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Two-Dimensional Gel Electrophoresis Image Analysis. Methods Mol Biol 2021; 2361:3-13. [PMID: 34236652 DOI: 10.1007/978-1-0716-1641-3_1] [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: 12/27/2022]
Abstract
Gel-based proteomics is still quite widespread due to its high-resolution power; the experimental approach is based on differential analysis, where groups of samples (e.g., control vs diseased) are compared to identify panels of potential biomarkers. However, the reliability of the result of the differential analysis is deeply influenced by 2D-PAGE maps image analysis procedures. The analysis of 2D-PAGE images consists of several steps, such as image preprocessing, spot detection and quantitation, image warping and alignment, spot matching. Several approaches are present in literature, and classical or last-generation commercial software packages exploit different algorithms for each step of the analysis. Here, the most widespread approaches and a comparison of the different strategies are presented.
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Van Vleet TR, Liguori MJ, Lynch JJ, Rao M, Warder S. Screening Strategies and Methods for Better Off-Target Liability Prediction and Identification of Small-Molecule Pharmaceuticals. SLAS DISCOVERY 2018; 24:1-24. [PMID: 30196745 DOI: 10.1177/2472555218799713] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Pharmaceutical discovery and development is a long and expensive process that, unfortunately, still results in a low success rate, with drug safety continuing to be a major impedance. Improved safety screening strategies and methods are needed to more effectively fill this critical gap. Recent advances in informatics are now making it possible to manage bigger data sets and integrate multiple sources of screening data in a manner that can potentially improve the selection of higher-quality drug candidates. Integrated screening paradigms have become the norm in Pharma, both in discovery screening and in the identification of off-target toxicity mechanisms during later-stage development. Furthermore, advances in computational methods are making in silico screens more relevant and suggest that they may represent a feasible option for augmenting the current screening paradigm. This paper outlines several fundamental methods of the current drug screening processes across Pharma and emerging techniques/technologies that promise to improve molecule selection. In addition, the authors discuss integrated screening strategies and provide examples of advanced screening paradigms.
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Affiliation(s)
- Terry R Van Vleet
- 1 Department of Investigative Toxicology and Pathology, AbbVie, N Chicago, IL, USA
| | - Michael J Liguori
- 1 Department of Investigative Toxicology and Pathology, AbbVie, N Chicago, IL, USA
| | - James J Lynch
- 2 Department of Integrated Science and Technology, AbbVie, N Chicago, IL, USA
| | - Mohan Rao
- 1 Department of Investigative Toxicology and Pathology, AbbVie, N Chicago, IL, USA
| | - Scott Warder
- 3 Department of Target Enabling Science and Technology, AbbVie, N Chicago, IL, USA
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Abstract
2D-DIGE is still a very widespread technique in proteomics for the identification of panels of biomarkers, allowing to tackle with some important drawback of classical two-dimensional gel-electrophoresis. However, once 2D-gels are obtained, they must undergo a quite articulated multistep image analysis procedure before the final differential analysis via statistical mono- and multivariate methods. Here, the main steps of image analysis software are described and the most recent procedures reported in the literature are briefly presented.
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Affiliation(s)
- Elisa Robotti
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121, Alessandria, Italy.
| | - Emilio Marengo
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121, Alessandria, Italy
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Wetmore BA, Merrick BA. Invited Review: Toxicoproteomics: Proteomics Applied to Toxicology and Pathology. Toxicol Pathol 2016; 32:619-42. [PMID: 15580702 DOI: 10.1080/01926230490518244] [Citation(s) in RCA: 122] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Global measurement of proteins and their many attributes in tissues and biofluids defines the field of proteomics. Toxicoproteomics, as part of the larger field of toxicogenomics, seeks to identify critical proteins and pathways in biological systems that are affected by and respond to adverse chemical and environmental exposures using global protein expression technologies. Toxicoproteomics integrates 3 disciplinary areas: traditional toxicology and pathology, differential protein and gene expression analysis, and systems biology. Key topics to be reviewed are the evolution of proteomics, proteomic technology platforms and their capabilities with exemplary studies from biology and medicine, a review of over 50 recent studies applying proteomic analysis to toxicological research, and the recent development of databases designed to integrate -Omics technologies with toxicology and pathology. Proteomics is examined for its potential in discovery of new biomarkers and toxicity signatures, in mapping serum, plasma, and other biofluid proteomes, and in parallel proteomic and transcriptomic studies. The new field of toxicoproteomics is uniquely positioned toward an expanded understanding of protein expression during toxicity and environmental disease for the advancement of public health.
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Affiliation(s)
- Barbara A Wetmore
- National Center for Toxicogenomics, National Institute of Environmental Health Sciences, Research Triangle Park, North Caroline 27709, USA
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Robotti E, Marengo E, Quasso F. Image Pretreatment Tools II: Normalization Techniques for 2-DE and 2-D DIGE. Methods Mol Biol 2016; 1384:91-107. [PMID: 26611411 DOI: 10.1007/978-1-4939-3255-9_6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Gel electrophoresis is usually applied to identify different protein expression profiles in biological samples (e.g., control vs. pathological, control vs. treated). Information about the effect to be investigated (a pathology, a drug, a ripening effect, etc.) is however generally confounded with experimental variability that is quite large in 2-DE and may arise from small variations in the sample preparation, reagents, sample loading, electrophoretic conditions, staining and image acquisition. Obtaining valid quantitative estimates of protein abundances in each map, before the differential analysis, is therefore fundamental to provide robust candidate biomarkers. Normalization procedures are applied to reduce experimental noise and make the images comparable, improving the accuracy of differential analysis. Certainly, they may deeply influence the final results, and to this respect they have to be applied with care. Here, the most widespread normalization procedures are described both for what regards the applications to 2-DE and 2D Difference Gel-electrophoresis (2-D DIGE) maps.
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Affiliation(s)
- Elisa Robotti
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121, Alessandria, Italy.
| | - Emilio Marengo
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121, Alessandria, Italy
| | - Fabio Quasso
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121, Alessandria, Italy
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Marengo E, Robotti E, Quasso F. Differential Analysis of 2-D Maps by Pixel-Based Approaches. Methods Mol Biol 2015; 1384:299-327. [PMID: 26611422 DOI: 10.1007/978-1-4939-3255-9_17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
Two approaches to the analysis of 2-D maps are available: the first one involves a step of spot detection on each gel image; the second one is based instead on the direct differential analysis of 2-D map images, following a pixel-based procedure. Both approaches strongly depend on the proper alignment of the gel images, but the pixel-based approach allows to solve important drawbacks of the spot-volume procedure, i.e., the problem of missing data and of overlapping spots. However, this approach is quite computationally intensive and requires the use of algorithms able to separate the information (i.e., spot-related information) from the background. Here, the most recent pixel-based approaches are described.
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Affiliation(s)
- Emilio Marengo
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121, Alessandria, Italy.
| | - Elisa Robotti
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121, Alessandria, Italy.
| | - Fabio Quasso
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121, Alessandria, Italy
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Marengo E, Robotti E. Biomarkers for pancreatic cancer: Recent achievements in proteomics and genomics through classical and multivariate statistical methods. World J Gastroenterol 2014; 20:13325-13342. [PMID: 25309068 PMCID: PMC4188889 DOI: 10.3748/wjg.v20.i37.13325] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2013] [Revised: 06/04/2014] [Accepted: 06/26/2014] [Indexed: 02/06/2023] Open
Abstract
Pancreatic cancer (PC) is one of the most aggressive and lethal neoplastic diseases. A valid alternative to the usual invasive diagnostic tools would certainly be the determination of biomarkers in peripheral fluids to provide less invasive tools for early diagnosis. Nowadays, biomarkers are generally investigated mainly in peripheral blood and tissues through high-throughput omics techniques comparing control vs pathological samples. The results can be evaluated by two main strategies: (1) classical methods in which the identification of significant biomarkers is accomplished by monovariate statistical tests where each biomarker is considered as independent from the others; and (2) multivariate methods, taking into consideration the correlations existing among the biomarkers themselves. This last approach is very powerful since it allows the identification of pools of biomarkers with diagnostic and prognostic performances which are superior to single markers in terms of sensitivity, specificity and robustness. Multivariate techniques are usually applied with variable selection procedures to provide a restricted set of biomarkers with the best predictive ability; however, standard selection methods are usually aimed at the identification of the smallest set of variables with the best predictive ability and exhaustivity is usually neglected. The exhaustive search for biomarkers is instead an important alternative to standard variable selection since it can provide information about the etiology of the pathology by producing a comprehensive set of markers. In this review, the most recent applications of the omics techniques (proteomics, genomics and metabolomics) to the identification of exploratory biomarkers for PC will be presented with particular regard to the statistical methods adopted for their identification. The basic theory related to classical and multivariate methods for identification of biomarkers is presented and then, the most recent applications in this field are discussed.
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Dávalos LM, Velazco PM, Warsi OM, Smits PD, Simmons NB. Integrating Incomplete Fossils by Isolating Conflicting Signal in Saturated and Non-Independent Morphological Characters. Syst Biol 2014; 63:582-600. [DOI: 10.1093/sysbio/syu022] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
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George J, Shukla Y. Pesticides and cancer: Insights into toxicoproteomic-based findings. J Proteomics 2011; 74:2713-22. [DOI: 10.1016/j.jprot.2011.09.024] [Citation(s) in RCA: 51] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2011] [Revised: 09/21/2011] [Accepted: 09/25/2011] [Indexed: 12/19/2022]
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Esquivel-Velázquez M, Larralde C, Morales J, Ostoa-Saloma P. Protein and antigen diversity in the vesicular fluid of Taenia solium cysticerci dissected from naturally infected pigs. Int J Biol Sci 2011; 7:1287-97. [PMID: 22110381 PMCID: PMC3221365 DOI: 10.7150/ijbs.7.1287] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2011] [Accepted: 10/01/2011] [Indexed: 11/05/2022] Open
Abstract
Cysticercosis caused by Taenia solium is a health threat for humans and pigs living in developing countries, for which there is neither a flawless immunodiagnostic test nor a totally effective vaccine. Suspecting of individual diversity of hosts and parasites as possible sources of the variations of the parasite loads among cysticercotic animals and of the limited success of such immunological applications as well as, we explored and measured both in nine cases of naturally acquired porcine cysticercosis. For this purpose, 2-Dimensional IgG immunoblots were performed by reacting the sera of each cysticercotic pig with the antigens contained in the vesicular fluid (VF) of their own cysticerci. We found an unexpectedly large diversity among the proteins and antigens contained in each of the nine VFs. Also diverse were the serum IgG antibody responses of the nine pigs, as none of their 2D- immunoblot images exhibited the same number of spots and resembled each other in only 6.3% to 65.3% of their features. So large an individual immunological diversity of the cysticercal antigens and of the infected pigs´ IgG antibody response should be taken into account in the design of immunological tools for diagnosis and prevention of cysticercosis and should also be considered as a possibly significant source of diversity in Taenia solium´s infectiveness and pathogenicity.
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Affiliation(s)
- Marcela Esquivel-Velázquez
- Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Ciudad Universitaria, A.P. 70228, México D.F 04510, México
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11
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Faergestad EM, Rye MB, Nhek S, Hollung K, Grove H. The use of chemometrics to analyse protein patterns from gel electrophoresis. ACTA CHROMATOGR 2011. [DOI: 10.1556/achrom.23.2011.1.1] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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George J, Singh R, Mahmood Z, Shukla Y. Toxicoproteomics: New paradigms in toxicology research. Toxicol Mech Methods 2010; 20:415-23. [DOI: 10.3109/15376511003667842] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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13
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Strategies of statistical image analysis of 2D immunoblots: The case of IgG response in experimental Taenia crassiceps cysticercosis. J Immunol Methods 2009; 351:46-54. [DOI: 10.1016/j.jim.2009.09.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2009] [Revised: 09/18/2009] [Accepted: 09/22/2009] [Indexed: 11/24/2022]
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Wang R, Wang JS, Liu GR, Han J, Chen YZ. Simulation of DNA electrophoresis in systems of large number of solvent particles by coarse-grained hybrid molecular dynamics approach. J Comput Chem 2009; 30:505-13. [DOI: 10.1002/jcc.21081] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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An Integrated Strategy in Two-Dimensional Electrophoresis Analysis Able to Identify Discriminants Between Different Clinical Conditions. Exp Biol Med (Maywood) 2008; 233:483-91. [DOI: 10.3181/0707-rm-187] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
Two-dimensional gel electrophoresis (2DE) is an indispensable tool in proteomics for the analysis of protein expression in complex biological systems such as cells and tissues. However, the automatic extraction of information from gel images is still a challenging task. In this paper we propose a strategy that represents a computational procedure of support to the discrimination of different clinical conditions associated with the samples. The analyzed gel images were acquired within the framework of a study of peripheral neuropathies: twenty-four 2DE maps generated from cerebrospinal fluid (16 pathologic and 8 control subjects) were processed. Quantitative features were defined to describe each image and treated with a method of dimensionality reduction. The informativeness of the descriptors allowed us to see the gel of the data set as items in a three-dimensional space, segregating according to the clinical conditions. Moreover, information with prognostic value was obtained for a single outsider gel of a patient who was included in a clinical subgroup at the first diagnosis but whose disease progressed with clinical features belonging to a different clinical subgroup. The method developed may represent an effective tool of classification that can be used repeatedly to capture the essential impression from separation images.
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González-Díaz H, González-Díaz Y, Santana L, Ubeira FM, Uriarte E. Proteomics, networks and connectivity indices. Proteomics 2008; 8:750-78. [DOI: 10.1002/pmic.200700638] [Citation(s) in RCA: 145] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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Abstract
Due to the low reproducibility affecting 2D gel-electrophoresis and the complex maps provided by this technique, the use of effective and robust methods for the comparison and classification of 2D maps is a fundamental tool for the development of automated diagnostic methods. A review of classical and recently developed methods for the comparison of 2D maps is presented here. The methods proposed regard both the analysis of spot volume datasets through multivariate statistical tools (pattern recognition methods, cluster analysis, and classification methods) and the analysis of 2D map images through fuzzy logic, three-way PCA, and the use of moment functions. The theoretical basis of each procedure is briefly introduced, together with a review of the most interesting applications present in recent literature.
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Affiliation(s)
- Emilio Marengo
- Department of Environmental and Life Sciences, University of Eastern Piedmont, Alessandria, Italy
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18
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Multivariate comparison between peptide mass fingerprints obtained by liquid chromatography–electrospray ionization-mass spectrometry with different trypsin digestion procedures. J Chromatogr A 2007; 1171:69-79. [DOI: 10.1016/j.chroma.2007.09.042] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2007] [Revised: 09/10/2007] [Accepted: 09/18/2007] [Indexed: 11/21/2022]
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Eslava-Schmalbach J, Alfonso H, Oliveros H, Gaitán H, Agudelo C. A new Inequity-in-Health Index based on Millennium Development Goals: methodology and validation. J Clin Epidemiol 2007; 61:142-50. [PMID: 18177787 DOI: 10.1016/j.jclinepi.2007.05.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2006] [Revised: 04/22/2007] [Accepted: 05/03/2007] [Indexed: 11/27/2022]
Abstract
OBJECTIVES Developing a new Inequity-in-Health Index (IHI) assuming inequity as "inequality of health outcomes," based on Millennium Development Goals (MDG). STUDY DESIGN AND SETTING Ecological study. Countries from around the world were included from United Nations, the World Bank, and a nonprofit organization's databases. The reliability and validity of this bidimensional IHI was tested. Main factor analysis (promax rotation) and main component analysis were used. RESULTS Six variables were used for constructing the IHI was constructed with six variables: underweight children, child mortality, death from malaria in children aged 0-4, death from malaria at all ages, births attended by skilled health personnel, and immunization against measles. The IHI had high internal consistency (Cronbach's alpha=0.8504), was reliable (Spearman>0.9, P=0.0000), and had 0.3033pi around the world (range: 0pi-0.5984pi). IHI had high correlation with the human development and poverty indexes, health gap indicator, life expectancy at birth, probability of dying before 40 years of age, and Gini coefficients (Spearman>0.7, P=0.0000). IHI discriminated countries by income, region, indebtedness, and corruption level (Kruskal Wallis, P<0.01). IHI had sensitivity to change (P=0.0000). CONCLUSION IHI is a bidimensional, valid and reliable index to monitor MDG. A new reliable methodology for developing bidimensional indicators is shown, which could be used for constructing other ones with their corresponding scores and graphs.
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Eravci M, Fuxius S, Broedel O, Weist S, Eravci S, Mansmann U, Schluter H, Tiemann J, Baumgartner A. Improved comparative proteome analysis based on two-dimensional gel electrophoresis. Proteomics 2007; 7:513-523. [PMID: 17309096 DOI: 10.1002/pmic.200600648] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The purpose of this study was to test the extent to which differences in spot intensity can be reliably recognized between two groups of two-dimensional electrophoresis gels (pH 4-7, visualized with ruthenium fluorescent stain) each loaded with different amounts of protein from rat brain (power analysis). Initial experiments yielded only unsatisfactory results: 546 spots were matched from two groups of 6 gels each loaded with 200 microg and 250 microg protein, respectively. Only 72 spots were higher (p<0.05), while 58 spots were significantly lower in the 250-microg group. The construction of new apparatuses that allowed the simultaneous processing of 24 gels throughout all steps between rehydration and staining procedure considerably lowered the between-gel variation. This resulted in the detection of significant differences in spot intensities in 77-90% of all matched spots on gel groups with a 25% difference in protein load. This applied both when protein from 24 biological replicates was loaded onto two groups of 12 gels and when two pooled tissue samples were each loaded onto 6 gels. At a difference of 50% in protein load, more than 90% of all spots differed significantly between two experimental groups.
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Affiliation(s)
- Murat Eravci
- Department of Radiology and Nuclear Medicine (Radiochemistry), Charité Universitätsmedizin, Campus Benjamin Franklin, Berlin, Germany
- A+M Proteome Science, Berlin, Germany
| | - Sandra Fuxius
- Department of Radiology and Nuclear Medicine (Radiochemistry), Charité Universitätsmedizin, Campus Benjamin Franklin, Berlin, Germany
- A+M Proteome Science, Berlin, Germany
| | | | | | | | - Ulrich Mansmann
- Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Germany
| | - Hartmut Schluter
- Department of Internal Medicine IV, Charité Universitätsmedizin, Campus Benjamin Franklin, Berlin, Germany
| | - Joachim Tiemann
- Department of Internal Medicine IV, Charité Universitätsmedizin, Campus Benjamin Franklin, Berlin, Germany
| | - Andreas Baumgartner
- Department of Radiology and Nuclear Medicine (Radiochemistry), Charité Universitätsmedizin, Campus Benjamin Franklin, Berlin, Germany
- A+M Proteome Science, Berlin, Germany
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Biron DG, Brun C, Lefevre T, Lebarbenchon C, Loxdale HD, Chevenet F, Brizard JP, Thomas F. The pitfalls of proteomics experiments without the correct use of bioinformatics tools. Proteomics 2006; 6:5577-96. [PMID: 16991202 DOI: 10.1002/pmic.200600223] [Citation(s) in RCA: 56] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The elucidation of the entire genomic sequence of various organisms, from viruses to complex metazoans, most recently man, is undoubtedly the greatest triumph of molecular biology since the discovery of the DNA double helix. Over the past two decades, the focus of molecular biology has gradually moved from genomes to proteomes, the intention being to discover the functions of the genes themselves. The postgenomic era stimulated the development of new techniques (e.g. 2-DE and MS) and bioinformatics tools to identify the functions, reactions, interactions and location of the gene products in tissues and/or cells of living organisms. Both 2-DE and MS have been very successfully employed to identify proteins involved in biological phenomena (e.g. immunity, cancer, host-parasite interactions, etc.), although recently, several papers have emphasised the pitfalls of 2-DE experiments, especially in relation to experimental design, poor statistical treatment and the high rate of 'false positive' results with regard to protein identification. In the light of these perceived problems, we review the advantages and misuses of bioinformatics tools - from realisation of 2-DE gels to the identification of candidate protein spots - and suggest some useful avenues to improve the quality of 2-DE experiments. In addition, we present key steps which, in our view, need to be to taken into consideration during such analyses. Lastly, we present novel biological entities named 'interactomes', and the bioinformatics tools developed to analyse the large protein-protein interaction networks they form, along with several new perspectives of the field.
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Affiliation(s)
- David G Biron
- GEMI, UMR CNRS/IRD 2724, Centre IRD, Montpellier, France.
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22
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Ottens AK, Kobeissy FH, Golden EC, Zhang Z, Haskins WE, Chen SS, Hayes RL, Wang KKW, Denslow ND. Neuroproteomics in neurotrauma. MASS SPECTROMETRY REVIEWS 2006; 25:380-408. [PMID: 16498609 DOI: 10.1002/mas.20073] [Citation(s) in RCA: 50] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Neurotrauma in the form of traumatic brain injury (TBI) afflicts more Americans annually than Alzheimer's and Parkinson's disease combined, yet few researchers have used neuroproteomics to investigate the underlying complex molecular events that exacerbate TBI. Discussed in this review is the methodology needed to explore the neurotrauma proteome-from the types of samples used to the mass spectrometry identification and quantification techniques available. This neuroproteomics survey presents a framework for large-scale protein research in neurotrauma, as applied for immediate TBI biomarker discovery and the far-reaching systems biology understanding of how the brain responds to trauma. Ultimately, knowledge attained through neuroproteomics could lead to clinical diagnostics and therapeutics to lessen the burden of neurotrauma on society.
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Affiliation(s)
- Andrew K Ottens
- Center of Neuroproteomics and Biomarkers Research, McKnight Brain Institute, University of Florida, Gainesville, FL, USA.
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Marengo E, Robotti E, Bobba M, Liparota MC, Rustichelli C, Zamò A, Chilosi M, Righetti PG. Multivariate statistical tools applied to the characterization of the proteomic profiles of two human lymphoma cell lines by two-dimensional gel electrophoresis. Electrophoresis 2006; 27:484-94. [PMID: 16372308 DOI: 10.1002/elps.200500323] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Mantle cell lymphoma (MCL) cell lines have been difficult to generate, since only few have been described so far and even fewer have been thoroughly characterized. Among them, there is only one cell line, called GRANTA-519, which is well established and universally adopted for most lymphoma studies. We succeeded in establishing a new MCL cell line, called MAVER-1, from a leukemic MCL, and performed a thorough phenotypical, cytogenetical and molecular characterization of the cell line. In the present report, the phenotypic expression of GRANTA-519 and MAVER-1 cell lines has been compared and evaluated by a proteomic approach, exploiting 2-D map analysis. By univariate statistical analysis (Student's t-test, as commonly used in most commercial software packages), most of the protein spots were found to be identical between the two cell lines. Thirty spots were found to be unique for the GRANTA-519, whereas another 11 polypeptides appeared to be expressed only by the MAVER-1 cell line. A number of these spots could be identified by MS. These data were confirmed and expanded by multivariate statistical tools (principal component analysis and soft-independent model of class analogy) that allowed identification of a larger number of differently expressed spots. Multivariate statistical tools have the advantage of reducing the risk of false positives and of identifying spots that are significantly altered in terms of correlated expression rather than absolute expression values. It is thus suggested that, in future work in differential proteomic profiling, both univariate and multivariate statistical tools should be adopted.
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Affiliation(s)
- Emilio Marengo
- Department of Environmental and Life Sciences, University of Eastern Piedmont, Alessandria, Italy.
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Marengo E, Robotti E, Antonucci F, Cecconi D, Campostrini N, Righetti PG. Numerical approaches for quantitative analysis of two-dimensional maps: A review of commercial software and home-made systems. Proteomics 2005; 5:654-66. [PMID: 15669000 DOI: 10.1002/pmic.200401015] [Citation(s) in RCA: 84] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The present review attempts to cover a number of methods that have appeared in the last few years for performing quantitative proteome analysis. However, due to the large number of methods described for both electrophoretic and chromatographic approaches, we have limited this review to conventional two-dimensional (2-D) map analysis which couples orthogonally a charge-based step (isoelectric focusing) to a size-based separation step (sodium dodecyl sulfate-electrophoresis). The first and oldest method applied to 2-D map data reduction is based on statistical analysis performed on sets of gels via powerful software packages, such as Melanie, PDQuest, Z3 and Z4000, Phoretix and Progenesis. This method calls for separately running a number of replicas for control and treated samples. The two sets of data are then merged and compared via a number of software packages which we describe. In addition to commercially-available systems, a number of home made approaches for 2-D map comparison have been recently described and are also reviewed. They are based on fuzzyfication of the digitized 2-D gel image coupled to linear discriminant analysis, three-way principal component analysis or a combination of principal component analysis and soft-independent modeling of class analogy. These statistical tools appear to perform well in differential proteomic studies.
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Affiliation(s)
- Emilio Marengo
- Department of Environmental and Life Sciences, University of Eastern Piedmont, Alessandria, Italy
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Marengo E, Robotti E, Righetti PG, Campostrini N, Pascali J, Ponzoni M, Hamdan M, Astner H. Study of proteomic changes associated with healthy and tumoral murine samples in neuroblastoma by principal component analysis and classification methods. Clin Chim Acta 2005; 345:55-67. [PMID: 15193978 DOI: 10.1016/j.cccn.2004.02.027] [Citation(s) in RCA: 15] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2003] [Revised: 02/15/2004] [Accepted: 02/16/2004] [Indexed: 01/18/2023]
Abstract
BACKGROUND The adrenal gland is the election organ forming primary neuroblastoma (NB) tumours, the most common extracranial solid tumours of infancy and childhood. METHODS Samples of adrenal gland belonging to healthy and diseased nude mouse were analysed by 2D gel-electrophoresis. The resulting 2D-PAGE maps were digitized by PDQuest and investigated by principal component analysis (PCA). RESULTS The analysis of the loadings of the first principal component (PC) permitted the evaluation of the spots characterising each class of samples. Moreover, the soft-independent model of class analogy (SIMCA) method confirmed the separation of the samples in the two classes and allowed the identification of the modelling and discriminating spots. Very good correlation was found between the data obtained by analysis of 2D maps via the commercial software PDQuest and the present PCA analysis. In both cases, the comparison between such maps showed up- and down-regulation of 84 polypeptide chains, out of a total of 700 spots detected by a fluorescent stain, Sypro Ruby. Spots that were differentially expressed between the two groups were analysed by matrix-assisted laser desorption time-of-flight (MALDI-TOF) mass spectrometry and 14 of these spots were identified so far.
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Affiliation(s)
- Emilio Marengo
- Department of Environmental and Life Sciences, University of Eastern Piedmont, Spalto Marengo 33-15100 Alessandria, Italy.
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Marengo E, Robotti E, Righetti PG, Antonucci F. New approach based on fuzzy logic and principal component analysis for the classification of two-dimensional maps in health and disease. Application to lymphomas. J Chromatogr A 2003; 1004:13-28. [PMID: 12929957 DOI: 10.1016/s0021-9673(03)00852-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Two-dimensional (2D) electrophoresis is the most wide spread technique for the separation of proteins in biological systems. This technique produces 2D maps of high complexity, which creates difficulties in the comparison of different samples. The method proposed in this paper for the comparison of different 2D maps can be summarised in four steps: (a) digitalisation of the image; (b) fuzzyfication of the digitalised map in order to consider the variability of the two-dimensional electrophoretic separation; (c) decoding by principal component analysis of the previously obtained fuzzy maps, in order to reduce the system dimensionality; (d) classification analysis (linear discriminant analysis), in order to separate the samples contained in the dataset according to the classes present in said dataset. This method was applied to a dataset constituted by eight samples: four belonging to healthy human lymph-nodes and four deriving from non-Hodgkin lymphomas. The amount of fuzzyfication of the original map is governed by the sigma parameter. The larger the value, the more fuzzy theresulting transformed map. The effect of the fuzzyfication parameter was investigated, the optimal results being obtained for sigma = 1.75 and 2.25. Principal component analysis and linear discriminant analysis allowed the separation of the two classes of samples without any misclassification.
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Affiliation(s)
- Emilio Marengo
- Dipartimento di Scienze e Tecnologie Avanzate, Università del Piemonte Orientale, 15100 Alessandria, Italy.
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