Master equation simulation analysis of immunostained Bicoid morphogen gradient

Background  

The concentration gradient of Bicoid protein which determines the developmental pathways in early Drosophila embryo is the best characterized morphogen gradient at the molecular level. Because different developmental fates can be elicited by different concentrations of Bicoid, it is important to probe the limits of this specification by analyzing intrinsic fluctuations of the Bicoid gradient arising from small molecular number. Stochastic simulations can be applied to further the understanding of the dynamics of Bicoid morphogen gradient formation at the molecular number level, and determine the source of the nucleus-to-nucleus expression variation (noise) observed in the Bicoid gradient.     

Interactively optimizing signal-to-noise ratios in expression profiling: project-specific algorithm selection and detection p-value weighting in Affymetrix microarrays

MOTIVATION: The most commonly utilized microarrays for mRNA profiling (Affymetrix) include 'probe sets' of a series of perfect match and mismatch probes (typically 22 oligonucleotides per probe set). There are an increasing number of reported 'probe set algorithms' that differ in their interpretation of a probe set to derive a single normalized 'signal' representative of expression of each mRNA. These algorithms are known to differ in accuracy and sensitivity, and optimization has been done using a small set of standardized control microarray data. We hypothesized that different mRNA profiling projects have varying sources and degrees of confounding noise, and that these should alter the choice of a specific probe set algorithm. Also, we hypothesized that use of the Microarray Suite (MAS) 5.0 probe set detection p-value as a weighting function would improve the performance of all probe set algorithms. RESULTS: We built an interactive visual analysis software tool (HCE2W) to test and define parameters in Affymetrix analyses that optimize the ratio of signal (desired biological variable) versus noise (confounding uncontrolled variables). Five probe set algorithms were studied with and without statistical weighting of probe sets using the MAS 5.0 probe set detection p-values. The signal-to-noise ratio optimization method was tested in two large novel microarray datasets with different levels of confounding noise, a 105 sample U133A human muscle biopsy dataset (11 groups: mutation-defined, extensive noise), and a 40 sample U74A inbred mouse lung dataset (8 groups: little noise). Performance was measured by the ability of the specific probe set algorithm, with and without detection p-value weighting, to cluster samples into the appropriate biological groups (unsupervised agglomerative clustering with F-measure values). Of the total random sampling analyses, 50% showed a highly statistically significant difference between probe set algorithms by ANOVA [F(4,10) > 14, p < 0.0001], with weighting by MAS 5.0 detection p-value showing significance in the mouse data by ANOVA [F(1,10) > 9, p < 0.013] and paired t-test [t(9) = -3.675, p = 0.005]. Probe set detection p-value weighting had the greatest positive effect on performance of dChip difference model, ProbeProfiler and RMA algorithms. Importantly, probe set algorithms did indeed perform differently depending on the specific project, most probably due to the degree of confounding noise. Our data indicate that significantly improved data analysis of mRNA profile projects can be achieved by optimizing the choice of probe set algorithm with the noise levels intrinsic to a project, with dChip difference model with MAS 5.0 detection p-value continuous weighting showing the best overall performance in both projects. Furthermore, both existing and newly developed probe set algorithms should incorporate a detection p-value weighting to improve performance. AVAILABILITY: The Hierarchical Clustering Explorer 2.0 is available at http://www.cs.umd.edu/hcil/hce/ Murine arrays (40 samples) are publicly available at the PEPR resource (http://microarray.cnmcresearch.org/pgadatatable.asp http://pepr.cnmcresearch.org Chen et al., 2004).     

The noise of membrane capacitance measurements in the whole-cell recording configuration

High-resolution measurement of membrane capacitance in the whole-cell-recording configuration can be used to detect small changes in membrane surface area that accompany exocytosis and endocytosis. We have investigated the noise of membrane capacitance measurements to determine the fundamental limits of resolution in actual cells in the whole-cell mode. Two previously overlooked sources of noise are particularly evident at low frequencies. The first noise source is accompanied by a correlation between capacitance estimates, whereas the second noise source is due to "1/f-like" current noise. An analytic expression that summarizes the noise from thermal and 1/f sources is derived, which agrees with experimental measurements from actual cells over a large frequency range. Our results demonstrate that the optimal frequencies for capacitance measurements are higher than previously believed. Finally, we demonstrate that the capacitance noise at high frequencies can be reduced by compensating for the voltage drop of the sine wave across the series resistance.     

Characterization of the expression ratio noise structure in high-density oligonucleotide arrays

Background

High-density oligonucleotide microarrays provide a powerful tool for assessing differential mRNA expression levels. Characterizing the noise resulting from the enzymatic and hybridization steps, called type I noise, is essential for attributing significance measures to the differential expression scores. We introduce scoring functions for expression ratios, and associated quality measures. Both the PM (Perfect Match) probes and PM-MM differentials (MM is the single MisMatch) are considered as raw intensities. We then characterize the log-ratio noise structure using robust estimates of their intensity dependent variance.

Results

We show the relationships between the obtained ratios and their quality measures. The complementarity of PM and PM-MM methods is emphasized by the probe sets signal to noise measures. Using a large set of replicate experiments, we demonstrate that the noise structure in the log-ratios very closely follows a local log-normal distribution for both the PM and PM-MM cases. Therefore, significance relative to the type I noise can be quantified reliably using the local STD. We discuss the intensity dependence of the STD and show that ratio scores >1.25 are significant in the mid- to high-intensity range.

Conclusions

The ratio noise structure inherent to high-density oligonucleotide arrays can be well described in terms of local log-normal ratio distributions with characteristic intensity dependence. Therefore, robust estimates of the local STD of these distributions provide a simple and powerful way for assessing significance (relative to type I noise) in differential gene expression. This approach will be helpful for improving the reliability of predictions from hybridization experiments in general.     

Discrimination of spectral patterns of sound signals in conditions of interfering noise

The method of probes with rippled spectra makes possible to measure the frequency resolving power (FRP) of human hearing using no frequency-selective masking techniques. This allows studying the influence of interfering noises on the FRP. In conditions of diotic presentation (parallel to both ears), FRP markedly decreases on- or low-frequency noise markedly decreases FRP. The dependence of this effect on sound intensity and noise-to-probe ratio is different for the on- and low-frequence noise, which indicates different mechanisms of action of these two kinds of interfering noise. However, in both cases, a loud enough interfering noise results in complete inability to discriminate the fine spectral pattern of the probe. On the contrary, in conditions of dichotic presentation (the probe to one ear and the noise to the other), the interfering noise does not influence FRP noticeably within a wide range of frequency relations of the probe and noise and noise-to-probe level ratios. Thus, almost entire dichotic release of influence of interfering noise on FRP takes place. This feature can be used for designing bearing aids.     

Characterization of mismatch and high-signal intensity probes associated with Affymetrix genechips

MOTIVATION: For Affymetrix microarray platforms, gene expression is determined by computing the difference in signal intensities between perfect match (PM) and mismatch (MM) probesets. Although the use of PM is not controversial, MM probesets have been associated with variance and ultimately inaccurate gene expression calls. A principal focus of this study was to investigate the nature of the MM signal intensities and demonstrate its contribution to the experimental results. RESULTS: While most MM intensities were likely associated with random noise, a subset of approximately 20% (99,485) of the MM probes displayed relatively high signal intensities to the corresponding PM probes (MM > PM) in a non-random fashion; 13,440 of these probes demonstrated exceptionally high 'outlier' intensities. About 15,938 PM probes also demonstrated exceptionally high outlier intensities consistently across all hybridizations. About 92% of the MM > PM probes had either a dThymidine (dT) or a dCytidine (dC) at the 13th position of the probe sequence. MM and PM probes displaying extremely high outlier intensities contained high dC rich nucleotides, and low dA contents at other nucleotides positions along the 25mer probe sequence. Differentially expressed genes generated using Genechip Operating System (GCOS) or modified PM-only methods were also examined. Of those candidate genes identified in the PM-only method, 157 of them were designated by GCOS as absent across all datasets and many others contained probes with MM > PM signal intensities. Our data suggests that MM intensity from PM signal can be a major source of error analysis, leading to fewer potentially biologically important candidate genes. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

持续与间断噪声前掩蔽条件下小鼠下丘神经元的不同反应模式

有关听中枢神经元纯音前掩蔽效应的神经表征已进行了大量研究,但是,噪声前掩蔽尤其是间断噪声前掩蔽效应的神经表征却鲜有报道。本研究观察了自由声场条件下,昆明小鼠下丘神经元在持续与间断噪声前掩蔽条件下对纯音探测声的反应。共记录到96个下丘神经元,测量了其中51个神经元在不同声刺激条件下的强度一放电率函数。结果显示,掩蔽声强度分布较广（探测声阈下21dB至阈上19dB之间）。在将近一半的神经元中,间断噪声的前掩蔽效应比持续噪声强（Ⅰ型,45．10％,P〈0．001）,但也有少数神经元其间断噪声的掩蔽效应较持续噪声的弱（Ⅲ型,17．65％,P〈0．001）,部分神经元无显著性差异（Ⅱ型,37．25％,P〉0．05）。无论Ⅰ型还是Ⅲ型神经元,持续噪声和间断噪声均在探测声强度较低时产生较强的抑制效应,随着探测声强度的升高,抑制效应逐渐降低（P〈0．001）;同时,持续噪声和间断噪声之间前掩蔽效应差异亦不复存在（P〉0．05）。此外,当掩蔽声由持续噪声换为间断噪声后,部分Ⅰ型神经元掩蔽时相的类型发生转变,其中最主要的转变为由前期抑制转变为均衡抑制（53．85％,7／13）。对下丘神经元声反应的时间域以及强度域,持续与间断噪声具有分化性前掩蔽效应,提示噪声前掩蔽并非简单的神经元发放压抑源,某些主动性神经调制机制可能参与了噪声条件下时相声信息的编码过程。     

Normalized Affymetrix expression data are biased by G-quadruplex formation

Probes with runs of four or more guanines (G-stacks) in their sequences can exhibit a level of hybridization that is unrelated to the expression levels of the mRNA that they are intended to measure. This is most likely caused by the formation of G-quadruplexes, where inter-probe guanines form Hoogsteen hydrogen bonds, which probes with G-stacks are capable of forming. We demonstrate that for a specific microarray data set using the Human HG_U133A Affymetrix GeneChip and RMA normalization there is significant bias in the expression levels, the fold change and the correlations between expression levels. These effects grow more pronounced as the number of G-stack probes in a probe set increases. Approximately 14% of the probe sets are directly affected. The analysis was repeated for a number of other normalization pipelines and two, FARMS and PLIER, minimized the bias to some extent. We estimate that ～15% of the data sets deposited in the GEO database are susceptible to the effect. The inclusion of G-stack probes in the affected data sets can bias key parameters used in the selection and clustering of genes. The elimination of these probes from any analysis in such affected data sets outweighs the increase of noise in the signal.     

How high is the level of technical noise in microarray data?

Background  

Microarray gene expression data are commonly perceived as being extremely noisy because of many imperfections inherent in the current technology. A recent study conducted by the MicroArray Quality Control (MAQC) Consortium and published in Nature Biotechnology provides a unique opportunity to probe into the true level of technical noise in such data.     

Bacterium in a box: sensing of quorum and environment by the <Emphasis Type="BoldItalic">LuxI/LuxR</Emphasis> gene regulatory circuit

The chemical signaling mechanism known as “bacterial quorum sensing” (QS) is normally interpreted as allowing bacteria to detect their own population density, in order to coordinate gene expression across a colony. However, the release of the chemical signal can also be interpreted as a means for one or a few cells to probe the local physical properties of their microenvironment. We have studied the behavior of the LuxI/LuxR QS circuit of Vibrio fischeri in tightly confining environments where individual cells detect their own released signals. We find that the lux genes become activated in these environments, although the activation onset time shows substantial cell-to-cell variability and little sensitivity to the confining volume. Our data suggest that noise in gene expression could significantly impact the utility of LuxI/LuxR as a probe of the local physical environment.     

Monitoring signal transduction in cancer: cDNA microarray for semiquantitative analysis.

This study targeted the development of a novel microarray tool to allow rapid determination of the expression levels of 58 different tyrosine kinase (tk) genes in small tumor samples. The goals were to define a reference probe for multi-sample comparison and to investigate the variability and reproducibility of the image acquisition and RT-PCR procedures. The small number of tk genes on our arrays enabled us to define a reference probe by artificially mixing all genes on the arrays. Such a probe provided contrast reference for comparative hybridization of control and sample DNA and enabled cross-comparison of more than two samples against one another. Comparison of signals generated from multiple scanning eliminated the concern of photo bleaching and scanner intrinsic noise. Tests performed with breast, thyroid, and prostate cancer samples yielded distinctive patterns and suggest the feasibility of our approach. Repeated experiments indicated reproducibility of such arrays. Up- or downregulated genes identified by this rapid screening are now being investigated with techniques such as in situ hybridization.