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González-Domínguez Á, Estanyol-Torres N, Brunius C, Landberg R, González-Domínguez R. QC omics: Recommendations and Guidelines for Robust, Easily Implementable and Reportable Quality Control of Metabolomics Data. Anal Chem 2024; 96:1064-1072. [PMID: 38179935 PMCID: PMC10809278 DOI: 10.1021/acs.analchem.3c03660] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 11/03/2023] [Accepted: 12/21/2023] [Indexed: 01/06/2024]
Abstract
The implementation of quality control strategies is crucial to ensure the reproducibility, accuracy, and meaningfulness of metabolomics data. However, this pivotal step is often overlooked within the metabolomics workflow and frequently relies on the use of nonstandardized and poorly reported protocols. To address current limitations in this respect, we have developed QComics, a robust, easily implementable and reportable method for monitoring and controlling data quality. The protocol operates in various sequential steps aimed to (i) correct for background noise and carryover, (ii) detect signal drifts and "out-of-control" observations, (iii) deal with missing data, (iv) remove outliers, (v) monitor quality markers to identify samples affected by improper collection, preprocessing, or storage, and (vi) assess overall data quality in terms of precision and accuracy. Notably, this tool considers important issues often neglected along quality control, such as the need of separately handling missing values and truly absent data to avoid losing relevant biological information, as well as the large impact that preanalytical factors may elicit on metabolomics results. Altogether, the guidelines compiled in QComics might contribute to establishing gold standard recommendations and best practices for quality control within the metabolomics community.
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Affiliation(s)
- Álvaro González-Domínguez
- Instituto
de Investigación e Innovación Biomédica de Cádiz
(INiBICA), Hospital Universitario Puerta del Mar, Universidad de Cádiz, Cádiz 11009, Spain
| | - Núria Estanyol-Torres
- Division
of Food and Nutrition Science, Department of Life Sciences, Chalmers University of Technology,SE-412 96Gothenburg ,Sweden
| | - Carl Brunius
- Division
of Food and Nutrition Science, Department of Life Sciences, Chalmers University of Technology,SE-412 96Gothenburg ,Sweden
| | - Rikard Landberg
- Division
of Food and Nutrition Science, Department of Life Sciences, Chalmers University of Technology,SE-412 96Gothenburg ,Sweden
| | - Raúl González-Domínguez
- Instituto
de Investigación e Innovación Biomédica de Cádiz
(INiBICA), Hospital Universitario Puerta del Mar, Universidad de Cádiz, Cádiz 11009, Spain
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2
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Fang Y, Li QR, Zhang ZH, Wu JR, Zheng HR, Zeng R. FIGS: Featured Ion-Guided Stoichiometry for Data-Independent Proteomics through Dynamic Deconvolution. J Proteome Res 2021; 20:4131-4138. [PMID: 34310138 DOI: 10.1021/acs.jproteome.1c00438] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Data-independent acquisition (DIA) has significant advantages for mass spectrometry (MS)-based peptide quantification, while mixed spectra remain challenging for precise stoichiometry. We here choose to analyze the library spectra in specific sets preferentially and locally. Accordingly, the featured ions are defined as the fragment ions uniquely assigned to corresponding precursors in a given spectrum set, which are generated by dynamic deconvolution of the mixed mass spectra. Then, we present featured ion-guided stoichiometry (FIGS), a universal method for accurate and robust peptide quantification for the DIA-MS data. We validate the high performance on the quantification sensitivity, accuracy, and efficiency of FIGS. Notably, our FIGS dramatically improves the quantification accuracy for the full dynamic range, especially for low-abundance peptides.
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Affiliation(s)
- Yan Fang
- School of Computer Science and Technology, University of Science and Technology of China, 96 JinZhai Road, Hefei 230026, China
| | - Qing-Run Li
- CAS Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Sciences, Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, 320 YueYang Road, Shanghai 200031, China
| | - Zhen-Hang Zhang
- School of Computer Science and Technology, University of Science and Technology of China, 96 JinZhai Road, Hefei 230026, China
| | - Jia-Rui Wu
- CAS Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Sciences, Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, 320 YueYang Road, Shanghai 200031, China
| | - Hao-Ran Zheng
- School of Computer Science and Technology, University of Science and Technology of China, 96 JinZhai Road, Hefei 230026, China.,Anhui Key Laboratory of Software Engineering in Computing and Communication, University of Science and Technology of China, 96 JinZhai Road, Hefei 230026, China.,Department of Systems Biology, University of Science and Technology of China, 96 JinZhai Road, Hefei 230026, China
| | - Rong Zeng
- CAS Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Sciences, Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, 320 YueYang Road, Shanghai 200031, China
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3
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Matthiesen R, Carvalho AS. Methods and Algorithms for Quantitative Proteomics by Mass Spectrometry. Methods Mol Biol 2020; 2051:161-197. [PMID: 31552629 DOI: 10.1007/978-1-4939-9744-2_7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Protein quantitation by mass spectrometry has always been a resourceful technique in protein discovery, and more recently it has leveraged the advent of clinical proteomics. A single mass spectrometry analysis experiment provides identification and quantitation of proteins as well as information on posttranslational modifications landscape. By contrast, protein array technologies are restricted to quantitation of targeted proteins and their modifications. Currently, there are an overwhelming number of quantitative mass spectrometry methods for protein and peptide quantitation. The aim here is to provide an overview of the most common mass spectrometry methods and algorithms used in quantitative proteomics and discuss the computational aspects to obtain reliable quantitative measures of proteins, peptides and their posttranslational modifications. The development of a pipeline using commercial or freely available software is one of the main challenges in data analysis of many experimental projects. Recent developments of R statistical programming language make it attractive to fully develop pipelines for quantitative proteomics. We discuss concepts of quantitative proteomics that together with current R packages can be used to build highly customizable pipelines.
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Affiliation(s)
- Rune Matthiesen
- Computational and Experimental Biology Group, CEDOC, Chronic Diseases Research Centre, NOVA Medical School, Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Lisboa, Portugal
| | - Ana Sofia Carvalho
- Computational and Experimental Biology Group, CEDOC, Chronic Diseases Research Centre, NOVA Medical School, Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Lisboa, Portugal.
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4
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Rathahao-Paris E, Alves S, Boussaid N, Picard-Hagen N, Gayrard V, Toutain PL, Tabet JC, Rutledge DN, Paris A. Evaluation and validation of an analytical approach for high-throughput metabolomic fingerprinting using direct introduction-high-resolution mass spectrometry: Applicability to classification of urine of scrapie-infected ewes. EUROPEAN JOURNAL OF MASS SPECTROMETRY (CHICHESTER, ENGLAND) 2019; 25:251-258. [PMID: 30335517 DOI: 10.1177/1469066718806450] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Direct injection-mass spectrometry can be used to perform high-throughput metabolomic fingerprinting. This work aims to evaluate a global analytical workflow in terms of sample preparation (urine sample dilution), high-resolution detection (quality of generated data based on criteria such as mass measurement accuracy and detection sensitivity) and data analysis using dedicated bioinformatics tools. Investigation was performed on a large number of biological samples collected from sheep infected or not with scrapie. Direct injection-mass spectrometry approach is usually affected by matrix effects, eventually hampering detection of some relevant biomarkers. Reference compounds were spiked in biological samples to help evaluate the quality of direct injection-mass spectrometry data produced by Fourier Transform mass spectrometry. Despite the potential of high-resolution detection, some drawbacks still remain. The most critical is the presence of matrix effects, which could be minimized by optimizing the sample dilution factor. The data quality in terms of mass measurement accuracy and reproducible intensity was evaluated. Good repeatability was obtained for the chosen dilution factor (i.e., 2000). More than 150 analyses were performed in less than 16 hours using the optimized direct injection-mass spectrometry approach. Discrimination of different status of sheeps in relation to scrapie infection (i.e., scrapie-affected, preclinical scrapie or healthy) was obtained from the application of Shrinkage Discriminant Analysis to the direct injection-mass spectrometry data. The most relevant variables related to this discrimination were selected and annotated. This study demonstrated that the choice of appropriated dilution faction is indispensable for producing quality and informative direct injection-mass spectrometry data. Successful application of direct injection-mass spectrometry approach for high throughput analysis of a large number of biological samples constitutes the proof of the concept.
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Affiliation(s)
- Estelle Rathahao-Paris
- 1 UMR Ingénierie Procédés Aliments, AgroParisTech, Inra, Université Paris-Saclay, Massy, France
- 2 Sorbonne Université, Faculté des Sciences et de l'Ingénierie, Institut Parisien de Chimie Moléculaire (IPCM), Paris, France
| | - Sandra Alves
- 2 Sorbonne Université, Faculté des Sciences et de l'Ingénierie, Institut Parisien de Chimie Moléculaire (IPCM), Paris, France
| | - Nawel Boussaid
- 1 UMR Ingénierie Procédés Aliments, AgroParisTech, Inra, Université Paris-Saclay, Massy, France
| | - Nicole Picard-Hagen
- 3 Toxalim, Université de Toulouse, INRA (Institut National de la Recherche Agronomique), INP (Institut National Polytechnique de Toulouse)-ENVT (Ecole Nationale Vétérinaire de Toulouse), Toulouse, France
| | - Véronique Gayrard
- 3 Toxalim, Université de Toulouse, INRA (Institut National de la Recherche Agronomique), INP (Institut National Polytechnique de Toulouse)-ENVT (Ecole Nationale Vétérinaire de Toulouse), Toulouse, France
| | | | - Jean-Claude Tabet
- 2 Sorbonne Université, Faculté des Sciences et de l'Ingénierie, Institut Parisien de Chimie Moléculaire (IPCM), Paris, France
- 5 CEA-INRA, Service de Pharmacologie et d'Immunoanalyse, Laboratoire d'Etude du Métabolisme des Médicaments, MetaboHUB, Université Paris-Saclay, Gif-sur-Yvette cedex, France
| | - Douglas N Rutledge
- 1 UMR Ingénierie Procédés Aliments, AgroParisTech, Inra, Université Paris-Saclay, Massy, France
| | - Alain Paris
- 6 Unité Molécules de Communication et Adaptation des Microorganismes (MCAM), Muséum National d'Histoire Naturelle, CNRS, CP54, Paris, France
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Specter: linear deconvolution for targeted analysis of data-independent acquisition mass spectrometry proteomics. Nat Methods 2018; 15:371-378. [PMID: 29608554 PMCID: PMC5924490 DOI: 10.1038/nmeth.4643] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Accepted: 02/23/2018] [Indexed: 01/11/2023]
Abstract
Mass spectrometry with data-independent acquisition (DIA) has emerged as a promising method to greatly improve the comprehensiveness and reproducibility of targeted and discovery proteomics, in theory systematically measuring all peptide precursors within a biological sample. Despite the technical maturity of DIA, the analytical challenges involved in discriminating between peptides with similar sequences in convoluted spectra have limited its applicability in important cases, such as the detection of single-nucleotide polymorphisms and alternative site localizations in phosphoproteomics data. We have developed Specter, an open-source software tool that uses linear algebra to deconvolute DIA mixture spectra directly in terms of a spectral library, circumventing the problems associated with typical fragment correlation-based approaches. We validate the sensitivity of Specter and its performance relative to other methods by means of several complex datasets, and show that Specter is able to successfully analyze cases involving highly similar peptides that are typically challenging for DIA analysis methods.
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6
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Wahab MF, Patel DC, Armstrong DW. Total peak shape analysis: detection and quantitation of concurrent fronting, tailing, and their effect on asymmetry measurements. J Chromatogr A 2017. [DOI: 10.1016/j.chroma.2017.06.031] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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7
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Bryant AE, Aldape MJ, Bayer CR, Katahira EJ, Bond L, Nicora CD, Fillmore TL, Clauss TRW, Metz TO, Webb-Robertson BJ, Stevens DL. Effects of delayed NSAID administration after experimental eccentric contraction injury - A cellular and proteomics study. PLoS One 2017; 12:e0172486. [PMID: 28245256 PMCID: PMC5330483 DOI: 10.1371/journal.pone.0172486] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2016] [Accepted: 02/06/2017] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Acute muscle injuries are exceedingly common and non-steroidal anti-inflammatory drugs (NSAIDs) are widely consumed to reduce the associated inflammation, swelling and pain that peak 1-2 days post-injury. While prophylactic use or early administration of NSAIDs has been shown to delay muscle regeneration and contribute to loss of muscle strength after healing, little is known about the effects of delayed NSAID use. Further, NSAID use following non-penetrating injury has been associated with increased risk and severity of infection, including that due to group A streptococcus, though the mechanisms remain to be elucidated. The present study investigated the effects of delayed NSAID administration on muscle repair and sought mechanisms supporting an injury/NSAID/infection axis. METHODS A murine model of eccentric contraction (EC)-induced injury of the tibialis anterior muscle was used to profile the cellular and molecular changes induced by ketorolac tromethamine administered 47 hr post injury. RESULTS NSAID administration inhibited several important muscle regeneration processes and down-regulated multiple cytoprotective proteins known to inhibit the intrinsic pathway of programmed cell death. These activities were associated with increased caspase activity in injured muscles but were independent of any NSAID effect on macrophage influx or phenotype switching. CONCLUSIONS These findings provide new molecular evidence supporting the notion that NSAIDs have a direct negative influence on muscle repair after acute strain injury in mice and thus add to renewed concern about the safety and benefits of NSAIDS in both children and adults, in those with progressive loss of muscle mass such as the elderly or patients with cancer or AIDS, and those at risk of secondary infection after trauma or surgery.
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Affiliation(s)
- Amy E. Bryant
- U.S. Department of Veterans Affairs, Office of Research and Development, Boise, ID, United States of America
- University of Washington School of Medicine, Seattle, WA, United States of America
| | - Michael J. Aldape
- U.S. Department of Veterans Affairs, Office of Research and Development, Boise, ID, United States of America
- Northwest Nazarene University, Nampa, ID, United States of America
| | - Clifford R. Bayer
- U.S. Department of Veterans Affairs, Office of Research and Development, Boise, ID, United States of America
| | - Eva J. Katahira
- U.S. Department of Veterans Affairs, Office of Research and Development, Boise, ID, United States of America
| | - Laura Bond
- Boise State University, Boise, ID, United States of America
| | - Carrie D. Nicora
- Pacific Northwest National Laboratory, Richland, WA, United States of America
| | - Thomas L. Fillmore
- Pacific Northwest National Laboratory, Richland, WA, United States of America
| | | | - Thomas O. Metz
- Pacific Northwest National Laboratory, Richland, WA, United States of America
| | | | - Dennis L. Stevens
- U.S. Department of Veterans Affairs, Office of Research and Development, Boise, ID, United States of America
- University of Washington School of Medicine, Seattle, WA, United States of America
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8
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Simader AM, Kluger B, Neumann NKN, Bueschl C, Lemmens M, Lirk G, Krska R, Schuhmacher R. QCScreen: a software tool for data quality control in LC-HRMS based metabolomics. BMC Bioinformatics 2015; 16:341. [PMID: 26498454 PMCID: PMC4619325 DOI: 10.1186/s12859-015-0783-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2015] [Accepted: 10/19/2015] [Indexed: 11/23/2022] Open
Abstract
Background Metabolomics experiments often comprise large numbers of biological samples resulting in huge amounts of data. This data needs to be inspected for plausibility before data evaluation to detect putative sources of error e.g. retention time or mass accuracy shifts. Especially in liquid chromatography-high resolution mass spectrometry (LC-HRMS) based metabolomics research, proper quality control checks (e.g. for precision, signal drifts or offsets) are crucial prerequisites to achieve reliable and comparable results within and across experimental measurement sequences. Software tools can support this process. Results The software tool QCScreen was developed to offer a quick and easy data quality check of LC-HRMS derived data. It allows a flexible investigation and comparison of basic quality-related parameters within user-defined target features and the possibility to automatically evaluate multiple sample types within or across different measurement sequences in a short time. It offers a user-friendly interface that allows an easy selection of processing steps and parameter settings. The generated results include a coloured overview plot of data quality across all analysed samples and targets and, in addition, detailed illustrations of the stability and precision of the chromatographic separation, the mass accuracy and the detector sensitivity. The use of QCScreen is demonstrated with experimental data from metabolomics experiments using selected standard compounds in pure solvent. The application of the software identified problematic features, samples and analytical parameters and suggested which data files or compounds required closer manual inspection. Conclusions QCScreen is an open source software tool which provides a useful basis for assessing the suitability of LC-HRMS data prior to time consuming, detailed data processing and subsequent statistical analysis. It accepts the generic mzXML format and thus can be used with many different LC-HRMS platforms to process both multiple quality control sample types as well as experimental samples in one or more measurement sequences. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0783-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Alexandra Maria Simader
- Center for Analytical Chemistry, Department of Agrobiotechnology (IFA-Tulln), University of Natural Resources and Life Sciences, Vienna (BOKU), Konrad Lorenz Strasse 20, 3430, Tulln, Austria.
| | - Bernhard Kluger
- Center for Analytical Chemistry, Department of Agrobiotechnology (IFA-Tulln), University of Natural Resources and Life Sciences, Vienna (BOKU), Konrad Lorenz Strasse 20, 3430, Tulln, Austria.
| | - Nora Katharina Nicole Neumann
- Center for Analytical Chemistry, Department of Agrobiotechnology (IFA-Tulln), University of Natural Resources and Life Sciences, Vienna (BOKU), Konrad Lorenz Strasse 20, 3430, Tulln, Austria.
| | - Christoph Bueschl
- Center for Analytical Chemistry, Department of Agrobiotechnology (IFA-Tulln), University of Natural Resources and Life Sciences, Vienna (BOKU), Konrad Lorenz Strasse 20, 3430, Tulln, Austria.
| | - Marc Lemmens
- Institute for Biotechnology in Plant Production, Department of Agrobiotechnology (IFA-Tulln), University of Natural Resources and Life Sciences, Vienna (BOKU), Konrad Lorenz Strasse 20, 3430, Tulln, Austria.
| | - Gerald Lirk
- University of Applied Sciences Upper Austria, School of Informatics, Communications and Media, Softwarepark 11, 4232, Hagenberg, Austria.
| | - Rudolf Krska
- Center for Analytical Chemistry, Department of Agrobiotechnology (IFA-Tulln), University of Natural Resources and Life Sciences, Vienna (BOKU), Konrad Lorenz Strasse 20, 3430, Tulln, Austria.
| | - Rainer Schuhmacher
- Center for Analytical Chemistry, Department of Agrobiotechnology (IFA-Tulln), University of Natural Resources and Life Sciences, Vienna (BOKU), Konrad Lorenz Strasse 20, 3430, Tulln, Austria.
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9
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Weddell JC, Imoukhuede PI. Quantitative characterization of cellular membrane-receptor heterogeneity through statistical and computational modeling. PLoS One 2014; 9:e97271. [PMID: 24827582 PMCID: PMC4020774 DOI: 10.1371/journal.pone.0097271] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2014] [Accepted: 04/16/2014] [Indexed: 12/20/2022] Open
Abstract
Cell population heterogeneity can affect cellular response and is a major factor in drug resistance. However, there are few techniques available to represent and explore how heterogeneity is linked to population response. Recent high-throughput genomic, proteomic, and cellomic approaches offer opportunities for profiling heterogeneity on several scales. We have recently examined heterogeneity in vascular endothelial growth factor receptor (VEGFR) membrane localization in endothelial cells. We and others processed the heterogeneous data through ensemble averaging and integrated the data into computational models of anti-angiogenic drug effects in breast cancer. Here we show that additional modeling insight can be gained when cellular heterogeneity is considered. We present comprehensive statistical and computational methods for analyzing cellomic data sets and integrating them into deterministic models. We present a novel method for optimizing the fit of statistical distributions to heterogeneous data sets to preserve important data and exclude outliers. We compare methods of representing heterogeneous data and show methodology can affect model predictions up to 3.9-fold. We find that VEGF levels, a target for tuning angiogenesis, are more sensitive to VEGFR1 cell surface levels than VEGFR2; updating VEGFR1 levels in the tumor model gave a 64% change in free VEGF levels in the blood compartment, whereas updating VEGFR2 levels gave a 17% change. Furthermore, we find that subpopulations of tumor cells and tumor endothelial cells (tEC) expressing high levels of VEGFR (>35,000 VEGFR/cell) negate anti-VEGF treatments. We show that lowering the VEGFR membrane insertion rate for these subpopulations recovers the anti-angiogenic effect of anti-VEGF treatment, revealing new treatment targets for specific tumor cell subpopulations. This novel method of characterizing heterogeneous distributions shows for the first time how different representations of the same data set lead to different predictions of drug efficacy.
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Affiliation(s)
- Jared C. Weddell
- Department of Bioengineering, University of Illinois Urbana Champaign, Urbana, Illinois, United States of America
| | - P. I. Imoukhuede
- Department of Bioengineering, University of Illinois Urbana Champaign, Urbana, Illinois, United States of America
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10
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Zamanzad Ghavidel F, Claesen J, Burzykowski T, Valkenborg D. Comparison of the Mahalanobis distance and Pearson's χ² statistic as measures of similarity of isotope patterns. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2014; 25:293-6. [PMID: 24249044 DOI: 10.1007/s13361-013-0773-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2013] [Revised: 10/07/2013] [Accepted: 10/14/2013] [Indexed: 05/23/2023]
Abstract
To extract a genuine peptide signal from a mass spectrum, an observed series of peaks at a particular mass can be compared with the isotope distribution expected for a peptide of that mass. To decide whether the observed series of peaks is similar to the isotope distribution, a similarity measure is needed. In this short communication, we investigate whether the Mahalanobis distance could be an alternative measure for the commonly employed Pearson's χ(2) statistic. We evaluate the performance of the two measures by using a controlled MALDI-TOF experiment. The results indicate that Pearson's χ(2) statistic has better discriminatory performance than the Mahalanobis distance and is a more robust measure.
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11
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Ayoglu B, Häggmark A, Neiman M, Igel U, Uhlén M, Schwenk JM, Nilsson P. Systematic antibody and antigen-based proteomic profiling with microarrays. Expert Rev Mol Diagn 2014; 11:219-34. [DOI: 10.1586/erm.10.110] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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12
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Sandin M, Teleman J, Malmström J, Levander F. Data processing methods and quality control strategies for label-free LC-MS protein quantification. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2013; 1844:29-41. [PMID: 23567904 DOI: 10.1016/j.bbapap.2013.03.026] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2012] [Revised: 01/18/2013] [Accepted: 03/08/2013] [Indexed: 12/20/2022]
Abstract
Protein quantification using different LC-MS techniques is becoming a standard practice. However, with a multitude of experimental setups to choose from, as well as a wide array of software solutions for subsequent data processing, it is non-trivial to select the most appropriate workflow for a given biological question. In this review, we highlight different issues that need to be addressed by software for quantitative LC-MS experiments and describe different approaches that are available. With focus on label-free quantification, examples are discussed both for LC-MS/MS and LC-SRM data processing. We further elaborate on current quality control methodology for performing accurate protein quantification experiments. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.
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Affiliation(s)
- Marianne Sandin
- Department of Immunotechnology, Lund University, BMC D13, 22184 Lund, Sweden
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13
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Ho TJ, Kuo CH, Wang SY, Chen GY, Tseng YJ. True ion pick (TIPick): a denoising and peak picking algorithm to extract ion signals from liquid chromatography/mass spectrometry data. JOURNAL OF MASS SPECTROMETRY : JMS 2013; 48:234-242. [PMID: 23378096 DOI: 10.1002/jms.3154] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2012] [Revised: 12/06/2012] [Accepted: 12/06/2012] [Indexed: 06/01/2023]
Abstract
Liquid Chromatography-Time of Flight Mass Spectrometry has become an important technique for toxicological screening and metabolomics. We describe TIPick a novel algorithm that accurately and sensitively detects target compounds in biological samples. TIPick comprises two main steps: background subtraction and peak picking. By subtracting a blank chromatogram, TIPick eliminates chemical signals of blank injections and reduces false positive results. TIPick detects peaks by calculating the S(CC(INI)) values of extracted ion chromatograms (EICs) without considering peak shapes, and it is able to detect tailing and fronting peaks. TIPick also uses duplicate injections to enhance the signals of the peaks and thus improve the peak detection power. Commonly seen split peaks caused by either saturation of the mass spectrometer detector or a mathematical background subtraction algorithm can be resolved by adjusting the mass error tolerance of the EICs and by comparing the EICs before and after background subtraction. The performance of TIPick was tested in a data set containing 297 standard mixtures; the recall, precision and F-score were 0.99, 0.97 and 0.98, respectively. TIPick was successfully used to construct and analyze the NTU MetaCore metabolomics chemical standards library, and it was applied for toxicological screening and metabolomics studies.
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Affiliation(s)
- Tsung-Jung Ho
- The Metabolomics Core Laboratory, Center of Genomic Medicine, National Taiwan University, No. 1 Sec. 4, Roosevelt Road, Taipei, Taiwan, 106
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14
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Matthiesen R, Carvalho AS. Methods and algorithms for quantitative proteomics by mass spectrometry. Methods Mol Biol 2013; 1007:183-217. [PMID: 23666727 DOI: 10.1007/978-1-62703-392-3_8] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Protein quantitation by mass spectrometry (MS) is attractive since it is possible to obtain both identification and quantitative values of proteins and their posttranslational modifications in a single experiment. In contrast, protein arrays only provide quantitative values of targeted proteins and their modifications. There are an overwhelming number of quantitative MS methods for protein and peptide quantitation. The aim here is to provide an overview of the most common MS methods and algorithms used in quantitative proteomics and discuss the computational algorithms needed to reliably quantitate proteins, peptides, and their posttranslational modifications. One of the main challenges in data analysis of many experimental projects is to pipe together a number of software solutions that are either commercial or freely available. The aim of this chapter is to provide a good set of algorithms, ideas, and resources that can easily be implemented in scripting language like R, Python, or Perl. By understanding the algorithmic ideas presented here, data from any instrument or modified experimental protocol can be analyzed and is therefore in the authors' opinion more valuable than a black box concept.
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Affiliation(s)
- Rune Matthiesen
- Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), Porto, Portugal
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15
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Abstract
Metabolomics aims at identification and quantitation of small molecules involved in metabolic reactions. LC-MS has enjoyed a growing popularity as the platform for metabolomic studies due to its high throughput, soft ionization, and good coverage of metabolites. The success of a LC-MS-based metabolomic study often depends on multiple experimental, analytical, and computational steps. This review presents a workflow of a typical LC-MS-based metabolomic analysis for identification and quantitation of metabolites indicative of biological/environmental perturbations. Challenges and current solutions in each step of the workflow are reviewed. The review intends to help investigators understand the challenges in metabolomic studies and to determine appropriate experimental, analytical, and computational methods to address these challenges.
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Affiliation(s)
- Bin Zhou
- Lombardi Comprehensive Cancer Center, Georgetown University, 4000 Reservoir Rd., NW, Washington, DC 20057, USA. Fax: 202-687-0227; Tel: 202-687-2283
| | - Jun Feng Xiao
- Lombardi Comprehensive Cancer Center, Georgetown University, 4000 Reservoir Rd., NW, Washington, DC 20057, USA. Fax: 202-687-0227; Tel: 202-687-2283
| | - Leepika Tuli
- Lombardi Comprehensive Cancer Center, Georgetown University, 4000 Reservoir Rd., NW, Washington, DC 20057, USA. Fax: 202-687-0227; Tel: 202-687-2283
| | - Habtom W. Ressom
- Lombardi Comprehensive Cancer Center, Georgetown University, 4000 Reservoir Rd., NW, Washington, DC 20057, USA. Fax: 202-687-0227; Tel: 202-687-2283
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16
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Leitch MC, Mitra I, Sadygov RG. Generalized Linear and Mixed Models for Label-Free Shotgun Proteomics. STATISTICS AND ITS INTERFACE 2012; 5:89-98. [PMID: 22822415 PMCID: PMC3399744 DOI: 10.4310/sii.2012.v5.n1.a8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Label-free shotgun proteomics holds great promise, and has already had some great successes in pinpointing which proteins are up or down regulated in certain disease states. However, there are still some pressing issues concerning the statistical analysis of label-free shotgun proteomics, and this field has not enjoyed as much dedication of statistical research towards it as microarray research has. Here we reapply previously used statistical methods, the QSpec and quasi-Poisson, as well as apply the negative binomial distribution to both a control data set and a data set with known differential expression to determine the successes and failure of each of the three methods.
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Affiliation(s)
| | | | - Rovshan G. Sadygov
- Department of Biochemistry and Molecular Biology, Sealy Center for Molecular Medicine, The University of Texas Medical Branch, Galveston, TX 77555
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17
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Matzke MM, Waters KM, Metz TO, Jacobs JM, Sims AC, Baric RS, Pounds JG, Webb-Robertson BJM. Improved quality control processing of peptide-centric LC-MS proteomics data. ACTA ACUST UNITED AC 2011; 27:2866-72. [PMID: 21852304 PMCID: PMC3187650 DOI: 10.1093/bioinformatics/btr479] [Citation(s) in RCA: 78] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Motivation: In the analysis of differential peptide peak intensities (i.e. abundance measures), LC-MS analyses with poor quality peptide abundance data can bias downstream statistical analyses and hence the biological interpretation for an otherwise high-quality dataset. Although considerable effort has been placed on assuring the quality of the peptide identification with respect to spectral processing, to date quality assessment of the subsequent peptide abundance data matrix has been limited to a subjective visual inspection of run-by-run correlation or individual peptide components. Identifying statistical outliers is a critical step in the processing of proteomics data as many of the downstream statistical analyses [e.g. analysis of variance (ANOVA)] rely upon accurate estimates of sample variance, and their results are influenced by extreme values. Results: We describe a novel multivariate statistical strategy for the identification of LC-MS runs with extreme peptide abundance distributions. Comparison with current method (run-by-run correlation) demonstrates a significantly better rate of identification of outlier runs by the multivariate strategy. Simulation studies also suggest that this strategy significantly outperforms correlation alone in the identification of statistically extreme liquid chromatography-mass spectrometry (LC-MS) runs. Availability:https://www.biopilot.org/docs/Software/RMD.php Contact:bj@pnl.gov Supplementary information:Supplementary material is available at Bioinformatics online.
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18
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Bielow C, Gröpl C, Kohlbacher O, Reinert K. Bioinformatics for qualitative and quantitative proteomics. Methods Mol Biol 2011; 719:331-349. [PMID: 21370091 DOI: 10.1007/978-1-61779-027-0_15] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Mass spectrometry is today a key analytical technique to elucidate the amount and content of proteins expressed in a certain cellular context. The degree of automation in proteomics has yet to reach that of genomic techniques, but even current technologies make a manual inspection of the data infeasible. This article addresses the key algorithmic problems bioinformaticians face when handling modern proteomic samples and shows common solutions to them. We provide examples on how algorithms can be combined to build relatively complex analysis pipelines, point out certain pitfalls and aspects worth considering and give a list of current state-of-the-art tools.
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Affiliation(s)
- Chris Bielow
- AG Algorithmische Bioinformatik, Institut für Informatik, Freie Universität Berlin, Berlin, Germany.
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19
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Proteomics in clinical chemistry: will it be long? Trends Biotechnol 2010; 28:225-9. [DOI: 10.1016/j.tibtech.2010.02.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2009] [Revised: 02/11/2010] [Accepted: 02/23/2010] [Indexed: 12/11/2022]
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