[6] Scanning Microarrays: Current Methods and Future Directions

Photobleaching of organic fluorophores: quantitative characterization, mechanisms, protection

Photochemical stability is one of the most important parameters that determine the usefulness of organic dyes in different applications. This Review addresses key factors that determine the dye photostability. It is shown that photodegradation can follow different oxygen-dependent and oxygen-independent mechanisms and may involve both ¹S₁-³T₁ and higher-energy ¹S_n-³T_n excited states. Their involvement and contribution depends on dye structure, medium conditions, irradiation power. Fluorescein, rhodamine, BODIPY and cyanine dyes, as well as conjugated polymers are discussed as selected examples illustrating photobleaching mechanisms. The strategies for modulating and improving the photostability are overviewed. They include the improvement of fluorophore design, particularly by attaching protective and anti-fading groups, creating proper medium conditions in liquid, solid and nanoscale environments. The special conditions for biological labeling, sensing and imaging are outlined.

Gene Expression Measurement Module (GEMM) for space application: Design and validation

In order to facilitate studies on the impact of the space environment on biological systems, we have developed a prototype of GEMM (Gene Expression Measurement Module) - an automated, miniaturized, integrated fluidic system for in-situ measurements of gene expression in microbial samples. The GEMM instrument is capable of (1) lysing bacterial cell walls, (2) extracting and purifying RNA released from cells, (3) hybridizing the RNA to probes attached to a microarray and (4) providing electrochemical readout, all in a microfluidics cartridge. To function on small, uncrewed spacecraft, the conventional, laboratory protocols for both sample preparation and hybridization required significant modifications. Biological validation of the instrument was carried out on Synechococcus elongatus, a photosynthetic cyanobacterium known for its metabolic diversity and resilience to adverse conditions. It was demonstrated that GEMM yielded reliable, reproducible gene expression profiles. GEMM is the only high throughput instrument that can be deployed in near future on space platforms other than the ISS to advance biological research in space. It can also prove useful for numerous terrestrial applications in the field.

Improved filtering of DNA methylation microarray data by detection p values and its impact on downstream analyses

Background

DNA methylation microarrays are popular for epigenome-wide association studies (EWAS), but spurious values complicate downstream analysis and threaten replication. Conventional cut-offs for detection p values for filtering out undetected probes were demonstrated in a single previous study as insufficient leading to many apparent methylation calls in samples from females in probes targeting the Y-chromosome. We present an alternative approach to calculate more accurate detection p values utilizing non-specific background fluorescence. We evaluate and compare our proposed approach of filtering observations with conventional ones by assessing the detection of Y-chromosome probes among males and females in 2755 samples from 17 studies on the 450K microarray and masking of large outliers between technical replicates and their impact downstream via an EWAS reanalysis.

Results

In contrast to conventional approaches, ours marks most Y-chromosome probes in females as undetected while removing a median of only 0.14% of the data per sample, catches more (30% vs. 6%) of large outliers (more than 20 percentage point difference between technical replicates), and helps to identify strong associations previously obfuscated by outliers between whole blood DNA methylation and chronological age in a well-powered EWAS (n = 729).

Conclusions

We provide guidance for filtering both 450K and EPIC microarrays as an essential preprocessing step to reduce spurious values. An implementation (including a function compatible with objects from the popular minfi package) was added to ewastools, an R package for comprehensive quality control of DNA methylation microarrays. Scripts to reproduce all analyses are available at doi.org/10.5281/zenodo.1443561.

Evaluating single molecule detection methods for microarrays with high dynamic range for quantitative single cell analysis

Single molecule microarrays have been used in quantitative proteomics, in particular, single cell analysis requiring high sensitivity and ultra-low limits of detection. In this paper, several image analysis methods are evaluated for their ability to accurately enumerate single molecules bound to a microarray spot. Crucially, protein abundance in single cells can vary significantly and may span several orders of magnitude. This poses a challenge to single molecule image analysis. In order to quantitatively assess the performance of each method, synthetic image datasets are generated with known ground truth whereby the number of single molecules varies over 5 orders of magnitude with a range of signal to noise ratios. Experiments were performed on synthetic datasets whereby the number of single molecules per spot corresponds to realistic single cell distributions whose ground truth summary statistics are known. The methods of image analysis are assessed in their ability to accurately estimate the distribution parameters. It is shown that super-resolution image analysis methods can significantly improve counting accuracy and better cope with single molecule congestion. The results highlight the challenge posed by quantitative single cell analysis and the implications to performing such analyses using microarray based approaches are discussed.

Evaluation of quantum dot immunofluorescence and a digital CMOS imaging system as an alternative to conventional organic fluorescence dyes and laser scanning for quantifying protein microarrays

Organic fluorescent dyes are widely used for the visualization of bound antibody in a variety of immunofluorescence assays. However, the detection equipment is often expensive, fragile, and hard to deploy widely. Quantum dots (Qdot) are nanocrystals made of semiconductor materials that emit light at different wavelengths according to the size of the crystal, with increased brightness and stability. Here, we have evaluated a small benchtop "personal" optical imager (ArrayCAM) developed for quantification of protein arrays probed by Qdot-based indirect immunofluorescence. The aim was to determine if the Qdot imager system provides equivalent data to the conventional organic dye-labeled antibody/laser scanner system. To do this, duplicate proteome microarrays of Vaccinia virus, Brucella melitensis and Plasmodium falciparum were probed with identical samples of immune sera, and IgG, IgA, and IgM profiles visualized using biotinylated secondary antibodies followed by a tertiary reagent of streptavidin coupled to either P3 (an organic cyanine dye typically used for microarrays) or Q800 (Qdot). The data show excellent correlation for all samples tested (R > 0.8) with no significant change of antibody reactivity profiles. We conclude that Qdot detection provides data equivalent to that obtained using conventional organic dye detection. The portable imager offers an economical, more robust, and deployable alternative to conventional laser array scanners.

Data Mining and Meta-Analysis on DNA Microarray Data

Microarray technology enables high-throughput parallel gene expression analysis, and use has grown exponentially thanks to the development of a variety of applications for expression, genetics and epigenetic studies. A wealth of data is now available from public repositories, providing unprecedented opportunities for meta-analysis approaches, which could generate new biological information, unrelated to the original scope of individual studies. This study provides a guideline for identification of biological significance of the statistically-selected differentially-expressed genes derived from gene expression arrays as well as to suggest further analysis pathways. The authors review the prerequisites for data-mining and meta-analysis, summarize the conceptual methods to derive biological information from microarray data and suggest software for each category of data mining or meta-analysis.

Oligonucleotide array-in-well platform for detection and genotyping human adenoviruses by utilizing upconverting phosphor label technology

We have developed a robust array-in-well test platform based on an oligonucleotide array, combining advantages of simple instrumentation and new upconverting phosphor reporter technology. Upconverting inorganic lanthanide phosphors have a unique property of photoluminescence emission at visible wavelengths under near-infrared excitation. No autofluorescence is produced from the sample or support material, enabling a highly sensitive assay. In this study, the assay is performed in standard 96-well microtiter plates, making the technique easily adaptable to high-throughput analysis. The oligonucleotide array-in-well assay is employed to detect a selection of ten common adenovirus genotypes causing human infections. The study provides a demonstration of the advantages and potential of the upconverting phosphor-based reporter technology in multianalyte assays and anti-Stokes photoluminescence detection with an anti-Stokes photoluminescence imaging device.

Multiplexed spectral signature detection for microfluidic color-coded bioparticle flow

Here, we report a high-speed photospectral detection technique capable of discriminating subtle variations of spectral signature among fluorescently labeled cells and microspheres flowing in a microfluidic channel. The key component used in our study is a strain-tunable nanoimprinted grating microdevice coupled with a photomultiplier tube (PMT). The microdevice permits acquisition of the continuous spectral profiles of multiple fluorescent emission sources at 1 kHz. Optically connected to a microfluidic flow chamber via a multimode optical fiber, our multiwavelength detection platform allows for cytometric measurement of cell groups emitting nearly identical fluorescence signals with a maximum emission wavelength difference as small as 5 nm. The same platform also allows us to demonstrate microfluidic flow cytometry of four different microsphere types in a wavelength bandwidth as narrow as 40 nm at a high (>85%) confidence level. Our study shows that detection of fluorescent spectral signatures at high speed and high resolution can expand specificity of multicolor flow cytometry. The enhanced capability enables multiplexed analysis of color-coded bioparticles based on single-laser excitation and single-detector spectroscopy in a microfluidic setting. The fluorescence signal discrimination power achieved by the optofluidic technology holds great promise to enable quantification of cellular parameters with higher accuracy as well as enumeration of a larger number of cell types than conventional flow cytometric methods.

Challenges in defining predictive markers for response to endocrine therapy in breast cancer

Endocrine therapy is a major treatment modality for hormone-dependent breast cancer. It has a relatively low morbidity, and there is evidence that antihormonal treatments have had a significant effect in reducing mortality for breast cancer. Despite this, resistance to endocrine therapy, either primary or acquired during treatment, occurs in the majority of patients, and is a major obstacle to optimal clinical management. There is therefore an urgent need to identify, on an individual basis, those tumors that are most likely to respond to endocrine therapy (so sparing patients with resistant tumors the needless side effects of ineffective therapy), and the mechanisms of resistance in tumors that are nonresponsive to treatment (so these can be bypassed). These needs are the focus of this review, which discusses the particular issues encountered when investigating the potential of multigene expression signatures as predictive factors for response to aromatase inhibitors, which have recently become front-line endocrine therapies for postmenopausal patients with breast cancer.

Characterisation and correction of signal fluctuations in successive acquisitions of microarray images

Background

There are many sources of variation in dual labelled microarray experiments, including data acquisition and image processing. The final interpretation of experiments strongly relies on the accuracy of the measurement of the signal intensity. For low intensity spots in particular, accurately estimating gene expression variations remains a challenge as signal measurement is, in this case, highly subject to fluctuations.

Results

To evaluate the fluctuations in the fluorescence intensities of spots, we used series of successive scans, at the same settings, of whole genome arrays. We measured the decrease in fluorescence and we evaluated the influence of different parameters (PMT gain, resolution and chemistry of the slide) on the signal variability, at the level of the array as a whole and by intensity interval. Moreover, we assessed the effect of averaging scans on the fluctuations. We found that the extent of photo-bleaching was low and we established that 1) the fluorescence fluctuation is linked to the resolution e.g. it depends on the number of pixels in the spot 2) the fluorescence fluctuation increases as the scanner voltage increases and, moreover, is higher for the red as opposed to the green fluorescence which can introduce bias in the analysis 3) the signal variability is linked to the intensity level, it is higher for low intensities 4) the heterogeneity of the spots and the variability of the signal and the intensity ratios decrease when two or three scans are averaged.

Conclusion

Protocols consisting of two scans, one at low and one at high PMT gains, or multiple scans (ten scans) can introduce bias or be difficult to implement. We found that averaging two, or at most three, acquisitions of microarrays scanned at moderate photomultiplier settings (PMT gain) is sufficient to significantly improve the accuracy (quality) of the data and particularly those for spots having low intensities and we propose this as a general approach. For averaging and precise image alignment at sub-pixel levels we have made a program freely available on our web-site to facilitate implementation of this approach.

Protein microarrays: effective tools for the study of inflammatory diseases

Inflammation is a defense reaction of an organism against harmful stimuli such as tissue injury or infectious agents. The relationship between the infecting microorganism and the immune, inflammatory, and coagulation responses of the host is intricately intertwined. Due to its complex nature, the molecular mechanisms of inflammation are not yet understood in detail and additional diagnostic tools are required to clarify further aspects. In recent years, protein microarray-based research has moved from being technology-based to application-oriented. Protein microarrays are perfect tools for studying inflammatory diseases. High-density protein arrays enable new classes of autoantibodies, which cause autoimmune diseases, to be discovered. Protein arrays consisting of miniaturized multiplexed sandwich immunoassays allow the simultaneous expression analysis of dozens of signaling molecules such as the cytokines and chemokines involved in the regulation of the immune system. The data enable statements to be made on the status of the disease and its progression as well as support for the clinicians in choosing patient-specific treatment. This chapter reviews the technology and the applications of protein microarrays in diagnosing and monitoring inflammatory diseases.

Microarray experiments to uncover Toll-like receptor function

This chapter is intended as a handbook for anyone interested in using microarrays to study Toll-like receptor (TLR) function or any other biological question. Although microarray technology has developed into a standard tool at many laboratories disposal, most of the actual microarray processing is done by core facilities using highly specialized equipment. This chapter only briefly describes these methods in principle and instead focus on the parts that investigators themselves can influence, such as the experimental design, RNA isolation, statistical analysis, cluster analysis, data visualization, and biological interpretation.

Basic science for the practicing physician: gene expression microarrays

OBJECTIVE

To provide a general overview of gene expression microarray technology and its relevance to physicians practicing allergy/immunology.

DATA SOURCES

The PubMed interface to MEDLINE was searched for primary and review articles on gene expression microarrays. Specific articles on clinical applications of microarrays were retrieved, along with articles on use of microarrays in models of allergy, asthma, and immunologic diseases.

STUDY SELECTION

The author's knowledge of the field was used to include sources of information other than those obtained through the MEDLINE search.

RESULTS

A synopsis of gene expression microarray technology, with emphasis on the relevance to allergy, asthma, and immunology, is presented.

CONCLUSIONS

Gene expression microarray technology allows investigators to measure gene expression across the genome. This has allowed researchers to improve our understanding of immunologic mechanisms in disease models. Initially used solely as a research tool, microarray-based clinical tests are now available, and many more are in development. Use of microarrays in allergy, asthma, and immunology will support the development of novel diagnostic tests for the physician and facilitate exploration of the basic mechanisms underlying allergic and immunologic diseases.

Review of the literature examining the correlation among DNA microarray technologies

DNA microarray technologies are used in a variety of biological disciplines. The diversity of platforms and analytical methods employed has raised concerns over the reliability, reproducibility and correlation of data produced across the different approaches. Initial investigations (years 2000-2003) found discrepancies in the gene expression measures produced by different microarray technologies. Increasing knowledge and control of the factors that result in poor correlation among the technologies has led to much higher levels of correlation among more recent publications (years 2004 to present). Here, we review the studies examining the correlation among microarray technologies. We find that with improvements in the technology (optimization and standardization of methods, including data analysis) and annotation, analysis across platforms yields highly correlated and reproducible results. We suggest several key factors that should be controlled in comparing across technologies, and are good microarray practice in general.

TM4 microarray software suite

Powerful specialized software is essential for managing, quantifying, and ultimately deriving scientific insight from results of a microarray experiment. We have developed a suite of software applications, known as TM4, to support such gene expression studies. The suite consists of open-source tools for data management and reporting, image analysis, normalization and pipeline control, and data mining and visualization. An integrated MIAME-compliant MySQL database is included. This chapter describes each component of the suite and includes a sample analysis walk-through.

Optimizing experiment and analysis parameters for spotted microarrays

This chapter outlines considerations and methods of experimental design for spotted microarray studies that contribute to the robustness of the acquired data. The chapter is divided into two principal sections: (1) a summary of factors that affect the quality of individual array results; and (2) a discussion of experimental design features and replication criteria that enable researchers to gain confidence in their findings. The goal of section (2) is to promote the careful design of microarray studies, with downstream statistical analysis in mind, such that a minimal amount of biological and/or technical replication is required to achieve reproducible results. Topics addressed include choosing appropriate samples to be compared, determining the optimal amount of sample to use, flagging uninformative data, establishing confidence limits in normalized data and the application of statistical tests to identify differentially expressed genes.