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Wu Q, Zheng J, Sui X, Fu C, Cui X, Liao B, Ji H, Luo Y, He A, Lu X, Xue X, Tan CSH, Tian R. High-throughput drug target discovery using a fully automated proteomics sample preparation platform. Chem Sci 2024; 15:2833-2847. [PMID: 38404368 PMCID: PMC10882491 DOI: 10.1039/d3sc05937e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 12/19/2023] [Indexed: 02/27/2024] Open
Abstract
Drug development is plagued by inefficiency and high costs due to issues such as inadequate drug efficacy and unexpected toxicity. Mass spectrometry (MS)-based proteomics, particularly isobaric quantitative proteomics, offers a solution to unveil resistance mechanisms and unforeseen side effects related to off-targeting pathways. Thermal proteome profiling (TPP) has gained popularity for drug target identification at the proteome scale. However, it involves experiments with multiple temperature points, resulting in numerous samples and considerable variability in large-scale TPP analysis. We propose a high-throughput drug target discovery workflow that integrates single-temperature TPP, a fully automated proteomics sample preparation platform (autoSISPROT), and data independent acquisition (DIA) quantification. The autoSISPROT platform enables the simultaneous processing of 96 samples in less than 2.5 hours, achieving protein digestion, desalting, and optional TMT labeling (requires an additional 1 hour) with 96-channel all-in-tip operations. The results demonstrated excellent sample preparation performance with >94% digestion efficiency, >98% TMT labeling efficiency, and >0.9 intra- and inter-batch Pearson correlation coefficients. By automatically processing 87 samples, we identified both known targets and potential off-targets of 20 kinase inhibitors, affording over a 10-fold improvement in throughput compared to classical TPP. This fully automated workflow offers a high-throughput solution for proteomics sample preparation and drug target/off-target identification.
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Affiliation(s)
- Qiong Wu
- Department of Chemistry, School of Science, Southern University of Science and Technology Shenzhen 518055 China
| | - Jiangnan Zheng
- Department of Chemistry, School of Science, Southern University of Science and Technology Shenzhen 518055 China
- Southern University of Science and Technology, Guangming Advanced Research Institute Shenzhen 518055 China
| | - Xintong Sui
- Department of Chemistry, School of Science, Southern University of Science and Technology Shenzhen 518055 China
| | - Changying Fu
- Department of Chemistry, School of Science, Southern University of Science and Technology Shenzhen 518055 China
| | - Xiaozhen Cui
- Department of Chemistry, School of Science, Southern University of Science and Technology Shenzhen 518055 China
| | - Bin Liao
- Department of Chemistry, School of Science, Southern University of Science and Technology Shenzhen 518055 China
| | - Hongchao Ji
- Department of Chemistry, School of Science, Southern University of Science and Technology Shenzhen 518055 China
| | - Yang Luo
- Department of Chemistry, School of Science, Southern University of Science and Technology Shenzhen 518055 China
| | - An He
- Department of Chemistry, School of Science, Southern University of Science and Technology Shenzhen 518055 China
| | - Xue Lu
- Department of Chemistry, School of Science, Southern University of Science and Technology Shenzhen 518055 China
| | - Xinyue Xue
- Department of Chemistry, School of Science, Southern University of Science and Technology Shenzhen 518055 China
| | - Chris Soon Heng Tan
- Department of Chemistry, School of Science, Southern University of Science and Technology Shenzhen 518055 China
- Research Center for Chemical Biology and Omics Analysis, School of Science, Southern University of Science and Technology 1088 Xueyuan Road Shenzhen 518055 China
- Southern University of Science and Technology, Guangming Advanced Research Institute Shenzhen 518055 China
| | - Ruijun Tian
- Department of Chemistry, School of Science, Southern University of Science and Technology Shenzhen 518055 China
- Research Center for Chemical Biology and Omics Analysis, School of Science, Southern University of Science and Technology 1088 Xueyuan Road Shenzhen 518055 China
- Southern University of Science and Technology, Guangming Advanced Research Institute Shenzhen 518055 China
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2
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Assress H, Ferruzzi MG, Lan RS. Optimization of Mass Spectrometric Parameters in Data Dependent Acquisition for Untargeted Metabolomics on the Basis of Putative Assignments. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023; 34:1621-1631. [PMID: 37419493 PMCID: PMC10402710 DOI: 10.1021/jasms.3c00084] [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: 03/02/2023] [Revised: 06/19/2023] [Accepted: 06/21/2023] [Indexed: 07/09/2023]
Abstract
Optimization of mass spectrometric parameters for a data dependent acquisition (DDA) experiment is essential to increase the MS/MS coverage and hence increase metabolite identifications in untargeted metabolomics. We explored the influence of mass spectrometric parameters including mass resolution, radio frequency (RF) level, signal intensity threshold, number of MS/MS events, cycle time, collision energy, maximum ion injection time (MIT), dynamic exclusion, and automatic gain control (AGC) target value on metabolite annotations on an Exploris 480-Orbitrap mass spectrometer. Optimal annotation results were obtained by performing ten data dependent MS/MS scans with a mass isolation window of 2.0 m/z and a minimum signal intensity threshold of 1 × 104 at a mass resolution of 180,000 for MS and 30,000 for MS/MS, while maintaining the RF level at 70%. Furthermore, combining an AGC target value of 5 × 106 and MIT of 100 ms for MS and an AGC target value of 1 × 105 and an MIT of 50 ms for MS/MS scans provided an improved number of annotated metabolites. A 10 s exclusion duration and a two stepped collision energy were optimal for higher spectral quality. These findings confirm that MS parameters do influence metabolomics results, and propose strategies for increasing metabolite coverage in untargeted metabolomics. A limitation of this work is that our parameters were only optimized for one RPLC method on single matrix and may be different for other protocols. Additionally, no metabolites were identified at level 1 confidence. The results presented here are based on metabolite annotations and need to be validated with authentic standards.
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Affiliation(s)
- Hailemariam
Abrha Assress
- Arkansas
Children’s Nutrition Center, Little Rock, Arkansas 72202, United States
- Department
of Pediatrics, University of Arkansas for
Medical Sciences, Little
Rock, Arkansas 72205, United States
| | - Mario G. Ferruzzi
- Arkansas
Children’s Nutrition Center, Little Rock, Arkansas 72202, United States
- Department
of Pediatrics, University of Arkansas for
Medical Sciences, Little
Rock, Arkansas 72205, United States
| | - Renny S. Lan
- Arkansas
Children’s Nutrition Center, Little Rock, Arkansas 72202, United States
- Department
of Pediatrics, University of Arkansas for
Medical Sciences, Little
Rock, Arkansas 72205, United States
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Zheng R, Stejskal K, Pynn C, Mechtler K, Boychenko A. Deep Single-Shot NanoLC-MS Proteome Profiling with a 1500 Bar UHPLC System, Long Fully Porous Columns, and HRAM MS. J Proteome Res 2022; 21:2545-2551. [PMID: 36068014 PMCID: PMC9552226 DOI: 10.1021/acs.jproteome.2c00270] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
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This study demonstrates how the latest ultrahigh-performance
liquid
chromatography (UHPLC) technology can be combined with high-resolution
accurate-mass (HRAM) mass spectrometry (MS) and long columns packed
with fully porous particles to improve bottom-up proteomics analysis
with nanoflow liquid chromatography–mass spectrometry (nanoLC-MS)
methods. The increased back pressures from the UHPLC system enabled
the use of 75 μm I.D. × 75 cm columns packed with 2 μm
particles at a typical 300 nL/min flow rate as well as elevated and
reduced flow rates. The constant pressure pump operation at 1500 bar
reduced sample loading and column washing/equilibration stages and
overall overhead time, which maximizes MS utilization time. The versatility
of flow rate optimization to balance the sensitivity, throughput with
sample loading amount, and capability of using longer gradients contributes
to a greater number of peptide and protein identifications for single-shot
bottom-up proteomics experiments. The routine proteome profiling and
precise quantification of >7000 proteins with single-shot nanoLC-MS
analysis open possibilities for large-scale discovery studies with
a deep dive into the protein level alterations. Data are available
via ProteomeXchange with identifier PXD035665.
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Affiliation(s)
- Runsheng Zheng
- Thermo Fisher Scientific, Dornierstrasse 4, 82110 Germering, Germany
| | - Karel Stejskal
- IMP─Institute of Molecular Pathology, Campus-Vienna-Biocenter 1, A-1030 Vienna, Austria.,IMBA─Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Dr. Bohr Gasse 3, A-1030 Vienna, Austria.,Gregor Mendel Institute of Molecular Plant Biology of the Austrian Academy of Sciences, Dr. Bohr Gasse 3, A-1030 Vienna, Austria
| | - Christopher Pynn
- Thermo Fisher Scientific, Dornierstrasse 4, 82110 Germering, Germany
| | - Karl Mechtler
- IMP─Institute of Molecular Pathology, Campus-Vienna-Biocenter 1, A-1030 Vienna, Austria.,IMBA─Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Dr. Bohr Gasse 3, A-1030 Vienna, Austria.,Gregor Mendel Institute of Molecular Plant Biology of the Austrian Academy of Sciences, Dr. Bohr Gasse 3, A-1030 Vienna, Austria
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4
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Pauwels J, Fijałkowska D, Eyckerman S, Gevaert K. Mass spectrometry and the cellular surfaceome. MASS SPECTROMETRY REVIEWS 2022; 41:804-841. [PMID: 33655572 DOI: 10.1002/mas.21690] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 02/05/2021] [Accepted: 02/09/2021] [Indexed: 06/12/2023]
Abstract
The collection of exposed plasma membrane proteins, collectively termed the surfaceome, is involved in multiple vital cellular processes, such as the communication of cells with their surroundings and the regulation of transport across the lipid bilayer. The surfaceome also plays key roles in the immune system by recognizing and presenting antigens, with its possible malfunctioning linked to disease. Surface proteins have long been explored as potential cell markers, disease biomarkers, and therapeutic drug targets. Despite its importance, a detailed study of the surfaceome continues to pose major challenges for mass spectrometry-driven proteomics due to the inherent biophysical characteristics of surface proteins. Their inefficient extraction from hydrophobic membranes to an aqueous medium and their lower abundance compared to intracellular proteins hamper the analysis of surface proteins, which are therefore usually underrepresented in proteomic datasets. To tackle such problems, several innovative analytical methodologies have been developed. This review aims at providing an extensive overview of the different methods for surfaceome analysis, with respective considerations for downstream mass spectrometry-based proteomics.
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Affiliation(s)
- Jarne Pauwels
- VIB Center for Medical Biotechnology, VIB, Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
| | | | - Sven Eyckerman
- VIB Center for Medical Biotechnology, VIB, Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
| | - Kris Gevaert
- VIB Center for Medical Biotechnology, VIB, Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
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Fritted tip capillary column with negligible dead volume facilitated ultrasensitive and deep proteomics. Anal Chim Acta 2022; 1201:339615. [DOI: 10.1016/j.aca.2022.339615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 01/18/2022] [Accepted: 02/14/2022] [Indexed: 11/24/2022]
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6
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Optimized Identification of Triacylglycerols in Milk by HPLC-HRMS. FOOD ANAL METHOD 2022. [DOI: 10.1007/s12161-022-02270-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
AbstractThis work has developed an optimized workflow for the targeted analysis of triacylglycerols (TAGs) in milk by liquid chromatography coupled with a Q-Exactive Orbitrap mass spectrometer. First, the effects of resolution (17,500; 35,000; 70,000; 140,000) and automatic gain control target (AGC, from 2×104, 2×105, 1×106, and 3×106) have been optimized with the goal to minimize the injection time, maximize the number of scans, and minimize the mass error. Then, the flow rate of the liquid chromatography system was also optimized by maximizing the number of theoretical plates. The resulting optimized parameters consisted of a flow rate of 200 μL/min, mass resolution of 35,000, and AGC target of 2×105. Such optimal conditions were applied for targeted TAG analysis of milk fat extracts. Up to 14 target triglycerides in milk fat were identified performing a data-dependent HPLC-HRMS-MS2 experiment (t-SIM-ddMS2). The findings reported here can be helpful for MS-based lipidomic workflows and targeted milk lipid analysis.
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7
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Sharma R, Lunge A, Mangla N, Agarwal N. Proteomic Landscape of a Drug-Tolerant Persister Subpopulation of Mycobacterium tuberculosis. J Proteome Res 2021; 20:4415-4426. [PMID: 34343006 DOI: 10.1021/acs.jproteome.1c00348] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Persisters are a subpopulation of bacteria that resist killing by antibiotics, even though they are genetically similar to their drug-susceptible counterpart. Like in several other bacteria, persisters are also reported in the human pathogen Mycobacterium tuberculosis (Mtb). Stochastic formation of Mtb persisters with a high level of antimicrobial tolerance set the stage for subsequent multidrug-resistant mutations. Despite significant advancement in our understanding, much remains to be learnt about the biology of this drug-recalcitrant bacterial subpopulation. Most of the information pertaining to the metabolic evolution required for emergence of drug tolerance in tuberculosis (TB) pathogens has come from transcriptional, metabolomic, and mutagenesis studies. Since proteins are the key functional molecules regulating the majority of metabolic activities in the cell, investigation of the whole-cell protein expression profile will further provide valuable insights into the physiology of Mtb persisters. We performed a quantitative proteomic analysis of Mtb H37Rv cultured under an in vitro persistence model to identify the proteomic profile of the phenotypic drug-tolerant bacterial population. Our study reveals that proteins related to intermediary metabolism and respiration, cell-wall and cell processes, lipid metabolism, information pathways, and virulence, detoxification and adaptation functional categories are primarily modulated in the persister subpopulation. Further, we demonstrate that various surface-localized mycobacterial membrane protein large (MmpL) proteins, which exhibit a high level of expression in Mtb persisters, are crucial for the mycobacterial survival during persistent growth state. A drug-induced persister subpopulation of Mtb exhibit various differentially regulated proteins that might be critical in mitigating the antimicrobial effect of drugs and can be further explored to develop novel anti-TB agents. The peptide identifications and tandem mass spectra (MS/MS) have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD013621.
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Affiliation(s)
- Rishabh Sharma
- NCR Biotech Science Cluster, Translational Health Science and Technology Institute, 3rd Milestone, Faridabad-Gurgaon Expressway, Faridabad, 121001 Haryana, India
| | - Ajitesh Lunge
- NCR Biotech Science Cluster, Translational Health Science and Technology Institute, 3rd Milestone, Faridabad-Gurgaon Expressway, Faridabad, 121001 Haryana, India.,Jawaharlal Nehru University, New Mehrauli Road, New Delhi, 110067 Delhi, India
| | - Nikita Mangla
- NCR Biotech Science Cluster, Translational Health Science and Technology Institute, 3rd Milestone, Faridabad-Gurgaon Expressway, Faridabad, 121001 Haryana, India.,Jawaharlal Nehru University, New Mehrauli Road, New Delhi, 110067 Delhi, India
| | - Nisheeth Agarwal
- NCR Biotech Science Cluster, Translational Health Science and Technology Institute, 3rd Milestone, Faridabad-Gurgaon Expressway, Faridabad, 121001 Haryana, India
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Smythers AL, Hicks LM. Mapping the plant proteome: tools for surveying coordinating pathways. Emerg Top Life Sci 2021; 5:203-220. [PMID: 33620075 PMCID: PMC8166341 DOI: 10.1042/etls20200270] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 02/07/2021] [Accepted: 02/09/2021] [Indexed: 12/14/2022]
Abstract
Plants rapidly respond to environmental fluctuations through coordinated, multi-scalar regulation, enabling complex reactions despite their inherently sessile nature. In particular, protein post-translational signaling and protein-protein interactions combine to manipulate cellular responses and regulate plant homeostasis with precise temporal and spatial control. Understanding these proteomic networks are essential to addressing ongoing global crises, including those of food security, rising global temperatures, and the need for renewable materials and fuels. Technological advances in mass spectrometry-based proteomics are enabling investigations of unprecedented depth, and are increasingly being optimized for and applied to plant systems. This review highlights recent advances in plant proteomics, with an emphasis on spatially and temporally resolved analysis of post-translational modifications and protein interactions. It also details the necessity for generation of a comprehensive plant cell atlas while highlighting recent accomplishments within the field.
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Affiliation(s)
- Amanda L Smythers
- Department of Chemistry, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, U.S.A
| | - Leslie M Hicks
- Department of Chemistry, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, U.S.A
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