Amanda M. White

ELISA microarray technology as a high-throughput system for cancer biomarker validation

A large gap currently exists between the ability to discover potential biomarkers and the ability to assess the real value of these proteins for cancer screening. One major challenge in biomarker validation is the inherent variability in biomarker levels. This variability stems from the diversity across the human population and the considerable molecular heterogeneity between individual tumors, even those that originate from a single tissue. An additional challenge with cancer screening is that most cancers are rare in the general population, meaning that assay specificity must be very high. Otherwise, the number of false positives will be much greater than the number of true positives. Due to these challenges associated with biomarker validation, it is necessary to analyze thousands of samples in order to obtain a clear idea of the utility of a screening assay. Enzyme-linked immunosorbent assay (ELISA) microarray technology can simultaneously quantify levels of multiple proteins and, thus, has the potential to accelerate validation of protein biomarkers for clinical use. This review will discuss current ELISA microarray technology and potential advances that could help to achieve the reproducibility and throughput that are required to evaluate cancer biomarkers.

Evaluating concentration estimation errors in ELISA microarray experiments.

BackgroundEnzyme-linked immunosorbent assay (ELISA) is a standard immunoassay to estimate a proteins concentration in a sample. Deploying ELISA in a microarray format permits simultaneous estimation of the concentrations of numerous proteins in a small sample. These estimates, however, are uncertain due to processing error and biological variability. Evaluating estimation error is critical to interpreting biological significance and improving the ELISA microarray process. Estimation error evaluation must be automated to realize a reliable high-throughput ELISA microarray system.In this paper, we present a statistical method based on propagation of error to evaluate concentration estimation errors in the ELISA microarray process. Although propagation of error is central to this method and the focus of this paper, it is most effective only when comparable data are available. Therefore, we briefly discuss the roles of experimental design, data screening, normalization, and statistical diagnostics when evaluating ELISA microarray concentration estimation errors.ResultsWe use an ELISA microarray investigation of breast cancer biomarkers to illustrate the evaluation of concentration estimation errors. The illustration begins with a description of the design and resulting data, followed by a brief discussion of data screening and normalization. In our illustration, we fit a standard curve to the screened and normalized data, review the modeling diagnostics, and apply propagation of error.We summarize the results with a simple, three-panel diagnostic visualization featuring a scatterplot of the standard data with logistic standard curve and 95% confidence intervals, an annotated histogram of sample measurements, and a plot of the 95% concentration coefficient of variation, or relative error, as a function of concentration.ConclusionsThis statistical method should be of value in the rapid evaluation and quality control of high-throughput ELISA microarray analyses. Applying propagation of error to a variety of ELISA microarray concentration estimation models is straightforward. Displaying the results in the three-panel layout succinctly summarizes both the standard and sample data while providing an informative critique of applicability of the fitted model, the uncertainty in concentration estimates, and the quality of both the experiment and the ELISA microarray process.

ProMAT: protein microarray analysis tool

SUMMARY ProMAT is a software tool for statistically analyzing data from enzyme-linked immunosorbent assay microarray experiments. The software estimates standard curves, sample protein concentrations and their uncertainties for multiple assays. ProMAT generates a set of comprehensive figures for assessing results and diagnosing process quality. The tool is available for Windows or Mac, and is distributed as open-source Java and R code. AVAILABILITY ProMAT is available at http://www.pnl.gov/statistics/ProMAT. ProMAT requires Java version 1.5.0 and R version 1.9.1 (or more recent versions). ProMAT requires either Windows XP or Mac OS 10.4 or newer versions.

Automated Microarray Image Analysis Toolbox for MATLAB

UNLABELLED The Automated Microarray Image Analysis (AMIA) Toolbox for MATLAB is a flexible, open-source, microarray image analysis tool that allows the user to customize analyses of microarray image sets. This tool provides several methods to identify and quantify spot statistics, as well as extensive diagnostic statistics and images to evaluate data quality and array processing. The open, modular nature of AMIA provides access to implementation details and encourages modification and extension of AMIAs capabilities. AVAILABILITY The AMIA Toolbox is freely available at http://www.pnl.gov/statistics/amia. The AMIA Toolbox requires MATLAB 6.5 (R13) (MathWorks, Inc. Natick, MA), as well as the Statistics Toolbox 4.1 and Image Processing Toolbox 4.1 for MATLAB or more recent versions. CONTACT amanda.white@pnl.gov

Diagnostic Oligonucleotide Microarray Fingerprinting of Bacillus Isolates

ABSTRACT A genome-independent microarray and new statistical techniques were used to genotype Bacillus strains and quantitatively compare DNA fingerprints with the known taxonomy of the genus. A synthetic DNA standard was used to understand process level variability and lead to recommended standard operating procedures for microbial forensics and clinical diagnostics.

ProMAT calibrator: A tool for reducing experimental bias in antibody microarrays.

Our research group has been developing enzyme-linked immunosorbent assays (ELISA) microarray technology for the rapid and quantitative evaluation of biomarker panels. Studies using antibody microarrays are susceptible to systematic bias from the various steps in the experimental process, and these biases can mask biologically significant differences. For this reason, we have developed a calibration system that can identify and reduce systematic bias due to processing factors. Specifically, we developed a sandwich ELISA for green fluorescent protein (GFP) that is included on each chip. The GFP antigen is spiked into each biological sample or standard mixture and the resulting signal is used for calibration between chips. We developed ProMAT Calibrator, an open-source bioinformatics tool, for the rapid visualization and interpretation of the calibrator data and, if desired, data normalization. We demonstrate that data normalization using this system markedly reduces bias from processing factors. Equally useful, this calibrator system can help reveal the source of the bias, thereby facilitating the elimination of the underlying problem. ProMAT Calibrator can be downloaded at http://www.pnl.gov/statistics/ProMAT .

A Bayesian estimator of protein–protein association probabilities

UNLABELLED The Bayesian Estimator of Protein-Protein Association Probabilities (BEPro aff3) is a software tool for estimating probabilities of protein-protein association between bait and prey protein pairs using data from multiple-bait, multiple-replicate, protein liquid chromatography tandem mass spectrometry LC-MS/MS affinity isolation experiments. AVAILABILITY BEPro (3) is public domain software, has been tested on WIndows XP, Linux and Mac OS, and is freely available from http://www.pnl.gov/statistics/BEPro3. SUPPLEMENTARY INFORMATION A user guide, example dataset with analysis and additional documentation are included with the BEPro (3) download.

An internal calibration method for protein-array studies.

Nuisance factors in a protein-array study add obfuscating variation to spot intensity measurements, diminishing the accuracy and precision of protein concentration predictions. The effects of nuisance factors may be reduced by design of experiments, and by estimating and then subtracting nuisance effects. Estimated nuisance effects also inform about the quality of the study and suggest refinements for future studies.We demonstrate a method to reduce nuisance effects by incorporating a non-interfering internal calibration in the study design and its complemental analysis of variance. We illustrate this method by applying a chip-level internal calibration in a biomarker discovery study.The variability of sample intensity estimates was reduced 16% to 92% with a median of 58%; confidence interval widths were reduced 8% to 70% with a median of 35%. Calibration diagnostics revealed processing nuisance trends potentially related to spot print order and chip location on a slide.The accuracy and precision of a protein-array study may be increased by incorporating a non-interfering internal calibration. Internal calibration modeling diagnostics improve confidence in study results and suggest process steps that may need refinement. Though developed for our protein-array studies, this internal calibration method is applicable to other targeted array-based studies.

An analysis pipeline for the inference of protein-protein interaction networks

We present a platform for the reconstruction of protein-protein interaction networks inferred from Mass Spectrometry (MS) bait-prey data. The Software Environment for Biological Network Inference (SEBINI), an environment for the deployment of network inference algorithms that use high-throughput data, forms the platform core. Among the many algorithms available in SEBINI is the Bayesian Estimator of Probabilities of Protein-Protein Associations (BEPro3) algorithm, which is used to infer interaction networks from such MS affinity isolation data. Also, the pipeline incorporates the Collective Analysis of Biological Interaction Networks (CABIN) software. We have thus created a structured workflow for protein-protein network inference and supplemental analysis.

ELISA-BASE

SUMMARY ELISA-BASE is an open source database for capturing, organizing and analyzing enzyme-linked immunosorbent assay (ELISA) microarray data. ELISA-BASE is an extension of the BioArray Software Environment (BASE) database system. AVAILABILITY http://www.pnl.gov/statistics/ProMAT/ELISA-BASE.stm.