases and Intensive Care, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074 Aachen, Germany; (L.F.); (N.G.); (K.R.); (A.S.K.)
| | - Christian Preisinger
- Proteomics Facility, Interdisciplinary Centre for Clinical Research (IZKF), Medical School, RWTH Aachen University, Pauwelsstraße 30, 52074 Aachen, Germany;
| | - Pavel Strnad
- Department of Internal Medicine III, Gastroenterology, Metabolic Diseases and Intensive Care, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074 Aachen, Germany; (L.F.); (N.G.); (K.R.); (A.S.K.)
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19
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Lacar B, Ferdosi S, Alavi A, Stukalov A, Venkataraman GR, de Geus M, Dodge H, Wu CY, Kivisakk P, Das S, Guturu H, Hyman B, Batzoglou S, Arnold SE, Siddiqui A. Identification of Novel Biomarkers for Alzheimer's Disease and Related Dementias Using Unbiased Plasma Proteomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.05.574446. [PMID: 38260620 PMCID: PMC10802486 DOI: 10.1101/2024.01.05.574446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Alzheimer's disease (AD) and related dementias (ADRD) is a complex disease with multiple pathophysiological drivers that determine clinical symptomology and disease progression. These diseases develop insidiously over time, through many pathways and disease mechanisms and continue to have a huge societal impact for affected individuals and their families. While emerging blood-based biomarkers, such as plasma p-tau181 and p-tau217, accurately detect Alzheimer neuropthology and are associated with faster cognitive decline, the full extension of plasma proteomic changes in ADRD remains unknown. Earlier detection and better classification of the different subtypes may provide opportunities for earlier, more targeted interventions, and perhaps a higher likelihood of successful therapeutic development. In this study, we aim to leverage unbiased mass spectrometry proteomics to identify novel, blood-based biomarkers associated with cognitive decline. 1,786 plasma samples from 1,005 patients were collected over 12 years from partcipants in the Massachusetts Alzheimer's Disease Research Center Longitudinal Cohort Study. Patient metadata includes demographics, final diagnoses, and clinical dementia rating (CDR) scores taken concurrently. The Proteograph™ Product Suite (Seer, Inc.) and liquid-chromatography mass-spectrometry (LC-MS) analysis were used to process the plasma samples in this cohort and generate unbiased proteomics data. Data-independent acquisition (DIA) mass spectrometry results yielded 36,259 peptides and 4,007 protein groups. Linear mixed effects models revealed 138 differentially abundant proteins between AD and healthy controls. Machine learning classification models for AD diagnosis identified potential candidate biomarkers including MBP, BGLAP, and APoD. Cox regression models were created to determine the association of proteins with disease progression and suggest CLNS1A, CRISPLD2, and GOLPH3 as targets of further investigation as potential biomarkers. The Proteograph workflow provided deep, unbiased coverage of the plasma proteome at a speed that enabled a cohort study of almost 1,800 samples, which is the largest, deep, unbiased proteomics study of ADRD conducted to date.
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Affiliation(s)
| | | | | | | | | | - Matthijs de Geus
- Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, Massachusetts 02114
| | - Hiroko Dodge
- Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, Massachusetts 02114
| | - Chao-Yi Wu
- Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, Massachusetts 02114
| | - Pia Kivisakk
- Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, Massachusetts 02114
| | - Sudeshna Das
- Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, Massachusetts 02114
| | | | - Brad Hyman
- Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, Massachusetts 02114
| | | | - Steven E. Arnold
- Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, Massachusetts 02114
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20
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Zhu Y. Plasma/Serum Proteomics based on Mass Spectrometry. Protein Pept Lett 2024; 31:192-208. [PMID: 38869039 PMCID: PMC11165715 DOI: 10.2174/0109298665286952240212053723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 01/22/2024] [Accepted: 01/31/2024] [Indexed: 06/14/2024]
Abstract
Human blood is a window of physiology and disease. Examination of biomarkers in blood is a common clinical procedure, which can be informative in diagnosis and prognosis of diseases, and in evaluating treatment effectiveness. There is still a huge demand on new blood biomarkers and assays for precision medicine nowadays, therefore plasma/serum proteomics has attracted increasing attention in recent years. How to effectively proceed with the biomarker discovery and clinical diagnostic assay development is a question raised to researchers who are interested in this area. In this review, we comprehensively introduce the background and advancement of technologies for blood proteomics, with a focus on mass spectrometry (MS). Analyzing existing blood biomarkers and newly-built diagnostic assays based on MS can shed light on developing new biomarkers and analytical methods. We summarize various protein analytes in plasma/serum which include total proteome, protein post-translational modifications, and extracellular vesicles, focusing on their corresponding sample preparation methods for MS analysis. We propose screening multiple protein analytes in the same set of blood samples in order to increase success rate for biomarker discovery. We also review the trends of MS techniques for blood tests including sample preparation automation, and further provide our perspectives on their future directions.
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Affiliation(s)
- Yiying Zhu
- Department of Chemistry, Tsinghua University, Beijing, China
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21
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Mahoney SA, Dey AK, Basisty N, Herman AB. Identification and functional analysis of senescent cells in the cardiovascular system using omics approaches. Am J Physiol Heart Circ Physiol 2023; 325:H1039-H1058. [PMID: 37656130 PMCID: PMC10908411 DOI: 10.1152/ajpheart.00352.2023] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 08/28/2023] [Accepted: 08/28/2023] [Indexed: 09/02/2023]
Abstract
Cardiovascular disease (CVD) is a leading cause of morbidity and mortality worldwide, and senescent cells have emerged as key contributors to its pathogenesis. Senescent cells exhibit cell cycle arrest and secrete a range of proinflammatory factors, termed the senescence-associated secretory phenotype (SASP), which promotes tissue dysfunction and exacerbates CVD progression. Omics technologies, specifically transcriptomics and proteomics, offer powerful tools to uncover and define the molecular signatures of senescent cells in cardiovascular tissue. By analyzing the comprehensive molecular profiles of senescent cells, omics approaches can identify specific genetic alterations, gene expression patterns, protein abundances, and metabolite levels associated with senescence in CVD. These omics-based discoveries provide insights into the mechanisms underlying senescence-induced cardiovascular damage, facilitating the development of novel diagnostic biomarkers and therapeutic targets. Furthermore, integration of multiple omics data sets enables a systems-level understanding of senescence in CVD, paving the way for precision medicine approaches to prevent or treat cardiovascular aging and its associated complications.
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Affiliation(s)
- Sophia A Mahoney
- Department of Integrative Physiology, University of Colorado at Boulder, Boulder, Colorado, United States
| | - Amit K Dey
- Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States
| | - Nathan Basisty
- Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States
| | - Allison B Herman
- Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States
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22
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Fu L, Zhang Y, Farokhzad RA, Mendes BB, Conde J, Shi J. 'Passive' nanoparticles for organ-selective systemic delivery: design, mechanism and perspective. Chem Soc Rev 2023; 52:7579-7601. [PMID: 37817741 PMCID: PMC10623545 DOI: 10.1039/d2cs00998f] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/12/2023]
Abstract
Nanotechnology has shown tremendous success in the drug delivery field for more effective and safer therapy, and has recently enabled the clinical approval of RNA medicine, a new class of therapeutics. Various nanoparticle strategies have been developed to improve the systemic delivery of therapeutics, among which surface modification of targeting ligands on nanoparticles has been widely explored for 'active' delivery to a specific organ or diseased tissue. Meanwhile, compelling evidence has recently been reported that organ-selective targeting may also be achievable by systemic administration of nanoparticles without surface ligand modification. In this Review, we highlight this unique set of 'passive' nanoparticles and their compositions and mechanisms for organ-selective delivery. In particular, the lipid-based, polymer-based, and biomimetic nanoparticles with tropism to different specific organs after intravenous administration are summarized. The underlying mechanisms (e.g., protein corona and size effect) of these nanosystems for organ selectivity are also extensively discussed. We further provide perspectives on the opportunities and challenges in this exciting area of organ-selective systemic nanoparticle delivery.
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Affiliation(s)
- Liyi Fu
- Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, 230031, China
- Center for Nanomedicine and Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Yang Zhang
- Center for Nanomedicine and Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Ryan A Farokhzad
- Center for Nanomedicine and Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Bárbara B Mendes
- ToxOmics, NOVA Medical School, Faculdade de Ciências Médicas, NMS|FCM, Universidade Nova de Lisboa, Lisboa, Portugal
| | - João Conde
- ToxOmics, NOVA Medical School, Faculdade de Ciências Médicas, NMS|FCM, Universidade Nova de Lisboa, Lisboa, Portugal
| | - Jinjun Shi
- Center for Nanomedicine and Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA
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23
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Jin L, Wang F, Wang X, Harvey BP, Bi Y, Hu C, Cui B, Darcy AT, Maull JW, Phillips BR, Kim Y, Jenkins GJ, Sornasse TR, Tian Y. Identification of Plasma Biomarkers from Rheumatoid Arthritis Patients Using an Optimized Sequential Window Acquisition of All THeoretical Mass Spectra (SWATH) Proteomics Workflow. Proteomes 2023; 11:32. [PMID: 37873874 PMCID: PMC10594463 DOI: 10.3390/proteomes11040032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 09/28/2023] [Accepted: 10/02/2023] [Indexed: 10/25/2023] Open
Abstract
Rheumatoid arthritis (RA) is a systemic autoimmune and inflammatory disease. Plasma biomarkers are critical for understanding disease mechanisms, treatment effects, and diagnosis. Mass spectrometry-based proteomics is a powerful tool for unbiased biomarker discovery. However, plasma proteomics is significantly hampered by signal interference from high-abundance proteins, low overall protein coverage, and high levels of missing data from data-dependent acquisition (DDA). To achieve quantitative proteomics analysis for plasma samples with a balance of throughput, performance, and cost, we developed a workflow incorporating plate-based high abundance protein depletion and sample preparation, comprehensive peptide spectral library building, and data-independent acquisition (DIA) SWATH mass spectrometry-based methodology. In this study, we analyzed plasma samples from both RA patients and healthy donors. The results showed that the new workflow performance exceeded that of the current state-of-the-art depletion-based plasma proteomic platforms in terms of both data quality and proteome coverage. Proteins from biological processes related to the activation of systemic inflammation, suppression of platelet function, and loss of muscle mass were enriched and differentially expressed in RA. Some plasma proteins, particularly acute-phase reactant proteins, showed great power to distinguish between RA patients and healthy donors. Moreover, protein isoforms in the plasma were also analyzed, providing even deeper proteome coverage. This workflow can serve as a basis for further application in discovering plasma biomarkers of other diseases.
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Affiliation(s)
- Liang Jin
- Research & Development, AbbVie, North Chicago, IL 60064, USA; (L.J.); (B.P.H.); (B.C.); (A.T.D.); (J.W.M.); (T.R.S.)
| | - Fei Wang
- Research & Development, AbbVie, North Chicago, IL 60064, USA; (L.J.); (B.P.H.); (B.C.); (A.T.D.); (J.W.M.); (T.R.S.)
| | - Xue Wang
- Research & Development, AbbVie, North Chicago, IL 60064, USA; (L.J.); (B.P.H.); (B.C.); (A.T.D.); (J.W.M.); (T.R.S.)
| | - Bohdan P. Harvey
- Research & Development, AbbVie, North Chicago, IL 60064, USA; (L.J.); (B.P.H.); (B.C.); (A.T.D.); (J.W.M.); (T.R.S.)
| | - Yingtao Bi
- Research & Development, AbbVie, North Chicago, IL 60064, USA; (L.J.); (B.P.H.); (B.C.); (A.T.D.); (J.W.M.); (T.R.S.)
| | - Chenqi Hu
- DMPK, Takeda Development Center Americas Inc., Cambridge, MA 02142, USA; (C.H.)
| | - Baoliang Cui
- Research & Development, AbbVie, North Chicago, IL 60064, USA; (L.J.); (B.P.H.); (B.C.); (A.T.D.); (J.W.M.); (T.R.S.)
| | - Anhdao T. Darcy
- Research & Development, AbbVie, North Chicago, IL 60064, USA; (L.J.); (B.P.H.); (B.C.); (A.T.D.); (J.W.M.); (T.R.S.)
| | - John W. Maull
- Research & Development, AbbVie, North Chicago, IL 60064, USA; (L.J.); (B.P.H.); (B.C.); (A.T.D.); (J.W.M.); (T.R.S.)
| | - Ben R. Phillips
- Research & Development, AbbVie, North Chicago, IL 60064, USA; (L.J.); (B.P.H.); (B.C.); (A.T.D.); (J.W.M.); (T.R.S.)
| | - Youngjae Kim
- DMPK, Takeda Development Center Americas Inc., Cambridge, MA 02142, USA; (C.H.)
| | - Gary J. Jenkins
- Research & Development, AbbVie, North Chicago, IL 60064, USA; (L.J.); (B.P.H.); (B.C.); (A.T.D.); (J.W.M.); (T.R.S.)
| | - Thierry R. Sornasse
- Research & Development, AbbVie, North Chicago, IL 60064, USA; (L.J.); (B.P.H.); (B.C.); (A.T.D.); (J.W.M.); (T.R.S.)
| | - Yu Tian
- Research & Development, AbbVie, North Chicago, IL 60064, USA; (L.J.); (B.P.H.); (B.C.); (A.T.D.); (J.W.M.); (T.R.S.)
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24
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Dey AK, Banarjee R, Boroumand M, Rutherford DV, Strassheim Q, Nyunt T, Olinger B, Basisty N. Translating Senotherapeutic Interventions into the Clinic with Emerging Proteomic Technologies. BIOLOGY 2023; 12:1301. [PMID: 37887011 PMCID: PMC10604147 DOI: 10.3390/biology12101301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 09/25/2023] [Accepted: 09/28/2023] [Indexed: 10/28/2023]
Abstract
Cellular senescence is a state of irreversible growth arrest with profound phenotypic changes, including the senescence-associated secretory phenotype (SASP). Senescent cell accumulation contributes to aging and many pathologies including chronic inflammation, type 2 diabetes, cancer, and neurodegeneration. Targeted removal of senescent cells in preclinical models promotes health and longevity, suggesting that the selective elimination of senescent cells is a promising therapeutic approach for mitigating a myriad of age-related pathologies in humans. However, moving senescence-targeting drugs (senotherapeutics) into the clinic will require therapeutic targets and biomarkers, fueled by an improved understanding of the complex and dynamic biology of senescent cell populations and their molecular profiles, as well as the mechanisms underlying the emergence and maintenance of senescence cells and the SASP. Advances in mass spectrometry-based proteomic technologies and workflows have the potential to address these needs. Here, we review the state of translational senescence research and how proteomic approaches have added to our knowledge of senescence biology to date. Further, we lay out a roadmap from fundamental biological discovery to the clinical translation of senotherapeutic approaches through the development and application of emerging proteomic technologies, including targeted and untargeted proteomic approaches, bottom-up and top-down methods, stability proteomics, and surfaceomics. These technologies are integral for probing the cellular composition and dynamics of senescent cells and, ultimately, the development of senotype-specific biomarkers and senotherapeutics (senolytics and senomorphics). This review aims to highlight emerging areas and applications of proteomics that will aid in exploring new senescent cell biology and the future translation of senotherapeutics.
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Affiliation(s)
| | | | | | | | | | | | | | - Nathan Basisty
- Translational Geroproteomics Unit, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA; (A.K.D.); (R.B.); (M.B.); (D.V.R.); (Q.S.); (T.N.); (B.O.)
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25
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Schulz F, Hühn J, Werner M, Hühn D, Kvelstad J, Koert U, Wutke N, Klapper M, Fröba M, Baulin V, Parak WJ. Local Environments Created by the Ligand Coating of Nanoparticles and Their Implications for Sensing and Surface Reactions. Acc Chem Res 2023; 56:2278-2285. [PMID: 37607332 PMCID: PMC10552541 DOI: 10.1021/acs.accounts.3c00139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Indexed: 08/24/2023]
Abstract
ConspectusThe ligand shells of colloidal nanoparticles (NPs) can serve different purposes. In general, they provide colloidal stability by introducing steric repulsion between NPs. In the context of biological applications, the ligand shell plays a critical role in targeting, enabling NPs to achieve specific biodistributions. However, there is also another important feature of the ligand shell of NPs, namely, the creation of a local environment differing from the bulk of the solvent in which the NPs are dispersed. It is known that charged ligand shells can attract or repel ions and change the effective charge of a NP through Debye-Hückel screening. Positively charged ions, such as H+ (or H3O+) are attracted to negatively charged surfaces, whereas negatively charged ions, such as Cl- are repelled. The distribution of the ions around charged NP surfaces is a radial function of distance from the center of the NP, which is governed by a balance of electrostatic forces and entropy of ions and ligands. As a result, the ion concentration at the NP surface is different from its bulk equilibrium concentration, i.e., the charged ligand shell around the NPs has formed a distinct local environment. This not only applies to charged ligand shells but also follows a more general principle of induced condensation and depletion. Polar/apolar ligand shells, for example, result in a locally increased concentration of polar/apolar molecules. Similar effects can be seen for biocatalysts like enzymes immobilized in nanoporous host structures, which provide a special environment due to their surface chemistry and geometrical nanoconfinement. The formation of a local environment close to the ligand shell of NPs has profound implications for NP sensing applications. As a result, analyte concentrations close to the ligand shell, which are the ones that are measured, may be very different from the analyte concentrations in bulk. Based on previous work describing this effect, it will be discussed herein how such local environments, created by the choice of used ligands, may allow for tailoring the NPs' sensing properties. In general, the ligand shell around NPs can be attractive/repulsive for molecules with distinct properties and thus forms an environment that can modulate the specific response. Such local environments can also be optimized to modulate chemical reactions close to the NP surface (for example, by size filtering within pores) or to attract specific low abundance proteins. The importance hereby is that this is based on interaction with low selectivity between the ligands and the target molecules.
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Affiliation(s)
- Florian Schulz
- Fachbereich
Physik, Universität Hamburg, 22607 Hamburg, Germany
| | - Jonas Hühn
- Fachbereich
Physik, Philipps Universität Marburg, 35037 Marburg, Germany
| | - Marco Werner
- Leibniz-Institut
fur Polymerforschung Dresden e.V., 01069 Dresden, Germany
| | - Dominik Hühn
- Fachbereich
Physik, Philipps Universität Marburg, 35037 Marburg, Germany
| | - Julia Kvelstad
- Fachbereich
Chemie, Philipps Universität Marburg, 35043 Marburg, Germany
| | - Ulrich Koert
- Fachbereich
Chemie, Philipps Universität Marburg, 35043 Marburg, Germany
| | - Nicole Wutke
- Max Planck
Institute für Polymerforschung, 55128 Mainz, Germany
| | - Markus Klapper
- Max Planck
Institute für Polymerforschung, 55128 Mainz, Germany
| | - Michael Fröba
- Fachbereich
Chemie, Universität Hamburg, 20146 Hamburg, Germany
| | - Vladimir Baulin
- Departament
Quimica Fisica i Inorganica, Universitat
Rovira i Virgili, 43007 Tarragona, Spain
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26
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Hornburg D, Wu S, Moqri M, Zhou X, Contrepois K, Bararpour N, Traber GM, Su B, Metwally AA, Avina M, Zhou W, Ubellacker JM, Mishra T, Schüssler-Fiorenza Rose SM, Kavathas PB, Williams KJ, Snyder MP. Dynamic lipidome alterations associated with human health, disease and ageing. Nat Metab 2023; 5:1578-1594. [PMID: 37697054 PMCID: PMC10513930 DOI: 10.1038/s42255-023-00880-1] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 07/28/2023] [Indexed: 09/13/2023]
Abstract
Lipids can be of endogenous or exogenous origin and affect diverse biological functions, including cell membrane maintenance, energy management and cellular signalling. Here, we report >800 lipid species, many of which are associated with health-to-disease transitions in diabetes, ageing and inflammation, as well as cytokine-lipidome networks. We performed comprehensive longitudinal lipidomic profiling and analysed >1,500 plasma samples from 112 participants followed for up to 9 years (average 3.2 years) to define the distinct physiological roles of complex lipid subclasses, including large and small triacylglycerols, ester- and ether-linked phosphatidylethanolamines, lysophosphatidylcholines, lysophosphatidylethanolamines, cholesterol esters and ceramides. Our findings reveal dynamic changes in the plasma lipidome during respiratory viral infection, insulin resistance and ageing, suggesting that lipids may have roles in immune homoeostasis and inflammation regulation. Individuals with insulin resistance exhibit disturbed immune homoeostasis, altered associations between lipids and clinical markers, and accelerated changes in specific lipid subclasses during ageing. Our dataset based on longitudinal deep lipidome profiling offers insights into personalized ageing, metabolic health and inflammation, potentially guiding future monitoring and intervention strategies.
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Affiliation(s)
- Daniel Hornburg
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Si Wu
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Mahdi Moqri
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Xin Zhou
- Department of Genetics, Stanford University, Stanford, CA, USA
| | | | - Nasim Bararpour
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Gavin M Traber
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Baolong Su
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Monica Avina
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Wenyu Zhou
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Jessalyn M Ubellacker
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Molecular Metabolism, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | | | | | - Paula B Kavathas
- Departments of Laboratory Medicine and Immunobiology, Yale School of Medicine, New Haven, CT, USA
| | - Kevin J Williams
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Lipidomics Laboratory, University of California, Los Angeles, Los Angeles, CA, USA
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Abdelkader Y, Perez-Davalos L, LeDuc R, Zahedi RP, Labouta HI. Omics approaches for the assessment of biological responses to nanoparticles. Adv Drug Deliv Rev 2023; 200:114992. [PMID: 37414362 DOI: 10.1016/j.addr.2023.114992] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 06/08/2023] [Accepted: 07/03/2023] [Indexed: 07/08/2023]
Abstract
Nanotechnology has enabled the development of innovative therapeutics, diagnostics, and drug delivery systems. Nanoparticles (NPs) can influence gene expression, protein synthesis, cell cycle, metabolism, and other subcellular processes. While conventional methods have limitations in characterizing responses to NPs, omics approaches can analyze complete sets of molecular entities that change upon exposure to NPs. This review discusses key omics approaches, namely transcriptomics, proteomics, metabolomics, lipidomics and multi-omics, applied to the assessment of biological responses to NPs. Fundamental concepts and analytical methods used for each approach are presented, as well as good practices for omics experiments. Bioinformatics tools are essential to analyze, interpret and visualize large omics data, and to correlate observations in different molecular layers. The authors envision that conducting interdisciplinary multi-omics analyses in future nanomedicine studies will reveal integrated cell responses to NPs at different omics levels, and the incorporation of omics into the evaluation of targeted delivery, efficacy, and safety will improve the development of nanomedicine therapies.
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Affiliation(s)
- Yasmin Abdelkader
- Unity Health Toronto - St. Michael's Hospital, University of Toronto, 209 Victoria St., Toronto, Ontario M5B 1T8, Canada; College of Pharmacy, Apotex Centre, University of Manitoba, 750 McDermot Av. W, Winnipeg, Manitoba R3E 0T5, Canada; Department of Cell Biology, Biotechnology Research Institute, National Research Centre, 33 El Buhouth St., Cairo 12622, Egypt
| | - Luis Perez-Davalos
- Unity Health Toronto - St. Michael's Hospital, University of Toronto, 209 Victoria St., Toronto, Ontario M5B 1T8, Canada; College of Pharmacy, Apotex Centre, University of Manitoba, 750 McDermot Av. W, Winnipeg, Manitoba R3E 0T5, Canada
| | - Richard LeDuc
- Children's Hospital Research Institute of Manitoba, 513 - 715 McDermot Av. W, Winnipeg, Manitoba R3E 3P4, Canada; Department of Biochemistry and Medical Genetics, University of Manitoba, 745 Bannatyne Av., Winnipeg, Manitoba R3E 0J9, Canada
| | - Rene P Zahedi
- Department of Biochemistry and Medical Genetics, University of Manitoba, 745 Bannatyne Av., Winnipeg, Manitoba R3E 0J9, Canada; Department of Internal Medicine, 715 McDermot Av., Winnipeg, Manitoba R3E 3P4, Canada; Manitoba Centre for Proteomics and Systems Biology, 799 JBRC, 715 McDermot Av., Winnipeg, Manitoba R3E 3P4, Canada; CancerCare Manitoba Research Institute, 675 McDermot Av., Manitoba R3E 0V9, Canada
| | - Hagar I Labouta
- Unity Health Toronto - St. Michael's Hospital, University of Toronto, 209 Victoria St., Toronto, Ontario M5B 1T8, Canada; College of Pharmacy, Apotex Centre, University of Manitoba, 750 McDermot Av. W, Winnipeg, Manitoba R3E 0T5, Canada; Leslie Dan Faculty of Pharmacy, University of Toronto, 144 College St., Toronto, Ontario M5S 3M2, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, M5S 3G9, Canada; Faculty of Pharmacy, Alexandria University, 1 Khartoum Square, Azarita, Alexandria, Egypt, 21521.
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Huang T, Wang J, Stukalov A, Donovan MKR, Ferdosi S, Williamson L, Just S, Castro G, Cantrell LS, Elgierari E, Benz RW, Huang Y, Motamedchaboki K, Hakimi A, Arrey T, Damoc E, Kreimer S, Farokhzad OC, Batzoglou S, Siddiqui A, Van Eyk JE, Hornburg D. Protein Coronas on Functionalized Nanoparticles Enable Quantitative and Precise Large-Scale Deep Plasma Proteomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.28.555225. [PMID: 37693476 PMCID: PMC10491250 DOI: 10.1101/2023.08.28.555225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Background The wide dynamic range of circulating proteins coupled with the diversity of proteoforms present in plasma has historically impeded comprehensive and quantitative characterization of the plasma proteome at scale. Automated nanoparticle (NP) protein corona-based proteomics workflows can efficiently compress the dynamic range of protein abundances into a mass spectrometry (MS)-accessible detection range. This enhances the depth and scalability of quantitative MS-based methods, which can elucidate the molecular mechanisms of biological processes, discover new protein biomarkers, and improve comprehensiveness of MS-based diagnostics. Methods Investigating multi-species spike-in experiments and a cohort, we investigated fold-change accuracy, linearity, precision, and statistical power for the using the Proteograph™ Product Suite, a deep plasma proteomics workflow, in conjunction with multiple MS instruments. Results We show that NP-based workflows enable accurate identification (false discovery rate of 1%) of more than 6,000 proteins from plasma (Orbitrap Astral) and, compared to a gold standard neat plasma workflow that is limited to the detection of hundreds of plasma proteins, facilitate quantification of more proteins with accurate fold-changes, high linearity, and precision. Furthermore, we demonstrate high statistical power for the discovery of biomarkers in small- and large-scale cohorts. Conclusions The automated NP workflow enables high-throughput, deep, and quantitative plasma proteomics investigation with sufficient power to discover new biomarker signatures with a peptide level resolution.
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Affiliation(s)
| | - Jian Wang
- Seer, Inc., Redwood City, CA, 94065 USA
| | | | | | | | | | - Seth Just
- Seer, Inc., Redwood City, CA, 94065 USA
| | | | | | | | | | | | | | | | | | - Eugen Damoc
- Thermo Fisher Scientific, (Bremen) GmbH, Germany
| | - Simion Kreimer
- Advanced Clinical Biosystems Research Institute, Precision Health, Barbra Streisand Women’s Heart Center at the Smidt Heart Institute, Cedars-Sinai Medical Center, 127 S. San Vicente Blvd., Los Angeles, CA, 90048, USA
| | | | | | | | - Jennifer E. Van Eyk
- Advanced Clinical Biosystems Research Institute, Precision Health, Barbra Streisand Women’s Heart Center at the Smidt Heart Institute, Cedars-Sinai Medical Center, 127 S. San Vicente Blvd., Los Angeles, CA, 90048, USA
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29
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Kong X, Gao P, Wang J, Fang Y, Hwang KC. Advances of medical nanorobots for future cancer treatments. J Hematol Oncol 2023; 16:74. [PMID: 37452423 PMCID: PMC10347767 DOI: 10.1186/s13045-023-01463-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 05/31/2023] [Indexed: 07/18/2023] Open
Abstract
Early detection and diagnosis of many cancers is very challenging. Late stage detection of a cancer always leads to high mortality rates. It is imperative to develop novel and more sensitive and effective diagnosis and therapeutic methods for cancer treatments. The development of new cancer treatments has become a crucial aspect of medical advancements. Nanobots, as one of the most promising applications of nanomedicines, are at the forefront of multidisciplinary research. With the progress of nanotechnology, nanobots enable the assembly and deployment of functional molecular/nanosized machines and are increasingly being utilized in cancer diagnosis and therapeutic treatment. In recent years, various practical applications of nanobots for cancer treatments have transitioned from theory to practice, from in vitro experiments to in vivo applications. In this paper, we review and analyze the recent advancements of nanobots in cancer treatments, with a particular emphasis on their key fundamental features and their applications in drug delivery, tumor sensing and diagnosis, targeted therapy, minimally invasive surgery, and other comprehensive treatments. At the same time, we discuss the challenges and the potential research opportunities for nanobots in revolutionizing cancer treatments. In the future, medical nanobots are expected to become more sophisticated and capable of performing multiple medical functions and tasks, ultimately becoming true nanosubmarines in the bloodstream.
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Affiliation(s)
- Xiangyi Kong
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital and Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, China
| | - Peng Gao
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
- Division of Breast Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, 610041, China
- Breast Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Jing Wang
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
| | - Yi Fang
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
| | - Kuo Chu Hwang
- Department of Chemistry, National Tsing Hua University, Hsinchu, 30013, Taiwan ROC.
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30
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Bader JM, Albrecht V, Mann M. MS-based proteomics of body fluids: The end of the beginning. Mol Cell Proteomics 2023:100577. [PMID: 37209816 PMCID: PMC10388585 DOI: 10.1016/j.mcpro.2023.100577] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 05/07/2023] [Accepted: 05/11/2023] [Indexed: 05/22/2023] Open
Abstract
Accurate biomarkers are a crucial and necessary precondition for precision medicine, yet existing ones are often unspecific and new ones have been very slow to enter the clinic. Mass spectrometry (MS)-based proteomics excels by its untargeted nature, specificity of identification and quantification making it an ideal technology for biomarker discovery and routine measurement. It has unique attributes compared to affinity binder technologies, such as OLINK Proximity Extension Assay and SOMAscan. In a previous review we described technological and conceptual limitations that had held back success (Geyer et al., 2017). We proposed a 'rectangular strategy' to better separate true biomarkers by minimizing cohort-specific effects. Today, this has converged with advances in MS-based proteomics technology, such as increased sample throughput, depth of identification and quantification. As a result, biomarker discovery studies have become more successful, producing biomarker candidates that withstand independent verification and, in some cases, already outperform state-of-the-art clinical assays. We summarize developments over the last years, including the benefits of large and independent cohorts, which are necessary for clinical acceptance. They are also required for machine learning or deep learning. Shorter gradients, new scan modes and multiplexing are about to drastically increase throughput, cross-study integration, and quantification, including proxies for absolute levels. We have found that multi-protein panels are inherently more robust than current single analyte tests and better capture the complexity of human phenotypes. Routine MS measurement in the clinic is fast becoming a viable option. The full set of proteins in a body fluid (global proteome) is the most important reference and the best process control. Additionally, it increasingly has all the information that could be obtained from targeted analysis although the latter may be the most straightforward way to enter into regular use. Many challenges remain, not least of a regulatory and ethical nature, but the outlook for MS-based clinical applications has never been brighter.
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Affiliation(s)
- Jakob M Bader
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, 82152 Martinsried, Germany
| | - Vincent Albrecht
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, 82152 Martinsried, Germany
| | - Matthias Mann
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, 82152 Martinsried, Germany; Novo Nordisk Foundation Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, 2200 Copenhagen, Denmark.
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31
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Ma C, Li Y, Li J, Song L, Chen L, Zhao N, Li X, Chen N, Long L, Zhao J, Hou X, Ren L, Yuan X. Comprehensive and deep profiling of the plasma proteome with protein corona on zeolite NaY. J Pharm Anal 2023; 13:503-513. [PMID: 37305782 PMCID: PMC10257194 DOI: 10.1016/j.jpha.2023.04.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 03/27/2023] [Accepted: 04/06/2023] [Indexed: 06/13/2023] Open
Abstract
Proteomic characterization of plasma is critical for the development of novel pharmacodynamic biomarkers. However, the vast dynamic range renders the profiling of proteomes extremely challenging. Here, we synthesized zeolite NaY and developed a simple and rapid method to achieve comprehensive and deep profiling of the plasma proteome using the plasma protein corona formed on zeolite NaY. Specifically, zeolite NaY and plasma were co-incubated to form plasma protein corona on zeolite NaY (NaY-PPC), followed by conventional protein identification using liquid chromatography-tandem mass spectrometry. NaY was able to significantly enhance the detection of low-abundance plasma proteins, minimizing the "masking" effect caused by high-abundance proteins. The relative abundance of middle- and low-abundance proteins increased substantially from 2.54% to 54.41%, and the top 20 high-abundance proteins decreased from 83.63% to 25.77%. Notably, our method can quantify approximately 4000 plasma proteins with sensitivity up to pg/mL, compared to only about 600 proteins identified from untreated plasma samples. A pilot study based on plasma samples from 30 lung adenocarcinoma patients and 15 healthy subjects demonstrated that our method could successfully distinguish between healthy and disease states. In summary, this work provides an advantageous tool for the exploration of plasma proteomics and its translational applications.
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Affiliation(s)
- Congcong Ma
- Tianjin Key Laboratory of Composite and Functional Materials, School of Materials Science and Engineering, Tianjin University, Tianjin, 300350, China
| | - Yanwei Li
- Department of Integrative Oncology, Tianjin Medical University Cancer Institute and Hospital and Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China
| | - Jie Li
- Department of Proteomics, Tianjin Key Laboratory of Clinical Multi-omics, Tianjin, 300308, China
| | - Lei Song
- Department of Proteomics, Tianjin Key Laboratory of Clinical Multi-omics, Tianjin, 300308, China
| | - Liangyu Chen
- Department of Proteomics, Tianjin Key Laboratory of Clinical Multi-omics, Tianjin, 300308, China
| | - Na Zhao
- Department of Proteomics, Tianjin Key Laboratory of Clinical Multi-omics, Tianjin, 300308, China
| | - Xueping Li
- Tianjin Key Laboratory of Composite and Functional Materials, School of Materials Science and Engineering, Tianjin University, Tianjin, 300350, China
| | - Ning Chen
- Tianjin Key Laboratory of Composite and Functional Materials, School of Materials Science and Engineering, Tianjin University, Tianjin, 300350, China
| | - Lixia Long
- Tianjin Key Laboratory of Composite and Functional Materials, School of Materials Science and Engineering, Tianjin University, Tianjin, 300350, China
| | - Jin Zhao
- Tianjin Key Laboratory of Composite and Functional Materials, School of Materials Science and Engineering, Tianjin University, Tianjin, 300350, China
| | - Xin Hou
- Tianjin Key Laboratory of Composite and Functional Materials, School of Materials Science and Engineering, Tianjin University, Tianjin, 300350, China
| | - Li Ren
- Department of Clinical Laboratory, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China
| | - Xubo Yuan
- Tianjin Key Laboratory of Composite and Functional Materials, School of Materials Science and Engineering, Tianjin University, Tianjin, 300350, China
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32
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Langlois NI, Ma KY, Clark HA. Nucleic acid nanostructures for in vivo applications: The influence of morphology on biological fate. APPLIED PHYSICS REVIEWS 2023; 10:011304. [PMID: 36874908 PMCID: PMC9869343 DOI: 10.1063/5.0121820] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 12/12/2022] [Indexed: 05/23/2023]
Abstract
The development of programmable biomaterials for use in nanofabrication represents a major advance for the future of biomedicine and diagnostics. Recent advances in structural nanotechnology using nucleic acids have resulted in dramatic progress in our understanding of nucleic acid-based nanostructures (NANs) for use in biological applications. As the NANs become more architecturally and functionally diverse to accommodate introduction into living systems, there is a need to understand how critical design features can be controlled to impart desired performance in vivo. In this review, we survey the range of nucleic acid materials utilized as structural building blocks (DNA, RNA, and xenonucleic acids), the diversity of geometries for nanofabrication, and the strategies to functionalize these complexes. We include an assessment of the available and emerging characterization tools used to evaluate the physical, mechanical, physiochemical, and biological properties of NANs in vitro. Finally, the current understanding of the obstacles encountered along the in vivo journey is contextualized to demonstrate how morphological features of NANs influence their biological fates. We envision that this summary will aid researchers in the designing novel NAN morphologies, guide characterization efforts, and design of experiments and spark interdisciplinary collaborations to fuel advancements in programmable platforms for biological applications.
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Affiliation(s)
- Nicole I. Langlois
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, Massachusetts 02115, USA
| | - Kristine Y. Ma
- Department of Bioengineering, Northeastern University, Boston, Massachusetts 02115, USA
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33
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Gao J, Karp JM, Langer R, Joshi N. The Future of Drug Delivery. CHEMISTRY OF MATERIALS : A PUBLICATION OF THE AMERICAN CHEMICAL SOCIETY 2023; 35:359-363. [PMID: 37799624 PMCID: PMC10553157 DOI: 10.1021/acs.chemmater.2c03003] [Citation(s) in RCA: 48] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/07/2023]
Affiliation(s)
- Jingjing Gao
- Center for Nanomedicine, Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115, United States
- Harvard Medical School, Boston, Massachusetts 02115, United States
| | - Jeffrey M Karp
- Center for Nanomedicine, Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115, United States
- Harvard Medical School, Boston, Massachusetts 02115, United States
- Harvard-MIT Program in Health Sciences and Technology, MIT, Cambridge, Massachusetts 02139, United States
- Harvard Stem Cell Institute, Cambridge, Massachusetts 02138, United States
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
| | - Robert Langer
- Harvard-MIT Program in Health Sciences and Technology, MIT, Cambridge, Massachusetts 02139, United States
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, United States
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, United States
| | - Nitin Joshi
- Center for Nanomedicine, Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115, United States
- Harvard Medical School, Boston, Massachusetts 02115, United States
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34
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Ferdosi S, Stukalov A, Hasan M, Tangeysh B, Brown TR, Wang T, Elgierari EM, Zhao X, Huang Y, Alavi A, Lee-McMullen B, Chu J, Figa M, Tao W, Wang J, Goldberg M, O'Brien ES, Xia H, Stolarczyk C, Weissleder R, Farias V, Batzoglou S, Siddiqui A, Farokhzad OC, Hornburg D. Enhanced Competition at the Nano-Bio Interface Enables Comprehensive Characterization of Protein Corona Dynamics and Deep Coverage of Proteomes. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2206008. [PMID: 35986672 DOI: 10.1002/adma.202206008] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 08/11/2022] [Indexed: 06/15/2023]
Abstract
Introducing engineered nanoparticles (NPs) into a biofluid such as blood plasma leads to the formation of a selective and reproducible protein corona at the particle-protein interface, driven by the relationship between protein-NP affinity and protein abundance. This enables scalable systems that leverage protein-nano interactions to overcome current limitations of deep plasma proteomics in large cohorts. Here the importance of the protein to NP-surface ratio (P/NP) is demonstrated and protein corona formation dynamics are modeled, which determine the competition between proteins for binding. Tuning the P/NP ratio significantly modulates the protein corona composition, enhancing depth and precision of a fully automated NP-based deep proteomic workflow (Proteograph). By increasing the binding competition on engineered NPs, 1.2-1.7× more proteins with 1% false discovery rate are identified on the surface of each NP, and up to 3× more proteins compared to a standard plasma proteomics workflow. Moreover, the data suggest P/NP plays a significant role in determining the in vivo fate of nanomaterials in biomedical applications. Together, the study showcases the importance of P/NP as a key design element for biomaterials and nanomedicine in vivo and as a powerful tuning strategy for accurate, large-scale NP-based deep proteomic studies.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - Amir Alavi
- Seer, Inc., Redwood City, CA, 94065, USA
| | | | | | - Mike Figa
- Seer, Inc., Redwood City, CA, 94065, USA
| | - Wei Tao
- Center for Nanomedicine and Department of Anesthesiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Jian Wang
- Seer, Inc., Redwood City, CA, 94065, USA
| | | | | | | | | | - Ralph Weissleder
- Department of Systems Biology, Harvard Medical School, 200 Longwood Ave, Boston, MA, 02115, USA
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge St, CPZN 5206, Boston, MA, 02114, USA
| | - Vivek Farias
- Sloan School and Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | | | | | - Omid C Farokhzad
- Seer, Inc., Redwood City, CA, 94065, USA
- Center for Nanomedicine and Department of Anesthesiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
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