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Korchak JA, Jeffery ED, Bandyopadhyay S, Jordan BT, Lehe MD, Watts EF, Fenix A, Wilhelm M, Sheynkman GM. IS-PRM-Based Peptide Targeting Informed by Long-Read Sequencing for Alternative Proteome Detection. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2024. [PMID: 39012054 DOI: 10.1021/jasms.4c00119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/17/2024]
Abstract
Alternative splicing is a major contributor of transcriptomic complexity, but the extent to which transcript isoforms are translated into stable, functional protein isoforms is unclear. Furthermore, detection of relatively scarce isoform-specific peptides is challenging, with many protein isoforms remaining uncharted due to technical limitations. Recently, a family of advanced targeted MS strategies, termed internal standard parallel reaction monitoring (IS-PRM), have demonstrated multiplexed, sensitive detection of predefined peptides of interest. Such approaches have not yet been used to confirm existence of novel peptides. Here, we present a targeted proteogenomic approach that leverages sample-matched long-read RNA sequencing (lrRNA-seq) data to predict potential protein isoforms with prior transcript evidence. Predicted tryptic isoform-specific peptides, which are specific to individual gene product isoforms, serve as "triggers" and "targets" in the IS-PRM method, Tomahto. Using the model human stem cell line WTC11, LR RNaseq data were generated and used to inform the generation of synthetic standards for 192 isoform-specific peptides (114 isoforms from 55 genes). These synthetic "trigger" peptides were labeled with super heavy tandem mass tags (TMT) and spiked into TMT-labeled WTC11 tryptic digest, predicted to contain corresponding endogenous "target" peptides. Compared to DDA mode, Tomahto increased detectability of isoforms by 3.6-fold, resulting in the identification of five previously unannotated isoforms. Our method detected protein isoform expression for 43 out of 55 genes corresponding to 54 resolved isoforms. This lrRNA-seq-informed Tomahto targeted approach is a new modality for generating protein-level evidence of alternative isoforms─a critical first step in designing functional studies and eventually clinical assays.
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Affiliation(s)
- Jennifer A Korchak
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia 22903, United States
| | - Erin D Jeffery
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia 22903, United States
| | - Saikat Bandyopadhyay
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia 22903, United States
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia 22903, United States
| | - Ben T Jordan
- Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
| | - Micah D Lehe
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia 22903, United States
| | - Emily F Watts
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia 22903, United States
| | - Aidan Fenix
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington 98195, United States
| | - Mathias Wilhelm
- Computational Mass Spectrometry, Technical University of Munich (TUM), D-85354 Freising, Germany
| | - Gloria M Sheynkman
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia 22903, United States
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia 22903, United States
- UVA Comprehensive Cancer Center, University of Virginia, Charlottesville, Virginia 22903, United States
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2
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Peters-Clarke TM, Coon JJ, Riley NM. Instrumentation at the Leading Edge of Proteomics. Anal Chem 2024; 96:7976-8010. [PMID: 38738990 DOI: 10.1021/acs.analchem.3c04497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2024]
Affiliation(s)
- Trenton M Peters-Clarke
- Department of Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Department of Biomolecular Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
| | - Joshua J Coon
- Department of Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Department of Biomolecular Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Morgridge Institute for Research, Madison, Wisconsin 53715, United States
| | - Nicholas M Riley
- Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
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3
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Peters-Clarke TM, Liang Y, Mertz KL, Lee KW, Westphall MS, Hinkle JD, McAlister GC, Syka JEP, Kelly RT, Coon JJ. Boosting the Sensitivity of Quantitative Single-Cell Proteomics with Infrared-Tandem Mass Tags. J Proteome Res 2024. [PMID: 38713017 DOI: 10.1021/acs.jproteome.4c00076] [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: 05/08/2024]
Abstract
Single-cell proteomics is a powerful approach to precisely profile protein landscapes within individual cells toward a comprehensive understanding of proteomic functions and tissue and cellular states. The inherent challenges associated with limited starting material demand heightened analytical sensitivity. Just as advances in sample preparation maximize the amount of material that makes it from the cell to the mass spectrometer, we strive to maximize the number of ions that make it from ion source to the detector. In isobaric tagging experiments, limited reporter ion generation limits quantitative accuracy and precision. The combination of infrared photoactivation and ion parking circumvents the m/z dependence inherent in HCD, maximizing reporter generation and avoiding unintended degradation of TMT reporter molecules in infrared-tandem mass tags (IR-TMT). The method was applied to single-cell human proteomes using 18-plex TMTpro, resulting in 4-5-fold increases in reporter signal compared to conventional SPS-MS3 approaches. IR-TMT enables faster duty cycles, higher throughput, and increased peptide identification and quantification. Comparative experiments showcase 4-5-fold lower injection times for IR-TMT, providing superior sensitivity without compromising accuracy. In all, IR-TMT enhances the dynamic range of proteomic experiments and is compatible with gas-phase fractionation and real-time searching, promising increased gains in the study of cellular heterogeneity.
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Affiliation(s)
- Trenton M Peters-Clarke
- Department of Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Department of Biomolecular Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
| | - Yiran Liang
- Department of Chemistry, Brigham Young University, Provo, Utah 84602, United States
| | - Keaton L Mertz
- Department of Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Department of Biomolecular Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
| | - Kenneth W Lee
- Department of Biomolecular Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
| | - Michael S Westphall
- Department of Biomolecular Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
| | - Joshua D Hinkle
- Thermo Fisher Scientific, San Jose, California 95134, United States
| | | | - John E P Syka
- Thermo Fisher Scientific, San Jose, California 95134, United States
| | - Ryan T Kelly
- Department of Chemistry, Brigham Young University, Provo, Utah 84602, United States
| | - Joshua J Coon
- Department of Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Department of Biomolecular Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- National Center for Quantitative Biology of Complex Systems, Madison, Wisconsin 53706, United States
- Morgridge Institute for Research, Madison, Wisconsin 53515, United States
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4
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Shuken SR, Yu Q, Gygi SP. Inserting Pre-analytical Chromatographic Priming Runs Significantly Improves Targeted Pathway Proteomics with Sample Multiplexing. J Proteome Res 2024; 23:1834-1843. [PMID: 38594897 PMCID: PMC11068481 DOI: 10.1021/acs.jproteome.4c00096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
Abstract
GoDig, a platform for targeted pathway proteomics without the need for manual assay scheduling or synthetic standards, is a powerful, flexible, and easy-to-use method that uses tandem mass tags to increase sample throughput up to 18-fold relative to label-free methods. Though the protein-level success rates of GoDig are high, the peptide-level success rates are more limited, hampering assays of harder-to-quantify proteins and site-specific phenomena. To guide the optimization of GoDig assays as well as improvements to the GoDig platform, we created GoDigViewer, a new stand-alone software that provides detailed visualizations of GoDig runs. GoDigViewer guided the implementation of "priming runs," an acquisition mode with significantly higher success rates. In this mode, two or more chromatographic priming runs are automatically performed to improve the accuracy and precision of target elution orders, followed by analytical runs which quantify targets. Using priming runs, success rates exceeded 97% for a list of 400 peptide targets and 95% for a list of 200 targets that are usually not quantified using untargeted mass spectrometry. We used priming runs to establish a quantitative assay of 125 macroautophagy proteins that had a >95% success rate and revealed differences in macroautophagy expression profiles across four human cell lines.
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Affiliation(s)
- Steven R Shuken
- Department of Cell Biology, Harvard Medical School, 240 Longwood Avenue, Boston, Massachusetts 02115, United States
| | - Qing Yu
- Department of Cell Biology, Harvard Medical School, 240 Longwood Avenue, Boston, Massachusetts 02115, United States
| | - Steven P Gygi
- Department of Cell Biology, Harvard Medical School, 240 Longwood Avenue, Boston, Massachusetts 02115, United States
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5
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Basharat AR, Xiong X, Xu T, Zang Y, Sun L, Liu X. TopDIA: A Software Tool for Top-Down Data-Independent Acquisition Proteomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.05.588302. [PMID: 38645171 PMCID: PMC11030422 DOI: 10.1101/2024.04.05.588302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Top-down mass spectrometry is widely used for proteoform identification, characterization, and quantification owing to its ability to analyze intact proteoforms. In the last decade, top-down proteomics has been dominated by top-down data-dependent acquisition mass spectrometry (TD-DDA-MS), and top-down data-independent acquisition mass spectrometry (TD-DIA-MS) has not been well studied. While TD-DIA-MS produces complex multiplexed tandem mass spectrometry (MS/MS) spectra, which are challenging to confidently identify, it selects more precursor ions for MS/MS analysis and has the potential to increase proteoform identifications compared with TD-DDA-MS. Here we present TopDIA, the first software tool for proteoform identification by TD-DIA-MS. It generates demultiplexed pseudo MS/MS spectra from TD-DIA-MS data and then searches the pseudo MS/MS spectra against a protein sequence database for proteoform identification. We compared the performance of TD-DDA-MS and TD-DIA-MS using Escherichia coli K-12 MG1655 cells and demonstrated that TD-DIA-MS with TopDIA increased proteoform and protein identifications compared with TD-DDA-MS.
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Affiliation(s)
- Abdul Rehman Basharat
- Department of BioHealth Informatics, Luddy School of Informatics, Computing, and Engineering, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202, USA
| | - Xingzhao Xiong
- Deming Department of Medicine, Tulane University School of Medicine, New Orleans, LA, 70112, USA
| | - Tian Xu
- Department of Chemistry, Michigan State University, East Lansing, MI, 48824, USA
| | - Yong Zang
- Department of Biostatistics and Health Data Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Liangliang Sun
- Department of Chemistry, Michigan State University, East Lansing, MI, 48824, USA
| | - Xiaowen Liu
- Deming Department of Medicine, Tulane University School of Medicine, New Orleans, LA, 70112, USA
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6
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Korchak JA, Jeffery ED, Bandyopadhyay S, Jordan BT, Lehe M, Watts EF, Fenix A, Wilhelm M, Sheynkman GM. IS-PRM-based peptide targeting informed by long-read sequencing for alternative proteome detection. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.01.587549. [PMID: 38617311 PMCID: PMC11014528 DOI: 10.1101/2024.04.01.587549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Alternative splicing is a major contributor of transcriptomic complexity, but the extent to which transcript isoforms are translated into stable, functional protein isoforms is unclear. Furthermore, detection of relatively scarce isoform-specific peptides is challenging, with many protein isoforms remaining uncharted due to technical limitations. Recently, a family of advanced targeted MS strategies, termed internal standard parallel reaction monitoring (IS-PRM), have demonstrated multiplexed, sensitive detection of pre-defined peptides of interest. Such approaches have not yet been used to confirm existence of novel peptides. Here, we present a targeted proteogenomic approach that leverages sample-matched long-read RNA sequencing (LR RNAseq) data to predict potential protein isoforms with prior transcript evidence. Predicted tryptic isoform-specific peptides, which are specific to individual gene product isoforms, serve as "triggers" and "targets" in the IS-PRM method, Tomahto. Using the model human stem cell line WTC11, LR RNAseq data were generated and used to inform the generation of synthetic standards for 192 isoform-specific peptides (114 isoforms from 55 genes). These synthetic "trigger" peptides were labeled with super heavy tandem mass tags (TMT) and spiked into TMT-labeled WTC11 tryptic digest, predicted to contain corresponding endogenous "target" peptides. Compared to DDA mode, Tomahto increased detectability of isoforms by 3.6-fold, resulting in the identification of five previously unannotated isoforms. Our method detected protein isoform expression for 43 out of 55 genes corresponding to 54 resolved isoforms. This LR RNA seq-informed Tomahto targeted approach, called LRP-IS-PRM, is a new modality for generating protein-level evidence of alternative isoforms - a critical first step in designing functional studies and eventually clinical assays.
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Affiliation(s)
- Jennifer A. Korchak
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
| | - Erin D. Jeffery
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
| | - Saikat Bandyopadhyay
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Ben T. Jordan
- Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Frederick, MD USA
| | - Micah Lehe
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
| | - Emily F. Watts
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
| | - Aidan Fenix
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Mathias Wilhelm
- Computational Mass Spectrometry, Technical University of Munich (TUM), D-85354 Freising, Germany
| | - Gloria M. Sheynkman
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, USA
- UVA Comprehensive Cancer Center, University of Virginia, Charlottesville, VA, USA
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7
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Pade LR, Stepler KE, Portero EP, DeLaney K, Nemes P. Biological mass spectrometry enables spatiotemporal 'omics: From tissues to cells to organelles. MASS SPECTROMETRY REVIEWS 2024; 43:106-138. [PMID: 36647247 PMCID: PMC10668589 DOI: 10.1002/mas.21824] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 09/14/2022] [Accepted: 09/17/2022] [Indexed: 06/17/2023]
Abstract
Biological processes unfold across broad spatial and temporal dimensions, and measurement of the underlying molecular world is essential to their understanding. Interdisciplinary efforts advanced mass spectrometry (MS) into a tour de force for assessing virtually all levels of the molecular architecture, some in exquisite detection sensitivity and scalability in space-time. In this review, we offer vignettes of milestones in technology innovations that ushered sample collection and processing, chemical separation, ionization, and 'omics analyses to progressively finer resolutions in the realms of tissue biopsies and limited cell populations, single cells, and subcellular organelles. Also highlighted are methodologies that empowered the acquisition and analysis of multidimensional MS data sets to reveal proteomes, peptidomes, and metabolomes in ever-deepening coverage in these limited and dynamic specimens. In pursuit of richer knowledge of biological processes, we discuss efforts pioneering the integration of orthogonal approaches from molecular and functional studies, both within and beyond MS. With established and emerging community-wide efforts ensuring scientific rigor and reproducibility, spatiotemporal MS emerged as an exciting and powerful resource to study biological systems in space-time.
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Affiliation(s)
- Leena R. Pade
- Department of Chemistry & Biochemistry, University of Maryland, 8051 Regents Drive, College Park, MD 20742
| | - Kaitlyn E. Stepler
- Department of Chemistry & Biochemistry, University of Maryland, 8051 Regents Drive, College Park, MD 20742
| | - Erika P. Portero
- Department of Chemistry & Biochemistry, University of Maryland, 8051 Regents Drive, College Park, MD 20742
| | - Kellen DeLaney
- Department of Chemistry & Biochemistry, University of Maryland, 8051 Regents Drive, College Park, MD 20742
| | - Peter Nemes
- Department of Chemistry & Biochemistry, University of Maryland, 8051 Regents Drive, College Park, MD 20742
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8
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McGann CD, Barshop WD, Canterbury JD, Lin C, Gabriel W, Huang J, Bergen D, Zabrouskov V, Melani RD, Wilhelm M, McAlister GC, Schweppe DK. Real-Time Spectral Library Matching for Sample Multiplexed Quantitative Proteomics. J Proteome Res 2023; 22:2836-2846. [PMID: 37557900 DOI: 10.1021/acs.jproteome.3c00085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/11/2023]
Abstract
Sample multiplexed quantitative proteomics assays have proved to be a highly versatile means to assay molecular phenotypes. Yet, stochastic precursor selection and precursor coisolation can dramatically reduce the efficiency of data acquisition and quantitative accuracy. To address this, intelligent data acquisition (IDA) strategies have recently been developed to improve instrument efficiency and quantitative accuracy for both discovery and targeted methods. Toward this end, we sought to develop and implement a new real-time spectral library searching (RTLS) workflow that could enable intelligent scan triggering and peak selection within milliseconds of scan acquisition. To ensure ease of use and general applicability, we built an application to read in diverse spectral libraries and file types from both empirical and predicted spectral libraries. We demonstrate that RTLS methods enable improved quantitation of multiplexed samples, particularly with consideration for quantitation from chimeric fragment spectra. We used RTLS to profile proteome responses to small molecule perturbations and were able to quantify up to 15% more significantly regulated proteins in half the gradient time compared to traditional methods. Taken together, the development of RTLS expands the IDA toolbox to improve instrument efficiency and quantitative accuracy for sample multiplexed analyses.
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Affiliation(s)
- Chris D McGann
- University of Washington, Seattle, Washington 98105, United States
| | | | | | - Chuwei Lin
- University of Washington, Seattle, Washington 98105, United States
| | | | - Jingjing Huang
- Thermo Fisher Scientific, San Jose, California 95134, United States
| | - David Bergen
- Thermo Fisher Scientific, San Jose, California 95134, United States
| | - Vlad Zabrouskov
- Thermo Fisher Scientific, San Jose, California 95134, United States
| | - Rafael D Melani
- Thermo Fisher Scientific, San Jose, California 95134, United States
| | | | | | - Devin K Schweppe
- University of Washington, Seattle, Washington 98105, United States
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9
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Brademan DR, Overmyer KA, He Y, Barshop WD, Canterbury JD, Bills BJ, Anderson BJ, Hutchins PD, Sharma S, Zabrouskov V, McAlister GC, Coon JJ. Improved Structural Characterization of Glycerophospholipids and Sphingomyelins with Real-Time Library Searching. Anal Chem 2023; 95:7813-7821. [PMID: 37172325 PMCID: PMC10840458 DOI: 10.1021/acs.analchem.2c04633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
In mass spectrometry-based lipidomics, complex lipid mixtures undergo chromatographic separation, are ionized, and are detected using tandem MS (MSn) to simultaneously quantify and structurally characterize eluting species. The reported structural granularity of these identified lipids is strongly reliant on the analytical techniques leveraged in a study. For example, lipid identifications from traditional collisionally activated data-dependent acquisition experiments are often reported at either species level or molecular species level. Structural resolution of reported lipid identifications is routinely enhanced by integrating both positive and negative mode analyses, requiring two separate runs or polarity switching during a single analysis. MS3+ can further elucidate lipid structure, but the lengthened MS duty cycle can negatively impact analysis depth. Recently, functionality has been introduced on several Orbitrap Tribrid mass spectrometry platforms to identify eluting molecular species on-the-fly. These real-time identifications can be leveraged to trigger downstream MSn to improve structural characterization with lessened impacts on analysis depth. Here, we describe a novel lipidomics real-time library search (RTLS) approach, which utilizes the lipid class of real-time identifications to trigger class-targeted MSn and to improve the structural characterization of phosphotidylcholines, phosphotidylethanolamines, phosphotidylinositols, phosphotidylglycerols, phosphotidylserine, and sphingomyelins in the positive ion mode. Our class-based RTLS method demonstrates improved selectivity compared to the current methodology of triggering MSn in the presence of characteristic ions or neutral losses.
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Affiliation(s)
- Dain R. Brademan
- The Morgridge Institute for Research, Madison, WI 53715, USA
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison WI 53706, USA
| | - Katherine A. Overmyer
- The Morgridge Institute for Research, Madison, WI 53715, USA
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison WI 53706, USA
| | - Yuchen He
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison WI 53706, USA
| | | | | | | | - Benton J. Anderson
- Department of Chemistry, University of Wisconsin-Madison, Madison WI 53706, USA
| | | | - Seema Sharma
- Thermo Fisher Scientific, San Jose, CA 95134, USA
| | | | | | - Joshua J. Coon
- The Morgridge Institute for Research, Madison, WI 53715, USA
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison WI 53706, USA
- Department of Chemistry, University of Wisconsin-Madison, Madison WI 53706, USA
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10
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Calvete JJ, Lomonte B, Saviola AJ, Calderón Celis F, Ruiz Encinar J. Quantification of snake venom proteomes by mass spectrometry-considerations and perspectives. MASS SPECTROMETRY REVIEWS 2023. [PMID: 37155340 DOI: 10.1002/mas.21850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 11/24/2022] [Accepted: 03/30/2023] [Indexed: 05/10/2023]
Abstract
The advent of soft ionization mass spectrometry-based proteomics in the 1990s led to the development of a new dimension in biology that conceptually allows for the integral analysis of whole proteomes. This transition from a reductionist to a global-integrative approach is conditioned to the capability of proteomic platforms to generate and analyze complete qualitative and quantitative proteomics data. Paradoxically, the underlying analytical technique, molecular mass spectrometry, is inherently nonquantitative. The turn of the century witnessed the development of analytical strategies to endow proteomics with the ability to quantify proteomes of model organisms in the sense of "an organism for which comprehensive molecular (genomic and/or transcriptomic) resources are available." This essay presents an overview of the strategies and the lights and shadows of the most popular quantification methods highlighting the common misuse of label-free approaches developed for model species' when applied to quantify the individual components of proteomes of nonmodel species (In this essay we use the term "non-model" organisms for species lacking comprehensive molecular (genomic and/or transcriptomic) resources, a circumstance that, as we detail in this review-essay, conditions the quantification of their proteomes.). We also point out the opportunity of combining elemental and molecular mass spectrometry systems into a hybrid instrumental configuration for the parallel identification and absolute quantification of venom proteomes. The successful application of this novel mass spectrometry configuration in snake venomics represents a proof-of-concept for a broader and more routine application of hybrid elemental/molecular mass spectrometry setups in other areas of the proteomics field, such as phosphoproteomics, metallomics, and in general in any biological process where a heteroatom (i.e., any atom other than C, H, O, N) forms integral part of its mechanism.
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Affiliation(s)
- Juan J Calvete
- Evolutionary and Translational Venomics Laboratory, Instituto de Biomedicina de Valencia, CSIC, Valencia, Spain
| | - Bruno Lomonte
- Unidad de Proteómica, Instituto Clodomiro Picado, Facultad de Microbiología, Universidad de Costa Rica, San José, Costa Rica
| | - Anthony J Saviola
- Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | | | - Jorge Ruiz Encinar
- Department of Physical and Analytical Chemistry, University of Oviedo, Oviedo, Spain
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11
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Huffman RG, Leduc A, Wichmann C, Di Gioia M, Borriello F, Specht H, Derks J, Khan S, Khoury L, Emmott E, Petelski AA, Perlman DH, Cox J, Zanoni I, Slavov N. Prioritized mass spectrometry increases the depth, sensitivity and data completeness of single-cell proteomics. Nat Methods 2023; 20:714-722. [PMID: 37012480 PMCID: PMC10172113 DOI: 10.1038/s41592-023-01830-1] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 02/27/2023] [Indexed: 04/05/2023]
Abstract
Major aims of single-cell proteomics include increasing the consistency, sensitivity and depth of protein quantification, especially for proteins and modifications of biological interest. Here, to simultaneously advance all these aims, we developed prioritized Single-Cell ProtEomics (pSCoPE). pSCoPE consistently analyzes thousands of prioritized peptides across all single cells (thus increasing data completeness) while maximizing instrument time spent analyzing identifiable peptides, thus increasing proteome depth. These strategies increased the sensitivity, data completeness and proteome coverage over twofold. The gains enabled quantifying protein variation in untreated and lipopolysaccharide-treated primary macrophages. Within each condition, proteins covaried within functional sets, including phagosome maturation and proton transport, similarly across both treatment conditions. This covariation is coupled to phenotypic variability in endocytic activity. pSCoPE also enabled quantifying proteolytic products, suggesting a gradient of cathepsin activities within a treatment condition. pSCoPE is freely available and widely applicable, especially for analyzing proteins of interest without sacrificing proteome coverage. Support for pSCoPE is available at http://scp.slavovlab.net/pSCoPE .
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Affiliation(s)
- R Gray Huffman
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center and Barnett Institute, Northeastern University, Boston, MA, USA
| | - Andrew Leduc
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center and Barnett Institute, Northeastern University, Boston, MA, USA
| | - Christoph Wichmann
- Computational Systems Biochemistry Research Group, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Marco Di Gioia
- Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Harrison Specht
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center and Barnett Institute, Northeastern University, Boston, MA, USA
| | - Jason Derks
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center and Barnett Institute, Northeastern University, Boston, MA, USA
| | - Saad Khan
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center and Barnett Institute, Northeastern University, Boston, MA, USA
| | - Luke Khoury
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center and Barnett Institute, Northeastern University, Boston, MA, USA
| | - Edward Emmott
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center and Barnett Institute, Northeastern University, Boston, MA, USA
- Centre for Proteome Research, Department of Biochemistry and Systems Biology, University of Liverpool, Liverpool, UK
| | - Aleksandra A Petelski
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center and Barnett Institute, Northeastern University, Boston, MA, USA
- Parallel Squared Technology Institute, Watertown, MA, USA
| | - David H Perlman
- Merck Exploratory Sciences Center, Merck Sharp and Dohme Corp., Cambridge, MA, USA
| | - Jürgen Cox
- Computational Systems Biochemistry Research Group, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Ivan Zanoni
- Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Nikolai Slavov
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center and Barnett Institute, Northeastern University, Boston, MA, USA.
- Parallel Squared Technology Institute, Watertown, MA, USA.
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12
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Yu Q, Liu X, Keller MP, Navarrete-Perea J, Zhang T, Fu S, Vaites LP, Shuken SR, Schmid E, Keele GR, Li J, Huttlin EL, Rashan EH, Simcox J, Churchill GA, Schweppe DK, Attie AD, Paulo JA, Gygi SP. Sample multiplexing-based targeted pathway proteomics with real-time analytics reveals the impact of genetic variation on protein expression. Nat Commun 2023; 14:555. [PMID: 36732331 PMCID: PMC9894840 DOI: 10.1038/s41467-023-36269-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 01/20/2023] [Indexed: 02/04/2023] Open
Abstract
Targeted proteomics enables hypothesis-driven research by measuring the cellular expression of protein cohorts related by function, disease, or class after perturbation. Here, we present a pathway-centric approach and an assay builder resource for targeting entire pathways of up to 200 proteins selected from >10,000 expressed proteins to directly measure their abundances, exploiting sample multiplexing to increase throughput by 16-fold. The strategy, termed GoDig, requires only a single-shot LC-MS analysis, ~1 µg combined peptide material, a list of up to 200 proteins, and real-time analytics to trigger simultaneous quantification of up to 16 samples for hundreds of analytes. We apply GoDig to quantify the impact of genetic variation on protein expression in mice fed a high-fat diet. We create several GoDig assays to quantify the expression of multiple protein families (kinases, lipid metabolism- and lipid droplet-associated proteins) across 480 fully-genotyped Diversity Outbred mice, revealing protein quantitative trait loci and establishing potential linkages between specific proteins and lipid homeostasis.
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Affiliation(s)
- Qing Yu
- Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Xinyue Liu
- Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Mark P Keller
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | | | - Tian Zhang
- Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Sipei Fu
- Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Laura P Vaites
- Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Steven R Shuken
- Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Ernst Schmid
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, 02115, USA
| | | | - Jiaming Li
- Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Edward L Huttlin
- Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Edrees H Rashan
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Judith Simcox
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | | | - Devin K Schweppe
- Department of Genome Sciences, University of Washington, Seattle, WA, 98105, USA
| | - Alan D Attie
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Joao A Paulo
- Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Steven P Gygi
- Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA.
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13
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FLASHIda enables intelligent data acquisition for top-down proteomics to boost proteoform identification counts. Nat Commun 2022; 13:4407. [PMID: 35906205 PMCID: PMC9338294 DOI: 10.1038/s41467-022-31922-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 07/07/2022] [Indexed: 11/17/2022] Open
Abstract
The detailed analysis and structural characterization of proteoforms by top-down proteomics (TDP) has gained a lot of interest in biomedical research. Data-dependent acquisition (DDA) of intact proteins is non-trivial due to the diversity and complexity of proteoforms. Dedicated acquisition methods thus have the potential to greatly improve TDP. Here, we present FLASHIda, an intelligent online data acquisition algorithm for TDP that ensures the real-time selection of high-quality precursors of diverse proteoforms. FLASHIda combines fast charge deconvolution algorithms and machine learning-based quality assessment for optimal precursor selection. In an analysis of E. coli lysate, FLASHIda increases the number of unique proteoform level identifications from 800 to 1500 or generates a near-identical number of identifications in one third of the instrument time when compared to standard DDA mode. Furthermore, FLASHIda enables sensitive mapping of post-translational modifications and detection of chemical adducts. As a software extension module to the instrument, FLASHIda can be readily adopted for TDP studies of complex samples to enhance proteoform identification rates. Data acquisition suitable for top-down proteomics (TDP) has the potential to significantly improve proteoform analysis. Here, the authors present FLASHIda, an intelligent online data acquisition algorithm for TDP that nearly doubles the number of proteoform-level identifications in complex samples.
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14
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Plank MJ. Modern Data Acquisition Approaches in Proteomics Based on Dynamic Instrument Control. J Proteome Res 2022; 21:1209-1217. [PMID: 35362319 DOI: 10.1021/acs.jproteome.2c00096] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Traditionally, data acquisition in mass spectrometry-based proteomics is directed by user-defined parameters and relatively simple decision making, such as selection of the highest MS1 peaks for fragmentation. In recent years, access to two-way-communication with instrument codebases has led to a surge in algorithms instructing more complex decision processes on-the-fly. A closer matching between the time windows for monitoring peptides in targeted proteomics and their actual chromatographic elution peaks has been addressed through dynamic retention time scheduling and through triggered acquisition. Strategies based on real-time database searching and spectral matching have, among others, been used to adjust acquisition parameters for selected peptides for improved quantitative accuracy. While initial studies were mainly performed on a proof-of-concept level, dynamic acquisition approaches recently became more broadly available through software and increasing integration into standard instrument control and are likely to transform the field of proteomics in the coming years.
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Affiliation(s)
- Michael J Plank
- Department of Molecular and Cellular Biology, University of Arizona, Tucson, Arizona 85721, United States.,Bio5 Institute, University of Arizona, Tucson, Arizona 85721, United States
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15
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Choi SB, Muñoz-LLancao P, Manzini MC, Nemes P. Data-Dependent Acquisition Ladder for Capillary Electrophoresis Mass Spectrometry-Based Ultrasensitive (Neuro)Proteomics. Anal Chem 2021; 93:15964-15972. [PMID: 34812615 DOI: 10.1021/acs.analchem.1c03327] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Measurement of broad types of proteins from a small number of cells to single cells would help to better understand the nervous system but requires significant leaps in sensitivity in high-resolution mass spectrometry (HRMS). Microanalytical capillary electrophoresis electrospray ionization (CE-ESI) offers a path to ultrasensitive proteomics by integrating scalability with sensitivity. Here, we systematically evaluate performance limitations in this technology to develop a data acquisition strategy with deeper coverage of the neuroproteome from trace amounts of starting materials than traditional dynamic exclusion. During standard data-dependent acquisition (DDA), compact migration challenged the duty cycle of second-stage transitions and redundant targeting of abundant peptide signals lowered their identification success rate. DDA was programmed to progressively exclude a static set of high-intensity peptide signals throughout replicate measurements, essentially forming rungs of a "DDA ladder." The method was tested for ∼500 pg portions of a protein digest from cultured hippocampal (primary) neurons (mouse), which estimated the total amount of protein from a single neuron. The analysis of ∼5 ng of protein digest over all replicates, approximating ∼10 neurons, identified 428 nonredundant proteins (415 quantified), an ∼35% increase over traditional DDA. The identified proteins were enriched in neuronal marker genes and molecular pathways of neurobiological importance. The DDA ladder enhances CE-HRMS sensitivity to single-neuron equivalent amounts of proteins, thus expanding the analytical toolbox of neuroscience.
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Affiliation(s)
- Sam B Choi
- Department of Chemistry & Biochemistry, University of Maryland, 8051 Regents Drive, College Park, Maryland 20742, United States
| | - Pablo Muñoz-LLancao
- Department of Neuroscience & Cell Biology, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey 08901, United States.,Department of Cell Biology, Yale University School of Medicine, New Haven, Connecticut 06510, United States
| | - M Chiara Manzini
- Department of Neuroscience & Cell Biology, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey 08901, United States
| | - Peter Nemes
- Department of Chemistry & Biochemistry, University of Maryland, 8051 Regents Drive, College Park, Maryland 20742, United States
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16
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Yu SH, Ferretti D, Schessner JP, Rudolph JD, Borner GHH, Cox J. Expanding the Perseus Software for Omics Data Analysis With Custom Plugins. ACTA ACUST UNITED AC 2021; 71:e105. [PMID: 32931150 DOI: 10.1002/cpbi.105] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The Perseus software provides a comprehensive framework for the statistical analysis of large-scale quantitative proteomics data, also in combination with other omics dimensions. Rapid developments in proteomics technology and the ever-growing diversity of biological studies increasingly require the flexibility to incorporate computational methods designed by the user. Here, we present the new functionality of Perseus to integrate self-made plugins written in C#, R, or Python. The user-written codes will be fully integrated into the Perseus data analysis workflow as custom activities. This also makes language-specific R and Python libraries from CRAN (cran.r-project.org), Bioconductor (bioconductor.org), PyPI (pypi.org), and Anaconda (anaconda.org) accessible in Perseus. The different available approaches are explained in detail in this article. To facilitate the distribution of user-developed plugins among users, we have created a plugin repository for community sharing and filled it with the examples provided in this article and a collection of already existing and more extensive plugins. © 2020 The Authors. Basic Protocol 1: Basic steps for R plugins Support Protocol 1: R plugins with additional arguments Basic Protocol 2: Basic steps for python plugins Support Protocol 2: Python plugins with additional arguments Basic Protocol 3: Basic steps and construction of C# plugins Basic Protocol 4: Basic steps of construction and connection for R plugins with C# interface Support Protocol 4: Advanced example of R Plugin with C# interface: UMAP Basic Protocol 5: Basic steps of construction and connection for python plugins with C# interface Support Protocol 5: Advanced example of python plugin with C# interface: UMAP Support Protocol 6: A basic workflow for the analysis of label-free quantification proteomics data using perseus.
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Affiliation(s)
- Sung-Huan Yu
- Computational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany
| | - Daniela Ferretti
- Computational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany
| | - Julia P Schessner
- Systems Biology of Membrane Trafficking Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany
| | - Jan Daniel Rudolph
- Computational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany.,Bosch Center for Artificial Intelligence, Robert-Bosch-Campus 1, Renningen, Germany
| | - Georg H H Borner
- Systems Biology of Membrane Trafficking Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany
| | - Jürgen Cox
- Computational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany.,Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
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17
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Tinte MM, Chele KH, van der Hooft JJJ, Tugizimana F. Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview. Metabolites 2021; 11:445. [PMID: 34357339 PMCID: PMC8305945 DOI: 10.3390/metabo11070445] [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: 05/25/2021] [Revised: 06/30/2021] [Accepted: 07/03/2021] [Indexed: 12/27/2022] Open
Abstract
Plants are constantly challenged by changing environmental conditions that include abiotic stresses. These are limiting their development and productivity and are subsequently threatening our food security, especially when considering the pressure of the increasing global population. Thus, there is an urgent need for the next generation of crops with high productivity and resilience to climate change. The dawn of a new era characterized by the emergence of fourth industrial revolution (4IR) technologies has redefined the ideological boundaries of research and applications in plant sciences. Recent technological advances and machine learning (ML)-based computational tools and omics data analysis approaches are allowing scientists to derive comprehensive metabolic descriptions and models for the target plant species under specific conditions. Such accurate metabolic descriptions are imperatively essential for devising a roadmap for the next generation of crops that are resilient to environmental deterioration. By synthesizing the recent literature and collating data on metabolomics studies on plant responses to abiotic stresses, in the context of the 4IR era, we point out the opportunities and challenges offered by omics science, analytical intelligence, computational tools and big data analytics. Specifically, we highlight technological advancements in (plant) metabolomics workflows and the use of machine learning and computational tools to decipher the dynamics in the chemical space that define plant responses to abiotic stress conditions.
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Affiliation(s)
- Morena M. Tinte
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa; (M.M.T.); (K.H.C.)
| | - Kekeletso H. Chele
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa; (M.M.T.); (K.H.C.)
| | | | - Fidele Tugizimana
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa; (M.M.T.); (K.H.C.)
- International Research and Development Division, Omnia Group, Ltd., Johannesburg 2021, South Africa
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18
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Yu SH, Kyriakidou P, Cox J. Isobaric Matching between Runs and Novel PSM-Level Normalization in MaxQuant Strongly Improve Reporter Ion-Based Quantification. J Proteome Res 2020; 19:3945-3954. [PMID: 32892627 PMCID: PMC7586393 DOI: 10.1021/acs.jproteome.0c00209] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
![]()
Isobaric
labeling has the promise of combining high sample multiplexing
with precise quantification. However, normalization issues and the
missing value problem of complete n-plexes hamper
quantification across more than one n-plex. Here,
we introduce two novel algorithms implemented in MaxQuant that substantially
improve the data analysis with multiple n-plexes.
First, isobaric matching between runs makes use of the three-dimensional
MS1 features to transfer identifications from identified to unidentified
MS/MS spectra between liquid chromatography–mass spectrometry
runs in order to utilize reporter ion intensities in unidentified
spectra for quantification. On typical datasets, we observe a significant
gain in MS/MS spectra that can be used for quantification. Second,
we introduce a novel PSM-level normalization, applicable to data with
and without the common reference channel. It is a weighted median-based
method, in which the weights reflect the number of ions that were
used for fragmentation. On a typical dataset, we observe complete
removal of batch effects and dominance of the biological sample grouping
after normalization. Furthermore, we provide many novel processing
and normalization options in Perseus, the companion software for the
downstream analysis of quantitative proteomics results. All novel
tools and algorithms are available with the regular MaxQuant and Perseus
releases, which are downloadable at http://maxquant.org.
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Affiliation(s)
- Sung-Huan Yu
- Computational Systems Biochemistry, Max-Planck Institute of Biochemistry, Am Klopferspitz 18, Martinsried 82152, Germany
| | - Pelagia Kyriakidou
- Computational Systems Biochemistry, Max-Planck Institute of Biochemistry, Am Klopferspitz 18, Martinsried 82152, Germany
| | - Jürgen Cox
- Computational Systems Biochemistry, Max-Planck Institute of Biochemistry, Am Klopferspitz 18, Martinsried 82152, Germany.,Department of Biological and Medical Psychology, University of Bergen, Jonas Liesvei 91, Bergen 5009, Norway
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19
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Schweppe DK, Eng JK, Yu Q, Bailey D, Rad R, Navarrete-Perea J, Huttlin EL, Erickson BK, Paulo JA, Gygi SP. Full-Featured, Real-Time Database Searching Platform Enables Fast and Accurate Multiplexed Quantitative Proteomics. J Proteome Res 2020; 19:2026-2034. [PMID: 32126768 DOI: 10.1021/acs.jproteome.9b00860] [Citation(s) in RCA: 147] [Impact Index Per Article: 36.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Multiplexed quantitative analyses of complex proteomes enable deep biological insight. While a multitude of workflows have been developed for multiplexed analyses, the most quantitatively accurate method (SPS-MS3) suffers from long acquisition duty cycles. We built a new, real-time database search (RTS) platform, Orbiter, to combat the SPS-MS3 method's longer duty cycles. RTS with Orbiter eliminates SPS-MS3 scans if no peptide matches to a given spectrum. With Orbiter's online proteomic analytical pipeline, which includes RTS and false discovery rate analysis, it was possible to process a single spectrum database search in less than 10 ms. The result is a fast, functional means to identify peptide spectral matches using Comet, filter these matches, and more efficiently quantify proteins of interest. Importantly, the use of Comet for peptide spectral matching allowed for a fully featured search, including analysis of post-translational modifications, with well-known and extensively validated scoring. These data could then be used to trigger subsequent scans in an adaptive and flexible manner. In this work we tested the utility of this adaptive data acquisition platform to improve the efficiency and accuracy of multiplexed quantitative experiments. We found that RTS enabled a 2-fold increase in mass spectrometric data acquisition efficiency. Orbiter's RTS quantified more than 8000 proteins across 10 proteomes in half the time of an SPS-MS3 analysis (18 h for RTS, 36 h for SPS-MS3).
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Affiliation(s)
- Devin K Schweppe
- Harvard Medical School, Department of Cell Biology, Cambridge, Massachusetts 02155, United States
| | - Jimmy K Eng
- University of Washington Proteomics Resource, Seattle, Washington 98109, United States
| | - Qing Yu
- Harvard Medical School, Department of Cell Biology, Cambridge, Massachusetts 02155, United States
| | - Derek Bailey
- Thermo Scientific LSMS, San Jose, California 95134, United States
| | - Ramin Rad
- Harvard Medical School, Department of Cell Biology, Cambridge, Massachusetts 02155, United States
| | - Jose Navarrete-Perea
- Harvard Medical School, Department of Cell Biology, Cambridge, Massachusetts 02155, United States
| | - Edward L Huttlin
- Harvard Medical School, Department of Cell Biology, Cambridge, Massachusetts 02155, United States
| | - Brian K Erickson
- Harvard Medical School, Department of Cell Biology, Cambridge, Massachusetts 02155, United States
| | - Joao A Paulo
- Harvard Medical School, Department of Cell Biology, Cambridge, Massachusetts 02155, United States
| | - Steven P Gygi
- Harvard Medical School, Department of Cell Biology, Cambridge, Massachusetts 02155, United States
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20
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Hutchins PD, Russell JD, Coon JJ. Accelerating Lipidomic Method Development through in Silico Simulation. Anal Chem 2019; 91:9698-9706. [PMID: 31298839 DOI: 10.1021/acs.analchem.9b01234] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Judicious selection of mass spectrometry (MS) acquisition parameters is essential for effectively profiling the broad diversity and dynamic range of biomolecules. Typically, acquisition parameters are individually optimized to maximally characterize analytes from each new sample matrix. This time-consuming process often ignores the synergistic relationship between MS method parameters, producing suboptimal results. Here we detail the creation of an algorithm which accurately simulates LC-MS/MS lipidomic data acquisition performance for a benchtop quadrupole-Orbitrap MS system. By coupling this simulation tool with a genetic algorithm for constrained parameter optimization, we demonstrate the efficient identification of LC-MS/MS method parameter sets individually suited for specific sample matrices. Finally, we utilize the in silico simulation to examine how continued developments in MS acquisition speed and sensitivity will further increase the power of MS lipidomics as a vital tool for impactful biochemical analysis.
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Affiliation(s)
- Paul D Hutchins
- Department of Chemistry , University of Wisconsin-Madison , Madison , Wisconsin 53706 , United States.,Genome Center of Wisconsin , Madison , Wisconsin 53706 , United States
| | - Jason D Russell
- Morgridge Institute for Research , Madison , Wisconsin 53715 , United States.,Genome Center of Wisconsin , Madison , Wisconsin 53706 , United States
| | - Joshua J Coon
- Department of Chemistry , University of Wisconsin-Madison , Madison , Wisconsin 53706 , United States.,Morgridge Institute for Research , Madison , Wisconsin 53715 , United States.,Genome Center of Wisconsin , Madison , Wisconsin 53706 , United States.,Department of Biomolecular Chemistry , University of Wisconsin-Madison , Madison , Wisconsin 53706 , United States
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21
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Van Houtven J, Agten A, Boonen K, Baggerman G, Hooyberghs J, Laukens K, Valkenborg D. QCQuan: A Web Tool for the Automated Assessment of Protein Expression and Data Quality of Labeled Mass Spectrometry Experiments. J Proteome Res 2019; 18:2221-2227. [PMID: 30942071 DOI: 10.1021/acs.jproteome.9b00072] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
In the context of omics disciplines and especially proteomics and biomarker discovery, the analysis of a clinical sample using label-based tandem mass spectrometry (MS) can be affected by sample preparation effects or by the measurement process itself, resulting in an incorrect outcome. Detection and correction of these mistakes using state-of-the-art methods based on mixed models can use large amounts of (computing) time. MS-based proteomics laboratories are high-throughput and need to avoid a bottleneck in their quantitative pipeline by quickly discriminating between high- and low-quality data. To this end we developed an easy-to-use web-tool called QCQuan (available at qcquan.net ) which is built around the CONSTANd normalization algorithm. It automatically provides the user with exploratory and quality control information as well as a differential expression analysis based on conservative, simple statistics. In this document we describe in detail the scientifically relevant steps that constitute the workflow and assess its qualitative and quantitative performance on three reference data sets. We find that QCQuan provides clear and accurate indications about the scientific value of both a high- and a low-quality data set. Moreover, it performed quantitatively better on a third data set than a comparable workflow assembled using established, reliable software.
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Affiliation(s)
- Joris Van Houtven
- VITO NV , Applied Bio & molecular Systems , Boeretang 200 , Mol 2400 , Belgium.,Universiteit Hasselt , Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat) , Agoralaan , Diepenbeek 3590 , Belgium.,Universiteit Antwerpen , Centre for Proteomics , Groenenborgerlaan 171 , Antwerpen 2020 , Belgium
| | - Annelies Agten
- Universiteit Hasselt , Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat) , Agoralaan , Diepenbeek 3590 , Belgium
| | - Kurt Boonen
- VITO NV , Applied Bio & molecular Systems , Boeretang 200 , Mol 2400 , Belgium.,Universiteit Antwerpen , Centre for Proteomics , Groenenborgerlaan 171 , Antwerpen 2020 , Belgium
| | - Geert Baggerman
- VITO NV , Applied Bio & molecular Systems , Boeretang 200 , Mol 2400 , Belgium.,Universiteit Antwerpen , Centre for Proteomics , Groenenborgerlaan 171 , Antwerpen 2020 , Belgium
| | - Jef Hooyberghs
- VITO NV , Applied Bio & molecular Systems , Boeretang 200 , Mol 2400 , Belgium.,Universiteit Hasselt , Theoretical Physics , Agoralaan , Diepenbeek 3590 , Belgium
| | - Kris Laukens
- Universiteit Antwerpen , Biomedical Informatics Research Center Antwerp (Biomina) , Middelheimlaan 1 , Antwerpen 2020 , Belgium.,Universiteit Antwerpen , Advanced Database Research and Modelling (ADReM), Department of Mathematics & Computer Sciences , Middelheimlaan 1 , Antwerpen 2020 , Belgium
| | - Dirk Valkenborg
- VITO NV , Applied Bio & molecular Systems , Boeretang 200 , Mol 2400 , Belgium.,Universiteit Hasselt , Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat) , Agoralaan , Diepenbeek 3590 , Belgium.,Universiteit Antwerpen , Centre for Proteomics , Groenenborgerlaan 171 , Antwerpen 2020 , Belgium
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22
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Wichmann C, Meier F, Virreira Winter S, Brunner AD, Cox J, Mann M. MaxQuant.Live Enables Global Targeting of More Than 25,000 Peptides. Mol Cell Proteomics 2019; 18:982-994. [PMID: 30755466 PMCID: PMC6495250 DOI: 10.1074/mcp.tir118.001131] [Citation(s) in RCA: 78] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Revised: 02/04/2019] [Accepted: 02/04/2019] [Indexed: 11/06/2022] Open
Abstract
Mass spectrometry (MS)-based proteomics is often performed in a shotgun format, in which as many peptide precursors as possible are selected from full or MS1 scans so that their fragment spectra can be recorded in MS2 scans. Although achieving great proteome depths, shotgun proteomics cannot guarantee that each precursor will be fragmented in each run. In contrast, targeted proteomics aims to reproducibly and sensitively record a restricted number of precursor/fragment combinations in each run, based on prescheduled mass-to-charge and retention time windows. Here we set out to unify these two concepts by a global targeting approach in which an arbitrary number of precursors of interest are detected in real-time, followed by standard fragmentation or advanced peptide-specific analyses. We made use of a fast application programming interface to a quadrupole Orbitrap instrument and real-time recalibration in mass, retention time and intensity dimensions to predict precursor identity. MaxQuant.Live is freely available (www.maxquant.live) and has a graphical user interface to specify many predefined data acquisition strategies. Acquisition speed is as fast as with the vendor software and the power of our approach is demonstrated with the acquisition of breakdown curves for hundreds of precursors of interest. We also uncover precursors that are not even visible in MS1 scans, using elution time prediction based on the auto-adjusted retention time alone. Finally, we successfully recognized and targeted more than 25,000 peptides in single LC-MS runs. Global targeting combines the advantages of two classical approaches in MS-based proteomics, whereas greatly expanding the analytical toolbox.
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Affiliation(s)
- Christoph Wichmann
- From the ‡Computational Systems Biochemistry, Max-Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany
| | - Florian Meier
- §Proteomics and Signal Transduction, Max-Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany.,¶NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen, Denmark
| | - Sebastian Virreira Winter
- §Proteomics and Signal Transduction, Max-Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany
| | - Andreas-David Brunner
- §Proteomics and Signal Transduction, Max-Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany
| | - Jürgen Cox
- From the ‡Computational Systems Biochemistry, Max-Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany;
| | - Matthias Mann
- §Proteomics and Signal Transduction, Max-Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany; .,¶NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen, Denmark
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23
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Erickson BK, Mintseris J, Schweppe DK, Navarrete-Perea J, Erickson AR, Nusinow DP, Paulo JA, Gygi SP. Active Instrument Engagement Combined with a Real-Time Database Search for Improved Performance of Sample Multiplexing Workflows. J Proteome Res 2019; 18:1299-1306. [PMID: 30658528 DOI: 10.1021/acs.jproteome.8b00899] [Citation(s) in RCA: 90] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Quantitative proteomics employing isobaric reagents has been established as a powerful tool for biological discovery. Current workflows often utilize a dedicated quantitative spectrum to improve quantitative accuracy and precision. A consequence of this approach is a dramatic reduction in the spectral acquisition rate, which necessitates the use of additional instrument time to achieve comprehensive proteomic depth. This work assesses the performance and benefits of online and real-time spectral identification in quantitative multiplexed workflows. A Real-Time Search (RTS) algorithm was implemented to identify fragment spectra within milliseconds as they are acquired using a probabilistic score and to trigger quantitative spectra only upon confident peptide identification. The RTS-MS3 was benchmarked against standard workflows using a complex two-proteome model of interference and a targeted 10-plex comparison of kinase abundance profiles. Applying the RTS-MS3 method provided the comprehensive characterization of a 10-plex proteome in 50% less acquisition time. These data indicate that the RTS-MS3 approach provides dramatic performance improvements for quantitative multiplexed experiments.
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Affiliation(s)
- Brian K Erickson
- Department of Cell Biology , Harvard Medical School , Boston , Massachusetts 02115 , United States
| | - Julian Mintseris
- Department of Cell Biology , Harvard Medical School , Boston , Massachusetts 02115 , United States
| | - Devin K Schweppe
- Department of Cell Biology , Harvard Medical School , Boston , Massachusetts 02115 , United States
| | - José Navarrete-Perea
- Department of Cell Biology , Harvard Medical School , Boston , Massachusetts 02115 , United States
| | - Alison R Erickson
- Department of Cell Biology , Harvard Medical School , Boston , Massachusetts 02115 , United States
| | - David P Nusinow
- Department of Cell Biology , Harvard Medical School , Boston , Massachusetts 02115 , United States
| | - Joao A Paulo
- Department of Cell Biology , Harvard Medical School , Boston , Massachusetts 02115 , United States
| | - Steven P Gygi
- Department of Cell Biology , Harvard Medical School , Boston , Massachusetts 02115 , United States
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24
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Zolg DP, Wilhelm M, Schnatbaum K, Zerweck J, Knaute T, Delanghe B, Bailey DJ, Gessulat S, Ehrlich HC, Weininger M, Yu P, Schlegl J, Kramer K, Schmidt T, Kusebauch U, Deutsch EW, Aebersold R, Moritz RL, Wenschuh H, Moehring T, Aiche S, Huhmer A, Reimer U, Kuster B. Building ProteomeTools based on a complete synthetic human proteome. Nat Methods 2017; 14:259-262. [PMID: 28135259 DOI: 10.1038/nmeth.4153] [Citation(s) in RCA: 140] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2016] [Accepted: 12/20/2016] [Indexed: 01/02/2023]
Abstract
We describe ProteomeTools, a project building molecular and digital tools from the human proteome to facilitate biomedical research. Here we report the generation and multimodal liquid chromatography-tandem mass spectrometry analysis of >330,000 synthetic tryptic peptides representing essentially all canonical human gene products, and we exemplify the utility of these data in several applications. The resource (available at http://www.proteometools.org) will be extended to >1 million peptides, and all data will be shared with the community via ProteomicsDB and ProteomeXchange.
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Affiliation(s)
- Daniel P Zolg
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
| | - Mathias Wilhelm
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
| | | | | | | | | | | | - Siegfried Gessulat
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany.,SAP SE, Potsdam, Germany
| | | | - Maximilian Weininger
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
| | - Peng Yu
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
| | | | - Karl Kramer
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
| | - Tobias Schmidt
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
| | | | | | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH, Zürich, Switzerland.,Faculty of Science, University of Zürich, Zürich, Switzerland
| | | | | | | | | | | | - Ulf Reimer
- JPT Peptide Technologies GmbH, Berlin, Germany
| | - Bernhard Kuster
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany.,Center for Integrated Protein Science Munich, Freising, Germany.,Bavarian Center for Biomolecular Mass Spectrometry, Freising, Germany
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25
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Erickson BK, Rose CM, Braun CR, Erickson AR, Knott J, McAlister GC, Wühr M, Paulo JA, Everley RA, Gygi SP. A Strategy to Combine Sample Multiplexing with Targeted Proteomics Assays for High-Throughput Protein Signature Characterization. Mol Cell 2017; 65:361-370. [PMID: 28065596 DOI: 10.1016/j.molcel.2016.12.005] [Citation(s) in RCA: 94] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2016] [Revised: 10/05/2016] [Accepted: 12/02/2016] [Indexed: 12/30/2022]
Abstract
Targeted mass spectrometry assays for protein quantitation monitor peptide surrogates, which are easily multiplexed to target many peptides in a single assay. However, these assays have generally not taken advantage of sample multiplexing, which allows up to ten analyses to occur in parallel. We present a two-dimensional multiplexing workflow that utilizes synthetic peptides for each protein to prompt the simultaneous quantification of >100 peptides from up to ten mixed sample conditions. We demonstrate that targeted analysis of unfractionated lysates (2 hr) accurately reproduces the quantification of fractionated lysates (72 hr analysis) while obviating the need for peptide detection prior to quantification. We targeted 131 peptides corresponding to 69 proteins across all 60 National Cancer Institute cell lines in biological triplicate, analyzing 180 samples in only 48 hr (the equivalent of 16 min/sample). These data further elucidated a correlation between the expression of key proteins and their cellular response to drug treatment.
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Affiliation(s)
- Brian K Erickson
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Christopher M Rose
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Craig R Braun
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Alison R Erickson
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA; Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA
| | | | - Graeme C McAlister
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Martin Wühr
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Joao A Paulo
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Robert A Everley
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA; Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA.
| | - Steven P Gygi
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA.
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26
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Kreimer S, Belov ME, Danielson WF, Levitsky LI, Gorshkov MV, Karger BL, Ivanov AR. Advanced Precursor Ion Selection Algorithms for Increased Depth of Bottom-Up Proteomic Profiling. J Proteome Res 2016; 15:3563-3573. [PMID: 27569903 DOI: 10.1021/acs.jproteome.6b00312] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Conventional TopN data-dependent acquisition (DDA) LC-MS/MS analysis identifies only a limited fraction of all detectable precursors because the ion-sampling rate of contemporary mass spectrometers is insufficient to target each precursor in a complex sample. TopN DDA preferentially targets high-abundance precursors with limited sampling of low-abundance precursors and repeated analyses only marginally improve sample coverage due to redundant precursor sampling. In this work, advanced precursor ion selection algorithms were developed and applied in the bottom-up analysis of HeLa cell lysate to overcome the above deficiencies. Precursors fragmented in previous runs were efficiently excluded using an automatically aligned exclusion list, which reduced overlap of identified peptides to ∼10% between replicates. Exclusion of previously fragmented high-abundance peptides allowed deeper probing of the HeLa proteome over replicate LC-MS runs, resulting in the identification of 29% more peptides beyond the saturation level achievable using conventional TopN DDA. The gain in peptide identifications using the developed approach translated to the identification of several hundred low-abundance protein groups, which were not detected by conventional TopN DDA. Exclusion of only identified peptides compared with the exclusion of all previously fragmented precursors resulted in an increase of 1000 (∼10%) additional peptide identifications over four runs, suggesting the potential for further improvement in the depth of proteomic profiling using advanced precursor ion selection algorithms.
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Affiliation(s)
- Simion Kreimer
- Barnett Institute of Chemical and Biological Analysis, Northeastern University , Boston, Massachusetts 02115, United States
| | - Mikhail E Belov
- Spectroglyph LLC , Kennewick, Washington 99338, United States
| | | | - Lev I Levitsky
- Institute for Energy Problems of Chemical Physics, Russian Academy of Sciences , 119334 Moscow, Russia.,Moscow Institute of Physics and Technology (State University) , 141700 Dolgoprudny, Moscow Region, Russia
| | - Mikhail V Gorshkov
- Institute for Energy Problems of Chemical Physics, Russian Academy of Sciences , 119334 Moscow, Russia.,Moscow Institute of Physics and Technology (State University) , 141700 Dolgoprudny, Moscow Region, Russia
| | - Barry L Karger
- Barnett Institute of Chemical and Biological Analysis, Northeastern University , Boston, Massachusetts 02115, United States
| | - Alexander R Ivanov
- Barnett Institute of Chemical and Biological Analysis, Northeastern University , Boston, Massachusetts 02115, United States
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27
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Recent developments in software tools for high-throughput in vitro ADME support with high-resolution MS. Bioanalysis 2016; 8:1723-33. [PMID: 27487387 DOI: 10.4155/bio-2016-0074] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
The last several years have seen the rapid adoption of the high-resolution MS (HRMS) for bioanalytical support of high throughput in vitro ADME profiling. Many capable software tools have been developed and refined to process quantitative HRMS bioanalysis data for ADME samples with excellent performance. Additionally, new software applications specifically designed for quan/qual soft spot identification workflows using HRMS have greatly enhanced the quality and efficiency of the structure elucidation process for high throughput metabolite ID in early in vitro ADME profiling. Finally, novel approaches in data acquisition and compression, as well as tools for transferring, archiving and retrieving HRMS data, are being continuously refined to tackle the issue of large data file size typical for HRMS analyses.
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28
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Potts GK, Voigt EA, Bailey DJ, Rose CM, Westphall MS, Hebert AS, Yin J, Coon JJ. Neucode Labels for Multiplexed, Absolute Protein Quantification. Anal Chem 2016; 88:3295-303. [PMID: 26882330 PMCID: PMC5141612 DOI: 10.1021/acs.analchem.5b04773] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
We describe a new method to accomplish multiplexed, absolute protein quantification in a targeted fashion. The approach draws upon the recently developed neutron encoding (NeuCode) metabolic labeling strategy and parallel reaction monitoring (PRM). Since PRM scanning relies upon high-resolution tandem mass spectra for targeted protein quantification, incorporation of multiple NeuCode labeled peptides permits high levels of multiplexing that can be accessed from high-resolution tandem mass spectra. Here we demonstrate this approach in cultured cells by monitoring a viral infection and the corresponding viral protein production over many infection time points in a single experiment. In this context the NeuCode PRM combination affords up to 30 channels of quantitative information in a single MS experiment.
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Affiliation(s)
- Gregory K Potts
- Department of Chemistry, University of Wisconsin , Madison, Wisconsin 53706, United States
- Genome Center of Wisconsin, University of Wisconsin , Madison, Wisconsin 53706, United States
| | - Emily A Voigt
- Department of Chemical and Biological Engineering, University of Wisconsin , Madison, Wisconsin 53706, United States
- Systems Biology Theme, Wisconsin Institute for Discovery, University of Wisconsin , Madison, Wisconsin 53706, United States
| | - Derek J Bailey
- Department of Chemistry, University of Wisconsin , Madison, Wisconsin 53706, United States
- Genome Center of Wisconsin, University of Wisconsin , Madison, Wisconsin 53706, United States
| | - Christopher M Rose
- Department of Chemistry, University of Wisconsin , Madison, Wisconsin 53706, United States
- Genome Center of Wisconsin, University of Wisconsin , Madison, Wisconsin 53706, United States
| | - Michael S Westphall
- Genome Center of Wisconsin, University of Wisconsin , Madison, Wisconsin 53706, United States
| | - Alexander S Hebert
- Genome Center of Wisconsin, University of Wisconsin , Madison, Wisconsin 53706, United States
- Department of Biomolecular Chemistry, University of Wisconsin , Madison, Wisconsin 53706, United States
| | - John Yin
- Department of Chemical and Biological Engineering, University of Wisconsin , Madison, Wisconsin 53706, United States
- Systems Biology Theme, Wisconsin Institute for Discovery, University of Wisconsin , Madison, Wisconsin 53706, United States
| | - Joshua J Coon
- Department of Chemistry, University of Wisconsin , Madison, Wisconsin 53706, United States
- Genome Center of Wisconsin, University of Wisconsin , Madison, Wisconsin 53706, United States
- Department of Biomolecular Chemistry, University of Wisconsin , Madison, Wisconsin 53706, United States
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29
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Affiliation(s)
- Nicholas M. Riley
- Genome Center of Wisconsin, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Joshua J. Coon
- Genome Center of Wisconsin, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
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30
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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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31
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Mayne J, Ning Z, Zhang X, Starr AE, Chen R, Deeke S, Chiang CK, Xu B, Wen M, Cheng K, Seebun D, Star A, Moore JI, Figeys D. Bottom-Up Proteomics (2013-2015): Keeping up in the Era of Systems Biology. Anal Chem 2015; 88:95-121. [PMID: 26558748 DOI: 10.1021/acs.analchem.5b04230] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Janice Mayne
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Zhibin Ning
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Xu Zhang
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Amanda E Starr
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Rui Chen
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Shelley Deeke
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Cheng-Kang Chiang
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Bo Xu
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Ming Wen
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Kai Cheng
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Deeptee Seebun
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Alexandra Star
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Jasmine I Moore
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Daniel Figeys
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
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32
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Yates JR. Pivotal role of computers and software in mass spectrometry - SEQUEST and 20 years of tandem MS database searching. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2015; 26:1804-13. [PMID: 26286455 PMCID: PMC4625908 DOI: 10.1007/s13361-015-1220-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2015] [Revised: 06/17/2015] [Accepted: 06/20/2015] [Indexed: 05/15/2023]
Abstract
Advances in computer technology and software have driven developments in mass spectrometry over the last 50 years. Computers and software have been impactful in three areas: the automation of difficult calculations to aid interpretation, the collection of data and control of instruments, and data interpretation. As the power of computers has grown, so too has the utility and impact on mass spectrometers and their capabilities. This has been particularly evident in the use of tandem mass spectrometry data to search protein and nucleotide sequence databases to identify peptide and protein sequences. This capability has driven the development of many new approaches to study biological systems, including the use of "bottom-up shotgun proteomics" to directly analyze protein mixtures. Graphical Abstract ᅟ.
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Affiliation(s)
- John R Yates
- Chemical Physiology and Molecular and Cellular Neurobiology, The Scripps Research Institute, 10550 North Torrey Pines Road, SR302B, La Jolla, CA, 92037, USA.
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33
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Gallien S, Kim SY, Domon B. Large-Scale Targeted Proteomics Using Internal Standard Triggered-Parallel Reaction Monitoring (IS-PRM). Mol Cell Proteomics 2015; 14:1630-44. [PMID: 25755295 DOI: 10.1074/mcp.o114.043968] [Citation(s) in RCA: 139] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2014] [Indexed: 12/22/2022] Open
Abstract
Targeted high-resolution and accurate mass analyses performed on fast sequencing mass spectrometers have opened new avenues for quantitative proteomics. More specifically, parallel reaction monitoring (PRM) implemented on quadrupole-orbitrap instruments exhibits exquisite selectivity to discriminate interferences from analytes. Furthermore, the instrument trapping capability enhances the sensitivity of the measurements. The PRM technique, applied to the analysis of limited peptide sets (typically 50 peptides or less) in a complex matrix, resulted in an improved detection and quantification performance as compared with the reference method of selected reaction monitoring performed on triple quadrupole instruments. However, the implementation of PRM for the analysis of large peptide numbers requires the adjustment of mass spectrometry acquisition parameters, which affects dramatically the quality of the generated data, and thus the overall output of an experiment. A newly designed data acquisition scheme enabled the analysis of moderate-to-large peptide numbers while retaining a high performance level. This new method, called internal standard triggered-parallel reaction monitoring (IS-PRM), relies on added internal standards and the on-the-fly adjustment of acquisition parameters to drive in real-time measurement of endogenous peptides. The acquisition time management was designed to maximize the effective time devoted to measure the analytes in a time-scheduled targeted experiment. The data acquisition scheme alternates between two PRM modes: a fast low-resolution "watch mode" and a "quantitative mode" using optimized parameters ensuring data quality. The IS-PRM method exhibited a highly effective use of the instrument time. Applied to the analysis of large peptide sets (up to 600) in complex samples, the method showed an unprecedented combination of scale and analytical performance, with limits of quantification in the low amol range. The successful analysis of various types of biological samples augurs a broad applicability of the method, which is likely to benefit a wide range of proteomics experiments.
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Affiliation(s)
- Sebastien Gallien
- From the ‡Luxembourg Clinical Proteomics Center (LCP), Luxembourg Institute of Health (LIH), Strassen, Luxembourg
| | - Sang Yoon Kim
- From the ‡Luxembourg Clinical Proteomics Center (LCP), Luxembourg Institute of Health (LIH), Strassen, Luxembourg
| | - Bruno Domon
- From the ‡Luxembourg Clinical Proteomics Center (LCP), Luxembourg Institute of Health (LIH), Strassen, Luxembourg
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34
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Tsou CC, Avtonomov D, Larsen B, Tucholska M, Choi H, Gingras AC, Nesvizhskii AI. DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics. Nat Methods 2015; 12:258-64, 7 p following 264. [PMID: 25599550 PMCID: PMC4399776 DOI: 10.1038/nmeth.3255] [Citation(s) in RCA: 419] [Impact Index Per Article: 46.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2014] [Accepted: 11/17/2014] [Indexed: 12/26/2022]
Abstract
As a result of recent improvements in mass spectrometry (MS), there is increased interest in data-independent acquisition (DIA) strategies in which all peptides are systematically fragmented using wide mass-isolation windows ('multiplex fragmentation'). DIA-Umpire (http://diaumpire.sourceforge.net/), a comprehensive computational workflow and open-source software for DIA data, detects precursor and fragment chromatographic features and assembles them into pseudo-tandem MS spectra. These spectra can be identified with conventional database-searching and protein-inference tools, allowing sensitive, untargeted analysis of DIA data without the need for a spectral library. Quantification is done with both precursor- and fragment-ion intensities. Furthermore, DIA-Umpire enables targeted extraction of quantitative information based on peptides initially identified in only a subset of the samples, resulting in more consistent quantification across multiple samples. We demonstrated the performance of the method with control samples of varying complexity and publicly available glycoproteomics and affinity purification-MS data.
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Affiliation(s)
- Chih-Chiang Tsou
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA
| | - Dmitry Avtonomov
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA
| | - Brett Larsen
- Lunenfeld-Tanenbaum Research Institute, Toronto, Canada
| | | | - Hyungwon Choi
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Anne-Claude Gingras
- Lunenfeld-Tanenbaum Research Institute, Toronto, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Canada
| | - Alexey I. Nesvizhskii
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA
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35
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Scott NE, Brown LM, Kristensen AR, Foster LJ. Development of a computational framework for the analysis of protein correlation profiling and spatial proteomics experiments. J Proteomics 2014; 118:112-29. [PMID: 25464368 DOI: 10.1016/j.jprot.2014.10.024] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2014] [Revised: 10/18/2014] [Accepted: 10/27/2014] [Indexed: 01/12/2023]
Abstract
UNLABELLED Standard approaches to studying an interactome do not easily allow conditional experiments but in recent years numerous groups have demonstrated the potential for co-fractionation/co-migration based approaches to assess an interactome at a similar sensitivity and specificity yet significantly lower cost and higher speed than traditional approaches. Unfortunately, there is as yet no implementation of the bioinformatics tools required to robustly analyze co-fractionation data in a way that can also integrate the valuable information contained in biological replicates. Here we have developed a freely available, integrated bioinformatics solution for the analysis of protein correlation profiling SILAC data. This modular solution allows the deconvolution of protein chromatograms into individual Gaussian curves enabling the use of these chromatography features to align replicates and assemble a consensus map of features observed across replicates; the chromatograms and individual curves are then used to quantify changes in protein interactions and construct the interactome. We have applied this workflow to the analysis of HeLa cells infected with a Salmonella enterica serovar Typhimurium infection model where we can identify specific interactions that are affected by the infection. These bioinformatics tools simplify the analysis of co-fractionation/co-migration data to the point where there is no specialized knowledge required to measure an interactome in this way. BIOLOGICAL SIGNIFICANCE We describe a set of software tools for the bioinformatics analysis of co-migration/co-fractionation data that integrates the results from multiple replicates to generate an interactome, including the impact on individual interactions of any external perturbation. This article is part of a Special Issue entitled: Protein dynamics in health and disease. Guest Editors: Pierre Thibault and Anne-Claude Gingras.
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Affiliation(s)
- Nichollas E Scott
- Centre for High-throughput Biology, University of British Columbia, Vancouver V6T 1Z4, British Columbia, Canada.
| | - Lyda M Brown
- Centre for High-throughput Biology, University of British Columbia, Vancouver V6T 1Z4, British Columbia, Canada
| | - Anders R Kristensen
- Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver V5Z 4S6, British Columbia, Canada
| | - Leonard J Foster
- Centre for High-throughput Biology, University of British Columbia, Vancouver V6T 1Z4, British Columbia, Canada.
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36
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Teleman J, Röst HL, Rosenberger G, Schmitt U, Malmström L, Malmström J, Levander F. DIANA--algorithmic improvements for analysis of data-independent acquisition MS data. ACTA ACUST UNITED AC 2014; 31:555-62. [PMID: 25348213 DOI: 10.1093/bioinformatics/btu686] [Citation(s) in RCA: 67] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
MOTIVATION Data independent acquisition mass spectrometry has emerged as a reproducible and sensitive alternative in quantitative proteomics, where parsing the highly complex tandem mass spectra requires dedicated algorithms. Recently, targeted data extraction was proposed as a novel analysis strategy for this type of data, but it is important to further develop these concepts to provide quality-controlled, interference-adjusted and sensitive peptide quantification. RESULTS We here present the algorithm DIANA and the classifier PyProphet, which are based on new probabilistic sub-scores to classify the chromatographic peaks in targeted data-independent acquisition data analysis. The algorithm is capable of providing accurate quantitative values and increased recall at a controlled false discovery rate, in a complex gold standard dataset. Importantly, we further demonstrate increased confidence gained by the use of two complementary data-independent acquisition targeted analysis algorithms, as well as increased numbers of quantified peptide precursors in complex biological samples. AVAILABILITY AND IMPLEMENTATION DIANA is implemented in scala and python and available as open source (Apache 2.0 license) or pre-compiled binaries from http://quantitativeproteomics.org/diana. PyProphet can be installed from PyPi (https://pypi.python.org/pypi/pyprophet). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Johan Teleman
- Department of Clinical Sciences, Lund University, BMC B14 221 84 Lund, Department of Immunotechnology, Lund University, Medicon Village (Building 406) 223 81 Lund, Sweden, Department of Biology, Institute of Molecular Systems Biology, ITS Scientific IT Services, ETH Zurich and SIT, University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland Department of Clinical Sciences, Lund University, BMC B14 221 84 Lund, Department of Immunotechnology, Lund University, Medicon Village (Building 406) 223 81 Lund, Sweden, Department of Biology, Institute of Molecular Systems Biology, ITS Scientific IT Services, ETH Zurich and SIT, University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland
| | - Hannes L Röst
- Department of Clinical Sciences, Lund University, BMC B14 221 84 Lund, Department of Immunotechnology, Lund University, Medicon Village (Building 406) 223 81 Lund, Sweden, Department of Biology, Institute of Molecular Systems Biology, ITS Scientific IT Services, ETH Zurich and SIT, University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland
| | - George Rosenberger
- Department of Clinical Sciences, Lund University, BMC B14 221 84 Lund, Department of Immunotechnology, Lund University, Medicon Village (Building 406) 223 81 Lund, Sweden, Department of Biology, Institute of Molecular Systems Biology, ITS Scientific IT Services, ETH Zurich and SIT, University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland
| | - Uwe Schmitt
- Department of Clinical Sciences, Lund University, BMC B14 221 84 Lund, Department of Immunotechnology, Lund University, Medicon Village (Building 406) 223 81 Lund, Sweden, Department of Biology, Institute of Molecular Systems Biology, ITS Scientific IT Services, ETH Zurich and SIT, University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland
| | - Lars Malmström
- Department of Clinical Sciences, Lund University, BMC B14 221 84 Lund, Department of Immunotechnology, Lund University, Medicon Village (Building 406) 223 81 Lund, Sweden, Department of Biology, Institute of Molecular Systems Biology, ITS Scientific IT Services, ETH Zurich and SIT, University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland
| | - Johan Malmström
- Department of Clinical Sciences, Lund University, BMC B14 221 84 Lund, Department of Immunotechnology, Lund University, Medicon Village (Building 406) 223 81 Lund, Sweden, Department of Biology, Institute of Molecular Systems Biology, ITS Scientific IT Services, ETH Zurich and SIT, University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland
| | - Fredrik Levander
- Department of Clinical Sciences, Lund University, BMC B14 221 84 Lund, Department of Immunotechnology, Lund University, Medicon Village (Building 406) 223 81 Lund, Sweden, Department of Biology, Institute of Molecular Systems Biology, ITS Scientific IT Services, ETH Zurich and SIT, University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland
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Prakash A, Peterman S, Ahmad S, Sarracino D, Frewen B, Vogelsang M, Byram G, Krastins B, Vadali G, Lopez M. Hybrid data acquisition and processing strategies with increased throughput and selectivity: pSMART analysis for global qualitative and quantitative analysis. J Proteome Res 2014; 13:5415-30. [PMID: 25244318 DOI: 10.1021/pr5003017] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Data-dependent acquisition (DDA) and data-independent acquisition strategies (DIA) have both resulted in improved understanding of proteomics samples. Both strategies have advantages and disadvantages that are well-published, where DDA is typically applied for deep discovery and DIA may be used to create sample records. In this paper, we present a hybrid data acquisition and processing strategy (pSMART) that combines the strengths of both techniques and provides significant benefits for qualitative and quantitative peptide analysis. The performance of pSMART is compared to published DIA strategies in an experiment that allows the objective assessment of DIA performance with respect to interrogation of previously acquired MS data. The results of this experiment demonstrate that pSMART creates fewer decoy hits than a standard DIA strategy. Moreover, we show that pSMART is more selective, sensitive, and reproducible than either standard DIA or DDA strategies alone.
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Affiliation(s)
- Amol Prakash
- Thermo Fisher Scientific, 790 Memorial Drive, Suite 202, Cambridge, Massachusetts 02139, United States
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