Comprehensive quality control utilizing the prehybridization third-dye image leads to accurate gene expression measurements by cDNA microarrays

Multiplexed method to calibrate and quantitate fluorescence signal for allergen-specific IgE

Using a microarray platform for allergy diagnosis allows for testing of specific IgE sensitivity to a multitude of allergens, while requiring only small volumes of serum. However, variation of probe immobilization on microarrays hinders the ability to make quantitative, assertive, and statistically relevant conclusions necessary in immunodiagnostics. To address this problem, we have developed a calibrated, inexpensive, multiplexed, and rapid protein microarray method that directly correlates surface probe density to captured labeled secondary antibody in clinical samples. We have identified three major technological advantages of our calibrated fluorescence enhancement (CaFE) technique: (i) a significant increase in fluorescence emission over a broad range of fluorophores on a layered substrate optimized specifically for fluorescence; (ii) a method to perform label-free quantification of the probes in each spot while maintaining fluorescence enhancement for a particular fluorophore; and (iii) a calibrated, quantitative technique that combines fluorescence and label-free modalities to accurately measure probe density and bound target for a variety of antibody-antigen pairs. In this paper, we establish the effectiveness of the CaFE method by presenting the strong linear dependence of the amount of bound protein to the resulting fluorescence signal of secondary antibody for IgG, β-lactoglobulin, and allergen-specific IgEs to Ara h 1 (peanut major allergen) and Phl p 1 (timothy grass major allergen) in human serum.

Label-free prehybridization DNA microarray imaging using photonic crystals for quantitative spot quality analysis

Technical variability during DNA capture probe printing remains an important obstacle to obtaining high quality data from microarray experiments. While methods that use fluorescent labels for visualizing printed arrays prior to hybridization have been presented, the ability to measure spot density using label-free techniques would provide valuable information on spot quality without altering standard microarray protocols. In this study, we present the use of a photonic crystal biosensor surface and a high resolution label-free imaging detection instrument to generate prehybridization images of spotted oligonucleotide microarrays. Spot intensity, size, level of saturation, and local background intensity were measured from these images. This information was used for the automated identification of missed spots (due to mechanical failure or sample depletion) as well as the assignment of a score that reflected the quality of each printed feature. Missed spots were identified with >95% sensitivity. Furthermore, filtering based on spot quality scores increased pairwise correlation of posthybridization spot intensity between replicate arrays, demonstrating that label-free spot quality scores captured the variability in the microarray data. This imaging modality can be applied for the quality control of printed cDNA, oligonucleotide, and protein microarrays.

Multiplexed, rapid point of care platform to quantify allergen-specific IgE

Variation of probe immobilization on microarrays hinders the ability to make high quality, assertive and statistically relevant conclusions needed in the healthcare setting. To address this problem, we have developed a calibrated, compact, inexpensive, multiplexed, dual modality point-of-care detection platform that calibrates and correlates surface probe density measured label-free to captured labeled secondary antibody, is independent of chip-to-chip variability, and improves upon existing diagnostic technology. We have identified four major technological advantages of our proposed platform: the capability to perform single spot analysis based on the fluorophore used for detection, a 10-fold gain in fluorescence signal due to optimized substrate, a calibrated, quantitative method that uses the combined fluorescent and label-free modalities to accurately measure the density of probe and bound target for a variety of systems, and a compact measurement platform offering reliable and rapid results at the doctor's office. Already, we have formulated over a 90% linear correlation between the amount of probe bound to surface and the resulting fluorescence of captured target for IgG, β-lactoglobulin, Ara h 1 peanut allergen, and Phl 5a Timothy grass allergen.

Global analysis of phase locking in gene expression during cell cycle: the potential in network modeling

Background

In nonlinear dynamic systems, synchrony through oscillation and frequency modulation is a general control strategy to coordinate multiple modules in response to external signals. Conversely, the synchrony information can be utilized to infer interaction. Increasing evidence suggests that frequency modulation is also common in transcription regulation.

Results

In this study, we investigate the potential of phase locking analysis, a technique to study the synchrony patterns, in the transcription network modeling of time course gene expression data. Using the yeast cell cycle data, we show that significant phase locking exists between transcription factors and their targets, between gene pairs with prior evidence of physical or genetic interactions, and among cell cycle genes. When compared with simple correlation we found that the phase locking metric can identify gene pairs that interact with each other more efficiently. In addition, it can automatically address issues of arbitrary time lags or different dynamic time scales in different genes, without the need for alignment. Interestingly, many of the phase locked gene pairs exhibit higher order than 1:1 locking, and significant phase lags with respect to each other. Based on these findings we propose a new phase locking metric for network reconstruction using time course gene expression data. We show that it is efficient at identifying network modules of focused biological themes that are important to cell cycle regulation.

Conclusions

Our result demonstrates the potential of phase locking analysis in transcription network modeling. It also suggests the importance of understanding the dynamics underlying the gene expression patterns.

The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments

BACKGROUND

Currently, a lack of consensus exists on how best to perform and interpret quantitative real-time PCR (qPCR) experiments. The problem is exacerbated by a lack of sufficient experimental detail in many publications, which impedes a reader's ability to evaluate critically the quality of the results presented or to repeat the experiments.

CONTENT

The Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines target the reliability of results to help ensure the integrity of the scientific literature, promote consistency between laboratories, and increase experimental transparency. MIQE is a set of guidelines that describe the minimum information necessary for evaluating qPCR experiments. Included is a checklist to accompany the initial submission of a manuscript to the publisher. By providing all relevant experimental conditions and assay characteristics, reviewers can assess the validity of the protocols used. Full disclosure of all reagents, sequences, and analysis methods is necessary to enable other investigators to reproduce results. MIQE details should be published either in abbreviated form or as an online supplement.

SUMMARY

Following these guidelines will encourage better experimental practice, allowing more reliable and unequivocal interpretation of qPCR results.

Quality Weighted Mean and T-test in Microarray Analysis Lead to Improved Accuracy in Gene Expression Measurements and Reduced Type I and II Errors in Differential Expression Detection

Previously we have reported a microarray image processing and data analysis package Matarray, where quality scores are defined for every spot that reflect the reliability and variability of the data acquired from each spot. In this article we present a new development in Matarray, where the quality scores are incorporated as weights in the statistical evaluation and data mining of microarray data. With this approach filtering of poor quality data is automatically achieved through the reduction in their weights, thereby eliminating the need to manually flag or remove bad data points, as well as the problem of missing values. More significantly, utilizing a set of control clones spiked in at known input ratios ranging from 1:30 to 30:1, we find that the quality-weighted statistics leads to more accurate gene expression measurements and more sensitive detection of their changes with significantly lower type II error rates. Further, we have applied the quality-weighted clustering to a time-course microarray data set, and find that the new algorithm improves grouping accuracy. In summary, incorporating quantitative quality measure of microarray data as weight in complex data analysis leads to improved reliability and convenience. In addition it provides a practical way to deal with the missing value issue in establishing automatic statistical tests.