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Antibody Microarrays and Multiplexing

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Bioinformatics of Human Proteomics

Part of the book series: Translational Bioinformatics ((TRBIO,volume 3))

Abstract

This chapter presents a range of statistical methods for antibody microarray normalization and data analysis. Commonly used techniques for cluster generation, differential analysis, and classification are covered. The focus is on the implementation of each technique to the technology and its suitability in relation to sample types and experiment design.

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References

  • Angenendt P, Glokler J, Murphy D, Lehrach H, Cahill DJ. Toward optimized antibody microarrays: a comparison of current microarray support materials. Anal Biochem. 2002;309:253–60.

    Article  PubMed  CAS  Google Scholar 

  • Armstrong NJ, van de Wiel MA. Microarray data analysis: from hypotheses to conclusions using gene expression data. Cell Oncol: Official J Int Soc Cell Oncol. 2004;26:279–90.

    CAS  Google Scholar 

  • Barrios-Rodiles M, Brown KR, Ozdamar B, Bose R, Liu Z, Donovan RS, Shinjo F, Liu Y, Dembowy J, Taylor IW, et al. High-throughput mapping of a dynamic signaling network in mammalian cells. Science. 2005;307:1621–5.

    Article  PubMed  CAS  Google Scholar 

  • Belov L, de la Vega O, dos Remedios CG, Mulligan SP, Christopherson RI. Immunophenotyping of leukemias using a cluster of differentiation antibody microarray. Cancer Res. 2001;61:4483–9.

    PubMed  CAS  Google Scholar 

  • Belov L, Mulligan SP, Barber N, Woolfson A, Scott M, Stoner K, Chrisp JS, Sewell WA, Bradstock KF, Bendall L, et al. Analysis of human leukaemias and lymphomas using extensive immunophenotypes from an antibody microarray. Br J Haematol. 2006;135:184–97.

    Article  PubMed  CAS  Google Scholar 

  • Benjamini Y, Hochberg Y. Controlling the false discovery rate – a practical and powerful approach to multiple testing. J R Stat Soc Ser B-Methodol. 1995;57:289–300.

    Google Scholar 

  • Bishop CM. Neural networks for pattern recognition. Oxford: Clarendon; 1995.

    Google Scholar 

  • Bittner M, Meltzer P, Chen Y, Jiang Y, Seftor E, Hendrix M, Radmacher M, Simon R, Yakhini Z, Ben-Dor A, et al. Molecular classification of cutaneous malignant melanoma by gene expression profiling. Nature. 2000;406:536–40.

    Article  PubMed  CAS  Google Scholar 

  • Bolstad BM, Irizarry RA, Astrand M, Speed TP. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics. 2003;19:185–93.

    Article  PubMed  CAS  Google Scholar 

  • Breitling R, Herzyk P. Rank-based methods as a non-parametric alternative of the T-statistic for the analysis of biological microarray data. J Bioinform Comput Biol. 2005;3:1171–89.

    Article  PubMed  CAS  Google Scholar 

  • Breitling R, Armengaud P, Amtmann A, Herzyk P. Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. FEBS Lett. 2004;573:83–92.

    Article  PubMed  CAS  Google Scholar 

  • Carlsson A, Wingren C, Ingvarsson J, Ellmark P, Baldertorp B, Ferno M, Olsson H, Borrebaeck CA. Serum proteome profiling of metastatic breast cancer using recombinant antibody microarrays. Eur J Cancer. 2008;44:472–80.

    Article  PubMed  CAS  Google Scholar 

  • Carlsson A, Wuttge DM, Ingvarsson J, Bengtsson AA, Sturfelt G, Borrebaeck CA, Wingren C. Serum protein profiling of systemic lupus erythematosus and systemic sclerosis using recombinant antibody microarrays. Mol Cell Proteomic: MCP. 2011;10:M110 005033.

    Google Scholar 

  • Chatziioannou A, Moulos P, Kolisis FN. Gene ARMADA: an integrated multi-analysis platform for microarray data implemented in MATLAB. BMC Bioinformatics. 2009;10:354.

    Article  PubMed  Google Scholar 

  • Cristianini N, Shawe-Taylor J. An introduction to support vector machines: and other kernel-based learning methods. Cambridge: Cambridge University Press; 2000.

    Book  Google Scholar 

  • Efron B, Tibshirani R. Empirical bayes methods and false discovery rates for microarrays. Genet Epidemiol. 2002;23:70–86.

    Article  PubMed  Google Scholar 

  • Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A. 1998;95:14863–8.

    Article  PubMed  CAS  Google Scholar 

  • Friedman N. Inferring cellular networks using probabilistic graphical models. Science. 2004;303:799–805.

    Article  PubMed  CAS  Google Scholar 

  • Friedman N, Linial M, Nachman I, Pe’er D. Using Bayesian networks to analyze expression data. J Comput Biol J Comput Mol Cell Biol. 2000;7:601–20.

    Article  CAS  Google Scholar 

  • Gao WM, Kuick R, Orchekowski RP, Misek DE, Qiu J, Greenberg AK, Rom WN, Brenner DE, Omenn GS, Haab BB, Hanash SM. Distinctive serum protein profiles involving abundant proteins in lung cancer patients based upon antibody microarray analysis. BMC Cancer. 2005;5:110.

    Article  PubMed  Google Scholar 

  • Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 2004;5:R80.

    Article  PubMed  Google Scholar 

  • Gruvberger S, Ringner M, Chen Y, Panavally S, Saal LH, Borg A, Ferno M, Peterson C, Meltzer PS. Estrogen receptor status in breast cancer is associated with remarkably distinct gene expression patterns. Cancer Res. 2001;61:5979–84.

    PubMed  CAS  Google Scholar 

  • Gulmann C, Sheehan KM, Kay EW, Liotta LA, Petricoin 3rd EF. Array-based proteomics: mapping of protein circuitries for diagnostics, prognostics, and therapy guidance in cancer. J Pathol. 2006;208:595–606.

    Article  PubMed  CAS  Google Scholar 

  • Hallborn J, Carlsson R. Automated screening procedure for high-throughput generation of antibody fragments. BioTechniques. 2002;(Suppl):30–7

    Google Scholar 

  • Hamelinck D, Zhou H, Li L, Verweij C, Dillon D, Feng Z, Costa J, Haab BB. Optimized normalization for antibody microarrays and application to serum-protein profiling. Mol Cell Proteomic MCP. 2005;4:773–84.

    Article  CAS  Google Scholar 

  • Hanes J, Schaffitzel C, Knappik A, Pluckthun A. Picomolar affinity antibodies from a fully synthetic naive library selected and evolved by ribosome display. Nat Biotechnol. 2000;18:1287–92.

    Article  PubMed  CAS  Google Scholar 

  • Hastie T, Tibshirani R, Eisen MB, Alizadeh A, Levy R, Staudt L, Chan WC, Botstein D, Brown P. ‘Gene shaving’ as a method for identifying distinct sets of genes with similar expression patterns. Genome Biol. 2000; 1:RESEARCH0003.

    Google Scholar 

  • Ingvarsson J, Wingren C, Carlsson A, Ellmark P, Wahren B, Engstrom G, Harmenberg U, Krogh M, Peterson C, Borrebaeck CA. Detection of pancreatic cancer using antibody microarray-based serum protein profiling. Proteomics. 2008;8:2211–19.

    Article  PubMed  CAS  Google Scholar 

  • Jafari P, Azuaje F. An assessment of recently published gene expression data analyses: reporting experimental design and statistical factors. BMC Med Inform Decis Mak. 2006;6:27.

    Article  PubMed  Google Scholar 

  • Joliffe T. Principle components analysis. Berlin: Springer; 1986.

    Book  Google Scholar 

  • Kerr MK, Churchill GA. Bootstrapping cluster analysis: assessing the reliability of conclusions from microarray experiments. Proc Natl Acad Sci U S A. 2001;98:8961–5.

    Article  PubMed  CAS  Google Scholar 

  • Kerr MK, Churchill GA. Statistical design and the analysis of gene expression microarray data. Genet Res. 2007;89:509–14.

    Article  PubMed  Google Scholar 

  • Kerr MK, Martin M, Churchill GA. Analysis of variance for gene expression microarray data. J Comput Biol J Comput Mol Cell Biol. 2000;7:819–37.

    Article  CAS  Google Scholar 

  • Khan J, Wei JS, Ringner M, Saal LH, Ladanyi M, Westermann F, Berthold F, Schwab M, Antonescu CR, Peterson C, Meltzer PS. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med. 2001;7:673–9.

    Article  PubMed  CAS  Google Scholar 

  • Kohonen T. Self organizing maps. Berlin: Springer; 1995.

    Book  Google Scholar 

  • Kukar T, Eckenrode S, Gu Y, Lian W, Megginson M, She JX, Wu D. Protein microarrays to detect protein-protein interactions using red and green fluorescent proteins. Anal Biochem. 2002;306:50–4.

    Article  PubMed  CAS  Google Scholar 

  • Kusnezow W, Jacob A, Walijew A, Diehl F, Hoheisel JD. Antibody microarrays: an evaluation of production parameters. Proteomics. 2003;3:254–64.

    Article  PubMed  CAS  Google Scholar 

  • Mestdagh P, Van Vlierberghe P, De Weer A, Muth D, Westermann F, Speleman F, Vandesompele J. A novel and universal method for microRNA RT-qPCR data normalization. Genome Biol. 2009;10:R64.

    Article  PubMed  Google Scholar 

  • Miller JC, Zhou H, Kwekel J, Cavallo R, Burke J, Butler EB, Teh BS, Haab BB. Antibody microarray profiling of human prostate cancer sera: antibody screening and identification of potential biomarkers. Proteomics. 2003;3:56–63.

    Article  PubMed  CAS  Google Scholar 

  • Mor G, Visintin I, Lai Y, Zhao H, Schwartz P, Rutherford T, Yue L, Bray-Ward P, Ward DC. Serum protein markers for early detection of ovarian cancer. Proc Natl Acad Sci U S A. 2005;102:7677–82.

    Article  PubMed  CAS  Google Scholar 

  • Noble WS. How does multiple testing correction work? Nat Biotechnol. 2009;27:1135–7.

    Article  PubMed  CAS  Google Scholar 

  • Olle EW, Sreekumar A, Warner RL, McClintock SD, Chinnaiyan AM, Bleavins MR, Anderson TD, Johnson KJ. Development of an internally controlled antibody microarray. Mol Cell Proteomic MCP. 2005;4:1664–72.

    Article  CAS  Google Scholar 

  • Pan W. A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments. Bioinformatics. 2002;18:546–54.

    Article  PubMed  CAS  Google Scholar 

  • Pan W, Lin J, Le CT. How many replicates of arrays are required to detect gene expression changes in microarray experiments? A mixture model approach. Genome Biol. 2002; 3:research0022.

    Google Scholar 

  • Pan W, Lin J, Le CT. A mixture model approach to detecting differentially expressed genes with microarray data. Funct Integr Genomics. 2003;3:117–24.

    Article  PubMed  CAS  Google Scholar 

  • Pollard HB, Ji XD, Jozwik C, Jacobowitz DM. High abundance protein profiling of cystic fibrosis lung epithelial cells. Proteomics. 2005;5:2210–26.

    Article  PubMed  CAS  Google Scholar 

  • Pollard HB, Srivastava M, Eidelman O, Jozwik C, Rothwell SW, Mueller GP, Jacobowitz DM, Darling T, Guggino WB, Wright J, et al. Protein microarray platforms for clinical proteomics. Proteomics Clin Appl. 2007;1:934–52.

    Article  PubMed  CAS  Google Scholar 

  • Poon TC, Yip TT, Chan AT, Yip C, Yip V, Mok TS, Lee CC, Leung TW, Ho SK, Johnson PJ. Comprehensive proteomic profiling identifies serum proteomic signatures for detection of hepatocellular carcinoma and its subtypes. Clin Chem. 2003;49:752–60.

    Article  PubMed  CAS  Google Scholar 

  • Quackenbush J. Computational analysis of microarray data. Nat Rev Genet. 2001;2:418–27.

    Article  PubMed  CAS  Google Scholar 

  • Raychaudhuri S, Stuart JM, Altman RB. Principal components analysis to summarize microarray experiments: application to sporulation time series. Pacific Symp Biocomput. 2000:455–66.

    Google Scholar 

  • Ringner M, Peterson C, Khan J. Analyzing array data using supervised methods. Pharmacogenomics. 2002;3:403–15.

    Article  PubMed  CAS  Google Scholar 

  • Ruxton GD. The unequal variance t-test is an underused alternative to Student’s t-test and the Mann–Whitney U test. Behav Ecol. 2006;17:688–90.

    Article  Google Scholar 

  • Saal LH, Troein C, Vallon-Christersson J, Gruvberger S, Borg A, Peterson C. BioArray Software Environment (BASE): a platform for comprehensive management and analysis of microarray data. Genome Biol. 2002; 3:SOFTWARE0003.

    Google Scholar 

  • Sachs K, Perez O, Pe’er D, Lauffenburger DA, Nolan GP. Causal protein-signaling networks derived from multiparameter single-cell data. Science. 2005;308:523–9.

    Article  PubMed  CAS  Google Scholar 

  • Saeed AI, Sharov V, White J, Li J, Liang W, Bhagabati N, Braisted J, Klapa M, Currier T, Thiagarajan M, et al. TM4: a free, open-source system for microarray data management and analysis. Biotechniques. 2003;34:374–8.

    PubMed  CAS  Google Scholar 

  • Saldanha AJ. Java Treeview–extensible visualization of microarray data. Bioinformatics. 2004;20:3246–8.

    Article  PubMed  CAS  Google Scholar 

  • Schweitzer B, Wiltshire S, Lambert J, O’Malley S, Kukanskis K, Zhu Z, Kingsmore SF, Lizardi PM, Ward DC. Immunoassays with rolling circle DNA amplification: a versatile platform for ultrasensitive antigen detection. Proc Natl Acad Sci U S A. 2000;97:10113–19.

    Article  PubMed  CAS  Google Scholar 

  • Schwender H, Krause A, Ickstadt K. Comparison of the empirical Bayes and the significance analysis of microarrays. Technical Report // Universität Dortmund, SFB 475 Komplexitäts­reduktion in Multivariaten Datenstrukturen, 2003;44. http://hdl.handle.net/10419/49325.

  • Shannon WD, Watson MA, Perry A, Rich K. Mantel statistics to correlate gene expression levels from microarrays with clinical covariates. Genet Epidemiol. 2002;23:87–96.

    Article  PubMed  Google Scholar 

  • Shannon W, Culverhouse R, Duncan J. Analyzing microarray data using cluster analysis. Pharmacogenomics. 2003;4:41–52.

    Article  PubMed  CAS  Google Scholar 

  • Smyth GK. Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 2004; 3:Article3.

    Google Scholar 

  • Srivastava M, Eidelman O, Jozwik C, Paweletz C, Huang W, Zeitlin PL, Pollard HB. Serum proteomic signature for cystic fibrosis using an antibody microarray platform. Mol Genet Metab. 2006;87:303–10.

    Article  PubMed  CAS  Google Scholar 

  • Story CM, Papa E, Hu CC, Ronan JL, Herlihy K, Ploegh HL, Love JC. Profiling antibody responses by multiparametric analysis of primary B cells. Proc Natl Acad Sci U S A. 2008;105:17902–7.

    Article  PubMed  CAS  Google Scholar 

  • Thomas JG, Olson JM, Tapscott SJ, Zhao LP. An efficient and robust statistical modeling approach to discover differentially expressed genes using genomic expression profiles. Genome Res. 2001;11:1227–36.

    Article  PubMed  CAS  Google Scholar 

  • Toronen P, Kolehmainen M, Wong G, Castren E. Analysis of gene expression data using self-organizing maps. FEBS Lett. 1999;451:142–6.

    Article  PubMed  CAS  Google Scholar 

  • Troyanskaya OG, Garber ME, Brown PO, Botstein D, Altman RB. Nonparametric methods for identifying differentially expressed genes in microarray data. Bioinformatics. 2002;18:1454–61.

    Article  PubMed  CAS  Google Scholar 

  • Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A. 2001;98:5116–21.

    Article  PubMed  CAS  Google Scholar 

  • Wang Y, Wu TR, Cai S, Welte T, Chin YE. Stat1 as a component of tumor necrosis factor alpha receptor 1-TRADD signaling complex to inhibit NF-kappaB activation. Mol Cell Biol. 2000;20:4505–12.

    Article  PubMed  CAS  Google Scholar 

  • Welch BL. The generalisation of student’s problems when several different population variances are involved. Biometrika. 1947;34:28–35.

    Google Scholar 

  • White CN, Chan DW, Zhang Z. Bioinformatics strategies for proteomic profiling. Clin Biochem. 2004;37:636–41.

    Article  PubMed  CAS  Google Scholar 

  • White SL, Belov L, Barber N, Hodgkin PD, Christopherson RI. Immunophenotypic changes induced on human HL60 leukaemia cells by 1alpha,25-dihydroxyvitamin D3 and 12-O-tetradecanoyl phorbol-13-acetate. Leuk Res. 2005;29:1141–51.

    Article  PubMed  CAS  Google Scholar 

  • Wolfinger RD, Gibson G, Wolfinger ED, Bennett L, Hamadeh H, Bushel P, Afshari C, Paules RS. Assessing gene significance from cDNA microarray expression data via mixed models. J Comput Biol J Comput Mol Cell Biol. 2001;8:625–37.

    Article  CAS  Google Scholar 

  • Wu X, Liu H, Liu J, Haley KN, Treadway JA, Larson JP, Ge N, Peale F, Bruchez MP. Immunofluorescent labeling of cancer marker Her2 and other cellular targets with semiconductor quantum dots. Nat Biotechnol. 2003;21:41–6.

    Article  PubMed  CAS  Google Scholar 

  • Wylie D, Shelton J, Choudhary A, Adai AT. A novel mean-centering method for normalizing microRNA expression from high-throughput RT-qPCR data. BMC Res Notes. 2011;4:555.

    Article  PubMed  CAS  Google Scholar 

  • Yang YH, Dudoit S, Luu P, Lin DM, Peng V, Ngai J, Speed TP. Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res. 2002;30:e15.

    Article  PubMed  Google Scholar 

  • Yang JY, Zong CS, Xia W, Wei Y, Ali-Seyed M, Li Z, Broglio K, Berry DA, Hung MC. MDM2 promotes cell motility and invasiveness by regulating E-cadherin degradation. Mol Cell Biol. 2006;26:7269–82.

    Article  PubMed  CAS  Google Scholar 

  • Zhou J, Belov L, Solomon MJ, Chan C, Clarke SJ, Christopherson RI. Colorectal cancer cell surface protein profiling using an antibody microarray and fluorescence multiplexing. J. Vis. Exp. 2011;(55):e3322. DOI:10.3791/3322.

    Google Scholar 

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Authors and Affiliations

  1. School of Molecular Bioscience, University of Sydney, Sydney, NSW, 2006, Australia

    Jerry Zhou, Larissa Belov & Richard I. Christopherson Ph.D.

  2. Cancer Research Program, Garvan Institute of Medical Research, Darlinghurst, NSW, 2120, Australia

    Nicola Armstrong

  3. School of Mathematics and Statistics and Prince of Wales Clinical School, University of New South Wales, Rensington, 2052, Australia

    Nicola Armstrong

Authors
  1. Jerry Zhou
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  2. Larissa Belov
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  3. Nicola Armstrong
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  4. Richard I. Christopherson Ph.D.
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Corresponding author

Correspondence to Jerry Zhou .

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  1. , Medicine, Biomedical Research Center, Fudan University Zhongshan Hospital, Shang Hai, China, People's Republic

    Xiangdong Wang

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Zhou, J., Belov, L., Armstrong, N., Christopherson, R.I. (2013). Antibody Microarrays and Multiplexing. In: Wang, X. (eds) Bioinformatics of Human Proteomics. Translational Bioinformatics, vol 3. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5811-7_15

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