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microRNA PROFILING USING A HIGH PERFORMANCE, FLEXIBLE μPARAFLO®  BIOCHIP PLATFORM Christoph Eicken PhD Head of Technical Services, Microarrays Chris Hebel Director of Business Development

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Agenda μParaflo® MicrofluidicsTechnology Customer Applications Q & A Practical Considerations

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LC Sciences headquarters in Houston, Texas - USA offices in USA & China, representatives inEurope, Japan, Korea, & India customers in >40 countries first miRNA array service provider (since 2005)

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Microfluidic Array Platform μParaflo®  Microfluidics Chip 10 µl total volume 4000 features

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The Core: 3 Levels of Flexibility Chemistry Digital Photolithography Microfluidics

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Chemistry Does not require electrochemical surface Nor specialty monomers Standard DMT chemistry allows use ofwide array of (non-regular) building blocks The Core: 3 Levels of Flexibility

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Standard t-Boc protecting group in an amino acid Deprotected using an acid or a photogenerated acid (PGA) Others: Photo-labile protecting group in an amino acid Deprotected using light irradiation 3 Levels of Flexibility No specialty monomers required

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3 Levels of Flexibility Digital Photolithography custom sequences on demand synthesis

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3 Levels of Flexibility Microfluidics multiple washing steps T-variation using the same chip, keeping all other parameters constant

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Si Substrate Cover Glass Distribution Channel Fluid Channel Reaction Chamber Light Beam Photogenerated Reagent μParaflo®  Microfluidics Chip

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DNA DNA array OligoMix® Modified Nucleotides miRNA array ncRNA array Peptides Kinase array Protein binding array Custom arrays Probe Diversity

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microRNA Related Publications Source: PubMed

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miRBase Version # of sequences (all species) miRBase Synchronicity

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Probe Content: miRBase and Beyond miRBase 13.0

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Microfluidics vs. Spotted Arrays - Raw Data

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Microfluidics vs. Spotted Arrays - Raw Data

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Spot Uniformity Enhanced Mass Transfer: improving sensitivity High Reproducibility Closed System:no dye “bleaching” Incremental Stringency Wash: higher specificity Stable for years Microfluidics vs. Spotted Arrays

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Specificity: Optimized RNA Hybridization Probes Sample: 20 mer RNA control spiked into total RNA sample Signal Intensity

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Brain qRT-PCR Validation Dual sample array using mouse brain and mouse thymus RNA samples from Ambion.  Results are compared with QPCR data for the same two RNA samples, also purchased from Ambion, published by Applied Biosystems (ABI)*. The comparison data includes all 12 microRNA transcripts published by ABI. * Data is obtained from http://www.appliedbiosystems.com

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To Sum It Up: Microfluidic Microarray Platform:Reliable and accurate results as validated with qPCR and Northern Blot Optimized RNA Hybridization Probes:Designed with optimized Tms to ensure uniform hybridization affinity under high stringency hybridization conditions enhancing both, sensitivity and specificity of the probes Most Current Content:Not 90% or 95% coverage but always 100% of experimentally verified miRNA sequences on all our arrays Complete Content Flexibility:The choice is yours! No Limitation on Use of Data:Full access and control over the results generated from your experiment

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Customer Publications: 75+ Cancer Research Cardiovascular Research Neuroscience Small RNA Discovery Reproductive Biology Plant Science Virology (HIV, Epstein-Barr) Stem Cell Research Endocrinology

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Pseudo color images Biomarker Discovery / Profiling: Differential Expression - One Dual Sample Chip control (Cy3) vs treated (Cy5) Cy3/Cy5 ratio

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Sample Data Repeats

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Raw Data in Publications

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Control / Treated Biological repeats t-Test Multi-array normalization and clustering analysis Array assay Differentiated miRNAs of Biological & Statistical Significance - Multiple Chips

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Disease - Marker identification(Olga, Olson, Cameron) Cameron JE, Yin Q, Fewell C, Lacey M, McBride J, Wang X, Lin Z, Schaefer BC, Flemington EK. (2008)The Epstein-Barr Virus latent membrane protein 1 (LMP1) induces cellular microRNA-146a, a modulator of lymphocyte signaling pathways. J Virol [Epub ahead of print]. Disease Marker Discovery

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Cameron JE, Yin Q, Fewell C, Lacey M, McBride J, Wang X, Lin Z, Schaefer BC, Flemington EK. (2008)The Epstein-Barr Virus latent membrane protein 1 (LMP1) induces cellular microRNA-146a, a modulator of lymphocyte signaling pathways. J Virol [Epub ahead of print]. Experiment included 5 biological repeats/group Probes for human (hsa) & virus (kshv) miRNA were used on the same chip

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From Biomarkers to Therapeutics Tavazoie SF, Alarcón C, Oskarsson T, Padua D, Wang Q, Bos PD, Gerald WL, Massagué J. (2008) Endogenous human microRNAs that suppress breast cancer metastasis. Nature 451(7175), 147-52.

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Discovery Applications: Potential miRNA or other small regulatory RNA genes array result

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Discovery Applications: mRNA 3’ 3’ UTR Protein miRNA Target Screening

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Vagin VV, Sigova A, Li C, Seitz H, Gvozdev V, Zamore PD. (2006) A distinct small RNA pathway silences selfish genetic elements in the germline. Science 313(5785), 320-324. Cummins JM, He Y, Leary RJ, Pagliarini R, Diaz L.A Jr, Sjoblom T, Barad O, Bentwich Z, Szafranska AE, Labourier E, et al. (2006) The colorectal microRNAome. Proc Natl Acad Sci USA 103, 3687-3692. Discovery Applications:

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microRNA Microarray Service - Reports

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Which Probe Content Should I Chose? Most current miRBase content If correlation to older data needed:chose same probe content single vs multi species chips:less can be more... custom probes:(predicted sequences, controls etc.) proprietary probes (additional paperwork, who will benefit from those data?)

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For differential studies, such as normal versus disease, untreated versus treated, and time course experiments, it is very important to have biological repeats in the experiment. Statistic identification of biologically significant calls can only be made based on comparing miRNA expression difference between different sample groups with the expression variations among samples within corresponding groups. Statistic significance is determined by a statistic test, such as T-test. The mathematical formulation of the T-test is T function where, A and B are the average value of group A and B, respectively; SA and SB are the standard deviations of group A and B, respectively; and nA and nB are the number of samples in group A and B, respectively. The higher the T function, the higher the probability of groups A and B being statistically different. Obviously, a statistic test can be performed only when each group contains more than one sample. Importance of biological repeats _ _

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Number of biological repeats needed The higher the biological variations are expected the more biological repeats should be used. Cell line samples: preferably 3 or more, at least 2 Lab animals: preferably 4 or more, at least 3 Human samples: 10 or more

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Technical repeats Generally there is no need for technical repeats. It is much more technically effective and financially efficient to use biological repeats instead of technical repeats. In most cases, our assay variations are less than biological-repeat variations. Pooled biological samples Generally, pooled samples are not recommended. When samples are pooled, critical formation on sample-to-sample variations (SA and SB in equation (1)) within the same groups are lost and therefore identification of biologically significant miRNA differentials may no longer be possible.

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Dual sample vs single sample assay A dual-sample assay is used when two samples are compared with each other. The assay does not involve chip-to-chip variations and therefore may reveal very small differences between the two samples. The assay is especially suitable when paired samples are studied, such as diseased tissue being compared with an adjacent non-diseased tissue. The assay also has an advantage of lower per-sample cost than single sample assay does. However, design considerations should be given to minimize dye-related bias. A single-sample assay is used when multiple independent samples are compared with each other. The assay has the advantage of free of dye-related bias although it has a higher per-sample cost.

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Minimize dye bias in dual-sample assays Dye swap can be used to eliminate false calls due to dye-related bias in dual sample assays. A good design should have half members of each group labeled with Cy3 and the other half labeled with Cy5. Example 1: 4 samples (A1, A2, B1, and B2) from an experiment of comparing untreated versus treated samples with 2 biological repeats for each sample group.

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Minimize dye bias in dual-sample assays Example 2: 12 samples from an experiment involving three sample groups of untreated (A1-A4), treated at dosage 1 (B1-B4), and treated at dosage 2 (C1-C4).

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Normalizaton LOWESS (Locally weighted scatterplot smoothing)is one of many "modern" modeling methods that build on "classical" methods, such as linear and nonlinear least squares regression. LOWESS combines much of the simplicity of linear least squares regression with the flexibility of nonlinear regression. It does this by fitting simple models to localized subsets of the data to build up a function that describes the deterministic part of the variation in the data, point by point. In fact, one of the chief attractions of this method is that the data analyst is not required to specify a global function of any form to fit a model to the data, only to fit segments of the data.The trade-off for these features is increased computation. Your controls (optional) Our controls (optional)

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