Protein Arrays in Functional Genome Research

Bioscience Reports, Vol. 25, Nos. 1/2, February/April 2005 (Ó 2005) DOI: 10.1007/s10540-005-2848-y

Christian Maercker1

Whole-genome analyses become more and more necessary for pharmaceutical research. DNA chip hybridizations are an important tool for monitoring gene expression profiles during diseases or medical treatment. However, drug target identification and validation as well as an increasing number of antibodies and other polypeptides tested as potential drugs produce an increasing demand for genome-wide functional assays. Protein arrays are an important step into this direction. Peptide arrays and protein expression libraries are useful for the identification of antibodies and for epitope mapping. Antibody arrays allow protein quantification, protein binding studies, and protein phosphorylation assays. Tissue microarrays give a detailed information about the localization of macromolecules. More complex interactions can be addressed in cells spotted in array format. Finally, microfluidics chips enable us to describe the communication between cells in a tissue. In this review, possibil- ities, limitations and chances of different protein array techniques are discussed.

KEY WORDS: Protein chips; antibody chips; peptide arrays; microﬂuidics; functional genome analysis; protein binding; protein phosphorylation.

ABBREVIATIONS: CGH: comparative genomic hybridization; CE-IVD: a certified product for in vitro diagnostics according to CE directive of the EU (‘‘Communautes Europeénnes’’); ChIP: chromatin immunoprecipitation; GFP: green fluorescent protein; MEA: microelectrode arrays; RNAi: RNA interference; SNP: single nucleotide polymor- phism.

INTRODUCTION High-throughput assays are indispensible for comprehensive functional genome research. The development of these techniques has been promoted again by the successful completion of the human genome sequencing project (Mc Pherson et al., 2001; Venter et al., 2001). This result, which was made possible by automated sequencing techniques and enormous progress in bioinformatics, is an important prerequisite for the application of whole-genome screens. The insertion of all sequence information into databases allows the design of comprehensive cDNA and oligonucleotide-based gene chips. DNA arrays are the state-of-the-art tool for gene expression analyses (Brown and Botstein, 1999), but also for the detection of chromosomal aberrations by comparative genomic hybridization on DNA microarrays (matrix-CGH; for review see Lichter et al.,

1RZPD German Resource Center for Genome Research GmbH, Berlin-Heidelberg, Im Neuenheimer Feld 580, D-69120, Heidelberg, Germany. E-mail: [email protected] 57 0144-8463/05/0400-0057/0 Ó 2005 Springer Science+Business Media, Inc. 58 Christian Maercker

2000), identification of protein binding DNA stretches (chromatin immunoprecipitation (ChIP); Cawley et al., 2004), and single nucleotide polymorphisms (SNPs; Woods et al., 2004). DNA–DNA and DNA–RNA hybridizations are established methods, which allow the detection of each single gene and its splice-variants. Not only RNA profiles are relevant for diagnostics, but also the genetic predisposition, which can be analyzed by hybridizing SNP chips or sequencing arrays. Very recently, Roche Diagnostics has launched the first pharmacogenetic gene chip with CE-IVD label. The AmpliChip CYP450 analyzes genes of the cytochrome P450 system involved in the metabolism of many different drugs. Metabolization rates of individual drugs can be calculated. Therefore, this tool helps to select certain drugs in appropriate concentrations for individual patients. RNA interference (RNAi) gene knock-downs and other genome-wide screens in pharmaceutical research are accompanied by gene expression profiles (for review see Carpenter and Sabatini, 2004). However, information about the expression level is not sufficient to describe gene function. This is not only due to the weak correlation between transcription activity and protein concentration (Gygi et al., 1999). Post-translational modifications cannot be recognized at the RNA level. For example, signal transduction processes in the cell are predominantly determined by phosphorylation steps. Also binding partners of proteins, localization of proteins in cellular compartments, enzymatic reactions, and many other functions are not appropriate to be analyzed by RNA expression analysis. Therefore, proteomics now plays a pivotal role in genome research. However, different from DNA- or RNA-based hybridization conditions, which are nearly the same for all of the genes, highly parallelized assays with proteins are very difficult to establish. Solubility, pH optima, and buffer conditions are very specific for each molecule. Different techniques are applied to investigate as many proteins in parallel as possible. 2D-gel electrophoresis allows the separation of thousands of proteins within the same experiment. Mass spectrometry methods (e.g., MALDI-TOF, SELDI) have evolved into very accurate, sensitive, and quick tools, which allow the extraction of many small spots from the gel and the analysis in a high-throughput manner. Database searches with those results make it possible to estimate peptide sequences and protein modifications (Wiesner and Bougmi, 2002; for review see Sickmann et al., 2003). Moreover, mass spectrometry can help to identify protein-protein interactions (Schulze and Mann, 2004). This review gives an overview about the state-of-the-art in protein chip technology. Polypeptides are immobilized on a matrix and are assayed individ- ually in parallel in a single experiment. Peptides, polypeptides, cell lysates, whole cells, or tissue samples are suitable as probes. Applications for different arrays are discussed.

PEPTIDE ARRAYS Ronald Frank’s group has developed a method which allows to immobilize up to 8.000 peptides on cellulose membranes (Frank, 2002). Recently, they also have started to use glass as matrix material. Step by step, activated amino acids are pipetted onto defined positions and built up to peptides. After each pipetting step, Protein Arrays in Functional Genome Research 59 a washing procedure removes excess of amino acids and chemicals to ensure the purity of the peptide library. The spotted molecules have a length of at most 30 amino acids with a concentration between 1 and 5 nmol. The peptides allow to detect interactions between enzymes and substrates, between antibodies and anti- genes, between receptors and hormones, and between nucleic acids and transcription factors. Even modifications of the macromolecules on the membrane are possible. In combination with compound libraries it is possible to detect and identify new active drug lead molecules. In further steps, these substances can be brought into solution and used for functional studies. An example is the coupling of the N-terminus of a peptide with a fluorescent dye to estimate the specificity of proteases. Together with kinases and radioactively labeled ATP the peptides can be tested as kinase substrates. By compatible peptide chemistry, drug candidates can be changed into active agents. Frank Breitling and collegues developed a very flexible method for the synthesis of peptides on membranes in a high density with the help of a classical laser printer (Bischoff et al., 2002; Breitling et al., 2002). The coloured toner particles are substituted by amino acid-toner particles, which consist of a ‘‘solid’’ solvent (diphenylformamide) and the amino acid. After a temperature increase, the amino acids are specifically released and coupled to the membrane or to the already syn- thesized peptide. This technique allows to build up peptides with lengths varying between 15 and 20 amino acids (Fig. 1). The low spreading caused by adjusting of the solvent by selective heating and high speed of the printer allows complexities of millions of different peptides on a single chip. This makes the arrays appropriate for the presentation of overlapping peptide sequences, which is applicable for the antigene mapping of autoreactive or infection related antibodies. In addition to the diagnostic aspect, libraries with different L- and D-peptides can be produced. D-peptides consist of D-amino acids, which are hardly degraded, because they do not occur in nature. Those molecules are particularly applicable for the development of new therapeutics.

Fig. 1. Amino acid (isoleucine) printed on glass surface by peptide laser printer (400 spots/cm2). Kindly provided by Frank Breitling, German Cancer Research Center, Heidelberg. 60 Christian Maercker

Fig. 2. (a, b). Diﬀerent coloured toner particles were coupled to pixel electrodes on a chip. Bar: 80 lm (Courtesy of Frank Breitling, F. Ralf Bischoﬀ, Volker Stadler, German Cancer Research Center, Heidelberg).

Novel chips, developed by the same group, are covered with pixel electrodes, which facilitate an accurate positioning of molecules. First trials with different col- ored toner particles have already been successful (Fig. 2). In ongoing experiments, particles containing derivatives of modified amino acids are transferred to the same chips with a modified surface chemistry (Bischoff et al., 2004).

PROTEIN ARRAYS Peptides are not appropriate for functional assays, because they only represent a small fraction of a whole protein, for example a protein kinase. For such assays complete proteins have to be immobilized to be able to detect enzymatic activities on a specific spot. Different publications showed that it is possible to produce specific proteins in bacteria, baculoviruses or mammalian cells, to purify the polypeptides, and to spot them on a membrane even on a whole-genome basis (Ge, 2000; Zhu et al., 2001; Espejo et al., 2002). These arrays enable to detect interactions between proteins as well as between protein and DNA or RNA. Despite of limitations due to purity and native conformation of the proteins, certain arrays are very convenient for special applications. One example are tissue-specific protein expression libraries, which are constructed at RZPD. Fusion proteins with a common 6ÂHistidine tag are overexpressed in E. coli, before the bacterial colonies are spotted onto membranes. With the help of an anti-histidine antibody, E. coli colonies are re-arrayed to create a library, which exclusively contains cells expressing His-tagged fusion proteins (for review see Cahill and Nordhoff, 2003). This enables to screen for epitopes recognized by specific antibodies or by autoantibodies in human patient sera. Since one 22Â22 cm membrane contains up to about 25,000 different protein fragments including controls spotted in duplicates, this is a very efficient method (http:// www.rzpd.de/products/proteinarrays/). A serum screening is shown in Fig. 3. Recently, a high-throughput method for the purification and spotting of fusion proteins has been developed (Lueking et al., 2003), and protein expression arrays also have successfully been used for phosphorylation assays and protein-protein binding studies (de Graaf et al., 2004). Another possibility to study protein-protein Protein Arrays in Functional Genome Research 61

Fig. 3. Serum screening of human colon gene expression library (constructed by Bernhard Korn and Sabine Henze, RZPD, Heidelberg; spotted by Uwe Radelof and Petra Gu¨nther, RZPD, Berlin). (a) Quality control with anti-His antibody (Sabine Henze). (b) Serum screening (Christine Schmitt, Christiane Rutenberg, Sabine Karolus, RZPD, Heidelberg). For details refer to the text.

interactions are two-hybrid screens in yeast or mammalian cells. There are recent publications which demonstrate the automation of these methods for high- throughput applications (Goehler et al., 2004). Ideal reaction conditions include purified proteins in their native form in a convenient buffer system. Micro- and nano-technology allow to mill little channels into the chip to produce reaction chambers for proteins and their interaction partners. Fluorescence techniques allow to address each micro-vial seperately (Angen- endt et al., 2004). An interesting alternative, although not containing a real chip format, was developed by Luminex. In this system, potential binding partners are coupled to beads, which can be discriminated by laser beams. Multiplexed experiments with up to 100 different beads in one vial of a 96 well microtiter plate are possible (Jia et al., 2004). An interesting technique, which combines the DNA spotting technique with the protein field, was published by the LaBaer group (Ramachandran et al., 2004). DNA driving the expression of tagged proteins was spotted together with an antibody against the tag and translated in vitro with mammalian reticulocyte lysate. This approach allows to immobilize native proteins in situ in large numbers for functional studies without the need of protein purification. At RZPD this method currently is established for the characterization of medically important proteins. 62 Christian Maercker

ANTIBODY ARRAYS Antibody arrays are a widely used sub-group of protein biochips. This has several reasons: First of all, antibodies against different proteins are relatively similar in structure, which allows standardized purification and spotting conditions. Sec- ondly, spotted antibodies very often have the same binding specificities as in solution. Thirdly, proteins in solution can be bound to spotted antibodies to display their native activity. Similar to RNA profilings on DNA chips, antibody arrays can be employed for overlay assays to quantify certain proteins specifically. This idea is also followed up at RZPD to confirm RNA hybridization results and complement them with important new information. At the moment we predominantly work on the 507 Ab array from Becton Dickinson (Anderson et al., 2003; Maercker et al., 2003). The standard assay compares two tissues or cell lines. The whole-protein lysates are labeled with NHS-ester linked fluorescent dyes (e.g., Cy3 for treated cells, and Cy5 for non-treated cells) and incubated with the array. A laser scan with subsequent image analysis using specialized software tools helps us to identify proteins which appear in increased concentration, e.g., in treated cells vs. non-treated cells or vice versa. The repetition of assays with inverted labeling reactions shows that certain antibodies might appear cross-reactive within a single experiment (Fig. 4). There- fore, a ‘‘dye-swop’’ experiment is strongly recommended. A second method is the protein binding assay. The antibody array is overlayed with protein complexes containing lysate from cells or tissue. Specific antibodies can detect potential binding partners of the proteins bound to the array antibodies (Wang et al., 2000). The advantage over the clone-based arrays mentioned above is the possibility to handle all involved proteins under native conditions. The company Hypromatrix and different research groups put effort into the further development of those assays. For phosphorylation assays on an antibody chip, the array is incubated with protein lysate without labeling. A monoclonal antibody or antiserum against phosphorylated aminoacids, e.g. anti-phosphotyrosine, can be used for the specific recognition of phosphorylation sites on all proteins bound to the antibodies on the array. This assay is very interesting, because it is possible to detect phosphorylations on many proteins in parallel, so that, for example, signaling pathways can be followed after treatment of cells with drug lead molecules (Abdollahi et al., 2004). Currently our group is working on new antibody arrays validated for phosphorylation assays, which are developed by BD Clontech (Fig. 5).

CELL-BASED ARRAYS The applications of antibody arrays are limited as well. Different solubilities of the proteins and difﬁculties to make suitable antibodies against all possible epitopes, makes genome-wide analyses nearly impossible. Therefore, in many cases cell-based assays are an interesting alternative. Cells as reaction chambers on arrays are very similar to the in vivo situation. cDNAs encoding full length proteins can be spotted on glass slides, which are covered with cells. The DNA is taken up by the cells, transcribed, and translated into proteins. Appropriate reporter genes (e.g., green Protein Arrays in Functional Genome Research 63

Fig. 4. Gene expression analysis on antibody arrays. Cell lysates from treated and non-treated cells were ﬂuorescently labeled with Cy5 and Cy3, respectively, and incubated with the array. The overlay of both images shows proteins with higher expression in treated cells (Cy5, red), with higher expression in non-treated cells (Cy3, green), and not regulated expression (yellow). The experiment was repeated with a dye-switch. Experiment by Christiane Rutenberg, RZPD, Heidelberg. 507 antibody array (spotted in duplicates) from BD Clontech.

fluorescent protein, GFP) help to control gene expression or serve as markers for the test of new therapeutic targets (Ziauddin and Sabatini, 2001). This kind of array is in use by RZPD and collaboration partners for gene knock-down experiments by small interference RNAs (siRNAs). A very promising approach is to combine the com- puter chip technology and micro-fluidic systems with the properties of cells. RZPD is in contact with partners who grow differentiated heart cells in silicium wavers. This setup allows to test new drug lead molecules before they go into clinical trials, and, very important, a quality control within the same experiment (‘‘beating heart’’; Fig. 6). Bio-electric chips are also popular for the simulation of neuronal networks with neural cells (Bayraktaroglu et al., 1999). Peter Fromherz and collegues have developed a neuro-chip, which contains 128Â128 sensors on one square millimeter (Fromherz, 2001). Microfluidics chips for hybridizations, the separation of cell types (Cheng et al., 1998) and other applications are offered by the US-companies Nanogen and Agilent Technologies, for instance.

TISSUE MICRO-ARRAYS Tissue arrays might even have more potential for functional assays as cell- based arrays, because different cell types can interact in their natural environment. Micro-electrode arrays (MEAs, Fig. 7) contain very small electrodes which are able to detect electrical signals from single cells or tissue from heart and brain, grown on these chips. This is an ideal system to investigate signaling defects in 64 Christian Maercker

Fig. 5. Phosphorylation assay on an antibody array. Endothelial cells were induced with bFGF for 30 min. In parallel, two arrays (60 antibodies) were overlayed with protein lysates from control cells and bFGF treated cells, respectively. Anti-phosphotyrosine antiserum recognized tyrosine phosphorylated proteins on the proteins bound to the antibodies on the array. The laser scanner detected the secondary antibody linked to Cy3. (a) Separate scans of the two arrays incubated with proteins from ctrl and bFGF treated cells. (b) Image overlay of the ﬁgures in (a). The green color of the array with proteins from bFGF treated cells was turned into red. Therefore, the red spots contain proteins, which are phosphorylated after bFGF treatment, and the green spots contain proteins which are dephosphorylated after bFGF treatment. The array was developed by Grigoriy Tchaga, BD Clon- tech, Palo Alto, USA. The experiment on the array was performed by Oskar Andersson (BD Clontech), Christiane Rutenberg (RZPD, Heidelberg) and Amir Abdollahi (German Cancer Research Center, Heidelberg).

human diseases, but also the effects of potential drugs. Electrode arrays even have been implanted in rabbit retina for subretinal electrical stimulation (Gekeler et al., 2004). In vitro tissue arrays for diagnostics are developed by BioTissue Technologies in Freiburg (Germany) and other companies. However, these complex assays are still very difficult and expensive and therefore, at this stage not applicable for routine laboratory work. In contrast, micro-arrays made from paraffin-embedded samples from many different tissues, are becoming more and more popular. The molecules in the cells are not accessible for functional studies. However, in situ assays like DNA and RNA Protein Arrays in Functional Genome Research 65

Fig. 6. Microﬂuidics chip with electrodes for liquid control. Kindly provided by Harald Mathis, Fraunhofer Institute, Sankt Augustin, Germany.

Fig. 7. Microelectrode array (MEA) for the detection of signaling processes within a tissue. Courtesy of Nadja Gugeler, NMI Institute Reutlingen, University of Tu¨bingen, Germany. The MEA Chip is owned by NMI Reutlingen. hybridizations and immunohistochemistry give very valuable results, because the bio-molecules can be localized in certain cell types of a tissue. Therefore, these microarrays are not only very useful for veriﬁying gene expression studies, but also for the diagnosis of diseases and prognostics for patients (Simon et al., 2003; Fig. 8). Currently, we and others are trying to improve the information from such assays by establishing methods for quantifying the results. 66 Christian Maercker

Fig. 8. Immunohistochemistry with anti-beta-catenin antibody on a tissue micro-array (TMA) with different human normal tissue. Different tissue sections are shown in 20-fold magnification. Exper- iment by Heidrun Ridinger. TMA by Oligene GmbH, Berlin.

CONCLUSIONS The functional analysis of the human proteome by highly parallelized assays is a big challenge for research and biotech industry. Only a small number of arrays is on the market (Table 1), many assays are under development. The chips are produced with very different methods (immobilization of bio-molecules on solid surfaces, micro-wells, micro-channels) and contain different probes (peptides, proteins, cells, tissues). The experimental results are scored with the help of numerous detection systems (mass spectrometry, fluorescence detection by laser beams, electric deduc- tion). The choice of an appropriate chip depends on the application (binding of molecules, modification of proteins, enzymatic activity) and the limitations of the particular systems (number of measuring points on the chip, backround fluorescence of immobilized probes and carrier material, structure and stability of samples).

ACKNOWLEDGMENTS The experimental results by Heidrun Ridinger und Christiane Rutenberg on antibody and tissue arrays are greatly acknowledged. Bernhard Korn is responsible for the development of protein expression libraries and siRNA applications at RZPD Heidelberg. I thank Manfred Koegl, Johannes Maurer, Harald Mathis rti rasi ucinlGnm Research Genome Functional in Arrays Protein

Table 1. Companies Involved in Protein arrays. This is Only A Selection of Firms Discussed in The Text

Applications of Proteinarrays

Array Company Address Application Service

SELDI Ciphergen www.ciphergen.com Comparative proteome analysis Identification of peptide substrates for Biosystems enzymes Peptide array Jerini www.jerini.com protein–protein interaction Optimization of drug leads DNA-protein interaction RNA-protein interaction Protein expression Protagen www.protagen.com characterization of antibodies Screening of human sera and antibodies libraries (Service RZPD) RZPD www.rzpd.de Bead systems Qiagen www.qigen.com protein–protein interacton DNA-protein interaction Antibody array BD Clontech http://bioinfo2.clontech.com/ Comparative proteome analysis Comparision of protein expression in different tissues (Service by RZPD) www.rzpd.de Antibody array Hypromatrix www.nanogen.com protein–protein interaction Protein binding assays (service by Hypromatrix, Biocat) Vertrieb durch Biocat www.biocat.de Microfluidics chip Nanogen www.nanogen.com Separation of cells Microfluidies chip Agilent www.agilent.com Separation of proteins and cells Tissue microarray Oligene www.oligene.com Immunohistochemistry Detection of proteins in different tissues (TMA) (Service by RZPD) (Vertrieb durch RZPD) www.rzpd.de 67 68 Christian Maercker

(Fraunhofer Institute, St. Augustin, Germany), Amir Abdollahi (DKFZ, Heidel- berg, Germany), Peter E. Huber (DKFZ, Heidelberg), Frank Breitling (DKFZ, Heidelberg), Grigoriy Tchaga (BD Clontech Palo Alto, USA), Oskar Andersson (BD Clontech Palo Alto), Petra Kioschis-Schneider (University of Applied Sciences, Mannheim, Germany), Iver Petersen (Charite´Berlin, Germany), Detlev Guessow (Merck, Darmstadt, Germany) und Jutta Reinhard-Rupp (Sanoﬁ-Aventis, Frank- furt, Germany) for helpful discussions, and Ralph Witzgall (University of Regens- burg, Germany) and Kerstin Amann (University of Erlangen, Germany) for their support, and Heidrun Ridinger for critical reading of the manuscript. Our explor- ative work is ﬁnanced by the German Minsitry for Science and Education (BMBF).

REFERENCES Abdollahi, A., Hahnfeldt, P., Maercker, C., Grone, H. J., Debus, J., Ansorge, W., Folkman, J., Hlatky, L., and Huber, P. E. (2004) Endostatin’s antiangiogenic signaling network. Mol. Cell. 13:649–663. Anderson, K., Potter, A., Baban, D., and Davies, K. E. (2003) Protein expression changes in spinal muscular atrophy revealed with a novel antibody array technology. Brain 126:2052–2064. Angenendt, P., Nyarsik, L., Szaflarski, W., Glokler, J., Nierhaus, K. H., Lehrach, H., Cahill, D. J., and Lueking, A. (2004) Cell-free protein expression and functional assay in nanowell chip format. Anal. Chem. 76:1844–1849. Bayraktaroglu, I., Selcuk, O. A., Dundar, G., Sina, B., and Alpaydin, E. (1999) ANNSyS: an Analog Neural Network Synthesis System. Neural. Netw. 12:325–338. Bischoff, F. R., Stadler, V., and Breitling, F. (2002) Hochkomplexe Peptidarrays – Techniken, Anwend- ungen und Perspektiven. Biospektrum 5:654–657. Bischoff, F. R., Stadler, V., and Breitling, F. (2004) Antiko¨rpern auf der Spur. GenomXpress 3:7–9. Breitling, F., Breitling, F. A., Felgenhauer, T., Fernandez, S., Leibe, K., Beyer, M., Stadler, V., Bischoff, F. R., and Poustka, A. (2002) Hochkomplexe Peptidarrays auf Computerchips. Laborwelt 3:4–6. Brown, P. O. and Botstein, D. (1999) Exploring the new world of the genome with DNA microarrays. Nat. Genet. 21(1 Suppl): 33–37. Cahill, D. J. and Nordhoff, E. (2003) Protein arrays and their role in proteomics. Adv. Biochem. Eng. Biotechnol. 83:177–187. Carpenter, A. E. and Sabatini, D. M. (2004) Systematic genome-wide screens of gene function. Nat. Rev. Genet. 5:11–22. Cawley, S., Bekiranov, S., Ng, H. H., Kapranov, P., Sekinger, E. A., Kampa, D., Piccolboni, A., Sementchenko, V., Cheng, J., Williams, A. J., Wheeler, R., Wong, B., Drenkow, J., Yamanaka, M., Patel, S., Brubaker, S., Tammana, H., Helt, G., Struhl, K., and Gingeras, T .R. (2004) Unbiased mapping of transcription factor binding sites along human chromosomes 21 and 22 points to widespread regulation of noncoding RNAs. Cell 116:499–509. Cheng, J., Sheldon, E. L., Wu, L., Heller, M. J., and O’Connell, J. P. (1998) Isolation of cultured cervical carcinoma cells mixed with peripheral blood cells on a bioelectronic chip. Anal. Chem. 70:2321–2326. de Graaf, K., Hekerman, P., Spelten, O., Herrmann, A., Packman, L. C., Bussow, K., Muller-Newen, G., and Becker, W. (2004) Characterization of cyclin L2, a novel cyclin with an arginine/serine-rich domain: phosphorylation by DYRK1A and colocalization with splicing factors. J. Biol. Chem. 279:4612–4624. Espejo, A., Cote, J., Bednarek, A., Richard, S., and Bedford, M. T. (2002) A protein-domain microarray identifies novel protein-protein interactions. Biochem. J. 367:697–702. Frank, R. (2002) High-density synthetic peptide microarrays: emerging tools for functional genomics and proteomics Comb. Chem. High Throughput Screen 5:429–440. Fromherz, P. (2001) Interfacing von Nervenzellen und Halbleiterchips Auf dem Weg zu Hirnchips und Neurocomputern? Physikalische Bla¨tter 57:43–48. Ge, H. (2000) UPA, a universal protein array system for quantitative detection of protein-protein, protein- DNA, protein-RNA and protein-ligand interactions Nucleic Acids Res. 28:e3. Protein Arrays in Functional Genome Research 69

Gekeler, F., Kobuch, K., Schwahn, H. N., Stett, A., Shinoda, K., and Zrenner, E. (2004) Subretinal electrical stimulation of the rabbit retina with acutely implanted electrode arrays. Graefes Arch. Clin. Exp. Ophthalmol. 242:587–596. Goehler, H., Lalowski, M., Stelzl, U., Waelter, S., Stroedicke, M., Worm, U., Droege, A., Lindenberg, K. S., Knoblich, M., Haenig, C., Herbst, M., Suopanki, J., Scherzinger, E., Abraham, C., Bauer, B., Hasenbank, R., Fritzsche, A., Ludewig, A. H., Buessow, K., Coleman, S. H., Gutekunst, C. A, Landwehrmeyer, B. G., Lehrach, H., and Wanker, E. E. (2004) A protein interaction network links GIT1an enhancer of huntingtin aggregation, to Huntington’s disease. Mol. Cell. 15:853–865. Gygi, S. P., Rochon, Y., Franza, B. R., and Aebersold, R. (1999) Correlation between protein and mRNA abundance in yeast. Mol. Cell. Biol. 19:1720–1730. Jia, X. C., Raya, R., Zhang, L., Foord, O., Walker, W. L., Gallo, M. L., Haak-Frendscho, M., Green, L. L., and Davis, C. G. (2004) A novel method of Multiplexed Competitive Antibody Binning for the characterization of monoclonal antibodies. J. Immunol. Meth. 288:91–98. Lichter, P., Joos, S., Bentz, M., and Lampel, S. (2000) Comparative genomic hybridization: uses and limitations. Semin. Hematol. 37:348–357. Lueking, A., Possling, A., Huber, O., Beveridge, A., Horn, M., Eickhoff, H., Schuchardt, J., Lehrach, H., and Cahill, D. J. (2003) A nonredundant human protein chip for antibody screening and serum profiling. Mol. Cell. Proteomics 12:1342–1349. Maercker, C., Abdollahi, A., Korn, B., and Huber, P. E (2003) Proteinexpressionsanalysen auf Antiko¨rper Arrays: Chip-Plattform am RZPD identifiziert Hypoxiefaktoren in menschlichen Zellen. GenomX- press 4:11–12. Mc Pherson, J. D., Marra, M., Hillier, L., Waterston, R. H., Chinwalla, A., Wallis, J., Sekhon, M., Wylie, K., Mardis, E. R., Wilson, R. K., Fulton, R., Kucaba, T. A., Wagner-McPherson, C., Barbazuk, W. B., Gregory, S. G., Humphray, S. J., French, L., Evans, R. S., Bethel, G., Whittaker, A., Holden, J. L., McCann, O. T., Dunham, A., Soderlund, C., Scott, C. E., Bentley, D. R., Schuler, G., Chen, H. C., Jang, W., Green, E. D., Idol, J. R., Maduro, V. V., Montgomery, K. T., Lee, E., Miller, A., Emerling, S., Kucherlapati, Gibbs, R., Scherer, S., Gorrell, J. H., Sodergren, E., Clerc-Blankenburg, K., Tabor, P., Naylor, S., Garcia, D., de Jong, P. J., Catanese, J. J., Nowak, N., Osoegawa, K., Qin, S., Rowen, L., Madan, A., Dors, M., Hood, L., Trask, B., Friedman, C., Massa, H., Cheung, V. G., Kirsch, I. R., Reid, T., Yonescu, R., Weissenbach, J., Bruls, T., Heilig, R., Branscomb, E., Olsen, A., Doggett, N., Cheng, J. F., Hawkins, T., Myers, R. M., Shang, J., Ramirez, L., Schmutz, J., Ve- lasquez, O., Dixon, K., Stone, N. E., Cox, D. R., Haussler, D., Kent, W. J., Furey, T., Rogic, S., Kennedy, S., Jones, S., Rosenthal, A., Wen, G., Schilhabel, M., Gloeckner, G., Nyakatura, G., Siebert, R., Schlegelberger, B., Korenberg, J., Chen, X. N., Fujiyama, A., Hattori, M., Toyoda, A., Yada, T., Park, H. S., Sakaki, Y., Shimizu, N., Asakawa, S., Kawasaki, K., Sasaki, T., Shintani, A., Shimizu, A., Shibuya, K., Kudoh, J., Minoshima, S., Ramser, J., Seranski, P., Hoff, C., Poustka, A., Reinhardt, R., and Lehrach, H., International Human Genome Mapping Consortium. (2001) A physical map of the human genome Nature 409:934–941. Ramachandran, N., Hainsworth, E., Bhullar, B., Eisenstein, S., Rosen, B., Lau, A. Y., Walter, J. C., and LaBaer, J. (2004) Self-assembling protein microarrays. Science 305:86–90. Schulze, W. X. and Mann, M. (2004) A novel proteomic screen for peptide-protein interactions. J. Biol. Chem. 279:10756–10764. Sickmann, A., Mreyen, M., and Meyer, H. E. (2003) Mass spectrometry – a key technology in proteome research. Adv. Biochem. Eng. Biotechnol. 83:141–176. Simon, R., Mirlacher, M., and Sauter, G. (2003) Tissue Microarrays for HT-molecular pathology. Euro. Biotech. News 4:38–42. Venter, J. C., Adams, M. D., Myers, E. W., Li, P. W., Mural, R. J., Sutton, G. G., Smith, H. O., Yandell, M., Evans, C. A., Holt, R. A., Gocayne, J. D., Amanatides, P., Ballew, R. M., Huson, D. H., Wortman, J. R., Zhang, Q., Kodira, C. D., Zheng, X. H., Chen, L., Skupski, M., Subramanian, G., Thomas, P. D., Zhang, J., Gabor Miklos, G. L., Nelson, C., Broder, S., Clark, A. G., Nadeau, J., McKusick, V. A., Zinder, N., Levine, A. J., Roberts, R. J., Simon, M., Slayman, C., Hunkapiller, M., Bolanos, R., Delcher, A., Dew, I., Fasulo, D., Flanigan, M., Florea, L., Halpern, A., Hannenhalli, S., Kravitz, S., Levy, S., Mobarry, C., Reinert, K., Remington, K., Abu-Threideh, J., Beasley, E., Biddick, K., Bonazzi, V., Brandon, R., Cargill, M., Chandramouliswaran, I., Charlab, R., Cha- turvedi, K., Deng, Z., Di Francesco, V., Dunn, P., Eilbeck, K., Evangelista, C., Gabrielian, A. E., Gan, W., Ge, W., Gong, F., Gu, Z., Guan, P., Heiman, T. J., Higgins, M. E., Ji, R. R., Ke, Z., 70 Christian Maercker

Ketchum, K. A., Lai, Z., Lei, Y., Li, Z., Li, J., Liang, Y., Lin, X., Lu, F., Merkulov, G. V., Milshina, N., Moore, H. M., Naik, A. K., Narayan, V. A., Neelam, B., Nusskern, D., Rusch, D. B., Salzberg, S., Shao, W., Shue, B., Sun, J., Wang, Z., Wang, A., Wang, X., Wang, J., Wei, M., Wides, R., Xiao, C., Yan, C., Yao, A., Ye, J., Zhan, M., Zhang, W., Zhang, H., Zhao, Q., Zheng, L., Zhong, F., Zhong, W., Zhu, S., Zhao, S., Gilbert, D., Baumhueter, S., Spier, G., Carter, C., Cravchik, A., Woodage, T., Ali, F., An, H., Awe, A., Baldwin, D., Baden, H., Barnstead, M., Barrow, I., Beeson, K., Busam, D., Carver, A., Center, A., Cheng, M. L., Curry, L., Danaher, S., Davenport, L., Desilets, R., Dietz, S., Dodson, K., Doup, L., Ferriera, S., Garg, N., Gluecksmann, A., Hart, B., Haynes, J., Haynes, C., Heiner, C., Hladun, S., Hostin, D., Houck, J., Howland, T., Ibegwam, C., Johnson, J., Kalush, F., Kline, L., Koduru, S., Love, A., Mann, F., May, D., McCawley, S., McIntosh, T., McMullen, I., Moy, M., Moy, L., Murphy, B., Nelson, K., Pfannkoch, C., Pratts, E., Puri, V., Qureshi, H., Reardon, M., Rodriguez, R., Rogers, Y.H., Romblad, D., Ruhfel, B., Scott, R., Sitter, C., Smallwood, M., Stewart, E., Strong, R., Suh, E., Thomas, R., Tint, N. N., Tse, S., Vech, C., Wang, G., Wetter, J., Williams, S., Williams, M., Windsor, S., Winn-Deen, E., Wolfe, K., Zaveri, J., Zaveri, K., Abril, J. F., Guigo, R., Campbell, M. J., Sjolander, K. V., Karlak, B., Kejariwal, A., Mi, H., Lazareva, B., Hatton, T., Narechania, A., Diemer, K., Muruganujan, A., Guo, N., Sato, S., Bafna, V., Istrail, S., Lippert, R., Schwartz, R., Walenz, B., Yooseph, S., Allen, D., Basu, A., Baxendale, J., Blick, L., Caminha, M., Carnes-Stine, J., Caulk, P., Chiang, Y. H., Coyne, M., Dahlke, C., Mays, A., Dombroski, M., Donnelly, M., Ely, D., Esparham, S., Fosler, C., Gire, H., Glanowski, S., Glasser, K., Glodek, A., Gorokhov, M., Graham, K., Gropman, B., Harris, M., Heil, J., Hen- derson, S., Hoover, J., Jennings, D., Jordan, C., Jordan, J., Kasha, J., Kagan, L., Kraft, C., Levitsky, A., Lewis, M., Liu, X., Lopez, J., Ma, D., Majoros, W., McDaniel, J., Murphy, S., Newman, M., Nguyen, T., Nguyen, N., Nodell, M., Pan, S., Peck, J., Peterson, M., Rowe, W., Sanders, R., Scott, J., Simpson, M., Smith, T., Sprague, A., Stockwell, T., Turner, R., Venter, E., Wang, M., Wen, M., Wu, D., Wu, M., Xia, A., Zandieh, A., and Zhu, X. (2001) The sequence of the human genome Science 291:1304–1351. Wang, Y., Wu, T. R., Cai, S., Welte, T., and Chin, Y. E. (2000) Stat1 as a component of tumor necrosis factor alpha receptor 1-TRADD signaling complex to inhibit NF-kappaB activation. Mol. Cell. Biol. 20:4505–4512. Wiesner, A. and Bogumi, R. (2002) Das ProteinChip-System in klinischer ForschungProteinisolierung und Proteinanalytik. TranskriptLaborwelt 2002:10–13. Woods, C. G., Valente, E. M., Bond, J., and Roberts, E. (2004) A new method for autozygosity mapping using single nucleotide polymorphisms (SNPs) and EXCLUDEAR. J. Med. Genet. 41:e101–e104. Zhu, H., Bilgin, M., Bangham, R., Hall, D., Casamayor, A., Bertone, P., Lan, N., Jansen, R., Bid- lingmaier, S., Houfek, T., Mitchell, T., Miller, P., Dean, R. A., Gerstein, M., and Snyder, M. (2001) Global analysis of protein activities using proteome chips. Science 293:2101–2105. Ziauddin, J. and Sabatini, D. M. (2001) Microarrays of cells expressing deﬁned cDNAs. Nature 411: 107–110.