Normalization for triple-target microarray experiments

<p>Abstract</p> <p>Background</p> <p>Most microarray studies are made using labelling with one or two dyes which allows the hybridization of one or two samples on the same slide. In such experiments, the most frequently used dyes are <it>Cy</it>3 and <it>Cy</it>5. Recent improvements in the technology (dye-labelling, scanner and, image analysis) allow hybridization up to four samples simultaneously. The two additional dyes are <it>Alexa</it>488 and <it>Alexa</it>494. The triple-target or four-target technology is very promising, since it allows more flexibility in the design of experiments, an increase in the statistical power when comparing gene expressions induced by different conditions and a scaled down number of slides. However, there have been few methods proposed for statistical analysis of such data. Moreover the lowess correction of the global dye effect is available for only two-color experiments, and even if its application can be derived, it does not allow simultaneous correction of the raw data.</p> <p>Results</p> <p>We propose a two-step normalization procedure for triple-target experiments. First the dye bleeding is evaluated and corrected if necessary. Then the signal in each channel is normalized using a generalized lowess procedure to correct a global dye bias. The normalization procedure is validated using triple-self experiments and by comparing the results of triple-target and two-color experiments. Although the focus is on triple-target microarrays, the proposed method can be used to normalize <it>p </it>differently labelled targets co-hybridized on a same array, for any value of <it>p </it>greater than 2.</p> <p>Conclusion</p> <p>The proposed normalization procedure is effective: the technical biases are reduced, the number of false positives is under control in the analysis of differentially expressed genes, and the triple-target experiments are more powerful than the corresponding two-color experiments. There is room for improving the microarray experiments by simultaneously hybridizing more than two samples.</p

Importance of randomization in microarray experimental designs with Illumina platforms

Measurements of gene expression from microarray experiments are highly dependent on experimental design. Systematic noise can be introduced into the data at numerous steps. On Illumina BeadChips, multiple samples are assayed in an ordered series of arrays. Two experiments were performed using the same samples but different hybridization designs. An experiment confounding genotype with BeadChip and treatment with array position was compared to another experiment in which these factors were randomized to BeadChip and array position. An ordinal effect of array position on intensity values was observed in both experiments. We demonstrate that there is increased rate of false-positive results in the confounded design and that attempts to correct for confounded effects by statistical modeling reduce power of detection for true differential expression. Simple analysis models without post hoc corrections provide the best results possible for a given experimental design. Normalization improved differential expression testing in both experiments but randomization was the most important factor for establishing accurate results. We conclude that lack of randomization cannot be corrected by normalization or by analytical methods. Proper randomization is essential for successful microarray experiments

Fluorescence Lifetime Imaging of Quantum Dot Labeled DNA Microarrays

Quantum dot (QD) labeling combined with fluorescence lifetime imaging microscopy is proposed as a powerful transduction technique for the detection of DNA hybridization events. Fluorescence lifetime analysis of DNA microarray spots of hybridized QD labeled target indicated a characteristic lifetime value of 18.8 ns, compared to 13.3 ns obtained for spots of free QD solution, revealing that QD labels are sensitive to the spot microenvironment. Additionally, time gated detection was shown to improve the microarray image contrast ratio by 1.8, achieving femtomolar target sensitivity. Finally, lifetime multiplexing based on Qdot525 and Alexa430 was demonstrated using a single excitation-detection readout channel

Regulatory Features Underlying Pollination-Dependent and -Independent Tomato Fruit Set Revealed by Transcript and Primary Metabolite Profiling

Indole Acetic Acid 9 (IAA9) is a negative auxin response regulator belonging to the Aux/IAA transcription factor gene family whose downregulation triggers fruit set before pollination, thus giving rise to parthenocarpy. In situ hybridization experiments revealed that a tissue-specific gradient of IAA9 expression is established during flower development, the release of which upon pollination triggers the initiation of fruit development. Comparative transcriptome and targeted metabolome analysis uncovered important features of the molecular events underlying pollination-induced and pollination-independent fruit set. Comprehensive transcriptomic profiling identified a high number of genescommonto both types of fruit set,amongwhich only a small subset are dependent on IAA9 regulation. The fine-tuning of Aux/IAA and ARF genes and the downregulation of TAG1 and TAGL6 MADS box genes are instrumental in triggering the fruit set program. Auxin and ethylene emerged as the most active signaling hormones involved in the flower-to-fruit transition. However, while these hormones affected only a small number of transcriptional events, dramatic shifts were observed at the metabolic and developmental levels. The activation of photosynthesis and sucrose metabolism-related genes is an integral regulatory component of fruit set process. The combined results allow a far greater comprehension of the regulatory and metabolic events controlling early fruit development both in the presence and absence of pollination/fertilization