Abstract
Two-Dimensional Polyacrylamide Gel Electrophoresis (2D-PAGE) provides two-dimensional maps where proteins appear separated according to their isoelectric point (pI) and molecular weight (MW). Usually these maps are very complex (i.e., hundreds or thousands of spots can be present in each map), and characterized by a low reproducibility, which hinders the possibility to identify reliable biomarkers unless robust methods are applied. The analysis of different sets of 2D-PAGE maps (e.g., control vs. pathological or control vs. drug-treated samples) to identify candidate biomarkers (proteins under- or over-expressed in different conditions) is usually carried out through image analysis systems providing a so-called spot volume dataset where each sample corresponds to a map described by the optical densities of all the detected spots. The identification of candidate biomarkers can be therefore accomplished by comparing different maps by classical monovariate statistical tests applied spotwise, or by multivariate chemometric tools applied to the entire set of spots present on each map. Here, the most exploited multivariate techniques will be considered, ranging from pattern recognition to classification methods.
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References
Robotti E, Marengo E (2016) Chemometric multivariate tools for candidate biomarker identification: LDA, PLS-DA, SIMCA, ranking-PCA. Methods Mol Biol 1384:237–267
Lomnytska M, Pinto R, Becker S, Engstrom U, Gustafsson S, Bjorklund C, Templin M, Bergstrand J, Xu L, Widengren J, Epstein E, Franzen B, Auer G (2018) Platelet protein biomarker panel for ovarian cancer diagnosis. Biomarker Res 6:2
Heger Z, Michalek P, Guran R, Cernei N, Duskova K, Vesely S, Anyz J, Stepankova O, Zitka O, Adam V, Kizek R (2015) Differences in urinary proteins related to surgical margin status after radical prostatectomy. Oncol Rep 34(6):3247–3255
Hadrevi J, Hellstrom F, Kieselbach T, Malm C, Pedrosa-Domellof F (2011) Protein differences between human trapezius and vastus lateralis muscles determined with a proteomic approach. BMC Musculoskelet Disord 12:181
Hatsugai M, Kurokawa MS, Kouro T, Nagai K, Arito M, Masuko K, Suematsu N, Okamoto K, Itoh F, Kato T (2010) Protein profiles of peripheral blood mononuclear cells are useful for differential diagnosis of ulcerative colitis and Crohn’s disease. J. Gastroenterol 45(5):488–500
Feng YN, Gong J, Yu H, Jin YP, Zhu J, Han YM (2010) Identification of changes in the composition of ileal bacterial microbiota of broiler chickens infected with Clostridium perfringens. Vet Microbiol 140(1–2):116–121
Rodriguez-Pineiro AM, Rodriguez-Berrocal FJ, de la Cadena MP (2007) Improvements in the search for potential biomarkers by proteomics: application of principal component and discriminant analyses for two-dimensional maps evaluation. J Chromatogr B Analyt Technol Biomed Life Sci 849(1–2):251–260
Lilley KS, Dupree P (2006) Methods of quantitative proteomics and their application to plant organelle characterization. J Exp Bot 57(7):1493–1499
Verhoeckx KCM, Gaspari M, Bijlsma S, van der Greef J, Witkamp RF, Doornbos RP, Rodenburg RJT (2005) In search of secreted protein biomarkers for the anti-inflammatory effect of beta(2)-adrenergic receptor agonists: application of DIGE technology in combination with multivariate and univariate data analysis tools. J Proteome Res 4(6):2015–2023
Karp NA, Griffin JL, Lilley KS (2005) Application of partial least squares discriminant analysis to two-dimensional difference gel studies in expression proteomics. Proteomics 5(1):81–90
Marengo E, Cocchi M, Demartini M, Robotti E, Bobba M, Righetti PG (2011) Investigation of the applicability of Zernike moments to the classification of SDS 2D-PAGE maps. Anal Bioanal Chem 400(5):1419–1431
Negri A, Robotti E, Prinsi B, Espen L (2011) Proteins involved in biotic and abiotic stress responses as the most significant biomarkers in the ripening of Pinot Noir skins. Funct Integr Genomics 11:341–355
Marengo E, Robotti E, Bobba M, Milli A, Campostrini N, Righetti SC, Cecconi D, Righetti PG (2008) Application of partial least squares discriminant analysis and variable selection procedures: a 2D-PAGE proteomic study. Anal Bioanal Chem 390(5):1327–1342
Marengo E, Robotti E, Righetti PG, Campostrini N, Pascali J, Ponzoni M, Hamdan M, Astner H (2004) Study of proteomic changes associated with healthy and tumoral murine samples in neuroblastoma by principal component analysis and classification methods. Clin Chim Acta 345:55–67
Marengo E, Robotti E, Bobba M, Liparota MC, Rustichelli C, Zamo A, Chilosi M, Righetti PG (2006) Multivariate statistical tools applied to the characterization of the proteomic profiles of two human lymphoma cell lines by two-dimensional gel electrophoresis. Electrophoresis 27:484–494
Marengo E, Robotti E, Bobba M, Righetti PG (2008) Evaluation of the variables characterized by significant discriminating power in the application of SIMCA classification method to proteomic studies. J Proteome Res 7:2789–2796
Robotti E, Demartini M, Gosetti F, Calabrese G, Marengo E (2011) Development of a classification and ranking method for the identification of possible biomarkers in twodimensional gel-electrophoresis based on principal component analysis and variable selection procedures. Mol Biosyst 7(3):677–686
Marengo E, Robotti E, Bobba M, Gosetti F (2010) The principle of exhaustiveness versus the principle of parsimony: a new approach for the identification of biomarkers from proteomic spot volume datasets based on principal component analysis. Anal Bioanal Chem 397(1):25–41
Polati R, Menini M, Robotti E, Millioni R, Marengo E, Novelli E, Balzan S, Cecconi D (2012) Proteomic changes involved in tenderization of bovine Longissimus dorsi muscle during prolonged ageing. Food Chem 135:2052–2069
Massart DL, Vandeginste BGM, Deming SM, Michotte Y, Kaufman L (1988) Chemometrics: a textbook. Elsevier, Amsterdam
Vandeginste BGM, Massart DL, Buydens LMC, De Yong S, Lewi PJ, Smeyers-Verbeke J (1988) Handbook of chemometrics and qualimetrics: part B. Elsevier, Amsterdam
Frank IE, Lanteri S (1989) Classification models: discriminant analysis, SIMCA, CART. Chemom Intell Lab Syst 5:247–256
Martens H, Naes T (1989) Multivariate calibration. Wiley, London
Oussama A, Elabadi F, Platikanov S, Kzaiber F, Tauler R (2012) Detection of olive oil adulteration using FT-IR spectroscopy and PLS with variable importance of projection (VIP) scores. J Am Oil Chem Soc 89:1807–1812
Manfredi M, Conte E, Barberis E, Buzzi A, Robotti E, Caneparo V, Cecconi D, Brandi J, Vanni E, Finocchiaro M, Astegiano M, Gariglio M, Marengo E, De Andrea M (2019) Integrated serum proteins and fatty acids analysis for putative biomarker discovery in inflammatory bowel disease. J Proteomics 195:138–149
Manfredi M, Chiariello C, Conte E, Castagna A, Robotti E, Gosetti F, Patrone M, Martinelli N, Bassi A, Cecconi D, Marengo E, Olivieri O (2019) Plasma proteome profiles of stable CAD patients stratified according to total Apo C-III levels. Proteomics Clin Appl 13(3):1800023
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Robotti, E., Calà, E., Marengo, E. (2021). Chemometric Tools for 2D-PAGE Data Analysis. In: Cecconi, D. (eds) Proteomics Data Analysis. Methods in Molecular Biology, vol 2361. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1641-3_2
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DOI: https://doi.org/10.1007/978-1-0716-1641-3_2
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