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Nalehua MR, Zaia J. A critical evaluation of ultrasensitive single-cell proteomics strategies. Anal Bioanal Chem 2024; 416:2359-2369. [PMID: 38358530 DOI: 10.1007/s00216-024-05171-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 01/20/2024] [Accepted: 01/23/2024] [Indexed: 02/16/2024]
Abstract
Success of mass spectrometry characterization of the proteome of single cells allows us to gain a greater understanding than afforded by transcriptomics alone but requires clear understanding of the tradeoffs between analytical throughput and precision. Recent advances in mass spectrometry acquisition techniques, including updated instrumentation and sample preparation, have improved the quality of peptide signals obtained from single cell data. However, much of the proteome remains uncharacterized, and higher throughput techniques often come at the expense of reduced sensitivity and coverage, which diminish the ability to measure proteoform heterogeneity, including splice variants and post-translational modifications, in single cell data analysis. Here, we assess the growing body of ultrasensitive single-cell approaches and their tradeoffs as researchers try to balance throughput and precision in their experiments.
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Affiliation(s)
| | - Joseph Zaia
- Bioinformatics Program, Boston University, Boston, MA, USA.
- Department of Biochemistry and Cell Biology, Boston University, Boston, MA, USA.
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2
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Ramirez-Sagredo A, Sunny A, Cupp-Sutton K, Chowdhury T, Zhao Z, Wu S, Ann Chiao Y. Characterizing Age-related Changes in Intact Mitochondrial Proteoforms in Murine Hearts using Quantitative Top-Down Proteomics. RESEARCH SQUARE 2024:rs.3.rs-3868218. [PMID: 38313302 PMCID: PMC10836115 DOI: 10.21203/rs.3.rs-3868218/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
Cardiovascular diseases (CVDs) are the leading cause of death worldwide, and the prevalence of CVDs increases markedly with age. Due to the high energetic demand, the heart is highly sensitive to mitochondrial dysfunction. The complexity of the cardiac mitochondrial proteome hinders the development of effective strategies that target mitochondrial dysfunction in CVDs. Mammalian mitochondria are composed of over 1000 proteins, most of which can undergo post-translational protein modifications (PTMs). Top-down proteomics is a powerful technique for characterizing and quantifying all protein sequence variations and PTMs. However, there are still knowledge gaps in the study of age-related mitochondrial proteoform changes using this technique. In this study, we used top-down proteomics to identify intact mitochondrial proteoforms in young and old hearts and determined changes in protein abundance and PTMs in cardiac aging. METHODS Intact mitochondria were isolated from the hearts of young (4-month-old) and old (24-25-month-old) mice. The mitochondria were lysed, and mitochondrial lysates were subjected to denaturation, reduction, and alkylation. For quantitative top-down analysis, there were 12 runs in total arising from 3 biological replicates in two conditions, with technical duplicates for each sample. The collected top-down datasets were deconvoluted and quantified, and then the proteoforms were identified. RESULTS From a total of 12 LC-MS/MS runs, we identified 134 unique mitochondrial proteins in the different sub-mitochondrial compartments (OMM, IMS, IMM, matrix). 823 unique proteoforms in different mass ranges were identified. Compared to cardiac mitochondria of young mice, 7 proteoforms exhibited increased abundance and 13 proteoforms exhibited decreased abundance in cardiac mitochondria of old mice. Our analysis also detected PTMs of mitochondrial proteoforms, including N-terminal acetylation, lysine succinylation, lysine acetylation, oxidation, and phosphorylation. CONCLUSION By combining mitochondrial protein enrichment using mitochondrial fractionation with quantitative top-down analysis using ultrahigh-pressure liquid chromatography (UPLC)-MS and label-free quantitation, we successfully identified and quantified intact proteoforms in the complex mitochondrial proteome. Using this approach, we detected age-related changes in abundance and PTMs of mitochondrial proteoforms in the heart.
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3
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Odenkirk MT, Zhang G, Marty MT. Do Nanodisc Assembly Conditions Affect Natural Lipid Uptake? JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023; 34:2006-2015. [PMID: 37524089 PMCID: PMC10528108 DOI: 10.1021/jasms.3c00170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/02/2023]
Abstract
Lipids play critical roles in modulating membrane protein structure, interactions, and activity. Nanodiscs provide a tunable membrane mimetic that can model these endogenous protein-lipid interactions in a nanoscale lipid bilayer. However, most studies of membrane proteins with nanodiscs use simple synthetic lipids that lack the headgroup and fatty acyl diversity of natural extracts. Prior research has successfully used natural lipid extracts in nanodiscs that more accurately mimic natural environments, but it is not clear how nanodisc assembly may bias the incorporated lipid profiles. Here, we applied lipidomics to investigate how nanodisc assembly conditions affect the profile of natural lipids in nanodiscs. Specifically, we tested the effects of assembly temperature, nanodisc size, and lipidome extract complexity. Globally, our analysis demonstrates that the lipids profiles are largely unaffected by nanodisc assembly conditions. However, a few notable changes emerged within individual lipids and lipid classes, such as a differential incorporation of cardiolipin and phosphatidylglycerol lipids from the E. coli polar lipid extract at different temperatures. Conversely, some classes of brain lipids were affected by nanodisc size at higher temperatures. Collectively, these data enable the application of nanodiscs to study protein-lipid interactions in complex lipid environments.
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Affiliation(s)
- Melanie T. Odenkirk
- Department of Chemistry and Biochemistry, University of Arizona, Tucson, AZ
- Bio5 Institute, University of Arizona, Tucson, AZ
| | - Guozhi Zhang
- Department of Chemistry and Biochemistry, University of Arizona, Tucson, AZ
| | - Michael T. Marty
- Department of Chemistry and Biochemistry, University of Arizona, Tucson, AZ
- Bio5 Institute, University of Arizona, Tucson, AZ
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4
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Ryan KA, Bruening ML. Online protein digestion in membranes between capillary electrophoresis and mass spectrometry. Analyst 2023; 148:1611-1619. [PMID: 36912593 DOI: 10.1039/d3an00106g] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/10/2023]
Abstract
This research employs pepsin-containing membranes to digest proteins online after a capillary electrophoresis (CE) separation and prior to tandem mass spectrometry. Proteolysis after the separation allows the peptides from a given protein to enter the mass spectrometer in a single plug. Thus, migration time can serve as an additional criterion for confirming the identification of a peptide. The membrane resides in a sheath-flow electrospray ionization (ESI) source to enable digestion immediately before spray into the mass spectrometer, thus limiting separation of the digested peptides. Using the same membrane, digestion occurred reproducibly during 20 consecutive CE analyses performed over a 10 h period. Additionally, after separating a mixture of six unreduced proteins with CE, online digestion facilitated protein identification with at least 2 identifiable peptides for all the proteins. Sequence coverages were >75% for myoglobin and carbonic anhydrase II but much lower for proteins containing disulfide bonds. Development of methods for efficient separation of reduced proteins or identification of cross-linked peptides should enhance sequence coverages for proteins with disulfide bonds. Migration times for the peptides identified from a specific protein differed by <∼30 s, which allows for rejection of some spurious peptide identifications.
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Affiliation(s)
- Kendall A Ryan
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556, USA.
| | - Merlin L Bruening
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556, USA. .,Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
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5
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Single-cell proteomics enabled by next-generation sequencing or mass spectrometry. Nat Methods 2023; 20:363-374. [PMID: 36864196 DOI: 10.1038/s41592-023-01791-5] [Citation(s) in RCA: 43] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 01/24/2023] [Indexed: 03/04/2023]
Abstract
In the last decade, single-cell RNA sequencing routinely performed on large numbers of single cells has greatly advanced our understanding of the underlying heterogeneity of complex biological systems. Technological advances have also enabled protein measurements, further contributing to the elucidation of cell types and states present in complex tissues. Recently, there have been independent advances in mass spectrometric techniques bringing us one step closer to characterizing single-cell proteomes. Here we discuss the challenges of detecting proteins in single cells by both mass spectrometry and sequencing-based methods. We review the state of the art for these techniques and propose that there is a space for technological advancements and complementary approaches that maximize the advantages of both classes of technologies.
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6
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Cupp-Sutton KA, Welborn T, Fang M, Langford JB, Wang Z, Smith K, Wu S. The Deuterium Calculator: An Open-Source Tool for Hydrogen-Deuterium Exchange Mass Spectrometry Analysis. J Proteome Res 2023; 22:532-538. [PMID: 36695755 DOI: 10.1021/acs.jproteome.2c00558] [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: 01/26/2023]
Abstract
Hydrogen-deuterium exchange mass spectrometry (HDX-MS) is a powerful protein footprinting technique to study protein dynamics and binding; however, HDX-MS data analysis is often challenging and time-consuming. Moreover, the HDX community is expanding to investigate multiprotein and highly complex protein systems which further complicates data analysis. Thus, a simple, open-source software package designed to analyze large and highly complex protein systems is needed. In this vein, we have developed "The Deuterium Calculator", a Python-based software package for HDX-MS data analysis. The Deuterium Calculator is capable of differential and nondifferential HDX-MS analysis, produces standardized data files according to recommendations from the International Conference on Hydrogen-Exchange Mass Spectrometry (IC-HDX) to increase transparency in data analysis, and generates Woods' plots for statistical analysis and data visualization. This standard output can be used to perform time dependent deuteration studies and for the study of protein folding kinetics or differential uptake. Moreover, The Deuterium Calculator is capable of performing these analyses on large HDX-MS data sets (e.g., LC-HDX-MS from cell lysate digest). The Deuterium Calculator is freely available for download at https://github.com/OUWuLab/TheDeuteriumCalculator.git. Data are available via ProteomeXchange with identifier PXD036813.
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Affiliation(s)
- Kellye A Cupp-Sutton
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, Oklahoma73012, United States
| | - Thomas Welborn
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, Oklahoma73012, United States
| | - Mulin Fang
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, Oklahoma73012, United States
| | - Joel B Langford
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, Oklahoma73012, United States
| | - Zhe Wang
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, Oklahoma73012, United States
| | - Kenneth Smith
- Department of Arthritis and Clinical Immunology, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma73104, United States
| | - Si Wu
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, Oklahoma73012, United States
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7
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Spatially Resolved Top-Down Proteomics of Tissue Sections Based on a Microfluidic Nanodroplet Sample Preparation Platform. Mol Cell Proteomics 2023; 22:100491. [PMID: 36603806 PMCID: PMC9944986 DOI: 10.1016/j.mcpro.2022.100491] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 12/10/2022] [Accepted: 12/20/2022] [Indexed: 01/04/2023] Open
Abstract
Conventional proteomic approaches measure the averaged signal from mixed cell populations or bulk tissues, leading to the dilution of signals arising from subpopulations of cells that might serve as important biomarkers. Recent developments in bottom-up proteomics have enabled spatial mapping of cellular heterogeneity in tissue microenvironments. However, bottom-up proteomics cannot unambiguously define and quantify proteoforms, which are intact (i.e., functional) forms of proteins capturing genetic variations, alternatively spliced transcripts and posttranslational modifications. Herein, we described a spatially resolved top-down proteomics (TDP) platform for proteoform identification and quantitation directly from tissue sections. The spatial TDP platform consisted of a nanodroplet processing in one pot for trace samples-based sample preparation system and an laser capture microdissection-based cell isolation system. We improved the nanodroplet processing in one pot for trace samples sample preparation by adding benzonase in the extraction buffer to enhance the coverage of nucleus proteins. Using ∼200 cultured cells as test samples, this approach increased total proteoform identifications from 493 to 700; with newly identified proteoforms primarily corresponding to nuclear proteins. To demonstrate the spatial TDP platform in tissue samples, we analyzed laser capture microdissection-isolated tissue voxels from rat brain cortex and hypothalamus regions. We quantified 509 proteoforms within the union of top-down mass spectrometry-based proteoform identification and characterization and TDPortal identifications to match with features from protein mass extractor. Several proteoforms corresponding to the same gene exhibited mixed abundance profiles between two tissue regions, suggesting potential posttranslational modification-specific spatial distributions. The spatial TDP workflow has prospects for biomarker discovery at proteoform level from small tissue sections.
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8
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Cupp-Sutton KA, Fang M, Wu S. Separation methods in single-cell proteomics: RPLC or CE? INTERNATIONAL JOURNAL OF MASS SPECTROMETRY 2022; 481:116920. [PMID: 36211475 PMCID: PMC9542495 DOI: 10.1016/j.ijms.2022.116920] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Cellular heterogeneity is commonly investigated using single-cell genomics and transcriptomics to investigate biological questions such as disease mechanism, therapeutic screening, and genomic and transcriptomic diversity between cellular populations and subpopulations at the cellular level. Single-cell mass spectrometry (MS)-based proteomics enables the high-throughput examination of protein expression at the single-cell level with wide applicability, and with spatial and temporal resolution, applicable to the study of cellular development, disease, effect of treatment, etc. The study of single-cell proteomics has lagged behind genomics and transcriptomics largely because proteins from single-cell samples cannot be amplified as DNA and RNA can using well established techniques such as PCR. Therefore, analytical methods must be robust, reproducible, and sensitive enough to detect the very small amount of protein within a single cell. To this end, nearly every step of the proteomics process has been extensively altered and improved to facilitate the proteomics analysis of single cells including cell counting and sorting, lysis, protein digestion, sample cleanup, separation, MS data acquisition, and data analysis. Here, we have reviewed recent advances in single-cell protein separation using nano reversed phase liquid chromatography (nRPLC) and capillary electrophoresis (CE) to inform application driven selection of separation techniques in the laboratory setting.
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Affiliation(s)
| | - Mulin Fang
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK 73019
| | - Si Wu
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK 73019
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9
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OGATA K, ISHIHAMA Y. Temperature Dependence of Retention Behavior of Phosphorylated Peptides in Ion-Pair Reversed-Phase Liquid Chromatography. CHROMATOGRAPHY 2022. [DOI: 10.15583/jpchrom.2022.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Kosuke OGATA
- Graduate School of Pharmaceutical Sciences, Kyoto University
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10
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Woo J, Clair GC, Williams SM, Feng S, Tsai CF, Moore RJ, Chrisler WB, Smith RD, Kelly RT, Paša-Tolić L, Ansong C, Zhu Y. Three-dimensional feature matching improves coverage for single-cell proteomics based on ion mobility filtering. Cell Syst 2022; 13:426-434.e4. [PMID: 35298923 PMCID: PMC9119937 DOI: 10.1016/j.cels.2022.02.003] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 12/04/2021] [Accepted: 02/17/2022] [Indexed: 12/13/2022]
Abstract
Single-cell proteomics (scProteomics) promises to advance our understanding of cell functions within complex biological systems. However, a major challenge of current methods is their inability to identify and provide accurate quantitative information for low-abundance proteins. Herein, we describe an ion-mobility-enhanced mass spectrometry acquisition and peptide identification method, transferring identification based on FAIMS filtering (TIFF), to improve the sensitivity and accuracy of label-free scProteomics. TIFF extends the ion accumulation times for peptide ions by filtering out singly charged ions. The peptide identities are assigned by a three-dimensional MS1 feature matching approach (retention time, accurate mass, and FAIMS compensation voltage). The TIFF method enabled unbiased proteome analysis to a depth of >1,700 proteins in single HeLa cells, with >1,100 proteins consistently identified. As a demonstration, we applied the TIFF method to obtain temporal proteome profiles of >150 single murine macrophage cells during lipopolysaccharide stimulation and identified time-dependent proteome changes. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Jongmin Woo
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Geremy C Clair
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Sarah M Williams
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Song Feng
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Chia-Feng Tsai
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Ronald J Moore
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - William B Chrisler
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Richard D Smith
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Ryan T Kelly
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, USA; Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT 84602, USA
| | - Ljiljana Paša-Tolić
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Charles Ansong
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Ying Zhu
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, USA.
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11
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Kelly V, Al-Rawi A, Lewis D, Kustatscher G, Ly T. Low Cell Number Proteomic Analysis Using In-Cell Protease Digests Reveals a Robust Signature for Cell Cycle State Classification. Mol Cell Proteomics 2022; 21:100169. [PMID: 34742921 PMCID: PMC8760417 DOI: 10.1016/j.mcpro.2021.100169] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 09/02/2021] [Accepted: 10/25/2021] [Indexed: 12/04/2022] Open
Abstract
Comprehensive proteome analysis of rare cell phenotypes remains a significant challenge. We report a method for low cell number MS-based proteomics using protease digestion of mildly formaldehyde-fixed cells in cellulo, which we call the "in-cell digest." We combined this with averaged MS1 precursor library matching to quantitatively characterize proteomes from low cell numbers of human lymphoblasts. About 4500 proteins were detected from 2000 cells, and 2500 proteins were quantitated from 200 lymphoblasts. The ease of sample processing and high sensitivity makes this method exceptionally suited for the proteomic analysis of rare cell states, including immune cell subsets and cell cycle subphases. To demonstrate the method, we characterized the proteome changes across 16 cell cycle states (CCSs) isolated from an asynchronous TK6 cells, avoiding synchronization. States included late mitotic cells present at extremely low frequency. We identified 119 pseudoperiodic proteins that vary across the cell cycle. Clustering of the pseudoperiodic proteins showed abundance patterns consistent with "waves" of protein degradation in late S, at the G2&M border, midmitosis, and at mitotic exit. These clusters were distinguished by significant differences in predicted nuclear localization and interaction with the anaphase-promoting complex/cyclosome. The dataset also identifies putative anaphase-promoting complex/cyclosome substrates in mitosis and the temporal order in which they are targeted for degradation. We demonstrate that a protein signature made of these 119 high-confidence cell cycle-regulated proteins can be used to perform unbiased classification of proteomes into CCSs. We applied this signature to 296 proteomes that encompass a range of quantitation methods, cell types, and experimental conditions. The analysis confidently assigns a CCS for 49 proteomes, including correct classification for proteomes from synchronized cells. We anticipate that this robust cell cycle protein signature will be crucial for classifying cell states in single-cell proteomes.
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Affiliation(s)
- Van Kelly
- Wellcome Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, UK; Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, UK
| | - Aymen Al-Rawi
- Wellcome Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, UK
| | - David Lewis
- Wellcome Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, UK
| | - Georg Kustatscher
- Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, UK
| | - Tony Ly
- Wellcome Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, UK; Centre for Gene Regulation and Expression, School of Life Sciences, University of Dundee, Dundee, UK.
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12
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Almeida N, Rodriguez J, Pla Parada I, Perez-Riverol Y, Woldmar N, Kim Y, Oskolas H, Betancourt L, Valdés JG, Sahlin KB, Pizzatti L, Szasz AM, Kárpáti S, Appelqvist R, Malm J, B. Domont G, C. S. Nogueira F, Marko-Varga G, Sanchez A. Mapping the Melanoma Plasma Proteome (MPP) Using Single-Shot Proteomics Interfaced with the WiMT Database. Cancers (Basel) 2021; 13:6224. [PMID: 34944842 PMCID: PMC8699267 DOI: 10.3390/cancers13246224] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 11/30/2021] [Accepted: 12/08/2021] [Indexed: 12/26/2022] Open
Abstract
Plasma analysis by mass spectrometry-based proteomics remains a challenge due to its large dynamic range of 10 orders in magnitude. We created a methodology for protein identification known as Wise MS Transfer (WiMT). Melanoma plasma samples from biobank archives were directly analyzed using simple sample preparation. WiMT is based on MS1 features between several MS runs together with custom protein databases for ID generation. This entails a multi-level dynamic protein database with different immunodepletion strategies by applying single-shot proteomics. The highest number of melanoma plasma proteins from undepleted and unfractionated plasma was reported, mapping >1200 proteins from >10,000 protein sequences with confirmed significance scoring. Of these, more than 660 proteins were annotated by WiMT from the resulting ~5800 protein sequences. We could verify 4000 proteins by MS1t analysis from HeLA extracts. The WiMT platform provided an output in which 12 previously well-known candidate markers were identified. We also identified low-abundant proteins with functions related to (i) cell signaling, (ii) immune system regulators, and (iii) proteins regulating folding, sorting, and degradation, as well as (iv) vesicular transport proteins. WiMT holds the potential for use in large-scale screening studies with simple sample preparation, and can lead to the discovery of novel proteins with key melanoma disease functions.
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Affiliation(s)
- Natália Almeida
- Laboratory of Proteomics/LADETEC, Universidade Federal Do Rio de Janeiro, Rio de Janeiro 21941-598, Brazil;
- Proteomics Unit, Institute of Chemistry, Universidade Federal Do Rio de Janeiro, Rio de Janeiro 21941-909, Brazil;
- Clinical Protein Science & Imaging, Biomedical Center, Department of Biomedical Engineering, Lund University, BMC D13, 22184 Lund, Sweden; (N.W.); (K.B.S.); (G.M.-V.)
| | - Jimmy Rodriguez
- Division of Chemistry I, Department of Biochemistry and Biophysics, Karolinska Institute, 17165 Stockholm, Sweden;
| | - Indira Pla Parada
- Section for Clinical Chemistry, Department of Translational Medicine, Lund University, Skåne University Hospital Malmö, 20502 Malmö, Sweden; (I.P.P.); (J.M.)
| | - Yasset Perez-Riverol
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK;
| | - Nicole Woldmar
- Clinical Protein Science & Imaging, Biomedical Center, Department of Biomedical Engineering, Lund University, BMC D13, 22184 Lund, Sweden; (N.W.); (K.B.S.); (G.M.-V.)
- Laboratory of Molecular Biology and Blood Proteomics—LADETEC, Institute of Chemistry, Federal University of Rio de Janeiro, Rio de Janeiro 21941-598, Brazil;
| | - Yonghyo Kim
- Data Convergence Drug Research Center, Therapeutics and Biotechnology Division, Korea Research Institute of Chemical Technology (KRICT), Daejeon 34114, Korea;
- Division of Oncology, Department of Clinical Sciences Lund, Lund University, 22185 Lund, Sweden; (H.O.); (L.B.); (J.G.V.); (R.A.)
| | - Henriett Oskolas
- Division of Oncology, Department of Clinical Sciences Lund, Lund University, 22185 Lund, Sweden; (H.O.); (L.B.); (J.G.V.); (R.A.)
| | - Lazaro Betancourt
- Division of Oncology, Department of Clinical Sciences Lund, Lund University, 22185 Lund, Sweden; (H.O.); (L.B.); (J.G.V.); (R.A.)
| | - Jeovanis Gil Valdés
- Division of Oncology, Department of Clinical Sciences Lund, Lund University, 22185 Lund, Sweden; (H.O.); (L.B.); (J.G.V.); (R.A.)
| | - K. Barbara Sahlin
- Clinical Protein Science & Imaging, Biomedical Center, Department of Biomedical Engineering, Lund University, BMC D13, 22184 Lund, Sweden; (N.W.); (K.B.S.); (G.M.-V.)
- Section for Clinical Chemistry, Department of Translational Medicine, Lund University, Skåne University Hospital Malmö, 20502 Malmö, Sweden; (I.P.P.); (J.M.)
| | - Luciana Pizzatti
- Laboratory of Molecular Biology and Blood Proteomics—LADETEC, Institute of Chemistry, Federal University of Rio de Janeiro, Rio de Janeiro 21941-598, Brazil;
| | | | - Sarolta Kárpáti
- Department of Dermatology, Venereology and Dermatooncology, Semmelweis University, 1085 Budapest, Hungary;
| | - Roger Appelqvist
- Division of Oncology, Department of Clinical Sciences Lund, Lund University, 22185 Lund, Sweden; (H.O.); (L.B.); (J.G.V.); (R.A.)
| | - Johan Malm
- Section for Clinical Chemistry, Department of Translational Medicine, Lund University, Skåne University Hospital Malmö, 20502 Malmö, Sweden; (I.P.P.); (J.M.)
| | - Gilberto B. Domont
- Proteomics Unit, Institute of Chemistry, Universidade Federal Do Rio de Janeiro, Rio de Janeiro 21941-909, Brazil;
| | - Fábio C. S. Nogueira
- Laboratory of Proteomics/LADETEC, Universidade Federal Do Rio de Janeiro, Rio de Janeiro 21941-598, Brazil;
- Proteomics Unit, Institute of Chemistry, Universidade Federal Do Rio de Janeiro, Rio de Janeiro 21941-909, Brazil;
| | - György Marko-Varga
- Clinical Protein Science & Imaging, Biomedical Center, Department of Biomedical Engineering, Lund University, BMC D13, 22184 Lund, Sweden; (N.W.); (K.B.S.); (G.M.-V.)
- Chemical Genomics Global Research Lab, Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Korea
- Department of Surgery, Tokyo Medical University, 6-7-1 Nishishinjiku Shinjiku-ku, Tokyo 160-0023, Japan
| | - Aniel Sanchez
- Section for Clinical Chemistry, Department of Translational Medicine, Lund University, Skåne University Hospital Malmö, 20502 Malmö, Sweden; (I.P.P.); (J.M.)
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13
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Ivanov MV, Bubis JA, Gorshkov V, Abdrakhimov DA, Kjeldsen F, Gorshkov MV. Boosting MS1-only Proteomics with Machine Learning Allows 2000 Protein Identifications in Single-Shot Human Proteome Analysis Using 5 min HPLC Gradient. J Proteome Res 2021; 20:1864-1873. [PMID: 33720732 DOI: 10.1021/acs.jproteome.0c00863] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Proteome-wide analyses rely on tandem mass spectrometry and the extensive separation of proteolytic mixtures. This imposes considerable instrumental time consumption, which is one of the main obstacles in the broader acceptance of proteomics in biomedical and clinical research. Recently, we presented a fast proteomic method termed DirectMS1 based on ultrashort LC gradients as well as MS1-only mass spectra acquisition and data processing. The method allows significant reduction of the proteome-wide analysis time to a few minutes at the depth of quantitative proteome coverage of 1000 proteins at 1% false discovery rate (FDR). In this work, to further increase the capabilities of the DirectMS1 method, we explored the opportunities presented by the recent progress in the machine-learning area and applied the LightGBM decision tree boosting algorithm to the scoring of peptide feature matches when processing MS1 spectra. Furthermore, we integrated the peptide feature identification algorithm of DirectMS1 with the recently introduced peptide retention time prediction utility, DeepLC. Additional approaches to improve the performance of the DirectMS1 method are discussed and demonstrated, such as using FAIMS for gas-phase ion separation. As a result of all improvements to DirectMS1, we succeeded in identifying more than 2000 proteins at 1% FDR from the HeLa cell line in a 5 min gradient LC-FAIMS/MS1 analysis. The data sets generated and analyzed during the current study have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the data set identifier PXD023977.
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Affiliation(s)
- Mark V Ivanov
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, 38 Leninsky Pr., Bld. 2, Moscow 119334, Russia
| | - Julia A Bubis
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, 38 Leninsky Pr., Bld. 2, Moscow 119334, Russia
| | - Vladimir Gorshkov
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, DK-5230 Odense M, Denmark
| | - Daniil A Abdrakhimov
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, 38 Leninsky Pr., Bld. 2, Moscow 119334, Russia.,Moscow Institute of Physics and Technology, Institutsky lane 9, Dolgoprudny, Moscow Region 141700, Russia
| | - Frank Kjeldsen
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, DK-5230 Odense M, Denmark
| | - Mikhail V Gorshkov
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, 38 Leninsky Pr., Bld. 2, Moscow 119334, Russia
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14
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Salovska B, Li W, Di Y, Liu Y. BoxCarmax: A High-Selectivity Data-Independent Acquisition Mass Spectrometry Method for the Analysis of Protein Turnover and Complex Samples. Anal Chem 2021; 93:3103-3111. [PMID: 33533601 DOI: 10.1021/acs.analchem.0c04293] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The data-independent acquisition (DIA) performed in the latest high-resolution, high-speed mass spectrometers offers a powerful analytical tool for biological investigations. The DIA mass spectrometry (DIA-MS) combined with the isotopic labeling approach holds a particular promise for increasing the multiplexity of DIA-MS analysis, which could assist the relative protein quantification and the proteome-wide turnover profiling. However, the wide MS1 isolation windows employed in conventional DIA methods lead to a limited efficiency in identifying and quantifying isotope-labeled peptide pairs through peptide fragment ions. Here, we optimized a high-selectivity DIA-MS named BoxCarmax that supports the analysis of complex samples, such as those generated from Stable isotope labeling by amino acids in cell culture (SILAC) and pulse SILAC (pSILAC) experiments. BoxCarmax enables multiplexed acquisition at both MS1 and MS2 levels, through the integration of BoxCar and MSX features, as well as a gas-phase separation strategy. We found BoxCarmax significantly improved the quantitative accuracy in SILAC and pSILAC samples by mitigating the ratio suppression of isotope-peptide pairs. We further applied BoxCarmax to measure protein degradation regulation during serum starvation stress in cultured cells, revealing valuable biological insights. Our study offered an alternative and accurate approach for the MS analysis of protein turnover and complex samples.
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Affiliation(s)
- Barbora Salovska
- Yale Cancer Biology Institute, Yale University, West Haven, Connecticut CT 06520, United States
| | - Wenxue Li
- Yale Cancer Biology Institute, Yale University, West Haven, Connecticut CT 06520, United States
| | - Yi Di
- Yale Cancer Biology Institute, Yale University, West Haven, Connecticut CT 06520, United States
| | - Yansheng Liu
- Yale Cancer Biology Institute, Yale University, West Haven, Connecticut CT 06520, United States.,Department of Pharmacology, Yale University School of Medicine, New Haven, Connecticut CT 06510, United States
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15
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Ogata K, Chang CH, Ishihama Y. Effect of Phosphorylation on the Collision Cross Sections of Peptide Ions in Ion Mobility Spectrometry. Mass Spectrom (Tokyo) 2021; 10:A0093. [PMID: 33552826 PMCID: PMC7843839 DOI: 10.5702/massspectrometry.a0093] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 12/09/2020] [Indexed: 11/23/2022] Open
Abstract
The insertion of ion mobility spectrometry (IMS) between LC and MS can improve peptide identification in both proteomics and phosphoproteomics by providing structural information that is complementary to LC and MS, because IMS separates ions on the basis of differences in their shapes and charge states. However, it is necessary to know how phosphate groups affect the peptide collision cross sections (CCS) in order to accurately predict phosphopeptide CCS values and to maximize the usefulness of IMS. In this work, we systematically characterized the CCS values of 4,433 pairs of mono-phosphopeptide and corresponding unphosphorylated peptide ions using trapped ion mobility spectrometry (TIMS). Nearly one-third of the mono-phosphopeptide ions evaluated here showed smaller CCS values than their unphosphorylated counterparts, even though phosphorylation results in a mass increase of 80 Da. Significant changes of CCS upon phosphorylation occurred mainly in structurally extended peptides with large numbers of basic groups, possibly reflecting intramolecular interactions between phosphate and basic groups.
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Affiliation(s)
- Kosuke Ogata
- Department of Molecular and Cellular BioAnalysis, Graduate School of Pharmaceutical Sciences, Kyoto University, Sakyo-ku, Kyoto 606–8501, Japan
| | - Chih-Hsiang Chang
- Department of Molecular and Cellular BioAnalysis, Graduate School of Pharmaceutical Sciences, Kyoto University, Sakyo-ku, Kyoto 606–8501, Japan
| | - Yasushi Ishihama
- Department of Molecular and Cellular BioAnalysis, Graduate School of Pharmaceutical Sciences, Kyoto University, Sakyo-ku, Kyoto 606–8501, Japan
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16
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Kelly RT. Single-cell Proteomics: Progress and Prospects. Mol Cell Proteomics 2020; 19:1739-1748. [PMID: 32847821 PMCID: PMC7664119 DOI: 10.1074/mcp.r120.002234] [Citation(s) in RCA: 173] [Impact Index Per Article: 43.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 08/20/2020] [Indexed: 01/19/2023] Open
Abstract
MS-based proteome profiling has become increasingly comprehensive and quantitative, yet a persistent shortcoming has been the relatively large samples required to achieve an in-depth measurement. Such bulk samples, typically comprising thousands of cells or more, provide a population average and obscure important cellular heterogeneity. Single-cell proteomics capabilities have the potential to transform biomedical research and enable understanding of biological systems with a new level of granularity. Recent advances in sample processing, separations and MS instrumentation now make it possible to quantify >1000 proteins from individual mammalian cells, a level of coverage that required an input of thousands of cells just a few years ago. This review discusses important factors and parameters that should be optimized across the workflow for single-cell and other low-input measurements. It also highlights recent developments that have advanced the field and opportunities for further development.
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Affiliation(s)
- Ryan T Kelly
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah, USA.
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17
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Wang S, Li W, Hu L, Cheng J, Yang H, Liu Y. NAguideR: performing and prioritizing missing value imputations for consistent bottom-up proteomic analyses. Nucleic Acids Res 2020; 48:e83. [PMID: 32526036 PMCID: PMC7641313 DOI: 10.1093/nar/gkaa498] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 04/20/2020] [Accepted: 06/08/2020] [Indexed: 02/05/2023] Open
Abstract
Mass spectrometry (MS)-based quantitative proteomics experiments frequently generate data with missing values, which may profoundly affect downstream analyses. A wide variety of imputation methods have been established to deal with the missing-value issue. To date, however, there is a scarcity of efficient, systematic, and easy-to-handle tools that are tailored for proteomics community. Herein, we developed a user-friendly and powerful stand-alone software, NAguideR, to enable implementation and evaluation of different missing value methods offered by 23 widely used missing-value imputation algorithms. NAguideR further evaluates data imputation results through classic computational criteria and, unprecedentedly, proteomic empirical criteria, such as quantitative consistency between different charge-states of the same peptide, different peptides belonging to the same proteins, and individual proteins participating protein complexes and functional interactions. We applied NAguideR into three label-free proteomic datasets featuring peptide-level, protein-level, and phosphoproteomic variables respectively, all generated by data independent acquisition mass spectrometry (DIA-MS) with substantial biological replicates. The results indicate that NAguideR is able to discriminate the optimal imputation methods that are facilitating DIA-MS experiments over those sub-optimal and low-performance algorithms. NAguideR further provides downloadable tables and figures supporting flexible data analysis and interpretation. NAguideR is freely available at http://www.omicsolution.org/wukong/NAguideR/ and the source code: https://github.com/wangshisheng/NAguideR/.
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Affiliation(s)
- Shisheng Wang
- West China-Washington Mitochondria and Metabolism Research Center; Key Lab of Transplant Engineering and Immunology, MOH, Regenerative Medicine Research Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Wenxue Li
- Yale Cancer Biology Institute, Yale University, West Haven, CT 06516, USA
| | - Liqiang Hu
- West China-Washington Mitochondria and Metabolism Research Center; Key Lab of Transplant Engineering and Immunology, MOH, Regenerative Medicine Research Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Jingqiu Cheng
- West China-Washington Mitochondria and Metabolism Research Center; Key Lab of Transplant Engineering and Immunology, MOH, Regenerative Medicine Research Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Hao Yang
- West China-Washington Mitochondria and Metabolism Research Center; Key Lab of Transplant Engineering and Immunology, MOH, Regenerative Medicine Research Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yansheng Liu
- Yale Cancer Biology Institute, Yale University, West Haven, CT 06516, USA.,Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06520, USA
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18
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Esser-Skala W, Segl M, Wohlschlager T, Reisinger V, Holzmann J, Huber CG. Exploring sample preparation and data evaluation strategies for enhanced identification of host cell proteins in drug products of therapeutic antibodies and Fc-fusion proteins. Anal Bioanal Chem 2020; 412:6583-6593. [PMID: 32691086 PMCID: PMC7442769 DOI: 10.1007/s00216-020-02796-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 06/22/2020] [Accepted: 06/30/2020] [Indexed: 01/17/2023]
Abstract
Manufacturing of biopharmaceuticals involves recombinant protein expression in host cells followed by extensive purification of the target protein. Yet, host cell proteins (HCPs) may persist in the final drug product, potentially reducing its quality with respect to safety and efficacy. Consequently, residual HCPs are closely monitored during downstream processing by techniques such as enzyme-linked immunosorbent assay (ELISA) or high-performance liquid chromatography combined with tandem mass spectrometry (HPLC-MS/MS). The latter is especially attractive as it provides information with respect to protein identities. Although the applied HPLC-MS/MS methodologies are frequently optimized with respect to HCP identification, acquired data is typically analyzed using standard settings. Here, we describe an improved strategy for evaluating HPLC-MS/MS data of HCP-derived peptides, involving probabilistic protein inference and peptide detection in the absence of fragment ion spectra. This data analysis workflow was applied to data obtained for drug products of various biotherapeutics upon protein A affinity depletion. The presented data evaluation strategy enabled in-depth comparative analysis of the HCP repertoires identified in drug products of the monoclonal antibodies rituximab and bevacizumab, as well as the fusion protein etanercept. In contrast to commonly applied ELISA strategies, the here presented workflow is process-independent and may be implemented into existing HPLC-MS/MS setups for drug product characterization and process development. Graphical abstract ![]()
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Affiliation(s)
- Wolfgang Esser-Skala
- Bioanalytical Research Labs, Department of Biosciences, University of Salzburg, Hellbrunner Straße 34, 5020, Salzburg, Austria.,Christian Doppler Laboratory for Innovative Tools for Biosimilar Characterization, University of Salzburg, Hellbrunner Straße 34, 5020, Salzburg, Austria
| | - Marius Segl
- Bioanalytical Research Labs, Department of Biosciences, University of Salzburg, Hellbrunner Straße 34, 5020, Salzburg, Austria.,Christian Doppler Laboratory for Innovative Tools for Biosimilar Characterization, University of Salzburg, Hellbrunner Straße 34, 5020, Salzburg, Austria
| | - Therese Wohlschlager
- Bioanalytical Research Labs, Department of Biosciences, University of Salzburg, Hellbrunner Straße 34, 5020, Salzburg, Austria.,Christian Doppler Laboratory for Innovative Tools for Biosimilar Characterization, University of Salzburg, Hellbrunner Straße 34, 5020, Salzburg, Austria
| | - Veronika Reisinger
- Christian Doppler Laboratory for Innovative Tools for Biosimilar Characterization, University of Salzburg, Hellbrunner Straße 34, 5020, Salzburg, Austria.,Technical Development Biosimilars, Global Drug Development, Novartis, Sandoz GmbH, Biochemiestraße 10, 6250, Kundl, Austria
| | - Johann Holzmann
- Christian Doppler Laboratory for Innovative Tools for Biosimilar Characterization, University of Salzburg, Hellbrunner Straße 34, 5020, Salzburg, Austria.,Technical Development Biosimilars, Global Drug Development, Novartis, Sandoz GmbH, Biochemiestraße 10, 6250, Kundl, Austria
| | - Christian G Huber
- Bioanalytical Research Labs, Department of Biosciences, University of Salzburg, Hellbrunner Straße 34, 5020, Salzburg, Austria. .,Christian Doppler Laboratory for Innovative Tools for Biosimilar Characterization, University of Salzburg, Hellbrunner Straße 34, 5020, Salzburg, Austria.
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19
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Prianichnikov N, Koch H, Koch S, Lubeck M, Heilig R, Brehmer S, Fischer R, Cox J. MaxQuant Software for Ion Mobility Enhanced Shotgun Proteomics. Mol Cell Proteomics 2020; 19:1058-1069. [PMID: 32156793 PMCID: PMC7261821 DOI: 10.1074/mcp.tir119.001720] [Citation(s) in RCA: 91] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 01/31/2020] [Indexed: 01/08/2023] Open
Abstract
Ion mobility can add a dimension to LC-MS based shotgun proteomics which has the potential to boost proteome coverage, quantification accuracy and dynamic range. Required for this is suitable software that extracts the information contained in the four-dimensional (4D) data space spanned by m/z, retention time, ion mobility and signal intensity. Here we describe the ion mobility enhanced MaxQuant software, which utilizes the added data dimension. It offers an end to end computational workflow for the identification and quantification of peptides and proteins in LC-IMS-MS/MS shotgun proteomics data. We apply it to trapped ion mobility spectrometry (TIMS) coupled to a quadrupole time-of-flight (QTOF) analyzer. A highly parallelizable 4D feature detection algorithm extracts peaks which are assembled to isotope patterns. Masses are recalibrated with a non-linear m/z, retention time, ion mobility and signal intensity dependent model, based on peptides from the sample. A new matching between runs (MBR) algorithm that utilizes collisional cross section (CCS) values of MS1 features in the matching process significantly gains specificity from the extra dimension. Prerequisite for using CCS values in MBR is a relative alignment of the ion mobility values between the runs. The missing value problem in protein quantification over many samples is greatly reduced by CCS aware MBR.MS1 level label-free quantification is also implemented which proves to be highly precise and accurate on a benchmark dataset with known ground truth. MaxQuant for LC-IMS-MS/MS is part of the basic MaxQuant release and can be downloaded from http://maxquant.org.
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Affiliation(s)
- Nikita Prianichnikov
- Computational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany
| | - Heiner Koch
- Bruker Daltonik GmbH, Farenheitstr. 4, 28359 Bremen, Germany
| | - Scarlet Koch
- Bruker Daltonik GmbH, Farenheitstr. 4, 28359 Bremen, Germany
| | - Markus Lubeck
- Bruker Daltonik GmbH, Farenheitstr. 4, 28359 Bremen, Germany
| | - Raphael Heilig
- Nuffield Department of Medicine, Target Discovery Institute, University of Oxford, Oxford, United Kingdom
| | - Sven Brehmer
- Bruker Daltonik GmbH, Farenheitstr. 4, 28359 Bremen, Germany
| | - Roman Fischer
- Nuffield Department of Medicine, Target Discovery Institute, University of Oxford, Oxford, United Kingdom
| | - Jürgen Cox
- Computational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany; Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway.
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20
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Van Gool A, Corrales F, Čolović M, Krstić D, Oliver-Martos B, Martínez-Cáceres E, Jakasa I, Gajski G, Brun V, Kyriacou K, Burzynska-Pedziwiatr I, Wozniak LA, Nierkens S, Pascual García C, Katrlik J, Bojic-Trbojevic Z, Vacek J, Llorente A, Antohe F, Suica V, Suarez G, t'Kindt R, Martin P, Penque D, Martins IL, Bodoki E, Iacob BC, Aydindogan E, Timur S, Allinson J, Sutton C, Luider T, Wittfooth S, Sammar M. Analytical techniques for multiplex analysis of protein biomarkers. Expert Rev Proteomics 2020; 17:257-273. [PMID: 32427033 DOI: 10.1080/14789450.2020.1763174] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
INTRODUCTION The importance of biomarkers for pharmaceutical drug development and clinical diagnostics is more significant than ever in the current shift toward personalized medicine. Biomarkers have taken a central position either as companion markers to support drug development and patient selection, or as indicators aiming to detect the earliest perturbations indicative of disease, minimizing therapeutic intervention or even enabling disease reversal. Protein biomarkers are of particular interest given their central role in biochemical pathways. Hence, capabilities to analyze multiple protein biomarkers in one assay are highly interesting for biomedical research. AREAS COVERED We here review multiple methods that are suitable for robust, high throughput, standardized, and affordable analysis of protein biomarkers in a multiplex format. We describe innovative developments in immunoassays, the vanguard of methods in clinical laboratories, and mass spectrometry, increasingly implemented for protein biomarker analysis. Moreover, emerging techniques are discussed with potentially improved protein capture, separation, and detection that will further boost multiplex analyses. EXPERT COMMENTARY The development of clinically applied multiplex protein biomarker assays is essential as multi-protein signatures provide more comprehensive information about biological systems than single biomarkers, leading to improved insights in mechanisms of disease, diagnostics, and the effect of personalized medicine.
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Affiliation(s)
- Alain Van Gool
- Translational Metabolic Laboratory, Department of Laboratory Medicine, Radboud Institute of Molecular Life Sciences, Radboud University Medical Center , Nijmegen, The Netherlands
| | - Fernado Corrales
- Functional Proteomics Laboratory, Centro Nacional De Biotecnología , Madrid, Spain
| | - Mirjana Čolović
- Department of Physical Chemistry, "Vinča" Institute of Nuclear Sciences, University of Belgrade , Belgrade, Serbia
| | - Danijela Krstić
- Institute of Medical Chemistry, Faculty of Medicine, University of Belgrade , Belgrade, Serbia
| | - Begona Oliver-Martos
- Neuroimmunology and Neuroinflammation Group. Instituto De Investigación Biomédica De Málaga-IBIMA. UGC Neurociencias, Hospital Regional Universitario De Málaga , Malaga, Spain
| | - Eva Martínez-Cáceres
- Immunology Division, LCMN, Germans Trias I Pujol University Hospital and Research Institute, Campus Can Ruti, Badalona, and Department of Cellular Biology, Physiology and Immunology, Universitat Autònoma De Barcelona , Cerdanyola Del Vallès, Spain
| | - Ivone Jakasa
- Laboratory for Analytical Chemistry, Department of Chemistry and Biochemistry, Faculty of Food Technology and Biotechnology, University of Zagreb , Zagreb, Croatia
| | - Goran Gajski
- Mutagenesis Unit, Institute for Medical Research and Occupational Health , Zagreb, Croatia
| | - Virginie Brun
- Université Grenoble Alpes, CEA, Inserm, IRIG, BGE , Grenoble, France
| | - Kyriacos Kyriacou
- Department of Electron Microscopy/Molecular Biology, The Cyprus School of Molecular Medicine/The Cyprus Institute of Neurology and Genetics , Nicosia, Cyprus
| | - Izabela Burzynska-Pedziwiatr
- Medical Faculty, Department of Biomedical Sciences, Chair of Medical Biology & Department of Structural Biology, Medical University of Lodz , Łódź, Poland
| | - Lucyna Alicja Wozniak
- Medical Faculty, Department of Biomedical Sciences, Chair of Medical Biology & Department of Structural Biology, Medical University of Lodz , Łódź, Poland
| | - Stephan Nierkens
- Center for Translational Immunology, University Medical Center Utrecht & Princess Máxima Center for Pediatric Oncology , Utrecht, The Netherlands
| | - César Pascual García
- Materials Research and Technology Department, Luxembourg Institute of Science and Technology (LIST) , Belvaux, Luxembourg
| | - Jaroslav Katrlik
- Department of Glycobiotechnology, Institute of Chemistry, Slovak Academy of Sciences , Bratislava, Slovakia
| | - Zanka Bojic-Trbojevic
- Laboratory for Biology of Reproduction, Institute for the Application of Nuclear Energy - INEP, University of Belgrade , Belgrade, Serbia
| | - Jan Vacek
- Department of Medical Chemistry and Biochemistry, Faculty of Medicine and Dentistry, Palacky University , Olomouc, Czech Republic
| | - Alicia Llorente
- Department of Molecular Cell Biology, Institute for Cancer Research, Oslo University Hospital , Oslo, Norway
| | - Felicia Antohe
- Proteomics Department, Institute of Cellular Biology and Pathology "N. Simionescu" of the Romanian Academy , Bucharest, Romania
| | - Viorel Suica
- Proteomics Department, Institute of Cellular Biology and Pathology "N. Simionescu" of the Romanian Academy , Bucharest, Romania
| | - Guillaume Suarez
- Center for Primary Care and Public Health (Unisanté), University of Lausanne , Lausanne, Switzerland
| | - Ruben t'Kindt
- Research Institute for Chromatography (RIC) , Kortrijk, Belgium
| | - Petra Martin
- Department of Medical Oncology, Midland Regional Hospital Tullamore/St. James's Hospital , Dublin, Ireland
| | - Deborah Penque
- Human Genetics Department, Instituto Nacional De Saúde Dr Ricardo Jorge, Lisboa, Portugal and Centre for Toxicogenomics and Human Health, Universidade Nova De Lisboa , Lisbon,Portugal
| | - Ines Lanca Martins
- Human Genetics Department, Instituto Nacional De Saúde Dr Ricardo Jorge, Lisboa, Portugal and Centre for Toxicogenomics and Human Health, Universidade Nova De Lisboa , Lisbon,Portugal
| | - Ede Bodoki
- Analytical Chemistry Department, Faculty of Pharmacy, "Iuliu Hatieganu" University of Medicine and Pharmacy , Cluj-Napoca, Romania
| | - Bogdan-Cezar Iacob
- Analytical Chemistry Department, Faculty of Pharmacy, "Iuliu Hatieganu" University of Medicine and Pharmacy , Cluj-Napoca, Romania
| | - Eda Aydindogan
- Department of Chemistry, Graduate School of Sciences and Engineering, Koç University , Istanbul, Turkey
| | - Suna Timur
- Institute of Natural Sciences, Department of Biochemistry, Ege University , Izmir, Turkey
| | | | | | - Theo Luider
- Department of Neurology, Erasmus MC , Rotterdam, The Netherlands
| | | | - Marei Sammar
- Ephraim Katzir Department of Biotechnology Engineering, ORT Braude College , Karmiel, Israel
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21
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Miller BF, Reid JJ, Price JC, Lin HJL, Atherton PJ, Smith K. CORP: The use of deuterated water for the measurement of protein synthesis. J Appl Physiol (1985) 2020; 128:1163-1176. [PMID: 32213116 DOI: 10.1152/japplphysiol.00855.2019] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023] Open
Abstract
The use of deuterium oxide (D2O) has greatly expanded the scope of what is possible for the measurement of protein synthesis. The greatest asset of D2O labeling is that it facilitates the measurement of synthesis rates over prolonged periods of time from single proteins through integrated tissue-based measurements. Because the ease of administration, the method is amenable for use in a variety of models and conditions. Although the method adheres to the same rules as other isotope methods, the flexibility can create conditions that are not the same as other approaches and thus requires careful execution to maintain validity and reliability. For this CORP article, we provide a history that gave rise to the method and discuss the advantages and disadvantages of the method, the critical assumptions, guidelines, and best practices based on instrumentation, models, and experimental design. The goal of this CORP article is to propagate additional use of D2O in a manner that produces reliable and valid data.
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Affiliation(s)
- Benjamin F Miller
- Aging and Metabolism Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma
| | - Justin J Reid
- Aging and Metabolism Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma
| | - John C Price
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah
| | - Hsien-Jung L Lin
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah
| | - Philip J Atherton
- MRC-ARUK Center for Musculoskeletal Ageing Research, School of Medicine, University of Nottingham, Derby, United Kingdom
| | - Kenneth Smith
- MRC-ARUK Center for Musculoskeletal Ageing Research, School of Medicine, University of Nottingham, Derby, United Kingdom
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22
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Searle BC, Swearingen KE, Barnes CA, Schmidt T, Gessulat S, Küster B, Wilhelm M. Generating high quality libraries for DIA MS with empirically corrected peptide predictions. Nat Commun 2020; 11:1548. [PMID: 32214105 PMCID: PMC7096433 DOI: 10.1038/s41467-020-15346-1] [Citation(s) in RCA: 112] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Accepted: 02/28/2020] [Indexed: 11/09/2022] Open
Abstract
Data-independent acquisition approaches typically rely on experiment-specific spectrum libraries, requiring offline fractionation and tens to hundreds of injections. We demonstrate a library generation workflow that leverages fragmentation and retention time prediction to build libraries containing every peptide in a proteome, and then refines those libraries with empirical data. Our method specifically enables rapid, experiment-specific library generation for non-model organisms, which we demonstrate using the malaria parasite Plasmodium falciparum, and non-canonical databases, which we show by detecting missense variants in HeLa.
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Affiliation(s)
- Brian C Searle
- Institute for Systems Biology, Seattle, WA, USA. .,Proteome Software, Inc., Portland, OR, USA.
| | | | | | | | - Siegfried Gessulat
- Technical University of Munich, Freising, Germany.,SAP SE, Potsdam, Germany
| | - Bernhard Küster
- Technical University of Munich, Freising, Germany.,Bavarian Center for Biomolecular Mass Spectrometry, Freising, Germany
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23
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Baird MA, Shliaha PV, Anderson GA, Moskovets E, Laiko V, Makarov AA, Jensen ON, Shvartsburg AA. High-Resolution Differential Ion Mobility Separations/Orbitrap Mass Spectrometry without Buffer Gas Limitations. Anal Chem 2019; 91:6918-6925. [DOI: 10.1021/acs.analchem.9b01309] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Matthew A. Baird
- Department of Chemistry, Wichita State University, 1845 Fairmount, Wichita, Kansas 67260, United States
| | - Pavel V. Shliaha
- Department of Biochemistry and Molecular Biology, VILLUM Center for Bioanalytical Sciences, University of Southern Denmark, DK-5230 Odense M, Denmark
| | - Gordon A. Anderson
- GAACE, 101904 Wiser Parkway Suite 105, Kennewick, Washington 99338, United States
| | - Eugene Moskovets
- MassTech Inc., 6992 Columbia Gateway Drive, Columbia, Maryland 21046, United States
| | - Victor Laiko
- MassTech Inc., 6992 Columbia Gateway Drive, Columbia, Maryland 21046, United States
| | - Alexander A. Makarov
- Thermo Fisher Scientific, Hanna-Kunath Strasse 11, Bremen 28199, Germany
- Department of Chemistry, University of Utrecht, 3508 TC Utrecht, Netherlands
| | - Ole N. Jensen
- Department of Biochemistry and Molecular Biology, VILLUM Center for Bioanalytical Sciences, University of Southern Denmark, DK-5230 Odense M, Denmark
| | - Alexandre A. Shvartsburg
- Department of Chemistry, Wichita State University, 1845 Fairmount, Wichita, Kansas 67260, United States
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Couvillion SP, Zhu Y, Nagy G, Adkins JN, Ansong C, Renslow RS, Piehowski PD, Ibrahim YM, Kelly RT, Metz TO. New mass spectrometry technologies contributing towards comprehensive and high throughput omics analyses of single cells. Analyst 2019; 144:794-807. [PMID: 30507980 PMCID: PMC6349538 DOI: 10.1039/c8an01574k] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Mass-spectrometry based omics technologies - namely proteomics, metabolomics and lipidomics - have enabled the molecular level systems biology investigation of organisms in unprecedented detail. There has been increasing interest for gaining a thorough, functional understanding of the biological consequences associated with cellular heterogeneity in a wide variety of research areas such as developmental biology, precision medicine, cancer research and microbiome science. Recent advances in mass spectrometry (MS) instrumentation and sample handling strategies are quickly making comprehensive omics analyses of single cells feasible, but key breakthroughs are still required to push through remaining bottlenecks. In this review, we discuss the challenges faced by single cell MS-based omics analyses and highlight recent technological advances that collectively can contribute to comprehensive and high throughput omics analyses in single cells. We provide a vision of the potential of integrating pioneering technologies such as Structures for Lossless Ion Manipulations (SLIM) for improved sensitivity and resolution, novel peptide identification tactics and standards free metabolomics approaches for future applications in single cell analysis.
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Affiliation(s)
- Sneha P Couvillion
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA.
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Abstract
INTRODUCTION Nanoproteomics, which is defined as quantitative proteome profiling of small populations of cells (<5000 cells), can reveal critical information related to rare cell populations, hard-to-obtain clinical specimens, and the cellular heterogeneity of pathological tissues. Areas covered: We present a brief review of the recent technological advances in nanoproteomics. These advances include new technologies or approaches covering major areas of proteomics workflow ranging from sample isolation, sample processing, high-resolution separations, to MS instrumentation. Expert commentary: We comment on the current state of nanoproteomics and discuss perspectives on both future technological directions and potential enabling applications.
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Affiliation(s)
- Ying Zhu
- a Environmental and Molecular Sciences Laboratory, Pacific Northwest National Laboratory , Richland , WA , USA
| | - Paul D Piehowski
- b Biological Sciences Division , Pacific Northwest National Laboratory , Richland , WA , USA
| | - Ryan T Kelly
- a Environmental and Molecular Sciences Laboratory, Pacific Northwest National Laboratory , Richland , WA , USA
| | - Wei-Jun Qian
- b Biological Sciences Division , Pacific Northwest National Laboratory , Richland , WA , USA
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26
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OGATA K, KROKHIN OV, ISHIHAMA Y. Retention Order Reversal of Phosphorylated and Unphosphorylated Peptides in Reversed-Phase LC/MS. ANAL SCI 2018; 34:1037-1041. [DOI: 10.2116/analsci.18scp11] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Affiliation(s)
- Kosuke OGATA
- Graduate School of Pharmaceutical Sciences, Kyoto University
| | - Oleg V. KROKHIN
- Manitoba Centre for Proteomics and Systems Biology and Department of Internal Medicine, University of Manitoba
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27
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Bittremieux W, Tabb DL, Impens F, Staes A, Timmerman E, Martens L, Laukens K. Quality control in mass spectrometry-based proteomics. MASS SPECTROMETRY REVIEWS 2018; 37:697-711. [PMID: 28802010 DOI: 10.1002/mas.21544] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2017] [Revised: 07/24/2017] [Accepted: 07/24/2017] [Indexed: 05/21/2023]
Abstract
Mass spectrometry is a highly complex analytical technique and mass spectrometry-based proteomics experiments can be subject to a large variability, which forms an obstacle to obtaining accurate and reproducible results. Therefore, a comprehensive and systematic approach to quality control is an essential requirement to inspire confidence in the generated results. A typical mass spectrometry experiment consists of multiple different phases including the sample preparation, liquid chromatography, mass spectrometry, and bioinformatics stages. We review potential sources of variability that can impact the results of a mass spectrometry experiment occurring in all of these steps, and we discuss how to monitor and remedy the negative influences on the experimental results. Furthermore, we describe how specialized quality control samples of varying sample complexity can be incorporated into the experimental workflow and how they can be used to rigorously assess detailed aspects of the instrument performance.
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Affiliation(s)
- Wout Bittremieux
- Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium
- Biomedical Informatics Research Center Antwerp (Biomina), University of Antwerp/Antwerp University Hospital, Edegem, Belgium
| | - David L Tabb
- Division of Molecular Biology and Human Genetics, Stellenbosch University Faculty of Medicine and Health Sciences, Tygerberg Hospital, Cape Town, South Africa
| | - Francis Impens
- VIB Proteomics Core, Ghent, Belgium
- VIB-UGent Center for Medical Biotechnology, Ghent, Belgium
- Faculty of Medicine and Health Sciences, Department of Biochemistry, Ghent University, Ghent, Belgium
| | - An Staes
- VIB Proteomics Core, Ghent, Belgium
- VIB-UGent Center for Medical Biotechnology, Ghent, Belgium
- Faculty of Medicine and Health Sciences, Department of Biochemistry, Ghent University, Ghent, Belgium
| | - Evy Timmerman
- VIB Proteomics Core, Ghent, Belgium
- VIB-UGent Center for Medical Biotechnology, Ghent, Belgium
- Faculty of Medicine and Health Sciences, Department of Biochemistry, Ghent University, Ghent, Belgium
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, Ghent, Belgium
- Faculty of Medicine and Health Sciences, Department of Biochemistry, Ghent University, Ghent, Belgium
- Bioinformatics Institute Ghent, Ghent University, Zwijnaarde, Belgium
| | - Kris Laukens
- Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium
- Biomedical Informatics Research Center Antwerp (Biomina), University of Antwerp/Antwerp University Hospital, Edegem, Belgium
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28
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Sinitcyn P, Rudolph JD, Cox J. Computational Methods for Understanding Mass Spectrometry–Based Shotgun Proteomics Data. Annu Rev Biomed Data Sci 2018. [DOI: 10.1146/annurev-biodatasci-080917-013516] [Citation(s) in RCA: 88] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Computational proteomics is the data science concerned with the identification and quantification of proteins from high-throughput data and the biological interpretation of their concentration changes, posttranslational modifications, interactions, and subcellular localizations. Today, these data most often originate from mass spectrometry–based shotgun proteomics experiments. In this review, we survey computational methods for the analysis of such proteomics data, focusing on the explanation of the key concepts. Starting with mass spectrometric feature detection, we then cover methods for the identification of peptides. Subsequently, protein inference and the control of false discovery rates are highly important topics covered. We then discuss methods for the quantification of peptides and proteins. A section on downstream data analysis covers exploratory statistics, network analysis, machine learning, and multiomics data integration. Finally, we discuss current developments and provide an outlook on what the near future of computational proteomics might bear.
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Affiliation(s)
- Pavel Sinitcyn
- Computational Systems Biochemistry Research Group, Max Planck Institute of Biochemistry, 82152 Martinsried, Germany
| | - Jan Daniel Rudolph
- Computational Systems Biochemistry Research Group, Max Planck Institute of Biochemistry, 82152 Martinsried, Germany
| | - Jürgen Cox
- Computational Systems Biochemistry Research Group, Max Planck Institute of Biochemistry, 82152 Martinsried, Germany
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29
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BoxCar acquisition method enables single-shot proteomics at a depth of 10,000 proteins in 100 minutes. Nat Methods 2018; 15:440-448. [DOI: 10.1038/s41592-018-0003-5] [Citation(s) in RCA: 229] [Impact Index Per Article: 38.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2017] [Accepted: 02/23/2018] [Indexed: 12/30/2022]
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30
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Manning AJ, Lee J, Wolfgeher DJ, Kron SJ, Greenberg JT. Simple strategies to enhance discovery of acetylation post-translational modifications by quadrupole-orbitrap LC-MS/MS. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2018; 1866:224-229. [DOI: 10.1016/j.bbapap.2017.10.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2017] [Revised: 09/07/2017] [Accepted: 10/13/2017] [Indexed: 12/26/2022]
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31
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Collins BC, Hunter CL, Liu Y, Schilling B, Rosenberger G, Bader SL, Chan DW, Gibson BW, Gingras AC, Held JM, Hirayama-Kurogi M, Hou G, Krisp C, Larsen B, Lin L, Liu S, Molloy MP, Moritz RL, Ohtsuki S, Schlapbach R, Selevsek N, Thomas SN, Tzeng SC, Zhang H, Aebersold R. Multi-laboratory assessment of reproducibility, qualitative and quantitative performance of SWATH-mass spectrometry. Nat Commun 2017; 8:291. [PMID: 28827567 PMCID: PMC5566333 DOI: 10.1038/s41467-017-00249-5] [Citation(s) in RCA: 338] [Impact Index Per Article: 48.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2017] [Accepted: 06/12/2017] [Indexed: 01/15/2023] Open
Abstract
Quantitative proteomics employing mass spectrometry is an indispensable tool in life science research. Targeted proteomics has emerged as a powerful approach for reproducible quantification but is limited in the number of proteins quantified. SWATH-mass spectrometry consists of data-independent acquisition and a targeted data analysis strategy that aims to maintain the favorable quantitative characteristics (accuracy, sensitivity, and selectivity) of targeted proteomics at large scale. While previous SWATH-mass spectrometry studies have shown high intra-lab reproducibility, this has not been evaluated between labs. In this multi-laboratory evaluation study including 11 sites worldwide, we demonstrate that using SWATH-mass spectrometry data acquisition we can consistently detect and reproducibly quantify >4000 proteins from HEK293 cells. Using synthetic peptide dilution series, we show that the sensitivity, dynamic range and reproducibility established with SWATH-mass spectrometry are uniformly achieved. This study demonstrates that the acquisition of reproducible quantitative proteomics data by multiple labs is achievable, and broadly serves to increase confidence in SWATH-mass spectrometry data acquisition as a reproducible method for large-scale protein quantification.SWATH-mass spectrometry consists of a data-independent acquisition and a targeted data analysis strategy that aims to maintain the favorable quantitative characteristics on the scale of thousands of proteins. Here, using data generated by eleven groups worldwide, the authors show that SWATH-MS is capable of generating highly reproducible data across different laboratories.
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Affiliation(s)
- Ben C Collins
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093, Zurich, Switzerland
| | | | - Yansheng Liu
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093, Zurich, Switzerland
| | - Birgit Schilling
- Buck Institute for Research on Aging, 8001 Redwood Boulevard, Novato, CA, 94945, USA
| | - George Rosenberger
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093, Zurich, Switzerland
- PhD. Program in Systems Biology, University of Zurich and ETH Zurich, Zurich, 8057, Switzerland
| | - Samuel L Bader
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, WA, 98109, USA
| | - Daniel W Chan
- Department of Pathology, Clinical Chemistry Division, Johns Hopkins University School of Medicine, Baltimore, MD, 21231, USA
| | - Bradford W Gibson
- Buck Institute for Research on Aging, 8001 Redwood Boulevard, Novato, CA, 94945, USA
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA, 94143, USA
| | - Anne-Claude Gingras
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, M5G 1X5, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, M5S 1A8, Ontario, Canada
| | - Jason M Held
- Departments of Medicine and Anesthesiology, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, MO, 63110, USA
| | - Mio Hirayama-Kurogi
- Department of Pharmaceutical Microbiology, Faculty of Life Sciences, Kumamoto University, 5-1 Oe-honmachi, Chuo-ku, Kumamoto, 862-0973, Japan
| | - Guixue Hou
- Proteomics Division, BGI-Shenzhen, Shenzhen, 518083, China
| | - Christoph Krisp
- Department of Chemistry and Biomolecular Sciences, Australian Proteome Analysis Facility (APAF), Macquarie University, Sydney, 2109, Australia
| | - Brett Larsen
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, M5G 1X5, Ontario, Canada
| | - Liang Lin
- Proteomics Division, BGI-Shenzhen, Shenzhen, 518083, China
| | - Siqi Liu
- Proteomics Division, BGI-Shenzhen, Shenzhen, 518083, China
| | - Mark P Molloy
- Department of Chemistry and Biomolecular Sciences, Australian Proteome Analysis Facility (APAF), Macquarie University, Sydney, 2109, Australia
| | - Robert L Moritz
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, WA, 98109, USA
| | - Sumio Ohtsuki
- Department of Pharmaceutical Microbiology, Faculty of Life Sciences, Kumamoto University, 5-1 Oe-honmachi, Chuo-ku, Kumamoto, 862-0973, Japan
| | - Ralph Schlapbach
- Functional Genomics Center Zurich, ETH Zurich/University of Zurich, Winterthurerstr. 190, 8057, Zurich, Switzerland
| | - Nathalie Selevsek
- Functional Genomics Center Zurich, ETH Zurich/University of Zurich, Winterthurerstr. 190, 8057, Zurich, Switzerland
| | - Stefani N Thomas
- Department of Pathology, Clinical Chemistry Division, Johns Hopkins University School of Medicine, Baltimore, MD, 21231, USA
| | - Shin-Cheng Tzeng
- Departments of Medicine and Anesthesiology, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, MO, 63110, USA
| | - Hui Zhang
- Department of Pathology, Clinical Chemistry Division, Johns Hopkins University School of Medicine, Baltimore, MD, 21231, USA
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093, Zurich, Switzerland.
- Faculty of Science, University of Zurich, Zurich, Switzerland.
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32
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Statistical characterization of therapeutic protein modifications. Sci Rep 2017; 7:7896. [PMID: 28801661 PMCID: PMC5554216 DOI: 10.1038/s41598-017-08333-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2017] [Accepted: 07/07/2017] [Indexed: 12/25/2022] Open
Abstract
Peptide mapping with liquid chromatography–tandem mass spectrometry (LC-MS/MS) is an important analytical method for characterization of post-translational and chemical modifications in therapeutic proteins. Despite its importance, there is currently no consensus on the statistical analysis of the resulting data. In this manuscript, we distinguish three statistical goals for therapeutic protein characterization: (1) estimation of site occupancy of modifications in one condition, (2) detection of differential site occupancy between conditions, and (3) estimation of combined site occupancy across multiple modification sites. We propose an approach, which addresses these goals in terms of summarizing the quantitative information from the mass spectra, statistical modeling, and model-based analysis of LC-MS/MS data. We illustrate the approach using an LC-MS/MS experiment from an antibody-drug conjugate and its monoclonal antibody intermediate. The performance was compared to a ‘naïve’ data analysis approach, by using computer simulation, evaluation of differential site occupancy in positive and negative controls, and comparisons of estimated site occupancy with orthogonal experimental measurements of N-linked glycoforms and total oxidation. The results demonstrated the importance of replicated studies of protein characterization, and of appropriate statistical modeling, for reproducible, accurate and efficient site occupancy estimation and differential analysis.
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33
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Banerjee U, Braga-Neto UM. Bayesian ABC-MCMC Classification of Liquid Chromatography-Mass Spectrometry Data. Cancer Inform 2017; 14:175-182. [PMID: 28096647 PMCID: PMC5224349 DOI: 10.4137/cin.s30798] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2016] [Revised: 08/23/2016] [Accepted: 08/28/2016] [Indexed: 11/15/2022] Open
Abstract
Proteomics promises to revolutionize cancer treatment and prevention by facilitating the discovery of molecular biomarkers. Progress has been impeded, however, by the small-sample, high-dimensional nature of proteomic data. We propose the application of a Bayesian approach to address this issue in classification of proteomic profiles generated by liquid chromatography–mass spectrometry (LC-MS). Our approach relies on a previously proposed model of the LC-MS experiment, as well as on the theory of the optimal Bayesian classifier (OBC). Computation of the OBC requires the combination of a likelihood-free methodology called approximate Bayesian computation (ABC) as well as Markov chain Monte Carlo (MCMC) sampling. Numerical experiments using synthetic LC-MS data based on an actual human proteome indicate that the proposed ABC-MCMC classification rule outperforms classical methods such as support vector machines, linear discriminant analysis, and 3-nearest neighbor classification rules in the case when sample size is small or the number of selected proteins used to classify is large.
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Affiliation(s)
- Upamanyu Banerjee
- Department of Electrical and Computer Engineering, Center for Bioinformatics and Genomics Systems Engineering, Texas A&M University, College Station, TX, USA
| | - Ulisses M Braga-Neto
- Department of Electrical and Computer Engineering, Center for Bioinformatics and Genomics Systems Engineering, Texas A&M University, College Station, TX, USA
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34
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Moulder R, Bhosale SD, Lahesmaa R, Goodlett DR. The progress and potential of proteomic biomarkers for type 1 diabetes in children. Expert Rev Proteomics 2016; 14:31-41. [PMID: 27997253 DOI: 10.1080/14789450.2017.1265449] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
INTRODUCTION Although it is possible to identify the genetic risk for type 1 diabetes (T1D), it is not possible to predict who will develop the disease. New biomarkers are needed that would help understand the mechanisms of disease onset and when to administer targeted therapies and interventions. Areas covered: An overview is presented of international study efforts towards understanding the cause of T1D, including the collection of several extensive temporal sample series that follow the development of T1D in at risk children. The results of the proteomics analysis of these materials are presented, which have included bodily fluids, such as serum or plasma and urine, as well as tissue samples from the pancreas. Expert commentary: Promising recent reports have indicated detection of early proteomic changes in the serum of patients prior to diagnosis, potentially providing new measures for risk assessment. Similarly, there has been evidence that post-translational modification (PTM) may result in the recognition of islet cell proteins as autoantigens; modified proteins could thus be used as targets for immunomodulation to overcome the threat of the autoimmune response.
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Affiliation(s)
- Robert Moulder
- a Turku Centre for Biotechnology , University of Turku , Turku , Finland
| | | | - Riitta Lahesmaa
- a Turku Centre for Biotechnology , University of Turku , Turku , Finland
| | - David Robinson Goodlett
- a Turku Centre for Biotechnology , University of Turku , Turku , Finland.,b School of Pharmacy , University of Maryland , Baltimore , MD , USA
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35
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Proteome Remodeling in Response to Sulfur Limitation in " Candidatus Pelagibacter ubique". mSystems 2016; 1:mSystems00068-16. [PMID: 27822545 PMCID: PMC5069961 DOI: 10.1128/msystems.00068-16] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2016] [Accepted: 06/16/2016] [Indexed: 11/20/2022] Open
Abstract
The alphaproteobacterium "Candidatus Pelagibacter ubique" strain HTCC1062 and most other members of the SAR11 clade lack genes for assimilatory sulfate reduction, making them dependent on organosulfur compounds that occur naturally in seawater. To investigate how these cells adapt to sulfur limitation, batch cultures were grown in defined medium containing either limiting or nonlimiting amounts of dimethylsulfoniopropionate (DMSP) as the sole sulfur source. Protein and mRNA expression were measured before, during, and after the transition from exponential growth to stationary phase. Two distinct responses were observed, one as DMSP became exhausted and another as the cells acclimated to a sulfur-limited environment. The first response was characterized by increased transcription and translation of all "Ca. Pelagibacter ubique" genes downstream from the previously confirmed S-adenosyl methionine (SAM) riboswitches bhmT, mmuM, and metY. The proteins encoded by these genes were up to 33 times more abundant as DMSP became limiting. Their predicted function is to shunt all available sulfur to methionine. The secondary response, observed during sulfur-limited stationary phase, was a 6- to 10-fold increase in the transcription of the heme c shuttle-encoding gene ccmC and two small genes of unknown function (SAR11_1163 and SAR11_1164). This bacterium's strategy for coping with sulfur stress appears to be intracellular redistribution to support methionine biosynthesis rather than increasing organosulfur import. Many of the genes and SAM riboswitches involved in this response are located in a hypervariable genome region (HVR). One of these HVR genes, ordL, is located downstream from a conserved motif that evidence suggests is a novel riboswitch. IMPORTANCE "Ca. Pelagibacter ubique" is a key driver of marine biogeochemistry cycles and a model for understanding how minimal genomes evolved in free-living anucleate organisms. This study explores the unusual sulfur acquisition strategy that has evolved in these cells, which lack assimilatory sulfate reduction and instead rely on reduced sulfur compounds found in oxic marine environments to meet their cellular quotas. Our findings demonstrate that the sulfur acquisition systems are constitutively expressed but the enzymatic steps leading to the essential sulfur-containing amino acid methionine are regulated by a unique array of riboswitches and genes, many of which are encoded in a rapidly evolving genome region. These findings support mounting evidence that streamlined cells have evolved regulatory mechanisms that minimize transcriptional switching and, unexpectedly, localize essential sulfur acquisition genes in a genome region normally associated with adaption to environmental variation.
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36
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Oxford KL, Wendler JP, McDermott JE, White III RA, Powell JD, Jacobs JM, Adkins JN, Waters KM. The landscape of viral proteomics and its potential to impact human health. Expert Rev Proteomics 2016; 13:579-91. [DOI: 10.1080/14789450.2016.1184091] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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37
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Zhang B, Käll L, Zubarev RA. DeMix-Q: Quantification-Centered Data Processing Workflow. Mol Cell Proteomics 2016; 15:1467-78. [PMID: 26729709 DOI: 10.1074/mcp.o115.055475] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2015] [Indexed: 12/31/2022] Open
Abstract
For historical reasons, most proteomics workflows focus on MS/MS identification but consider quantification as the end point of a comparative study. The stochastic data-dependent MS/MS acquisition (DDA) gives low reproducibility of peptide identifications from one run to another, which inevitably results in problems with missing values when quantifying the same peptide across a series of label-free experiments. However, the signal from the molecular ion is almost always present among the MS(1)spectra. Contrary to what is frequently claimed, missing values do not have to be an intrinsic problem of DDA approaches that perform quantification at the MS(1)level. The challenge is to perform sound peptide identity propagation across multiple high-resolution LC-MS/MS experiments, from runs with MS/MS-based identifications to runs where such information is absent. Here, we present a new analytical workflow DeMix-Q (https://github.com/userbz/DeMix-Q), which performs such propagation that recovers missing values reliably by using a novel scoring scheme for quality control. Compared with traditional workflows for DDA as well as previous DIA studies, DeMix-Q achieves deeper proteome coverage, fewer missing values, and lower quantification variance on a benchmark dataset. This quantification-centered workflow also enables flexible and robust proteome characterization based on covariation of peptide abundances.
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Affiliation(s)
- Bo Zhang
- From the ‡ Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Scheeles väg 2, SE-17177 Solna, Sweden
| | - Lukas Käll
- § Science for Life Laboratory, School of Biotechnology, Royal Institute of Technology-KTH, 17165 Solna, Sweden
| | - Roman A Zubarev
- From the ‡ Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Scheeles väg 2, SE-17177 Solna, Sweden.
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38
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Pocsfalvi G, Stanly C, Vilasi A, Fiume I, Capasso G, Turiák L, Buzas EI, Vékey K. Mass spectrometry of extracellular vesicles. MASS SPECTROMETRY REVIEWS 2016; 35:3-21. [PMID: 25705034 DOI: 10.1002/mas.21457] [Citation(s) in RCA: 84] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2014] [Accepted: 12/17/2014] [Indexed: 06/04/2023]
Abstract
The review briefly summaries main features of extracellular vesicles, a joint terminology for exosomes, microvesicles, and apoptotic vesicles. These vesicles are in the center of interest in biology and medical sciences, and form a very active field of research. Mass spectrometry (MS), with its specificity and sensitivity, has the potential to identify and characterize molecular composition of these vesicles; but as yet there are only a limited, but fast-growing, number of publications that use MS workflows in this field. MS is the major tool to assess protein composition of extracellular vesicles: qualitative and quantitative proteomics approaches are both reviewed. Beside proteins, lipid and metabolite composition of vesicles might also be best assessed by MS techniques; however there are few applications as yet in this respect. The role of alternative analytical approaches, like gel-based proteomics and antibody-based immunoassays, are also mentioned. The objective of the review is to give an overview of this fast-growing field to help orient MS-based research on extracellular vesicles.
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Affiliation(s)
- Gabriella Pocsfalvi
- Mass Spectrometry and Proteomics, Institute of Biosciences and BioResources, National Research Council of Italy, Naples, Italy
| | - Christopher Stanly
- Mass Spectrometry and Proteomics, Institute of Biosciences and BioResources, National Research Council of Italy, Naples, Italy
| | - Annalisa Vilasi
- Mass Spectrometry and Proteomics, Institute of Biosciences and BioResources, National Research Council of Italy, Naples, Italy
| | - Immacolata Fiume
- Mass Spectrometry and Proteomics, Institute of Biosciences and BioResources, National Research Council of Italy, Naples, Italy
| | - Giovambattista Capasso
- Division of Nephrology, Department of Cardio-Vascular Sciences, Second University of Naples, Naples, Italy
| | - Lilla Turiák
- Mass Spectrometry Proteomics Group, Institute of Organic Chemistry, Research Centre for Natural Sciences of the Hungarian Academy of Sciences, Budapest, Hungary
| | - Edit I Buzas
- Department of Genetics, Cell- and Immunobiology, Semmelweis University, Budapest, Hungary
| | - Károly Vékey
- Mass Spectrometry Proteomics Group, Institute of Organic Chemistry, Research Centre for Natural Sciences of the Hungarian Academy of Sciences, Budapest, Hungary
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Mitra V, Smilde A, Hoefsloot H, Suits F, Bischoff R, Horvatovich P. Inversion of peak elution order prevents uniform time alignment of complex liquid-chromatography coupled to mass spectrometry datasets. J Chromatogr A 2014; 1373:61-72. [PMID: 25482036 DOI: 10.1016/j.chroma.2014.10.101] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2014] [Revised: 10/24/2014] [Accepted: 10/27/2014] [Indexed: 12/01/2022]
Abstract
Retention time alignment is one of the most challenging steps in processing LC-MS datasets of complex proteomics samples acquired within a differential profiling study. A large number of time alignment methods have been developed for accurate pre-processing of such datasets. These methods generally assume that common compounds elute in the same order but they do not test whether this assumption holds. If this assumption is not valid, alignments based on a monotonic retention time function will lose accuracy for peaks that depart from the expected order of the retention time correspondence function. To address this issue, we propose a quality control method that assesses if a pair of complex LC-MS datasets can be aligned with the same alignment performance based on statistical tests before correcting retention time shifts. The algorithm first confirms the presence of an adequate number of common peaks (>∼100 accurately matched peak pairs), then determines if the probability for a conserved elution order of those common peaks is sufficiently high (>0.01) and finally performs retention time alignment of two LC-MS chromatograms. This procedure was applied to LC-MS and LC-MS/MS datasets from two different inter-laboratory proteomics studies showing that a large number of common peaks in chromatograms acquired by different laboratories change elution order with considerable retention time differences.
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Affiliation(s)
- Vikram Mitra
- Analytical Biochemistry, Department of Pharmacy, University of Groningen, A. Deusinglaan 1, 9713 AV Groningen, The Netherlands; Netherlands Bioinformatics Centre, Geert Grooteplein 28, 6525 GA Nijmegen, The Netherlands; Netherlands Proteomics Centre, Padualaan 8, 3584 CH Utrecht, The Netherlands
| | - Age Smilde
- Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
| | - Huub Hoefsloot
- Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
| | - Frank Suits
- IBM T.J. Watson Research Centre, 1101 Kitchawan Road, Yorktown Heights, 10598 NY, USA
| | - Rainer Bischoff
- Analytical Biochemistry, Department of Pharmacy, University of Groningen, A. Deusinglaan 1, 9713 AV Groningen, The Netherlands; Netherlands Bioinformatics Centre, Geert Grooteplein 28, 6525 GA Nijmegen, The Netherlands; Netherlands Proteomics Centre, Padualaan 8, 3584 CH Utrecht, The Netherlands
| | - Péter Horvatovich
- Analytical Biochemistry, Department of Pharmacy, University of Groningen, A. Deusinglaan 1, 9713 AV Groningen, The Netherlands; Netherlands Bioinformatics Centre, Geert Grooteplein 28, 6525 GA Nijmegen, The Netherlands; Netherlands Proteomics Centre, Padualaan 8, 3584 CH Utrecht, The Netherlands.
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40
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Keating DH, Zhang Y, Ong IM, McIlwain S, Morales EH, Grass JA, Tremaine M, Bothfeld W, Higbee A, Ulbrich A, Balloon AJ, Westphall MS, Aldrich J, Lipton MS, Kim J, Moskvin OV, Bukhman YV, Coon JJ, Kiley PJ, Bates DM, Landick R. Aromatic inhibitors derived from ammonia-pretreated lignocellulose hinder bacterial ethanologenesis by activating regulatory circuits controlling inhibitor efflux and detoxification. Front Microbiol 2014; 5:402. [PMID: 25177315 PMCID: PMC4132294 DOI: 10.3389/fmicb.2014.00402] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2014] [Accepted: 07/17/2014] [Indexed: 11/13/2022] Open
Abstract
Efficient microbial conversion of lignocellulosic hydrolysates to biofuels is a key barrier to the economically viable deployment of lignocellulosic biofuels. A chief contributor to this barrier is the impact on microbial processes and energy metabolism of lignocellulose-derived inhibitors, including phenolic carboxylates, phenolic amides (for ammonia-pretreated biomass), phenolic aldehydes, and furfurals. To understand the bacterial pathways induced by inhibitors present in ammonia-pretreated biomass hydrolysates, which are less well studied than acid-pretreated biomass hydrolysates, we developed and exploited synthetic mimics of ammonia-pretreated corn stover hydrolysate (ACSH). To determine regulatory responses to the inhibitors normally present in ACSH, we measured transcript and protein levels in an Escherichia coli ethanologen using RNA-seq and quantitative proteomics during fermentation to ethanol of synthetic hydrolysates containing or lacking the inhibitors. Our study identified four major regulators mediating these responses, the MarA/SoxS/Rob network, AaeR, FrmR, and YqhC. Induction of these regulons was correlated with a reduced rate of ethanol production, buildup of pyruvate, depletion of ATP and NAD(P)H, and an inhibition of xylose conversion. The aromatic aldehyde inhibitor 5-hydroxymethylfurfural appeared to be reduced to its alcohol form by the ethanologen during fermentation, whereas phenolic acid and amide inhibitors were not metabolized. Together, our findings establish that the major regulatory responses to lignocellulose-derived inhibitors are mediated by transcriptional rather than translational regulators, suggest that energy consumed for inhibitor efflux and detoxification may limit biofuel production, and identify a network of regulators for future synthetic biology efforts.
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Affiliation(s)
- David H Keating
- Great Lakes Bioenergy Research Center, University of Wisconsin-Madison Madison, WI, USA
| | - Yaoping Zhang
- Great Lakes Bioenergy Research Center, University of Wisconsin-Madison Madison, WI, USA
| | - Irene M Ong
- Great Lakes Bioenergy Research Center, University of Wisconsin-Madison Madison, WI, USA
| | - Sean McIlwain
- Great Lakes Bioenergy Research Center, University of Wisconsin-Madison Madison, WI, USA
| | - Eduardo H Morales
- Great Lakes Bioenergy Research Center, University of Wisconsin-Madison Madison, WI, USA ; Department of Biomolecular Chemistry, University of Wisconsin-Madison Madison, WI, USA
| | - Jeffrey A Grass
- Great Lakes Bioenergy Research Center, University of Wisconsin-Madison Madison, WI, USA ; Department of Biochemistry, University of Wisconsin-Madison Madison, WI, USA
| | - Mary Tremaine
- Great Lakes Bioenergy Research Center, University of Wisconsin-Madison Madison, WI, USA
| | - William Bothfeld
- Great Lakes Bioenergy Research Center, University of Wisconsin-Madison Madison, WI, USA
| | - Alan Higbee
- Great Lakes Bioenergy Research Center, University of Wisconsin-Madison Madison, WI, USA
| | - Arne Ulbrich
- Department of Chemistry, University of Wisconsin-Madison Madison, WI, USA
| | - Allison J Balloon
- Department of Chemistry, University of Wisconsin-Madison Madison, WI, USA
| | - Michael S Westphall
- Department of Biomolecular Chemistry, University of Wisconsin-Madison Madison, WI, USA ; Department of Chemistry, University of Wisconsin-Madison Madison, WI, USA
| | - Josh Aldrich
- Pacific Northwest National Laboratory Richland, WA, USA
| | - Mary S Lipton
- Pacific Northwest National Laboratory Richland, WA, USA
| | - Joonhoon Kim
- Great Lakes Bioenergy Research Center, University of Wisconsin-Madison Madison, WI, USA ; Department of Chemical and Biological Engineering, University of Wisconsin-Madison Madison, WI, USA
| | - Oleg V Moskvin
- Great Lakes Bioenergy Research Center, University of Wisconsin-Madison Madison, WI, USA
| | - Yury V Bukhman
- Great Lakes Bioenergy Research Center, University of Wisconsin-Madison Madison, WI, USA
| | - Joshua J Coon
- Great Lakes Bioenergy Research Center, University of Wisconsin-Madison Madison, WI, USA ; Department of Biomolecular Chemistry, University of Wisconsin-Madison Madison, WI, USA ; Department of Chemistry, University of Wisconsin-Madison Madison, WI, USA
| | - Patricia J Kiley
- Great Lakes Bioenergy Research Center, University of Wisconsin-Madison Madison, WI, USA ; Department of Biomolecular Chemistry, University of Wisconsin-Madison Madison, WI, USA
| | - Donna M Bates
- Great Lakes Bioenergy Research Center, University of Wisconsin-Madison Madison, WI, USA
| | - Robert Landick
- Great Lakes Bioenergy Research Center, University of Wisconsin-Madison Madison, WI, USA ; Department of Biochemistry, University of Wisconsin-Madison Madison, WI, USA ; Department of Bacteriology, University of Wisconsin-Madison Madison, WI, USA
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Ghanat Bari M, Ma X, Zhang J. PeakLink: a new peptide peak linking method in LC-MS/MS using wavelet and SVM. Bioinformatics 2014; 30:2464-70. [PMID: 24813213 DOI: 10.1093/bioinformatics/btu299] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION In liquid chromatography-mass spectrometry/tandem mass spectrometry (LC-MS/MS), it is necessary to link tandem MS-identified peptide peaks so that protein expression changes between the two runs can be tracked. However, only a small number of peptides can be identified and linked by tandem MS in two runs, and it becomes necessary to link peptide peaks with tandem identification in one run to their corresponding ones in another run without identification. In the past, peptide peaks are linked based on similarities in retention time (rt), mass or peak shape after rt alignment, which corrects mean rt shifts between runs. However, the accuracy in linking is still limited especially for complex samples collected from different conditions. Consequently, large-scale proteomics studies that require comparison of protein expression profiles of hundreds of patients can not be carried out effectively. METHOD In this article, we consider the problem of linking peptides from a pair of LC-MS/MS runs and propose a new method, PeakLink (PL), which uses information in both the time and frequency domain as inputs to a non-linear support vector machine (SVM) classifier. The PL algorithm first uses a threshold on an rt likelihood ratio score to remove candidate corresponding peaks with excessively large elution time shifts, then PL calculates the correlation between a pair of candidate peaks after reducing noise through wavelet transformation. After converting rt and peak shape correlation to statistical scores, an SVM classifier is trained and applied for differentiating corresponding and non-corresponding peptide peaks. RESULTS PL is tested in multiple challenging cases, in which LC-MS/MS samples are collected from different disease states, different instruments and different laboratories. Testing results show significant improvement in linking accuracy compared with other algorithms. AVAILABILITY AND IMPLEMENTATION M files for the PL alignment method are available at http://compgenomics.utsa.edu/zgroup/PeakLink. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mehrab Ghanat Bari
- Department of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, TX 78246, USA
| | - Xuepo Ma
- Department of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, TX 78246, USA
| | - Jianqiu Zhang
- Department of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, TX 78246, USA
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Wu S, Brown JN, Tolić N, Meng D, Liu X, Zhang H, Zhao R, Moore RJ, Pevzner P, Smith RD, Paša-Tolić L. Quantitative analysis of human salivary gland-derived intact proteome using top-down mass spectrometry. Proteomics 2014; 14:1211-22. [DOI: 10.1002/pmic.201300378] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2013] [Revised: 01/10/2014] [Accepted: 02/25/2014] [Indexed: 01/08/2023]
Affiliation(s)
- Si Wu
- Environmental Molecular Sciences Laboratory; Pacific Northwest National Laboratories; Richland WA USA
| | - Joseph N. Brown
- Biological Sciences Division; Pacific Northwest National Laboratories; Richland WA USA
| | - Nikola Tolić
- Environmental Molecular Sciences Laboratory; Pacific Northwest National Laboratories; Richland WA USA
| | - Da Meng
- Computational Mathematics Division; Pacific Northwest National Laboratories; Richland WA USA
| | - Xiaowen Liu
- Department of BioHealth Informatics; Indiana University-Purdue University Indianapolis; Indianapolis IN USA
| | - Haizhen Zhang
- Environmental Molecular Sciences Laboratory; Pacific Northwest National Laboratories; Richland WA USA
| | - Rui Zhao
- Environmental Molecular Sciences Laboratory; Pacific Northwest National Laboratories; Richland WA USA
| | - Ronald J. Moore
- Biological Sciences Division; Pacific Northwest National Laboratories; Richland WA USA
| | - Pavel Pevzner
- Department of Computer Science and Engineering; University of California, San Diego; La Jolla CA USA
| | - Richard D. Smith
- Biological Sciences Division; Pacific Northwest National Laboratories; Richland WA USA
| | - Ljiljana Paša-Tolić
- Environmental Molecular Sciences Laboratory; Pacific Northwest National Laboratories; Richland WA USA
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Metabolic reprogramming during purine stress in the protozoan pathogen Leishmania donovani. PLoS Pathog 2014; 10:e1003938. [PMID: 24586154 PMCID: PMC3937319 DOI: 10.1371/journal.ppat.1003938] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2013] [Accepted: 01/06/2014] [Indexed: 01/18/2023] Open
Abstract
The ability of Leishmania to survive in their insect or mammalian host is dependent upon an ability to sense and adapt to changes in the microenvironment. However, little is known about the molecular mechanisms underlying the parasite response to environmental changes, such as nutrient availability. To elucidate nutrient stress response pathways in Leishmania donovani, we have used purine starvation as the paradigm. The salvage of purines from the host milieu is obligatory for parasite replication; nevertheless, purine-starved parasites can persist in culture without supplementary purine for over three months, indicating that the response to purine starvation is robust and engenders parasite survival under conditions of extreme scarcity. To understand metabolic reprogramming during purine starvation we have employed global approaches. Whole proteome comparisons between purine-starved and purine-replete parasites over a 6–48 h span have revealed a temporal and coordinated response to purine starvation. Purine transporters and enzymes involved in acquisition at the cell surface are upregulated within a few hours of purine removal from the media, while other key purine salvage components are upregulated later in the time-course and more modestly. After 48 h, the proteome of purine-starved parasites is extensively remodeled and adaptations to purine stress appear tailored to deal with both purine deprivation and general stress. To probe the molecular mechanisms affecting proteome remodeling in response to purine starvation, comparative RNA-seq analyses, qRT-PCR, and luciferase reporter assays were performed on purine-starved versus purine-replete parasites. While the regulation of a minority of proteins tracked with changes at the mRNA level, for many regulated proteins it appears that proteome remodeling during purine stress occurs primarily via translational and/or post-translational mechanisms. Leishmania, the cause of a deadly spectrum of diseases in humans, surmounts a number of environmental challenges, including changes in the availability of salvageable nutrients, to successfully colonize its host. Adaptation to environmental stress is clearly of significance in parasite biology, but the underlying mechanisms are not well understood. To simulate the response to periodic nutrient scarcity in vivo, we have induced purine starvation in vitro. Purines are essential for growth and viability, and serve as the major energy currency of cells. Leishmania cannot synthesize purines and must salvage them from the surroundings. Extracellular purine depletion in culture induces a robust survival response in Leishmania, whereby growth arrests, but parasites persist for months. To profile the events that enable endurance of purine starvation, we used shotgun proteomics. Our data suggest that purine starvation induces extensive proteome remodeling, tailored to enhance purine capture and recycling, reduce energy expenditures, and maintain viability of the metabolically active, non-dividing population. Through global and targeted approaches, we reveal that proteome remodeling is multifaceted, and occurs through an array of responses at the mRNA, translational, and post-translational level. Our data provide one of the most inclusive views of adaptation to microenvironmental stress in Leishmania.
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Jagusztyn-Krynicka EK, Dadlez M, Grabowska A, Roszczenko P. Proteomic technology in the design of new effective antibacterial vaccines. Expert Rev Proteomics 2014; 6:315-30. [DOI: 10.1586/epr.09.47] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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45
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Umar A, Jaremko M, Burgers PC, Luider TM, Foekens JA, Paša-Tolic L. High-throughput proteomics of breast carcinoma cells: a focus on FTICR-MS. Expert Rev Proteomics 2014; 5:445-55. [DOI: 10.1586/14789450.5.3.445] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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46
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Dekker LJ, Burgers PC, Kros JM, Smitt PAES, Luider TM. Peptide profiling of cerebrospinal fluid by mass spectrometry. Expert Rev Proteomics 2014; 3:297-309. [PMID: 16771702 DOI: 10.1586/14789450.3.3.297] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The search for biomarkers is driven by the increasing clinical importance of early diagnosis. Reliable biomarkers can also assist in directing therapy, monitoring disease activity and the efficacy of treatment. In addition, the discovery of novel biomarkers might provide clues to the pathogenesis of a disease. The dynamic range of protein concentrations in body fluids exceeds 10 orders of magnitude. These huge differences in concentrations complicate the detection of proteins with low expression levels. Since all classical biomarkers have low expression levels (e.g., prostate-specific antigen: 2-4 microg/l; and CA125: 20-35 U/ml), new developments with respect to identification and validation techniques of the low-abundance proteins are required. This review will discuss the current status of profiling cerebrospinal fluid using mass spectrometry-based techniques, and new developments in this area.
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Affiliation(s)
- Lennard J Dekker
- Erasmus University Medical Center, Department of Neurology, PO Box 1738, 3000 DR Rotterdam, The Netherlands.
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47
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Vandermarliere E, Mueller M, Martens L. Getting intimate with trypsin, the leading protease in proteomics. MASS SPECTROMETRY REVIEWS 2013; 32:453-65. [PMID: 23775586 DOI: 10.1002/mas.21376] [Citation(s) in RCA: 136] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2012] [Revised: 02/15/2013] [Accepted: 02/15/2013] [Indexed: 05/21/2023]
Abstract
Nowadays, mass spectrometry-based proteomics is carried out primarily in a bottom-up fashion, with peptides obtained after proteolytic digest of a whole proteome lysate as the primary analytes instead of the proteins themselves. This experimental setup crucially relies on a protease to digest an abundant and complex protein mixture into a far more complex peptide mixture. Full knowledge of the working mechanism and specificity of the used proteases is therefore crucial, both for the digestion step itself as well as for the downstream identification and quantification of the (fragmentation) mass spectra acquired for the peptides in the mixture. Targeted protein analysis through selected reaction monitoring, a relative newcomer in the specific field of mass spectrometry-based proteomics, even requires a priori understanding of protease behavior for the proteins of interest. Because of the rapidly increasing popularity of proteomics as an analytical tool in the life sciences, there is now a renewed demand for detailed knowledge on trypsin, the workhorse protease in proteomics. This review addresses this need and provides an overview on the structure and working mechanism of trypsin, followed by a critical analysis of its cleavage behavior, typically simply accepted to occur exclusively yet consistently after Arg and Lys, unless they are followed by a Pro. In this context, shortcomings in our ability to understand and predict the behavior of trypsin will be highlighted, along with the downstream implications. Furthermore, an analysis is carried out on the inherent shortcomings of trypsin with regard to whole proteome analysis, and alternative approaches will be presented that can alleviate these issues. Finally, some reflections on the future of trypsin as the workhorse protease in mass spectrometry-based proteomics will be provided.
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Affiliation(s)
- Elien Vandermarliere
- Department of Medical Protein Research, VIB, B-9000 Ghent, Belgium; Department of Biochemistry, Ghent University, B-9000 Ghent, Belgium
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Taylor RC, Webb Robertson BJM, Markillie LM, Serres MH, Linggi BE, Aldrich JT, Hill EA, Romine MF, Lipton MS, Wiley HS. Changes in translational efficiency is a dominant regulatory mechanism in the environmental response of bacteria. Integr Biol (Camb) 2013; 5:1393-406. [PMID: 24081429 DOI: 10.1039/c3ib40120k] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
To understand how cell physiological state affects mRNA translation, we used Shewanella oneidensis MR-1 grown under steady state conditions at either 20% or 8.5% O2. Using a combination of quantitative proteomics and RNA-Seq, we generated high-confidence data on >1000 mRNA and protein pairs. By using a steady state model, we found that differences in protein-mRNA ratios were primarily due to differences in the translational efficiency of specific genes. When oxygen levels were lowered, 28% of the proteins showed at least a 2-fold change in expression. Transcription levels were sp. significantly altered for 26% of the protein changes; translational efficiency was significantly altered for 46% and a combination of both was responsible for the remaining 28%. Changes in translational efficiency were significantly correlated with the codon usage pattern of the genes and measurable tRNA pools changed in response to altered O2 levels. Our results suggest that changes in the translational efficiency of proteins, in part due to altered tRNA pools, is a major determinant of regulated alterations in protein expression levels in bacteria.
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Affiliation(s)
- Ronald C Taylor
- Computational Biosciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
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49
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Depuydt G, Xie F, Petyuk VA, Shanmugam N, Smolders A, Dhondt I, Brewer HM, Camp DG, Smith RD, Braeckman BP. Reduced insulin/insulin-like growth factor-1 signaling and dietary restriction inhibit translation but preserve muscle mass in Caenorhabditis elegans. Mol Cell Proteomics 2013; 12:3624-39. [PMID: 24002365 PMCID: PMC3861712 DOI: 10.1074/mcp.m113.027383] [Citation(s) in RCA: 65] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Reduced signaling through the C. elegans insulin/insulin-like growth factor-1-like tyrosine kinase receptor daf-2 and dietary restriction via bacterial dilution are two well-characterized lifespan-extending interventions that operate in parallel or through (partially) independent mechanisms. Using accurate mass and time tag LC-MS/MS quantitative proteomics, we detected that the abundance of a large number of ribosomal subunits is decreased in response to dietary restriction, as well as in the daf-2(e1370) insulin/insulin-like growth factor-1-receptor mutant. In addition, general protein synthesis levels in these long-lived worms are repressed. Surprisingly, ribosomal transcript levels were not correlated to actual protein abundance, suggesting that post-transcriptional regulation determines ribosome content. Proteomics also revealed the increased presence of many structural muscle cell components in long-lived worms, which appeared to result from the prioritized preservation of muscle cell volume in nutrient-poor conditions or low insulin-like signaling. Activation of DAF-16, but not diet restriction, stimulates mRNA expression of muscle-related genes to prevent muscle atrophy. Important daf-2-specific proteome changes include overexpression of aerobic metabolism enzymes and general activation of stress-responsive and immune defense systems, whereas the increased abundance of many protein subunits of the proteasome core complex is a dietary-restriction-specific characteristic.
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Affiliation(s)
- Geert Depuydt
- Biology Department, Ghent University, Proeftuinstraat 86 N1, B-9000 Ghent, Belgium
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50
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D'haeseleer P, Gladden JM, Allgaier M, Chain PSG, Tringe SG, Malfatti SA, Aldrich JT, Nicora CD, Robinson EW, Paša-Tolić L, Hugenholtz P, Simmons BA, Singer SW. Proteogenomic analysis of a thermophilic bacterial consortium adapted to deconstruct switchgrass. PLoS One 2013; 8:e68465. [PMID: 23894306 PMCID: PMC3716776 DOI: 10.1371/journal.pone.0068465] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2013] [Accepted: 05/29/2013] [Indexed: 12/02/2022] Open
Abstract
Thermophilic bacteria are a potential source of enzymes for the deconstruction of lignocellulosic biomass. However, the complement of proteins used to deconstruct biomass and the specific roles of different microbial groups in thermophilic biomass deconstruction are not well-explored. Here we report on the metagenomic and proteogenomic analyses of a compost-derived bacterial consortium adapted to switchgrass at elevated temperature with high levels of glycoside hydrolase activities. Near-complete genomes were reconstructed for the most abundant populations, which included composite genomes for populations closely related to sequenced strains of Thermus thermophilus and Rhodothermus marinus, and for novel populations that are related to thermophilic Paenibacilli and an uncultivated subdivision of the little-studied Gemmatimonadetes phylum. Partial genomes were also reconstructed for a number of lower abundance thermophilic Chloroflexi populations. Identification of genes for lignocellulose processing and metabolic reconstructions suggested Rhodothermus, Paenibacillus and Gemmatimonadetes as key groups for deconstructing biomass, and Thermus as a group that may primarily metabolize low molecular weight compounds. Mass spectrometry-based proteomic analysis of the consortium was used to identify >3000 proteins in fractionated samples from the cultures, and confirmed the importance of Paenibacillus and Gemmatimonadetes to biomass deconstruction. These studies also indicate that there are unexplored proteins with important roles in bacterial lignocellulose deconstruction.
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Affiliation(s)
- Patrik D'haeseleer
- Joint BioEnergy Institute, Emeryville, California, United States of America.
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