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Abstract Background Image analysis of microarrays and, in particular, spot quantification and spot quality control, is one of the most important steps in statistical analysis of microarray data. Recent methods of spot quality control are still in early age of development, often leading to underestimation of true positive microarray features and, consequently, to loss of important biological information. Therefore, improving and standardizing the statistical approaches of spot quality control are essential to facilitate the overall analysis of microarray data and subsequent extraction of biological information. Findings We evaluated the performance of two image analysis packages MAIA and GenePix (GP) using two complementary experimental approaches with a focus on the statistical analysis of spot quality factors. First, we developed control microarrays with a priori known fluorescence ratios to verify the accuracy and precision of the ratio estimation of signal intensities. Next, we developed advanced semi-automatic protocols of spot quality evaluation in MAIA and GP and compared their performance with available facilities of spot quantitative filtering in GP. We evaluated these algorithms for standardised spot quality analysis in a whole-genome microarray experiment assessing well-characterised transcriptional modifications induced by the transcription regulator SNAI1. Using a set of RT-PCR or qRT-PCR validated microarray data, we found that the semi-automatic protocol of spot quality control we developed with MAIA allowed recovering approximately 13% more spots and 38% more differentially expressed genes (at FDR = 5%) than GP with default spot filtering conditions. Conclusion Careful control of spot quality characteristics with advanced spot quality evaluation can significantly increase the amount of confident and accurate data resulting in more meaningful biological conclusions.

Abstract Background The actin cytoskeleton plays a crucial role in supporting and regulating numerous cellular processes. Mutations or alterations in the expression levels affecting the actin cytoskeleton system or related regulatory mechanisms are often associated with complex diseases such as cancer. Understanding how qualitative or quantitative changes in expression of the set of actin cytoskeleton genes are integrated to control actin dynamics and organisation is currently a challenge and should provide insights in identifying potential targets for drug discovery. Here we report the development of a dedicated microarray, the Actichip, containing 60-mer oligonucleotide probes for 327 genes selected for transcriptome analysis of the human actin cytoskeleton. Results Genomic data and sequence analysis features were retrieved from GenBank and stored in an integrative database called Actinome. From these data, probes were designed using a home-made program (CADO4MI) allowing sequence refinement and improved probe specificity by combining the complementary information recovered from the UniGene and RefSeq databases. Actichip performance was analysed by hybridisation with RNAs extracted from epithelial MCF-7 cells and human skeletal muscle. Using thoroughly standardised procedures, we obtained microarray images with excellent quality resulting in high data reproducibility. Actichip displayed a large dynamic range extending over three logs with a limit of sensitivity between one and ten copies of transcript per cell. The array allowed accurate detection of small changes in gene expression and reliable classification of samples based on the expression profiles of tissue-specific genes. When compared to two other oligonucleotide microarray platforms, Actichip showed similar sensitivity and concordant expression ratios. Moreover, Actichip was able to discriminate the highly similar actin isoforms whereas the two other platforms did not. Conclusion Our data demonstrate that Actichip is a powerful alternative to commercial high density microarrays for cytoskeleton gene profiling in normal or pathological samples. Actichip is available upon request.

Excitation diffusion is studied in 5-25-nm-thick films of zinc tetra-(p-octylphenyl)-porphyrin (ZnTOPP) spin-coated onto quartz slides by doping with quenchers using steady-state and time-resolved fluorescence spectroscopy. The neighboring stacks aggregate in a herringbone structure, forming ordered domains that were randomly oriented in the substrate plane, and an energy-transfer rate constant of (7 plusmn 2) x 10(super 10)s(super minus1).

A novel mathematical model of the actin dynamics in living cells under steady-state conditions has been developed for fluorescence recovery after photobleaching (FRAP) experiments. As opposed to other FRAP fitting models, which use the average lifetime of actins in filaments and the actin turnover rate as fitting parameters, our model operates with unbiased actin association/dissociation rate constants and accounts for the filament length. The mathematical formalism is based on a system of stochastic differential equations. The derived equations were validated on synthetic theoretical data generated by a stochastic simulation algorithm adapted for the simulation of FRAP experiments. Consistent with experimental findings, the results of this work showed that (1) fluorescence recovery is a function of the average filament length, (2) the F-actin turnover and the FRAP are accelerated in the presence of actin nucleating proteins, (3) the FRAP curves may exhibit both a linear and non-linear behaviour depending on the parameters of actin polymerisation, and (4) our model resulted in more accurate parameter estimations of actin dynamics as compared with other FRAP fitting models. Additionally, we provide a computational tool that integrates the model and that can be used for interpretation of FRAP data on actin cytoskeleton. [ABSTRACT FROM AUTHOR]

Background: Initially detected in leukocytes and cancer cells derived from solid tissues, L-plastin/fimbrin belongs to a large family of actin crosslinkers and is considered as a marker for many cancers. Phosphorylation of L-plastin on residue Ser5 increases its F-actin binding activity and is required for L-plastin-mediated cell invasion. Methodology/Principal Findings: To study the kinetics of L-plastin and the impact of L-plastin Ser5 phosphorylation on L-plastin dynamics and actin turn-over in live cells, simian Vero cells were transfected with GFP-coupled WT-L-plastin, Ser5 substitution variants (S5/A, S5/E) or actin and analyzed by fluorescence recovery after photobleaching (FRAP). FRAP data were explored by mathematical modeling to estimate steady-state reaction parameters. We demonstrate that in Vero cell focal adhesions L-plastin undergoes rapid cycles of association/dissociation following a two-binding-state model. Phosphorylation of L-plastin increased its association rates by two-fold, whereas dissociation rates were unaffected. Importantly, L-plastin affected actin turn-over by decreasing the actin dissociation rate by four-fold, increasing thereby the amount of F-actin in the focal adhesions, all these effects being promoted by Ser5 phosphorylation. In MCF-7 breast carcinoma cells, phorbol 12-myristate 13-acetate (PMA) treatment induced L-plastin translocation to de novo actin polymerization sites in ruffling membranes and spike-like structures and highly increased its Ser5 phosphorylation. Both inhibition studies and siRNA knock-down of PKC isozymes pointed to the involvement of the novel PKC-d isozyme in the PMA-elicited signaling pathway leading to L-plastin Ser5 phosphorylation. Furthermore, the L-plastin contribution to actin dynamics regulation was substantiated by its association with a protein complex comprising cortactin, which is known to be involved in this process. Conclusions/Significance: Altogether these findings quantitatively demonstrate for the first time that L-plastin contributes to the fine-tuning of actin turn-over, an activity which is regulated by Ser5 phosphorylation promoting its high affinity binding to the cytoskeleton. In carcinoma cells, PKC-d signaling pathways appear to link L-plastin phosphorylation to actin polymerization and invasion. [ABSTRACT FROM AUTHOR]

Simulation-based fitting has been applied to data analysis and parameter determination of complex experimental systems in many areas of chemistry and biophysics. However, this method is limited because of the time costs of the calculations. In this paper it is proposed to approximate and substitute a simulation model by an artificial neural network during the fitting procedure. Such a substitution significantly speeds up the parameter determination. This approach is tested on a model of fluorescence resonance energy transfer (FRET) within a system of site-directed fluorescence labeled M13 major coat protein mutants incorporated into a lipid bilayer. It is demonstrated that in our case the application of a trained artificial neural network for the substitution of the simulation model results in a significant gain in computing time by a factor of 5 x 10(4). Moreover, an artificial neural network produces a smooth approximation of the noisy results of a stochastic simulation.